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10 Applications of AI in Market Research (+ 4 Tools from the Future)

Concept testing and product innovation

The use of AI in market research has revolutionized the industry by providing groundbreaking solutions and unparalleled insights. By automating tedious tasks and uncovering hidden patterns within large datasets, AI has taken research to new heights. In this article, we will look at the current and future state of AI in market research, cover various applications of AI in this field, and provide a list of useful tools to consider.

With the digital business becoming increasingly competitive, marketers have their work cut out for them. At the same time, consumers are demanding increasingly personalized and enhanced experiences. The problem is that this requires gathering and analyzing endless amounts of market data to segment, understand, and tailor campaigns to potential customers. The most promising solution is to leverage the power of AI in market research to enhance the capabilities of marketers to generate actionable insights and make data-driven decisions in real time.

Below, we’ll look at how AI in market research is changing the landscape by giving marketers more flexibility and agility while accelerating traditional marketing processes.

In This Article

Market research in a nutshell – the current state of AI

Sentiment analysis.

• Predictive analysis

Consumer segmentation

Image and video analysis, voice and speech analysis, social media listening, chatbots and virtual assistants, customer journey analysis, demand forecasting, poll the people, future outlook of ai in market research.

• The latest trending AI news
• How AI boosts efficiency at 10Web
• In-depth reviews of AI tools
• Entrepreneurial wisdom and insights
• Valuable business growth tips

Advances in AI tools for market research are revolutionizing the field of market research, transforming the way brands gather and activate consumer insights. While valuable, traditional approaches to market research are often limited in their scope and ability to provide real-time, predictive, and prescriptive insights. 

However, AI-enabled Consumer Intelligence (AICI) platforms are changing the game by leveraging AI and data-mining algorithms to harness data from multiple sources such as social media, search engines, surveys, and more.

Recently, the importance of understanding consumers’ preferences, motivations, and opinions has grown beyond the capacity of market research and customer insights teams. Getting marketing data from a single source is no longer enough. 

Marketers need to analyze various streams , like social listening, the voice of the customer, and text analytics, into a single platform to gain actionable insights. Market research AI software is the only technology capable of processing this wide range of data efficiently enough to enable brands to tap into online conversations across social media, blogs, forums, and more in real time.

According to a recent Gartner survey , AI was the #1 technology CEOs expected to disrupt their industry. As a result, AI-driven consumer insights are gaining traction and being adopted by more organizations. This has made it increasingly possible for market leaders and teams to incorporate AI into their operations.

AI is also being used in various marketing activities :

• 47% of marketers use it for targeting ads ,
• 42% to personalize offers and content in real-time,
• 39% to optimize email send time.

What’s more 64% of marketers consider AI to be a valuable part of their marketing strategy , while 80% say AI is having the most significant effect on how customers is interacting with their brand. 

This means that you’re already falling behind if you haven’t started to dip into AI as part of your marketing strategy . However, the good news is that it’s never too late to start.

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10 applications of ai in market research.

AI is transforming the field of market research by providing advanced capabilities to gather, analyze, and get actionable insights from vast amounts of data. From sentiment analysis to predictive analytics, image, and video analysis to customer journey analysis, AI-powered applications give market researchers the power to understand consumers, optimize strategies, and make data-driven decisions. 

As technology continues to evolve, AI will play an increasingly vital role in shaping the future of market research in a number of applications:

AI-powered sentiment analysis enables market researchers to analyze the emotions, opinions, and attitudes expressed in large volumes of text data, such as social media posts, customer reviews, and survey responses. Natural Language Processing (NLP) algorithms categorize the sentiment as positive, negative, or neutral, providing valuable insights into customer perceptions and preferences.

Image source

Example: Cosmetics businesses can use AI in market research to quickly analyze thousands of social media posts to gauge customer sentiment towards their latest product launch, allowing them to make informed marketing decisions and improve their product.

Predictive analytics

AI algorithms can analyze historical data, identify patterns, and make predictions about future market trends and consumer behavior. Predictive analytics helps market researchers forecast demand, optimize pricing strategies, and make informed decisions about product development, marketing campaigns, and inventory management.

Example: eCommerce retailers can analyze historical customer data, market trends, etc. to accurately forecast future demand for products and optimize marketing campaigns.

AI-driven market research clustering algorithms group consumers into distinct segments based on similarities in their demographics, behaviors, and preferences. Market researchers can then tailor marketing strategies and messages to each segment , improving campaign effectiveness and enhancing customer targeting efforts.

Example: Fashion brands can analyze diverse data sets to identify distinct customer segments based on demographics, behaviors, and preferences, enabling them to tailor marketing strategies and create personalized experiences.

With the rise of visual content on social media and other platforms, AI-powered image and video analysis tools have become invaluable for market researchers. These tools can automatically analyze and categorize visual content , detect brand logos, and recognize objects, scenes, and emotions depicted in images or videos, providing deeper insights into how customers interact with visual media.

Example: A travel agency can automatically analyze user-generated content to identify popular destinations, detect visual trends, and gain insights into customer preferences, enabling them to create targeted marketing campaigns with captivating visuals.

Voice and speech-enabled AI marketing research tools allow market researchers to analyze and extract valuable insights from recorded phone calls, customer support interactions, and voice-based survey responses. NLP algorithms transcribe and analyze the audio data, identifying key topics, sentiment, and customer satisfaction levels, enabling businesses to improve customer service and identify issues.

Example: Telecom companies can analyze customer call recordings to identify key topics, sentiment, and satisfaction levels, enabling them to enhance overall customer experience.

AI-powered social media listening tools monitor and analyze conversations on various social media platforms in real time. These tools can identify trending topics, monitor brand mentions, track sentiment, and provide valuable insights into consumer preferences, behaviors, and emerging market trends.

Example: Beverage companies, like Coca-Cola , use AI in market research to monitor real-time conversations across various social media platforms to track brand mentions and make data-driven marketing decisions and engage with their audience effectively.

AI-driven chatbots and virtual assistants are increasingly used in market research to engage with consumers, collect data, and provide personalized recommendations . Used by 80% of marketers already , these conversational chatbots can conduct surveys, answer customer inquiries, and capture valuable insights from interactions, enabling organizations to gather data at scale and improve customer engagement. Some even use them to reduce support tickets and response times.

Example: eCommerce platforms can use chatbots to make personalized product recommendations, offer 24/7 support, and enhance customer engagement, which drives sales and improves overall customer satisfaction.

AI algorithms can analyze multiple touchpoints and interactions throughout the customer journey to identify critical moments, pain points, and areas of opportunity . By understanding the customer journey in detail, market researchers can optimize marketing strategies, improve customer experiences, and drive customer loyalty.

Example: A retail brand can analyze various touchpoints and interactions across multiple channels to identify key moments in the customer journey and optimize its marketing strategy to not miss out on conversions.

AI-based demand forecasting models leverage historical sales data, market trends, external factors, and even weather patterns to predict future demand for products or services. Accurate forecasting helps organizations optimize inventory levels, production planning, resource allocation, and reducing costs. It also helps boost the customer experience, by reducing delivery times and ensuring there is always stock for popular items.

Example: An consumer electronics company can use it to balance the demand for products based on historical sales data, market trends, and external factors (like chip shortages). This allows them to optimize warehousing and supply-chain costs while maintaining suitable inventory levels.

AI-powered concept testing tools enable market researchers to test and evaluate new product concepts or features before launch . Through advanced algorithms, consumer feedback, and predictive analytics, these tools help assess market acceptance, identify potential improvements and guide product innovation strategies.

Example: Tech startups can gather consumer feedback, analyze preferences, and predict market acceptance of a new product concept to guide their product development team.

5 AI tools for market research

Marketers already have access to many powerful tools to use AI in market research projects. Out-of-the-box solutions often provide the most cost-effective way to start leveraging AI and ML to enhance their marketing teams’ capabilities with lower lead times.

1. AI Market Research Assistant by Prelaunch.com

This market research tool helps physical product sellers improve their products by analyzing thousands of real customer comments on competitors’ Amazon listings.

You just need to define your product’s category (what it is) and give some of your biggest competitors’ Amazon URLs. In a matter of minutes, the tool returns the insights. The insights can include answers to important questions like:

• What’s the most liked or disliked feature,
• Unique selling points,
• Price/Benefit relationship,
• What’s the ideal customer persona, etc.

It’s all free for now.

Powered by OpenAI and Chat-GPT (yes, that Chat-GPT), this AI market research platform combines human intelligence and AI to enable data-driven decision-making. The platform uses a network of over 500,000 human panelists to gather survey data. You can create your own survey and disperse it to Poll the People’s panelists for real human responses. 

However, the secret ingredient is that Poll the People allows you to leverage its ChatGPT-powered analysis platform to effortlessly gain valuable insights from these survey responses.

SimilarWeb is a leading AI-powered market research tool that provides comprehensive insights into digital consumer behavior. Leveraging advanced algorithms and machine learning, SimilarWeb gathers data on website traffic, user demographics, engagement metrics, and competitive analysis. This enables businesses to uncover industry trends, identify market opportunities, and benchmark their performance against competitors. 

With its robust AI capabilities, including keyword analysis, audience segmentation, and app analytics, SimilarWeb empowers market researchers to make data-driven decisions, optimize marketing strategies, and gain a competitive edge in the digital landscape.

Latana is an AI research tool that specializes in brand performance tracking. It leverages advanced AI and machine learning algorithms to provide accurate and real-time insights into brand performance and audience perception. 

Latana collects data from a diverse range of sources, including online surveys, social media, and website analytics, to help businesses understand brand awareness, brand perception, customer sentiment, and competitive positioning. The tool offers customizable dashboards, robust analytics, and visualizations to assist market researchers in making data-driven decisions and optimizing their brand strategies.

Acquired by the leaders in business AI and CRM, Salesforce, Tableau is an AI-powered data visualization and analytics tool that empowers market researchers to gain valuable insights from their data. With its intuitive interface and advanced analytics capabilities, Tableau enables users to explore, analyze, and visualize complex market research data. 

By leveraging AI-driven features, such as automated data modeling and smart recommendations, this AI market research tool helps uncover patterns, trends, and correlations in market data. Its interactive dashboards and visualizations make it simple to communicate research findings effectively within your marketing team.

If you look at the statistics, it is clear that AI in market research is here to stay. 61.4% of marketers already use AI in one or more of their marketing activities. It also continues to grow in specific marketing areas. 68% of marketers intend to use AI to innovate their email marketing activities, while 63% plan to use it in their influencer campaigns .

It’s no wonder that the AI marketing market itself is expected to grow from a value of just $12.2 billion in 2022 to $77.4 billion by 2030 at an astronomical CAGR of 25.2%.

So, not only is AI in marketing research already a significant force, but its influence will only grow. Those who don’t invest in it now, risk falling further and further behind early adopters and industry leaders.

However, the future of consumer understanding lies in a combination of human intelligence and AI for market research capabilities. While traditional social listening techniques have their place, augmenting market research with AI and automation has become essential to cope with the overwhelming volume of data available.

Looking to the future, AI in market research will continue to play a vital role in shaping the field. The rise of hyper-personalization, augmented intelligence, and ethical considerations will further enhance the capabilities of AI in delivering tailored experiences and enabling human-machine collaboration. 

AI-driven consumer insights are gaining traction, and organizations that embrace AI as part of their marketing strategy will have a competitive edge in understanding customer behavior, optimizing marketing campaigns, and fostering meaningful customer relationships.

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9 Top AI Market Research Tools (2024)

Artificial intelligence (AI) and machine learning can help you conduct market research—and understand your data—faster than ever before.

Check out these seven AI market research tools to better understand your industry and customers this year.

1. Exploding Topics: Best For Discovering Trends

Exploding Topics is a trend-spotting and social listening platform.

We use AI to source our data—and then verify its accuracy and relevance with the help of human data analysis experts.

This AI+ human combo allows us to source data about new and growing trends quickly, while also ensuring only the most relevant information enters our database.

Plus, we update our data sets daily. This means that Exploding Topics users are often the first to know about trends affecting their business and customers.

By using Exploding Topics as part of your market research activities, you can easily gather reliable information about:

• Trending products, services, and companies
• Trend popularity across different social media platforms
• Changes in topic popularity over the past 15 years
• Typical e-commerce product prices
• Emerging startups

You can even discover the ways market trends relate to each other—and how we’re forecasting their popularity levels may change.

Who Should Use Exploding Topics?

Exploding Topics is a useful tool for anyone conducting market research activities.

It's great for use early in the research process and can help you narrow down the topics you need to focus on during your focus panels, interviews, and more.

How Much Does Exploding Topics Cost?

Exploding Topics is free to use. You can browse a portion of our database right now—no login required.

To unlock our full database and more features that are useful for market research you'll want to subscribe to an Exploding Topics Pro plan. Options start at just $39, billed annually.

You can give Exploding Topics Pro a try before committing, too—it's just $1 for your first week of access.

2. Gong: Best For Transcribing Customer Interviews

Gong is an advanced AI-powered call recording and customer intelligence platform.

Sales, marketing, and research teams can use Gong to record video and audio from calls with customers or survey participants.

Gong will automatically transcribe your calls and produce AI-generated summaries that include:

• Key highlights
• Contextual insights and discussion points
• Keywords that appear frequently during the call
• Valuable insights including references to meetings and times
• Actions for each person on the call

It works in multiple languages as well!

Who Should Use Gong?

Gong is a good choice for marketers and researchers that intend to do a lot of multilingual audience interviews. You'll need to regularly plan, schedule, and hold calls with Gong in order to make the tool worthwhile.

If you're seeking a service that conducts research for you, though, you'll want to try a top market research firm .

How Much Does Gong Cost?

Gong prices its licenses per user; you'll need to get in touch with the company to discuss a customized quote.

3. Speak: Best For Data Visualization

Speak is a data visualization service.

The platform uses natural language processing (NLP) to extract data and actionable insights from video, audio, or text files.

Like Gong, Speak can automatically record and transcribe your customer interviews. However, it's not limited to content captured directly through Speak.

You can upload files from any source, including public URLs, and have Speak extract data using AI.

From there, Speak can turn the data points into shareable visualizations. You can even ask the Speak AI questions about your data and get responses in a conversational format.

Who Should Use Speak?

Speak is a good choice if you plan to capture research data in multiple mediums, including text-based surveys and pre-recorded video responses.

It's also worth looking at Speak if you're interested in using AI to examine and report on your data using prompts—similar to how you interact with an AI chatbot.

How Much Does Speak Cost?

You can use Speak's basic AI transcription service on a pay-as-you go basis. To unlock everything that Speak offers, including chatting with the AI, you'll need to subscribe to a monthly paid plan.

Options start at $29 per month or $278.40 per year.

4. SurveyMonkey Genius: Best For Building Research Surveys

SurveyMonkey Genius is an AI-powered survey builder.

The team at SurveyMonkey trained their AI on data gleaned from over 25 years' worth of surveys hosted on the platform.

You can start the process by describing your goals for the survey, or by using one of SurveyMonkey's prepared prompts.

From there, the AI will generate an entire editable survey for you to review and tweak.

SurveyMonkey Genius can also:

• Suggest the best type of question to use for a topic
• Prepare balanced multiple choice answer options that help to remove bias
• Conduct sentiment analysis on open-ended question responses
• Highlight trends in your survey responses
• Evaluate the quality of each response and eliminate any that aren't readable or aren't in line with your quality standards

When we gave the Genius feature a try, we could create a complete survey in less than five minutes.

If you find that the SurveyMonkey AI doesn't perform as expected, though, you can give feedback directly in the app. SurveyMonkey will then use this feedback to continue training the AI.

Who Should Use SurveyMonkey Genius?

If you're already a SurveyMonkey user, it's worth checking out Genius to see if it can accelerate your workflow.

New users interested in an easier way to make surveys might also want to check out SurveyMonkey Genius, as it may help you launch market research initiatives (and get results) faster.

How Much Does SurveyMonkey Genius Cost?

You can use SurveyMonkey Genius as part of a free SurveyMonkey account.

5. Poll the People: Best For Finding Survey Respondents

Poll the People is a panel analysis program.

If you like the AI-powered aspect of SurveyMonkey Genius, but don’t have an audience to survey, then Poll the People is a solid alternative.

The platform uses OpenAI’s ChatGPT technology to help users create, distribute, and analyze the results of market research surveys.

You can use Poll the People to:

• Build surveys using prepared templates
• Select a panel from the platform’s pool of over 500,000 respondents
• View, filter, sort, and analyze results with the help of ChatGPT

As with Speak, you can chat with Poll the People's AI about your survey results and get direct answers that reference the data.

Who Should Use Poll the People?

Poll the People is a good choice for anyone who wants to collect primary research data from groups of people like their target audience.

It’s a particularly good fit if you don’t already have access to a survey panel.

How Much Does Poll the People Cost?

Poll the People offers a pay-as-you-go plan. It’s free to sign up—you just pay $1 per response, and get access to all of the platform’s features.

If you plan to run multiple large surveys, then you may prefer the Plus plan. It costs $50 monthly and lets you run up to 10 surveys with 500 responses each every month.

6. Remesh: Best For Running Online Focus Groups

Remesh is an AI-powered focus group platform.

You can use Remesh to:

• Establish a focus group schedule
• Build a discussion guide
• Upload media to share with your groups
• Create questions to ask each participant
• Ask participants open-ended and multiple-choice questions about your content
• Have participants vote on how closely they agree with statements made by other focus group members

While you run the focus group, the Remesh AI works to analyze feedback, find common themes, and highlight key points for your review.

This AI analysis happens at the same time you talk to your target audience. If you see specific consumer behavior reflected in the findings and want to learn more about it, you can change your discussion questions on the fly.

Remesh also lets you export ready-to-share slide decks and CSV files with your data once the focus group ends.

Who Should Use Remesh?

Remesh is a good choice for teams that plan to run their own focus groups. The included AI features can help you get a better understanding of your results in real time, without waiting for the session to end.

How Much Does Remesh Cost?

Unfortunately, Remesh doesn't publish their pricing publicly, so you'll need to schedule a demo to get a custom quote.

7. Lexalytics: Best For Custom Research Applications

Lexalytics is a text analytics platform.

The service uses NLP to evaluate text based documents and extract information including:

• Sentiment and intent
• Names of people, places, dates, products, and companies
• Common factors to create groups of related text

It's suitable for use when conducting all kinds of market research—even for pharmaceuticals, finance, and other heavily regulated industries.

Plus, you can incorporate Lexalytics technology into your own applications—giving it great versatility when compared to other AI analysis programs.

Who Should Use Lexalytics?

Lexalytics is a nice choice for any research teams that plan to develop their own custom survey, panel, or focus group platform.

If you're already paying to use a full-featured tool like Speak or Remesh, though, you might find Lexalytics to be an unnecessary add-on.

How Much Does Lexalytics Cost?

Lexalytics plans are customized based on your needs, so you'll have to get in touch with the company and get a price quote.

8. Claude: Best For Exploring Research Findings

Claude is a self-contained conversational AI chatbot.

Claude doesn't access the internet, so it can't conduct research for you. You can feed your own data files into it, though, and use the AI as a data analysis assistant.

When you share data with Claude, you can ask the AI a variety of questions about the content of your data upload. You can even have it create summaries and customer personas based on your research.

During a test, we fed a CSV file with 95 lines of data into Claude. Within a few seconds, Claude could start answering questions about our findings—eliminating the need to read through each data point ourselves.

Who Should Use Claude?

Claude is a useful tool for anyone who spends a lot of time reading data-rich reports and spreadsheets.

How Much Does Claude Cost?

It's free to try out Claude, though you're limited in the number of questions you can ask it each day.

To increase your usage limits, you'll need to sign up for Claude Pro—it costs $20 per month.

9. YouScan: Best For Social Media Market Research

YouScan is a social media analysis tool.

The platform uses AI to extract data from images and text shared across social media platforms like Instagram.

It doesn’t rely on post captions and hashtags, either—YouScan can recognize text and subjects depicted in images.

Use the tool to:

• Evaluate how frequently a brand or product appears in social media pictures
• Assess the sentiment of social media posts made by your target audience
• Compare brands and products that appear together in users’ photos
• Discover the platforms that attract discussions or posts about specific brands

And, as with Poll the People and Claude, YouScan includes a chatbot you can use to gather insights into your data. Simply ask it a question about the information you're looking for, and YouScan's AI will respond.

Who Should Use YouScan?

Companies that have a very social media friendly product line can use YouScan to learn more about how and where their brand is depicted on social media.

How Much Does YouScan Cost?

YouScan plans start at $299 per month, billed annually, and include unlimited user seats.

Deciding to use AI tools is a great way to streamline your market research process—and how you interpret related metrics. While each of the tools on this list still requires some human involvement, each service can help you find and understand data faster.

Whether you’re uncovering new trends to research with Exploding Topics, conducting surveys with Remesh and Poll the People, or exploring your data in depth with Claude, the nine AI-powered tools on this list are some of the best options for conducting market research this year.

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How to leverage AI for market research in 2024: top 7 tools, use cases, and best practices.

In this article, I'll walk you through the benefits of AI for market research , the best AI tools, the use cases we use at Predictable Innovation and concrete prompts for customer, competitor, and market research.

Imagine, as a strategist, founder, innovator, or product marketer, if you could transform internet and interview data into actionable insights in record time without tedious manual processing.

Today, thanks to artificial intelligence (AI), this is possible.

AI-powered tools for market research can scrape large amounts of data on the web, perform data analysis, and deliver it through easy-to-digest formats thanks to natural language processing models, marking a new era for market research.

"AI tools are not just assisting on decision making, they're making Predictable Innovation's team save up to 40% of time when conducting market research."

Let's get started!

Table of contents: best 7 tools in AI for market research

• Fathom or Grain for customer interview transcript and research
• Essense.io for competitor research
• Perplexity for generalist market and competitor research.
• Aomni as an agent to create industry or market research reports.
• The Gigabrain for audience research at Reddit
• ChatGPT for interview transcript research and customer research, including jobs-to-be-done.
• ChatGPT for positioning research and strategy idea generation by using it as a team member.
• Notion AI to have all in one place and automate AI prompts with database content.

1.Best AI-powered market research tools

Perplexity.ai.

‍ Perplexity is a market research tool that provides a clean and easy-to-use interface. Its key advantage over traditional models like ChatGPT is its ability to deliver more precise, context-aware responses.

This is achieved by integrating direct internet browsing capabilities within its system. Consequently, Perplexity offers users up-to-the-minute information, expanding their research capabilities beyond pre-trained data limits.

This feature makes it exceptionally useful for market researchers seeking the most current data and insights.

Aomni is an innovative AI agent for B2B sales. It excels in aggregating extensive internet data points to provide comprehensive reports, saving significant research time for sales professionals.

It is designed for sales, but the way it provides research reports is excellent for gaining a general market or industry understanding quickly.

The advantage of Aomni is that it executes an internal dialogue with different research queries related to your topic; this saves you the time to think on your own when using other tools like ChatGPT or Perplexity.

For this type of broader market research, aomni .com is an excellent solution. The pros of this solution are:

• The sources have up-to-date results from performing live research.
• Understanding the internal monologue of the agent to perform the research.
• The structure of the result is fantastic. Similar to what a human researcher would deliver in a document.

ChatGPT for Market Research

‍ I don't think I have to introduce you to ChatGPT, right? Its advantage over the other two tools is straightforward: developing a conversation and getting more profound, specific insights is excellent because of the conversational ability.

That makes ChatGPT an excellent tool for use cases that require back-and-forth questions to dig deeper into specific market challenges, customer pains, or competitor strengths.

Last, but not least, ChatGPT can also help build your market ecosystem map by finding key players in your target industry or markets. This step is very helpful to find people to interview and build relationships with to improve your go-to-market strategy .

The Gigabrain

‍ Here's what' Gigabrain claims to be: "GigaBrain finds the most useful discussions on Reddit and other communities . We sift through the noise and analyze billions of comments for you.". And that's precisely what they provide.

That makes this tool excellent for understanding market perceptions, beliefs, and challenges based on real-world conversations with prospective customers rather than internet articles or databases.

Head-to-head comparison of best AI for market research tools

Practical use cases and prompts in AI for market research

For the scenario of industry and market research, the concrete use cases and prompts to apply these tools are:

• Discovering market stats
• Discovering potential research sources
• Discovering market category differences and market perceptions
• Discovering barriers to product success (risks of adopting your product category)
• Discovering the perceived market risks of adopting a tech or product
• Understanding customer's behavioral change and the learning curve of adopting a new tech
• Researching inside a PDF (with ChatGPT)
• Understanding market maturity (stage of the innovation adoption curve)
• Exploring opportunities and potential targets
• Understanding challenges by [AUDIENCE] in [CATEGORY]
• Discovering problems for a target market from the perspective of the client
• Discovering problems your tech/product can solve from a product perspective
• Discovering potential segments and applications to target
• Prioritizing segments and applications to target (Bowling Pin Strategy)

2.Best AI tools for customer research

Fathom Video is an automated meeting assistant tool. It offers functionalities like recording, transcribing, and summarizing video meetings.

A standout feature is its ability to highlight and automatically summarize vital parts of a call, allowing users to focus on the conversation without worrying about taking notes.

One of the features that we love about Fathom is the ability to use templates to extract information from the calls. Grain offers a better feature as you can create your prompts to extract information. However, we use fathom because extracting information from the transcripts works better by uploading the transcript to ChatGPT (in private mode) and asking ChatGPT to extract the information we seek.

Examples of data you can extract with AI are competitors' names, pain points, struggles, points of frustration, jobs-to-be-done, technology adoption frictions, and or hesitations to buy.

We already talked about this tool. Its ability to scrape and understand information from real-world conversations makes it a great tool to uncover specific pains of your audience as soon as they are in publicly available forums like Reddit.

ChatGPT is a killer when it comes to researching customers or audiences.

Its conversational interface makes ChatGPT great for narrowing down discussions and getting to better, more specific points and angles about customer journeys, jobs to be done examples , pain points, or end-user workflows.

Also, its ability to learn as the conversation evolves is key to getting better answers because it uses the conversation to enrich its contextual information, which gives it a better understanding of the customer's or audience's context.

Regarding pure AI reasoning and reach power, Notion AI is over performed by the other tools.

However, if you are a Notion user and want all the use cases in one place, Notion is the best all-in-one solution to avoid interface switching and paying multiple subscriptions. As said, this comes with a lower reach, quality, and reasoning power for AI.

Practical use cases and prompts in AI for customer research

For the scenario of customer research, the concrete use cases and prompts to apply the previous AI toolset are:

• Extracting pain points from customer/prospect interviews
• Extracting jobs to be done from customer/prospect interviews
• Extracting  outcomes from customer/prospect interviews
• Summarizing patterns from multiple market and customer interviews
• Understanding budgets (is there a budget allocated to solve the problem my product solves?)
• Exploring pain points starting with an audience and category of product
• Assisting in a discussion of potential targets
• Discovering the customer's journey
• Exploring the economic penalties for an audience for not adopting a specific type of product/solution
• Discovering the end-user's workflow impact of implementing a given solution/tech
• Customer scenario discovery (before, after, bridge)
• Discovering buying trigger events

3.Best AI tools for competitive research

Essense.io is an AI platform that transforms qualitative customer feedback and competitor reviews into actionable insights. It focuses on guiding businesses to prioritize their product roadmap based on customer needs and competitive analysis.

This tool identifies customer pain points and feature requests.

Additionally, Essense.io aids in making informed marketing decisions by analyzing customer and prospect feedback and brand sentiment from various sources. It's especially useful for quickly producing insightful customer or competitor intelligence reports, saving significant research time.

This tool could also be situated in the customer research bucket because it helps uncover customer needs from the reviews scraped or analyzed.

Yes, ChatGPT is in all the buckets! And it's because of its versatility. ChatGPT is excellent for creating lists of potential competitors and performing a SWOT analysis. Head to the practical use cases section to get prompt examples.

Practical AI cases for competitive research

The concrete use cases and prompts to apply the previous AI toolset for competitive research are:

• Discovering competitor (or your company) whitespaces and weaknesses with essense.io and G2 reviews
• Discovering competitors in existing categories
• Discovering competitors in categories in formation or unknown
• Create a competitor table by providing a product/company URL
• Discovering perceptions about a competitor (or your company)
• Discover why competitors win
• Creating a SWOT analysis
• Getting an AI opinion on your product/company going head to head against XYZ competitor
• Understanding the market perception of your company or a competitor

How Predictable Innovation Strategy uses AI for market research

Predictable Innovation has completely embedded AI in its market research workflow to provide clients with quicker results while keeping the same high quality. We've found 25-40% time savings thanks to using Artificial Intelligence (AI) for market research and other use cases.

The role of AI in market research has been transformative for our business and competitiveness, providing our team with different benefits:

👀 Gaining analysis efficiency

‍ We were analyzing tons of Reddit posts, call transcripts, reading entire analysts' PDFs, or hundreds of G2 competitor feedback answers.

That's gone, using AI-powered market research. AI makes data collection from these sources almost automatic. It is a great tool for finding patterns, summarizing information, and extracting data from text or videos. We use all this to gather market intelligence faster.

🧠 Gaining a second brain and new perspectives

AI gives us fresh angles for product positioning and go-to-market strategies that are not biased by our beliefs. It's tough to get out of your own head. AI can help with this.

🔍 Getting broader research insights

AI tools can identify sources and reach internet corners we could not or did not have the time. We've gained more research findings, insights, understanding, and angles about the market, customers, and competitors we analyze.

🚀 Taking off research projects at light speed

With traditional market research methods, we had to conduct secondary internet research and analyze interviews. It would take around two weeks to warm up a market analysis or strategy project.

Now, by using AI-powered market research tools, we get up to speed on new client projects in just one day by using AI tools for market research.

🏎️ Faster market feedback loops & strategy development

Given the speed at which we can now analyze the information, we can deliver faster market testing and feedback loops, leading to a competitive edge.

Market learning speed is often an advantage.

😮‍💨 Overcoming the blank-page syndrome

We won't have to feel the pain of starting from 0 anymore to fill in our templates and create strategies. AI is helping us with a fresh starting point.

What is the business impact of using AI for market research for Predictable Innovation

We've measured the impact of integrating AI for market research and other use cases in our workflow. Here's what we've found:

→ 25-40% margins increase

Depending on the project's complexity, we've measured 25-40% working time savings in market/customer research, innovation, positioning strategy, messaging, and sales tasks.

P&L IMPACT: All that goes directly into Predictable Innovation's P&L bottom line .

→ x2 productivity

In strategy projects for clients from the kick-off to deliverables. An average project was three months; with AI, it takes 6 to 8 weeks. Most of the productivity gains are coming from AI market research use cases.

STRATEGIC IMPACT: Speed-to-delivery is now a key feature of Predictable Innovation . We now deliver high quality with more speed to results.

→ New research eyes and strategic angles

We spent tens of hours analyzing Reddit posts, interview transcripts, analysts' PDFs, google results, and G2 user interviews. That's now gone with all the AI tools in this article.

TEAM IMPACT: The AI is working as their team assistant to provide angles for their strategy team. These are new strategic angles that are not biased by preconceived human beliefs.

FAQs about AI for market research

What are the different ways to use ai in market research.

AI tools like ChatGPT, Aomin, or perplexity.ai can help with a large number of use cases like extraction of pain points from interview transcripts, sentiment analysis of interviews, create SWOT analysis, scraping G2 reviews to finding competitor whitespaces, discovering customer journeys and jobs-to-be-done or assess market opportunities.

At Predictable Innovation, we've identified and documented 67 use cases in which AI can be used for customer, competitor, and market research. Some of them have been detailed in this article.

What are the best practices for using AI in market research?

We've created a list of quick tips when approaching the use of AI for market research:

1. When using ChatGPT, create new chats whenever your questions require a new skill set or context

Think of ChatGPT the same way as hiring an employee. You won't hire someone who is an expert in car manufacturing to work on healthcare. That's a scenario in which you want to start a new ChatGPT conversation. Every time you switch contextual needs like skillset required (ex. think copywriting vs research vs positioning strategy), industry or product category, you have to start a new conversation.

2. The more specific you are, the more specific ChatGPT's answer

‍ AI is as good as the question you're asking and the context you're providing. Think of ChatGPT as talking to a child. You must be clear, specific, and relevant if you want specific and relevant answers.

3. AI is your assistant; you're the brain

‍ Let AI provide you with data, angles, and analysis. But you and your team MUST double-check it and make informed decisions.

4. AI is NOT a replacement for customer or market interviews

‍ What you'll find in a single customer interview, or even an interview with your sales team, will be way more detailed, specific, and relevant than what you'll find with AI research over the internet.

5. Protect sensitive data

When using proprietary data or interviews in ChatGPT, protect your data by turning off the Chat history & training under set, and carefully read the terms of service and privacy policy of AI tools.

6. Follow-up questions are a must to discover more specific answers

Think of ChatGPT as one more team member. Make the curiosity drive your questions to discover more and dig more deeply and more specifically into ChatGPT answers. You won't just take the first answer and use it; get curious, keep asking, and go deeper.

7. Push ChatGPT

If the answer is not what you expect, make your question / prompt more precise, specific, and relevant. You can also try tricks like pushing ChatGPT to provide new insights, angles, or ideas with this prompt:

What are the potential challenges when implementing AI in market research?

Data quality.

AI models are only as good as the data they are trained with. The model will produce unreliable results if the data is incomplete or inaccurate.

When using the previous SAAS tools, always double-check the results, find the sources of truth, and make sense of them.

If you have your own models, ensure that the data is cleaned and preprocessed before training the model.

Transparency

This is often referred to as the 'black box'. It is difficult to understand how these models are making their outputs, which can lead to mistrust and resistance from stakeholders.

The solution to this challenge is to use explainable AI techniques, start with pilots, and showcase the value of AI for market research.

Job displacement

‍ The solution to this problem is not easy. The best you can do is to focus on using AI to augment your team's capabilities rather than replace them.

This means freeing up your team to focus on more complex and strategic tasks while they use AI for the use cases I described today. As said, artificial intelligence is the assistant; human intelligence is the decision-maker.

If the data used to train the AI is biased, the predictions or analyses will also be biased.

This can lead to inaccurate market research results and potentially harmful decisions. To combat this, it's important to use diverse and representative data sets for training AI. Regular audits of AI systems can also help identify and correct any biases.

Rapid pace of AI development

AI technology constantly evolves, making it difficult for organizations to stay up-to-date with the latest tools and techniques. The solution to this challenge is to invest in continuous learning and development. This could involve attending industry conferences, participating in online courses, partnering with AI experts, or subscribing to AI newsletters like the Neuron or Superhuman .

Change management needs

Implementing AI may require significant changes to workflows and processes, and there can be resistance from employees who are comfortable with existing research approaches.

To overcome this, start with small pilots, communicate the benefits of AI clearly, and provide ample training and support during the transition. Involving employees in the implementation process and addressing their concerns can also help ensure a smooth transition.

While challenges are associated with implementing AI for market research, these can be overcome with the right strategies and approaches.

By focusing on data quality, transparency, ethical considerations, and continuous learning, you can successfully leverage AI to enhance your market research efforts. Just like we did!

Continue learning AI for market research with Predictable Innovation

Check this video on how Predictable Innovation uses AI to streamline for the concrete use case of Ideal Customer Analysis:

→ Take back 40% of your time in just 3h with my AI for market research online course

→ Try our custom ChatGPT tools ‍

Get free advice and angles by GPTs trained with our knowledge and strategy frameworks. Just bear in mind that they are experimental!

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AI in Market Research: How It Is Disrupting Our Industry

In this post, we touch on how AI is already redefining the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497423">consumer insights process to make it simpler for businesses to make high-quality, fast, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497417">informed decisions for their key business objectives.

By now you’re likely familiar with dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497395">Artificial Intelligence (AI) technology (being as it’s become hard to ignore in business applications and social media). However, you might be wondering about artificial intelligence in market research and the role that it plays. AI market research tools  are yet another example of how AI and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497436">machine-learning dropdown#toggle" data-dropdown-menu-id-param="menu_term_280497436" data-dropdown-placement-param="top" data-term-id="280497436"> dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497399">algorithms are positively transforming industries through highly intelligent and efficient processes. In the sections below, learn how AI tools for market research are already transforming the industry and the potential they have for the future. 

Table of Contents: 

• Where does AI already exist in market research
• New advances in market research AI 

6 use cases of AI in market research

• Which market research tasks AI cannot take over 
• Overall positive impact of AI on the market research industry

How quantilope is continuously integrating AI 

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="278475767">

Where does AI already exist in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research ?

In today’s age of digital, platform-based market research, many market research providers lean on AI for efficient data cleaning, predictive insights, text and data analysis, and more. In fact, AI is so seamlessly ingrained into many of these platforms that you may not have even recognized you’re using it! If you’ve ever used smart and intuitive survey building features (like drag & drop modules), a length of interview (LOI) prediction tool, automated data cleaning filters, automated analysis of advanced research methods, or real-time charting solutions, you’ve been benefiting from the power of AI. Compare this to the traditional days of manual and tedious market research processes, and it’s clear we’ve already come a long way. Even so, AI continues to make advances in market research. Back to Table of Contents

New advances in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research AI

AI for market research is something that will only continue to grow - making it a really exciting time for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394"> researchers who want to do more, with less.

One clear example of this is the excitement over Generative AI (or, Gen AI for short); that is, models that generate text, images, code, and other media in mere seconds. A notable GenAI tool that’s become widely used is ChatGPT. Using AI, ChatGPT can generate paragraphs of text, list a series of bullet points, provide research into a certain topic area, and even tell you a funny joke! So how does something like ChatGPT help dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market researchers ? Generative AI helps research teams quickly research a topic, provide a survey foundation, or understand the best survey layout - all from just a few prompted words. That’s efficiency at its core. However, other recent advances in AI can do more than just generate information - it can synthesize it. Synthezing AI (or, Synth AI for short) takes large dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497408">data sets or other mass information and summarizes it for a faster and cleaner analysis. Research teams can use Synth AI for things like insights summaries, fast dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497410">data analysis , knowledge management, and report creation. Both Gen AI and Synth AI in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research are modernizing what’s been an otherwise tedious and manual industry. With these efficiencies, researchers are in a position to focus more of their valuable time on strategic stories from the insights to make dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497415">data-driven decisions . Back to Table of Contents

Below are six practical use cases of AI for market research to support you and your research team in each step of your end-to-end insights process. 

1. Survey ideation: 

For researchers stuck on what to ask or include in their surveys, AI tools can help generate ideas and questions that will provide them with high dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497430">data quality and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497403">actionable insights . This might be particularly useful for applying advanced methods or for running dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497443">product innovation studies when a brand is exploring a new dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497442">target audience or unfamiliar venture.

2. Survey programming: 

AI can replace the need to manually write and program logic into survey questionnaires. Research platforms like quantilope offer the option to drag & drop pre-programmed survey questions or advanced research methods into a questionnaire. Automation during this step significantly speeds up the time from initial concepting, to actual fieldwork, and final decision-making. AI can also help with understanding participant experience during this phase - like quantilope’s use of AI to predict the length of interview (LOI).

3.  Data collection and analysis: 

AI tools streamline data collection and data analysis so that researchers can spend more time focusing on the insights and stories from their dataset. For example, quantilope leverages AI to automate the analysis of descriptive usage and attitude data as well as data from 12 advanced research methodologies so that platform users can spend more time with the findings than on the actual analysis. Rather than manually programming a survey for data collection and manually inputting chart metrics for analysis, automated algorithms can quickly analyze your data set and generate reports that highlight key takeaways. Some AI tools can even generate predictive analytics so you can start forecasting new market trends early on.

4. Data reporting: 

AI tools can populate dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497444">real-time dropdown#toggle" data-dropdown-menu-id-param="menu_term_280497444" data-dropdown-placement-param="top" data-term-id="280497444"> data so that you can get a jump start on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497414">market insights analysis before fieldwork even wraps up. Set up the charts you want to be populated and let AI take care of the rest as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497413">respondents complete your survey. Further, AI can even generate chart titles and descriptions to shorten the time from survey launch to stakeholder delivery.

5. Qualitative analysis : 

Are you using qualitative means of research like dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497428">focus groups or online video interviews? AI  dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market analysis allows researchers to quickly perform emotion or dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497409">sentiment analysis based on consumers’ qualitative responses and extract key insights using natural language processing (nlp). 

6. Customer support: 

AI has the potential to serve as a co-pilot, supporting each stage of the research process from initial planning to final reporting. For researchers who want to be more hands-on with their insights process but need a bit of help along the way, AI becomes their reliable partner to get more done in less time - time researchers can leverage for additional projects or to support other business needs (like developing marketing strategies around key insights).

Back to Table of Contents

Which dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research tasks AI cannot take over 

Though AI can (and does) transform the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research industry in many positive ways, there are some elements that simply can’t be fully replaced by technology (yet). 

For one, strategic consulting . No matter what level of AI you might be using, nothing can beat a human-to-human discussion to personalize dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497423">consumer insights and bring them to life.

Second, AI can’t totally take over the role of data scientists within a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research organization. You’ll still need these highly skilled professionals to set up tailored AI integrations, troubleshoot any AI issues, and improve your AI features over time.

Third, relationship building . dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">Market research requires a lot of relationships for projects to run smoothly. Even when using an all-in-one platform, you’ll often still need panel partners, software integrations, and other external elements.

Finally, AI can’t totally replace quantitative and qualitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497429">market research dropdown#toggle" data-dropdown-menu-id-param="menu_term_280497429" data-dropdown-placement-param="top" data-term-id="280497429"> projects where researchers gather consumer feedback and act on the insights. AI might help with certain steps in these projects but AI doesn’t have the ability to actually go and replace studies from start to finish.

Overall, the role of AI and machine learning in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research should be more about enhancing human processes rather than entirely replacing them. Back to Table of Contents

Overall positive impact of AI on the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research industry

Consumer trends, wants, and needs are changing at a pace that makes it difficult for businesses to keep up. As a result, executives are increasing their demand for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497396">real-time , high-quality insights, putting increasing pressure on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research professionals. With these increased requests for data-backed dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497423">consumer insights in very short turnarounds, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market researchers are challenged to meet these new demands alongside limited or shrinking capacities.

The good news is that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497412">AI technology can take over these tedious, manual processes like cleaning dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497426">unstructured data , inputting dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497445">data sources into charts, automating dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497446">statistical analyses , and creating final reports. AI is the solution for research teams that don’t have enough hands to run all the research they need to run, but still need decisions backed by insights. With the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497440">use of AI and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497400">automation , researchers will have more time to really analyze dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497407">consumer behavior insights and how they can support strategic dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497441">initiatives for the company. Overall, AI's impact on the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394" style="background-color: transparent;">market research industry is likely to be a combination of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497400" style="background-color: transparent;">automation , transformation, and the emergence of new roles.

As quantilope CEO and Co-Founder, Peter Aschmoneit, sees it:

"The continued rise in applicable uses of AI, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497395">artificial intelligence , and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497397">machine learning in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research is one of the most exciting movements in the industry...and only getting bigger." -Peter Aschmoneit, quantilope CEO and Co-Founder

For nearly a decade, quantilope has been leveraging dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497412">AI technologies to unlock high-quality dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497423">consumer insights in dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497396">real-time , empowering researchers and executives to elevate the consumer voice in boardroom conversations. quantilope was built with AI in its DNA to make high-quality dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research faster, more affordable, and more accessible to researchers of all skill sets. To continue growing alongside the exciting advancements in AI, quantilope established three core principles that you’ll see reflected across the Consumer Intelligence Platform :

Build AI into the end-to-end research process. For AI to reach its true potential in consumer research, it must be ingrained in an end-to-end workflow that researchers have direct and constant access to. Today’s researchers shouldn’t need to copy/paste data across platforms or dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497445">data sources .

Blend AI with advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497425">research methods . quantilope was built around the idea that speed to insights should never sacrifice the quality of insights. Only when using AI in combination with advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497425">research methods like Conjoint , TURF , Implicit Association Tests , and other advanced methods can researchers provide robust data that stakeholders can trust without compromising a project’s timeline.

Make AI your research copilot. The future of AI in the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research industry is through AI co-pilots that navigate you through each stage of the research process. Using an AI co-pilot, you’ll seamlessly weave AI capabilities into survey creation, survey fielding, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497410">data analysis , and insights reporting for an overall more productive process. In other words, an AI co-pilot allows you to do much more with much less (effort, time, resources, etc.).

Staying true to the above principles around AI, quantilope has recently announced the launch of all new AI features to add to the platform’s existing AI capabilities - which already include automated analysis of advanced methods, automated data cleaning, and automatically estimated LOI (length of interview). The new, additional AI features will lean on Gen AI and Synth AI to offer survey creation tools, automated project summaries, and more. With this announcement , quantilope is becoming the only research platform to blend market-leading advanced methods and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497412">AI technology - enabling researchers to unlock data they can trust, easier and faster than ever before.

“Over the short and long term, quantilope is reshaping dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research through practical applications of AI that finally solve the age-old research dilemma of having too many requests for projects & insights with too few hands or resources to complete them,” explained quantilope CEO and Co-Founder, Peter Aschmoneit. “Among all of the buzz and anticipation around AI, it’s our commitment to seamlessly weave AI tools into our existing platform that naturally fit into the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497394">market research workflow and make advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497425">research methods even easier for researchers to implement into their processes.” With quantilope’s expanded set of AI tools, researchers can build new projects and analyze dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497411">customer feedback with just a few clicks of their mouse. As for the long term, AI will continue to form the backbone of quantilope’s platform, further streamlining processes around fielding, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497410">data analysis , and data reporting. With this AI transformation, researchers are expected to get 50% of their time back on each project with quantilope - time they can leverage for additional projects or to support other business needs.

To learn more about AI and how quantilope is making the shift to AI to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="280497404">optimize our end-to-end consumer research process, get in touch below!

Get in touch to learn more about quantilope's new set of AI features!

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Market Research AI | Best Tools, Tested Tips, and Examples

Gain insights into the world of Market Research AI with this comprehensive article. Explore the top AI tools, proven tips, and real-world examples to unlock the potential of AI in market research. Stay ahead of the competition and make data-driven decisions in your business.

Sintra AI Editorial

Table of contents

Introduction.

Struggling to sift through mountains of data for that golden insight? Market research can be a minefield of complexity, leaving you overwhelmed and second-guessing your strategies. But what if there was a smarter, faster way to get the job done?

Welcome to the world of artificial intelligence in market research. This guide is your one-stop-shop for understanding how machine learning algorithms and market research tool can revolutionize your research process, complete with tried-and-true tips and real-world examples. Let's dive in and transform your market research challenges into opportunities.

Natural Language Processing and Machine Learning

Natural Language Processing ( NLP ) and Machine Learning are game-changers in market research. They dig deep to give you insights that are not just accurate but also actionable, taking your market research to the next level.

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Natural Language Processing (NLP)

NLP is a branch of AI that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language in a way that is meaningful. For market researchers, this has several applications:

1. Sentiment Analysis: By analyzing customer reviews, social media posts, and other textual data, NLP can determine the sentiment behind the words. This helps businesses gauge customer sentiment towards their products, services, or brand as a whole.

2. Chatbots and Virtual Assistants: These AI-driven tools use NLP to interact with users, gather data, answer queries, and even conduct surveys. They offer a more interactive and engaging way to collect data from respondents.

3. Text Analytics: NLP can sift through vast amounts of textual data to identify trends, patterns, and key themes. This is invaluable for businesses looking to understand market dynamics, customer preferences, and emerging trends.

Machine Learning (ML)

Machine Learning, a subset of AI, involves training machines to learn from a data collection and make decisions or predictions based on that data. In the context of market research, ML offers several advantages:

• Predictive Analytics: ML algorithms can analyze past data to predict future trends. For instance, by analyzing past sales data, ML can predict future sales trends, helping businesses plan better.
• Data Segmentation: ML can segment data into meaningful groups or clusters. This is particularly useful in market segmentation, where businesses can target specific groups based on their preferences, behaviors, or demographics.
• Anomaly Detection: ML can identify unusual patterns or outliers in data. This is crucial for businesses to detect potential issues, fraud, or areas that need attention.

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Understanding The Use Of AI in Market Research

AI is more than just a buzzword when it comes to market research; it's a transformative force if your business wants to gain deeper insights. It's redefining the way we gather, sift through, and make sense of the mountains of data that businesses collect every day. Here's how:

A New Level of Consumer Understanding

Brands are awash in data collection from social media, customer reviews, and other digital touchpoints. AI steps in as a lifesaver, turning this flood of information into meaningful insights. Consumer Intelligence platforms, powered by AI, go beyond mere data aggregation. They delve into the nuances of consumer behavior, preferences, and sentiment, offering brands a richer understanding of their audience.

Elevating Market Research Tools

AI isn't just a shiny new toy; it's a catalyst for change in market research. It amplifies the capabilities of traditional research methods, making them more efficient and insightful. For instance, AI algorithms can sift through social media chatter or customer reviews to identify emerging market trends or sentiments that might not be immediately obvious through manual analysis.

Transforming Surveys and Data Interpretation

Innovative companies like Qualtrics are leveraging AI to revolutionize the way surveys are designed and data of survey responses is analyzed. The aim is to make market research more intelligent, helping businesses not just collect data but also interpret it in a way that leads to deeper understanding, actionable insights and smarter decision-making.

Revitalizing Secondary Research

AI is also giving secondary research a new lease on life. It can quickly scan through academic papers, market reports, and other secondary sources to identify key insights that could easily be overlooked in a manual review. This makes your secondary research more robust and comprehensive.

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Bright Prospects Ahead

AI's role in market research is far from reaching its peak. As the technology continues to evolve, we'll see even more advanced tools that offer deeper, more nuanced insights. For those willing to adapt and integrate AI into their research methods, the potential benefits are enormous.

Benefits Of Using AI Instead Of Traditional Market Research Methods

While traditional methods have their merits, AI brings a fresh perspective, offering a plethora of benefits that are hard to ignore. Let's delve into the advantages of leveraging AI over conventional market research techniques.

1. Faster Data Processing

AI excels in handling large volumes of data at incredible speeds, something that traditional market research methods struggle with. This rapid data processing allows businesses to make quicker decisions and stay ahead of the competition.

2. More Accurate Insights

With its advanced algorithms and machine learning capabilities, AI reduces the risk of human error, providing more accurate and reliable insights. This level of precision is crucial for businesses looking to make data-driven decisions.

3. Cost Efficiency

Implementing AI in market research can lead to significant cost savings. By automating data collection and analysis, businesses can reduce the need for manual labor, saving both time and money.

4. Enhanced Data Analysis

AI goes beyond basic data analysis, offering deeper insights and identifying patterns that might be missed by traditional methods. This enhanced analysis leads to a better understanding of market trends and consumer behavior.

5. Real-Time Data Access

In today’s fast-moving market, having access to real-time data is a game-changer. AI enables businesses to gather and analyze data in real-time, ensuring that they are always up to date with the latest market developments.

6. Scalability

As a business grows, its market research needs also expand. AI offers scalability, allowing businesses to easily adjust their research efforts to match their current needs without a significant increase in cost or resources.

7. Elimination of Human Bias

Traditional market research methods often rely on human interpretation, which can introduce subjective biases. AI eliminates this risk, providing unbiased and objective insights.

8. Improved Customer Experience

By understanding consumer behavior and preferences, businesses can tailor their products and services to better meet customer needs. AI plays a crucial role in this, analyzing customer feedback and behavior to enhance the overall customer experience.

9. Access to Unstructured Data

AI has the capability to analyze unstructured data, such as social media posts, images, and videos, providing a more comprehensive view of the market. This is something that traditional methods struggle with, as they typically rely on structured data.

10. Continuous Learning and Improvement

One of the standout features of AI is its ability to learn and improve over time. As more data is collected and analyzed, the AI algorithms become smarter, leading to even more accurate and insightful market research.

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6 Best AI Tools for Market Research

In the ever-evolving landscape of market research, AI tools are emerging as game-changers, offering unparalleled insights and efficiency. Here's a look at six of the best AI tools that are making waves in the market research domain:

1. Sintra AI

This AI tool provides an exceptional blend of automation bots for streamlining business processes and a comprehensive collection of AI prompts tailored to optimize sales workflows and enhance decision-making. By seamlessly integrating AI into sales workflows, this comprehensive solution optimizes decision-making and maximizes sales opportunities. It offers an exceptional blend of efficiency, accuracy, and intelligence, allowing businesses to boost productivity and achieve better outcomes.

2. Quantilope

Quantilope is making significant strides in the market research arena with its automated consumer research platform. This tool is meticulously designed to elevate the quality, simplicity, and impact of research. By offering advanced consumer insights, Quantilope caters to international audiences in multiple languages, ensuring a global reach.

Speak AI is a standout tool specializing in AI for qualitative research. It boasts impressive transcription accuracy, especially with high-quality audio. This makes it a more cost-effective and efficient alternative compared to many of its counterparts. With its multilingual support and emphasis on reducing transcription and analysis time, Speak AI is a valuable asset for researchers.

Appen has carved a niche for itself in the domain of data labeling, a pivotal aspect of market research. Known for its reliability and user-friendly interface, Appen is the preferred choice for many professionals, especially those working in machine learning and computer vision fields.

Pecan AI is synonymous with predictive analytics. It offers a comprehensive suite of features ranging from marketing mix modeling and predictive campaign ROAS to campaign optimization and customer churn prediction. With Pecan AI, businesses can proactively retain customers, target audiences more cost-effectively, and optimize their marketing budgets.

Crayon is a competitive intelligence software designed to ensure businesses stay ahead of the curve. With its competitor monitoring and alert features, Crayon empowers businesses to concentrate on high-impact tasks, such as sales enablement, while staying updated with pertinent competitive intelligence.

Overcoming Challenges in AI-Driven Market Research

The integration of Artificial Intelligence (AI) in market research has opened up a world of possibilities, offering deeper insights and more efficient data processing. However, like any technological advancement, AI-driven market research comes with its own set of challenges. Let's explore some of these challenges and the strategies to overcome them.

Demonstrating the Value of AI

One of the primary challenges faced by researchers is demonstrating the value and benefits of AI for specific research objectives. It's essential to showcase how AI can enhance the quality of insights and streamline the research process. By presenting case studies and real-world examples, researchers can highlight the tangible benefits of AI-driven research.

Addressing Market Uncertainty

The dynamic nature of markets can pose challenges for AI-driven research. Predictive models based on historical data might not always accurately forecast future trends, especially in volatile markets. To address this, researchers need to continuously update their models and algorithms, taking into account the latest market developments.

Ensuring Data Quality

The accuracy of AI-driven insights heavily depends on the quality of data fed into the system. Poor quality data can lead to misleading results. Researchers need to be vigilant about data quality, ensuring that the data sources are reliable and the data is cleaned and pre-processed effectively.

Balancing Personalization with Privacy Concerns

While AI enables highly personalized insights, it also raises concerns about data privacy. Researchers need to strike a balance between personalization and privacy, ensuring that data is collected and processed ethically, and in compliance with data protection regulations.

Overcoming Technology Anxiety

The introduction of AI in market research can lead to technology anxiety among traditional researchers. It's crucial to foster a culture of learning and adaptation, offering training and support to help researchers transition smoothly to AI-driven methodologies.

Best Practices for Implementing AI in Market Research

The integration of Artificial Intelligence (AI) into market research has the potential to offer deeper insights and streamline processes. However, to ensure the successful adoption and implementation of AI in this domain, it's essential to adhere to certain best practices. Here's a guide to help you navigate the complexities of AI-driven market research:

1. Clearly Define Research Objectives

Before diving into AI-driven research, it's paramount to clarify the research objectives. Understand what you aim to achieve with AI and ensure it aligns with your overarching research goals. A well-defined objective serves as a roadmap, guiding the AI tools and methodologies to be employed, ensuring that the results are both relevant and actionable.

2. Prioritize Data Quality

Data is the lifeblood of AI-driven research. The quality of data directly impacts the accuracy of insights derived. It's essential to source data from reliable sources, ensuring it's cleaned, pre-processed, and free from biases. High-quality data not only ensures accurate insights but also builds trust in the AI-driven research process.

3. Stay Updated with AI Developments

The world of AI is dynamic, with new advancements and algorithms emerging regularly. To harness the full potential of AI in market research, it's crucial to stay updated with these developments. This ensures that the research methodologies employed are cutting-edge, optimizing the research outcomes.

4. Address Data Privacy Concerns

In an era where data privacy is a paramount concern, it's vital to ensure that AI-driven research adheres to data protection regulations. Transparency in data collection, processing, and storage is key. Ensure that participants are informed about how their data will be used and that all data activities are ethical and compliant.

5. Foster Collaboration and Training

AI is most effective when combined with human expertise. Encourage collaboration between AI specialists and traditional researchers. This synergy ensures that the research is holistic, combining the computational power of AI with the nuanced understanding of human researchers. Additionally, invest in training to familiarize researchers with AI tools, ensuring smooth integration.

6. Continuously Evaluate and Iterate

AI-driven research is not a one-time activity. It's essential to continuously evaluate the methodologies employed, gather feedback, and iterate. This ensures that the research remains relevant, adapting to changing market dynamics and emerging trends.

7. Emphasize the Human Touch

While AI offers computational prowess, the human touch in research is irreplaceable. Ensure that AI tools don't overshadow the human aspect. Incorporate quality checks, validations, and human interpretations to ensure that the insights derived are not just data-driven but also contextually relevant.

Take Your Business To New Heights With The Power Of AI

In today's competitive business landscape, Artificial Intelligence (AI) stands out as a transformative force, driving efficiency and innovation. By harnessing AI, businesses can automate mundane tasks, freeing up resources and allowing teams to focus on strategic initiatives. This not only streamlines operations but also enhances decision-making, ensuring that actions are timely and data-driven.

Personalization is another arena where AI shines. By analyzing customer data, AI tailors offerings to individual preferences, leading to increased engagement and loyalty. This bespoke approach ensures that businesses can meet customer needs more effectively, fostering stronger relationships and ensuring repeat business.

However, the true potential of AI lies in its ability to unlock new opportunities. Beyond optimizing current processes , AI paves the way for businesses to explore untapped markets, diversify their offerings, and innovate. By integrating AI, businesses position themselves at the forefront of their industries, ready to seize emerging opportunities and set new industry standards.

Start Growing with Sintra AI

In pursuing business success, discovering an AI-powered solution that meets your distinct requirements can be a game-changer. Meet Sintra AI . As a user-friendly and customizable platform, Sintra AI empowers you to harness the full potential of business automation tailored specifically to your needs.

We understand that every business is unique and deserves personalized attention. With our intuitive interface and cutting-edge AI technology, Sintra AI automation simplifies the process of creating a comprehensive marketing automation strategy that perfectly aligns with your goals and aspirations.

Imagine eliminating manual errors, reducing overhead costs , and saving valuable time. With Sintra AI's automation bots , you can do just that. These powerful tools can automate specific tasks within your workflow, tailored to meet your unique needs. Whether you want to automate data entry, generate reports, or manage customer interactions, these automation bots have covered you.

Moreover, Sintra AI automation boasts a vast library of carefully crafted AI prompts designed explicitly for businesses. These prompts act as invaluable virtual assistants, expertly guiding sales professionals through the entire sales process and providing real-time insights and recommendations. By harnessing AI-driven suggestions, businesses can make well-informed decisions and optimize their marketing automation strategy to drive maximum revenue.

Market research has evolved significantly, with AI taking center stage. While traditional methods served their purpose, AI is reshaping the landscape with its efficiency and valuable insights. We've highlighted some of the standout AI tools in the market, shared actionable insights and tips, and provided real-world examples to demonstrate the transformative power of AI in market research.

The advantages of integrating AI are numerous, from rapid data processing to cost savings and pinpoint accuracy in insights. Transitioning to AI-driven market research is not just a trend; it's a strategic move that offers a competitive edge in today's data-driven world.

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7 Best AI Market Research Tools in 2023

In recent years, the market research industry has begun to adopt AI-powered marketing tools to help gather and analyze data. 

AI market research tools offer a number of advantages over traditional methods, including the ability to process large amounts of data quickly, identify patterns and correlations, and make predictions about future trends.

We researched and compiled the best AI market research software that help businesses can make better decisions about product development, marketing strategies, and sales tactics.

• August 2, 2023

1. SemRush Market Explorer

SEMrush is a powerful and versatile marketing intelligence system for online marketing, from SEO and PPC to social media and competitor analysis.

SEMrush provides insights and ideas for improving your own online marketing efforts, as well as those of your competitors.

And more importantly for us today, SemRush also offers a tool solely dedicated to market research – SemRush Market Explorer.

Other than providing proper marketing analysis, insights, and metrics, Market Explorer grants comparative research that sheds some light on specific markets, niches, and demographics.

SemRush displays market data such as:

• The level of competition within a market, which is based on the Herfindahl–Hirschman Index
• The percentage of a competitor’s traffic in the traffic volume for the market
• Dynamics of the domain traffic against the market total traffic trends
• The distribution of the market traffic by country and traffic changes compared with the previous month

And you can also compare the domain growth with the market across different acquisition channels that can help you highlight the distribution of marketing budgets and other resources of your competitors.

Semrush employs its own machine learning algorithms and trusted data providers to present the data in their databases.

With this AI market research tool, SemRush wants to make sure that any company is able to gain knowledge about their every interaction with the public. Popular brands like Amazon, Forbes, SAMSUNG, and Tesla have all used SemRush to cover their market research needs.

2. SurveySparrow

SurveySparrow is seriously changing the game when it comes to market research thanks to its use of artificial intelligence. This tool taps into the power of AI algorithms and machine learning to take survey creation and analysis to the next level.

With SurveySparrow, you can easily build personalized, unbiased surveys that resonate with respondents. The AI automation increases response rates and gathers insights faster than old-school manual surveys. And we’re talking about deep insights here – SurveySparrow analyzes sentiments behind feedback to understand how customers truly feel.

The AI models can even predict future behaviors and trends based on historical data. This allows businesses to make proactive decisions to meet changing market needs. Plus, SurveySparrow integrates with tons of tools to automate workflows and reporting.

In short, SurveySparrow lets you dig deeper into the motivations and preferences shaping your customers’ choices. That means making strategic calls backed by data, instead of guesses.

CrawlQ is an audience insight tool powered by AI technology that builds a mock-up of your clients – employing highly intuitive algorithms and research tools that use the existing data of your company and produce a model of your target audience.

Providing a portal that is intuitive and easy to navigate, CrawlQ offers a navigation hub that is assisted by Athena, its AI algorithm. 

In turn, this market research tool allows the inclusion of the social media platforms like Google, Reddit, and Quora in the search engine – as it serves the purpose to process and analyze countless URLs, posts, replies, commentaries blogs, and forums on the web.

More than insights and reports, this market explorer tool allows the company to research the market from the point of view of the client – targeting the audience that will help to improve the strategies and facilitate the exponential growth of the business.

Like with many other machine learning software, the more you use Athena to navigate through markets, the more intelligent Athena becomes and the more helpful she is in the entire process. The Netherlands Education Group, eDifference, and Lean Case all use CrawlQ’s technologies to cover their needs for market research.

4. Momentive.ai

Momentive.ai is a technology that is developed to create a portal that serves the “experience management” purpose. To be constantly growing and constantly improving the product, the strategies and the way that the company is received is Momentive’s primary goal.

Their solutions seek to help companies improve their experiences with the people that make success possible: employees, customers, and the products by themselves. In addition to its experience management services, Momentive offers an AI-powered market research tool that helps the company understand its market.

Identifying the company’s needs, searching the industry for tailored results, placing issues on certain roles or purposes, and customizing the framework of the marketing process are all easy to do and possible thanks to Momentive’s highly intelligent AI – all while utilizing accurate methodologies that deliver actionable insights and advanced reports.

Products from Momentive, such as GetFeedback and SurveyMonkey as well as its brand and market analytics solutions, equip decision-makers at 345,000 companies throughout the globe to create memorable customer experiences.

Well-renowned brands such as IBM, The Hershey Company, Toyota, and LG all put their trust in Momentive and employ their market research tools and technologies.

To simplify the concept of this market explorer, it’s fair to say that Feedly is an organized portal that compiles all newsletters, blogs, and social media sources in the same place – including Youtube Channels, Twitter feeds, newspaper articles, official journals, niche industry publications and many other mainstream platforms such as Facebook, Instagram, and Reddit.

While using Feedly’s dashboard, the user in charge is able to rely on Leo, the Artificial Intelligence assistance that is built into the platform, to create a search topic and generate a wide-scope report that extracts the most relevant data out of all the previously mentioned sources.

Like good AI, the more Leo is used to search, the more it grows. Though it seems more complex than promised, Feedly’s premise is fairly simple: Using a 3 step process, you can search and track insights across all your social media platforms. Using Leo’s help to organize, process, and analyze.

By utilizing the data exposed after analyzing the insights, the company is able to perform actionable changes within its strategies – optimizing the marketing process and the customer experience. 

6. Remesh.ai

The experience of using Remesh.ai should feel like the user is speaking to the entire world and receiving absolutely all the information that they need to further tailor their strategies and analysis.

In the field of market research, the technologies offered by Remeash.ai allow the user to narrow their search and specify the purpose of their actions depending on the target – be it the overall category of the industry, the company’s own employees, or even specific demographics with tailored characteristics.

Remesh.ai has an AI market research tool that extends from products to companies, to institutional establishments and even includes higher entities like localized citizens and their governments.

Being one of the few tools that include civic analysis , Remesh.ai is not afraid of showing true data that is extracted from every and any group of people all over the world. Buyers, sellers, voters, employees… remesh.ai lets the data speak for itself.

Whether the search includes industries, markets or citizens, Remesh’s market research tool always provides feedback in real-time and allows the user to segment and analyze the data that it obtains from its search. 

Semeon believes that offering an artificial intelligence tool that automates research helps the preservation of healthy practices to cover the marketing needs of any and all companies. Their solutions extend to experience management including clients, customer services, and the experience of the product itself, and of course, they also offer an automated Market Research solution that is designed to expose weak points and prioritize timely action to boost engagement and increase sales.

Other than providing software to extract and analyze data, one of their top priorities is to provide automation software that reduces the need for human touch while generating the same result, if not better.

Semeon’s AI Intelligence is programmed to consider every detail and assist during the research process by utilizing technologies that will analyze the nuisance of context and situational occurrences to generate results that are optimized by human behavior and their organic inconsistencies.

This makes reporting and insights more accurate and less biased by small errors. Semeon wishes to utilize the data to become capable of facilitating the exploration of different segmentation and new markets while lowering the risk of failure and increasing the possibilities of success rate.

Companies like Ubisoft, The Healthcare of Ontario Pension Plan, and The Bellwether Institute all employ Semeon.

AI Market Research Tools Final Words

The bottom line is that AI market research tools beat old-school methods hands down. Unlike tedious manual surveys and analysis, AI platforms help you gather and process data lightyears faster with way more insights.

These cutting-edge tools can rapidly identify hidden patterns and trends that would take humans forever to spot. That means making smart marketing calls backed by data, instead of guesses.

AI eliminates all the grunt work that makes market research painful. No more wasting weeks creating surveys and crunching numbers. AI handles the busywork so you can focus on the big picture strategy.

AI market research tools are essential for any brand that wants to stay ahead of the curve. There’s no reason to still be stuck in the past, manually fighting with data.

Patryk Miszczak

Launch Profitable Sales Funnels

AI in market research: Charting a course from raw data to strategic excellence

In today’s dynamic business landscape, success hinges on understanding the market. Navigating the road to a successful product or service is like traversing a maze – it’s all about tapping into your audience’s wants, needs, and expectations and comprehending what they are willing to spend on your offerings. This essential journey is charted by market research, an invaluable tool for business success.

Traditionally, market research has been a hands-on, meticulous task. From manual data collections to in-person focus groups, the old ways were time-consuming and resource-intensive, but they helped gather crucial insights. However, these methods had their limits, struggling to unveil the deeper nuances of consumer behavior.

Welcome to a new era where AI transforms market research. AI isn’t just a tool; it’s a game-changing ally that rejuvenates the entire field. Imagine having the power to process and analyze colossal amounts of data swiftly and accurately—something that was previously unattainable. AI, with its robust capabilities like web scraping and sentiment analysis , enables us to feel the real-time pulse of the market, amplifying our insights and decisions.

Through AI, we now have a more profound, more nuanced lens to study consumer behaviors and trends. It unveils patterns and correlations that were once hidden and projects emerging trends with astounding precision. Join us as we explore how AI is enhancing and transforming market research into a realm of remarkable new possibilities.

What is market research?

The role of ai in market research, ways to use ai in market research, sentiment analysis, predictive analytics, social media listening, chatbots and virtual assistants, customer journey analysis, demand forecasting, consumer segmentation, image and video analysis, voice and speech analysis, concept testing and product innovation, how does leewayhertz’s generative ai platform enhance market research processes, traditional vs. ai-based market research, benefits of ai in market research, the future of ai in market research.

Market research is a pivotal, data-driven process essential for assessing the viability of new products or services and enhancing a brand’s allure. It equips businesses with crucial insights into the preferences and behaviors of their target audience, collates pertinent market information, and thoroughly analyzes customer feedback. This wealth of data serves as a robust foundation, helping navigate marketing challenges effectively. It becomes instrumental in devising potent marketing strategies, catalyzing brand innovation and fueling success. Market research typically involves two approaches:

Primary research

This involves the initial data collection and employs qualitative and quantitative research methods. Businesses interact with their customers through surveys and questionnaires to gather information. Primary research can be broadly categorized into two types:

• Exploratory research:  This approach uses open-ended interview questions, typically conducted with a sample group. It aims to uncover insights and gather preliminary information.
• Specific research:  Specific research is more focused and addresses issues or questions identified during exploratory research. It seeks to find solutions or specific answers to these identified problems.

Secondary research

In this phase, businesses rely on data compiled from external sources such as government agencies, media outlets, reports, studies, newspapers, and other publications. This existing information is valuable for gaining additional insights and context, supplementing the primary research findings.

Both primary and secondary research methods play critical roles in providing a comprehensive understanding of the market, enabling businesses to make well-informed decisions and develop effective marketing strategies tailored to their audience’s needs and preferences.

AI in market research involves integrating Machine Learning (ML) algorithms into traditional methods, such as interviews, discussions, and surveys, to enhance the research process. These algorithms enable real-time data collection and analysis, predicting trends and extracting valuable patterns. This process results in high-quality, up-to-date insights that transparently capture even minor market changes.

For instance, a health and fitness product brand can employ a custom AI model to scan online conversations about healthcare and fitness trends and competitors’ offerings across public domains. By doing so, they gain factual insights to brainstorm innovative product ideas and devise digital marketing strategies that align with market demand. This approach saves time, ensures logical decision-making, and facilitates the launch of products and services that effectively cater to the target customers’ needs, ultimately leading to a more informed and successful market presence.

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Now that we have established the perfect synergy between AI and market research, the next step is understanding how precisely this alignment works. So, let’s delve straight into the ways to use AI in market research:

Open-ended text analysis

In the sphere of AI-driven market research, customer feedback remains paramount; it’s the lifeblood of businesses. Without customers, even the most sophisticated AI in market research would yield little value. This underscores the importance of attentively listening to potential and existing customers. However, manually deciphering and understanding what each customer says can be as daunting as picking up spilled mustard seeds one by one – not impossible, but undeniably tedious.

Conversely, AI seamlessly integrates into your market research workflow and solves this challenge effortlessly. Here’s how: AI scrutinizes open-ended survey responses from various communication channels, from traditional emails to contemporary social media comments. It deepens into this textual data to extract the precise thoughts and sentiments concealed within.

However, analyzing sentiment is more complex than it seems. Consider this scenario: you are researching hotel reviews and come across two contrasting comments:

Review 1: “In one word, wow! Everything about our stay was perfect, from the food, cleanliness, and courteous staff. I loved it.”

Review 2: “Breakfast wasn’t served on time, nobody bothered to clean the space, took 10+ attempts to reach out to room service on call..wow! Could it be any better?”

Relying solely on keywords for sentiment analysis won’t suffice, as the “wow” in the first review conveys a vastly different sentiment than the “wow” in the second. A robust Natural Language Processing (NLP) and deep-learning-driven sentiment analysis module are needed to avoid such cognitive pitfalls in the research. Using AI elevates market research by autonomously analyzing text in real-time, deciphering implicit sentiments instead of merely capturing literal meanings.

Conversational insight collection

In research, gathering insights from your target audience is crucial. One critical aspect of this process is engaging in meaningful conversations with respondents to discover information that can shape strategies and decisions. Consider this scenario: You’re collecting data to create a menu for a new cafe, targeting youngsters who frequently visit cafes. You ask them, “What are the top 5 items you usually order when you visit a cafe?”

You receive responses like, “Cappuccino, brownie, sandwiches, cake, croissants, etc.” Does this provide enough information to finalize your menu? Clearly, it falls short.

However, if you follow up with questions like, “Any specific type of cappuccino?” You might get answers like “Nutella cappuccino,” “java chip cappuccino,” or “Pumpkin spice cappuccino.” Similarly, asking about cake preferences might yield responses like “strawberry tea cake,” “lemon yogurt cake,” or “fruit cake.”

This highlights the importance of follow-up questions in market research to gather valuable insights. Yet, manually conducting such a conversational follow-up survey can be challenging.

AI in market research empowers businesses to effortlessly collect precise, relevant information with exceptional efficiency and accuracy. Custom AI models monitor consumer interactions, identifying recurring patterns. AI chatbots designed for conversational interactions can be trained to offer users more advanced and enhanced experiences. These AI-powered chatbots can learn and adjust based on ongoing conversations, resulting in more comprehensive consumer market insights.

Auto report generation

Accelerating the transition from insightful data to actionable strategies necessitates seamless execution, achievable through clear, visually intuitive reports. AI-driven report generation makes this crucial step effortless.

AI-driven report generation empowers businesses to craft concise reports tailored to their needs, featuring customizable metrics. Each department within an enterprise can create reports to suit their requirements precisely. This agility ensures that decision-makers receive the most relevant information, streamlining the decision-making process.

Consider a shipping business harnessing AI in market research pursuits to make more informed decisions. The operations team can generate reports regarding regional segmentation, package placement, and freight scheduling, enhancing safety and optimizing operations for cost efficiency.

Simultaneously, the accounting team can review reports showcasing expenditures and profits over a custom-defined timeline. This insight aids in fine-tuning future financial decisions, ultimately contributing to the business’s growth and sustainability. Integrating AI into report generation significantly enhances the depth and accuracy of the insights, positioning organizations for strategic success.

Customer success automation

In today’s business landscape, post-purchase customer experience is vital for retaining customers. However, effectively managing and maintaining post-purchase interactions and communications can be resource-intensive for any customer success team.

AI handles a spectrum of tasks seamlessly, from scheduling follow-ups to crafting ‘stay-in-touch’ messages. It goes beyond automation by analyzing message content and frequencies, providing valuable insights and suggesting better-performing metrics. This precision and accuracy significantly elevate customer retention, fostering a loyal customer base.

AI plays a pivotal role in maintaining track of customer feedback through predictive modeling. This involves gathering comprehensive data points related to a customer’s digital interactions with your business. AI leverages this data to streamline the prediction of the potential churn rate of customers, empowering customer success professionals to intervene and prevent customer attrition proactively. This strategic approach safeguards customer satisfaction and fosters sustained business growth in the long run.

Advanced secondary research

Effective research is a multi-step process, where each phase serves a unique purpose to differentiate one stage from another. Secondary research, as the name implies, represents a deeper investigation conducted on the information gathered during the initial round of research. This practice holds immense value for companies of all sizes, offering insights into various aspects such as understanding emerging markets, devising competitive pricing strategies, and evaluating supplier relationships.

What distinguishes primary research from secondary research is the level of detail in the information gathered. To illustrate this distinction, let’s consider a scenario where a freight rental service company conducts market research to compile a list of potential routes.

In primary research, you would directly collect competitors’ data, strengths, weaknesses, and unique selling points (USPs). This approach offers a thorough understanding of the competitive landscape.

On the other hand, secondary research aims to distill this extensive data into a concise list of routes, accompanied by a rationale that justifies your specific requirements. This is where AI in market research proves valuable. It efficiently fulfills these secondary research objectives, helping businesses make informed decisions based on well-structured insights.

Preparing questionnaire

What is even better than getting surveys completed on time? It is the art of crafting precisely tailored questions—a foundational step in effective market research. The precision of these questions directly influences the quality of responses obtained. AI solves this challenge by presenting a predefined questionnaire, meticulously analyzing the responses, and adjusting subsequent questions accordingly. This dynamic process ensures that the results closely align with the core research objectives.

AI in market research allows businesses to analyze how closely past responses align with standard answers to the questions posed. Consequently, the following questions are fine-tuned based on this analysis, enhancing response quality in real-time. This innovative approach makes data collection more efficient and insightful than ever before.

Use cases of AI in market research

The key applications of AI in market research are:

AI-driven sentiment analysis empowers market researchers to understand emotions, opinions, and attitudes conveyed in extensive text data from different sources like social media posts, customer reviews, and survey responses. Utilizing Natural Language Processing (NLP) algorithms, sentiments can be categorized into positive, negative, or neutral, offering crucial insights into customer perceptions and inclinations. For instance, companies in the cosmetics industry can leverage AI for market research, swiftly examining various social media posts to grasp customer sentiment regarding their recent product introduction. This insight enables informed decision-making, improved marketing strategies and product enhancements.

AI algorithms can examine past data, recognize patterns, and predict forthcoming market trends and consumer behaviors. Predictive analytics is a valuable tool for market researchers, allowing them to anticipate demand, enhance pricing strategies, and make well-informed choices concerning product innovation, marketing initiatives, and inventory administration.

For instance, consider e-commerce businesses that harness AI-driven predictive analytics. By scrutinizing historical customer data and assessing market trends, they can accurately project the future demand for various products. This insight equips them to fine-tune their marketing campaigns, ensuring that the right products are promoted to the right audience at the right time. Additionally, predictive analytics can help these retailers optimize pricing strategies, ensuring that products are competitively priced to attract customers while maximizing profitability. Furthermore, it aids in making strategic decisions regarding inventory management , reducing the risk of overstocking or understocking products.

AI-powered social media listening tools are indispensable resources for market researchers, as they continuously monitor and analyze conversations across social media platforms in real time. These tools can identify trending topics, track brand mentions, and analyze sentiment, offering invaluable insights into consumer preferences, behaviors, and emerging market trends.

For example, let’s consider a global tech company launching a new smartphone. They employ AI-powered social media listening tools to monitor smartphone discussions across platforms like Twitter, Facebook, Instagram, and more. The tools automatically identify conversations about the latest smartphone models, the company’s brand mentions, and relevant trending topics.

AI-driven chatbots and virtual assistants have become pivotal in market research, engaging with consumers, gathering data, and offering personalized recommendations. These conversational tools conduct surveys, address inquiries, and extract valuable insights, enabling organizations to collect data at scale and enhance customer engagement.

For instance, in the e-commerce business, chatbots prove instrumental. They can deliver personalized product recommendations, offer round-the-clock support, and boost customer engagement. This, in turn, drives sales and elevates overall customer satisfaction, showcasing the potential of these AI-powered conversational tools in the market.

AI algorithms are crucial in dissecting the multifaceted customer journey, scrutinizing numerous touchpoints and interactions to pinpoint pivotal moments, pain points, and opportunities for enhancement. This comprehensive understanding empowers market researchers to fine-tune marketing strategies, enhance customer experiences, and foster customer loyalty.

For example, let’s take a retail brand’s scenario. They harness AI algorithms to scrutinize customer interactions across various channels, including website visits, email engagement, social media interactions, and in-store experiences. Through this analysis, they identify crucial moments within the customer journey, such as when a potential customer explores a product page or adds items to their cart but abandons the purchase.

AI-based demand forecasting models play a vital role in anticipating future product or service demand. These AI models analyze past sales records, market trends, external influences, and even weather patterns. This accurate forecasting enables organizations to optimize various aspects such as inventory levels, production planning, and resource allocation, ultimately leading to cost reduction. Furthermore, it significantly improves the customer experience by reducing delivery times and ensuring adequate stock availability for popular items.

For example, let’s consider a consumer electronics company. By leveraging AI-based demand forecasting , they can analyze historical sales data and incorporate market trends and external factors like chip shortages. This holistic analysis helps them balance product demand effectively. They can optimize warehousing and supply-chain costs by accurately anticipating demand fluctuations and adjusting inventory levels accordingly. This helps manage costs and ensures they always have the right amount of products in stock to meet customer demands.

AI-powered market research clustering algorithms are crucial in grouping consumers into well-defined segments based on shared characteristics such as demographics, behaviors, and preferences. This segmentation approach allows market researchers to craft highly targeted marketing strategies and messages for each segment, ultimately improving campaign effectiveness and more precise customer targeting efforts.

For instance, let’s consider how this works for fashion brands. By leveraging AI-driven clustering algorithms, fashion companies can analyze diverse data sets encompassing a wide range of customer information. This data may include age, gender, shopping habits, preferred clothing styles, brand affinities, etc. This data identifies natural groupings or segments within the customer base when processed using clustering algorithms.

Furthermore, these segments could include categories like “fashion-forward millennials,” “classic style enthusiasts,” “athleisure lovers,” and so on. Each segment represents a distinct group of customers with similar tastes and preferences. Once these segments are identified, fashion brands can tailor their marketing strategies accordingly. They can create personalized marketing campaigns that resonate with each segment’s preferences and behaviors.

The proliferation of visual content across social media and various online platforms has made AI-powered image and video analysis tools indispensable for market researchers. These advanced tools can autonomously assess and categorize visual content, detect brand logos, and recognize objects, scenes, and emotions conveyed in images or videos. This capability gives market researchers profound insights into how customers engage with visual media.

For instance, let’s consider a travel agency looking to leverage AI-powered image and video analysis. The agency can automatically analyze user-generated content shared on social media platforms by harnessing these tools. This analysis can help these travel agencies identify popular destinations, detect emerging visual trends, and gain valuable insights into customer preferences. Armed with this information, the agencies can create targeted marketing campaigns enriched with captivating visuals that resonate with their audience, enhancing their marketing strategies.

Voice and speech-enabled AI marketing research tools have ushered in a new era for market researchers, offering the capability to analyze and extract valuable insights from various sources, including recorded phone calls, customer support interactions, and voice-based survey responses. Powered by Natural Language Processing (NLP) algorithms, these tools transcribe and analyze the audio data, identifying essential topics, sentiments, and levels of customer satisfaction. This invaluable information empowers businesses to enhance customer service and swiftly address issues.

For instance, let’s consider how telecom companies can leverage these AI tools. Telecoms can analyze customer call recordings, often containing a wealth of information about customer interactions. With the help of NLP algorithms, they can automatically identify key discussion topics, assess the sentiment expressed during calls (positive, negative, or neutral), and gauge overall customer satisfaction. This analysis provides telecom companies with actionable insights such as identifying pain points, improving customer service, product and service enhancement, and targeted marketing.

Voice and speech-enabled AI marketing research tools open up exciting possibilities for businesses to tap into the insights hidden within audio data.

AI-powered concept testing tools are crucial for market researchers to evaluate potential market reactions to new product concepts or features before their actual launch. These tools use advanced algorithms and predictive analytics to assess reactions and feedback from potential consumers who have been presented with the product concept through methods like surveys or focus groups. This approach helps estimate market acceptance, identify improvement areas, and strategize effective product innovations based on potential consumer preferences and expectations.

Consider a scenario where a tech startup is conceptualizing a new product. Instead of employing AI-powered concept testing tools, they may engage with potential consumers through surveys, interviews, or focus groups to gather feedback. By presenting the proposed concept and directly interacting with the target audience, the startup can analyze preferences, reactions, and suggestions, enabling the product development team to gain essential insights about the market’s potential reception. This method allows them to gather valuable feedback before the product is officially launched.

LeewayHertz’s generative AI platform,  ZBrain , plays a transformative role in reshaping the market research landscape by providing organizations with exceptional insights and a distinctive competitive edge. As a comprehensive, enterprise-ready platform, ZBrain empowers businesses to design and implement applications tailored to their specific operational requirements. The platform uses clients’ data, whether in the form of text, images, or documents, to train advanced LLMs like GPT-4, Vicuna, Llama 2, or GPT-NeoX for developing contextually aware applications capable of performing diverse tasks.

In the field of market research, enterprises confront challenges like gaining a comprehensive understanding of customer needs, assessing market trends, mitigating risks, and making informed decisions for strategic growth, requiring a continuous stream of actionable insights. ZBrain effectively addresses these challenges through its distinctive feature called “Flow,” which provides an intuitive interface that allows users to create intricate business logic for their apps without the need for coding. Flow’s easy-to-use drag-and-drop interface enables the seamless integration of large language models, prompt templates, and media models into your app’s logic for its easy conceptualization, creation, or modification.

To comprehensively understand how ZBrain Flow works, explore this resource that outlines a range of industry-specific Flow processes. This compilation highlights ZBrain’s adaptability and resilience, showcasing how the platform effectively meets the diverse needs of various industries, ensuring enterprises stay ahead in today’s rapidly evolving business landscape.

ZBrain applications enable organizations to identify market trends, optimize resource allocation, expedite data analysis, enhance data accuracy, and formulate highly personalized and adaptive business strategies while upholding strict privacy standards. The outcomes include increased productivity, refined strategic decision-making, quicker research delivery, and elevated measures in market research for enhanced operational efficiency and sustained success.

It’s important to note that while AI-based market research offers many advantages, it should be used in conjunction with traditional methods to ensure a comprehensive understanding of the market. Additionally, ethical considerations and data privacy should always be prioritized in AI-based research.

Improved data collection:  AI-integrated tools expedite data collection, efficiently gathering real-time customer input and analyzing feedback for patterns and trends. Unlike traditional methods, often involving manual and time-consuming processes, AI automates these tasks precisely and efficiently. This accelerates the research process, enabling businesses to respond more rapidly to evolving customer preferences and market dynamics, ultimately gaining a competitive edge.

Enhanced data analysis:  AI, equipped with its computational power, swiftly and precisely deciphers extensive datasets. It reveals complex patterns and valuable insights that would prove challenging or nearly impossible for human analysts to identify through manual examination. This efficiency streamlines data analysis , leading to more informed decision-making across various domains.

Personalization:  AI harnesses customer data to provide personalized marketing recommendations that align with each individual’s preferences and behaviors. By analyzing a person’s past interactions and choices, AI can suggest products, services, or highly relevant content. This level of personalization fosters deeper customer engagement and increases satisfaction, as customers perceive that a brand comprehends and caters to their distinct needs and preferences. Ultimately, this results in more effective marketing campaigns and stronger customer loyalty.

Better customer segmentation:  AI enhances customer segmentation, enabling businesses to categorize their customer base precisely. This refined segmentation empowers companies to tailor marketing strategies to specific customer groups, effectively identifying those most likely to respond positively to particular campaigns.

Real-time insights:  AI provides instant updates on what customers are doing, helping businesses quickly adjust their plans to keep up with changes. This agility enhances a company’s ability to stay competitive and adaptive in a rapidly changing market landscape.

Predictive analytics:  AI anticipates customer behaviors and market trends, enabling businesses to sustain a competitive advantage and make well-informed choices for future product development and marketing strategies. This predictive capability equips companies with valuable foresight, allowing them to stay ahead in a dynamic business environment.

Improved decision-making:  AI provides valuable insights that surpass human capabilities, enabling businesses to make more informed decisions rooted in evolving trends and consumer preferences. This advanced data analysis equips organizations with a competitive edge, as they can better understand market dynamics and consumer behavior, leading to more strategic and effective decision-making.

Increased efficiency:  AI streamlines labor-intensive tasks like data entry, freeing market researchers to concentrate on more valuable endeavors such as in-depth analysis and strategic planning. This automation enhances productivity and empowers professionals to contribute more significantly to their research and business objectives.

Faster research delivery:  In market research, speed is key to maintaining relevance. Delays can lead to outdated insights and inaccurate sentiment analysis. A significant portion of a market researcher’s time is consumed in report writing, causing further delays and outdated data. AI-powered market research, on the other hand, provides results almost instantly. AI technology swiftly gathers data from a chosen target audience, automatically scans keywords or topics, and does it all faster than a human researcher would.

Flexible solution:  Effective research relies on data collection tools tailored to the target audience. Surveys, like customer satisfaction surveys, must be user-friendly to avoid low response rates that can distort scores or yield inaccurate information.

AI technology introduces flexibility and interactivity to surveys by adapting to customer responses. Machine learning enables dynamic analysis, enhancing existing tools to better align with customer preferences and needs. This adaptability ensures that data collection processes are more responsive and capable of delivering higher-quality information.

Let’s delve into how artificial intelligence might impact market research in the coming years, particularly in virtual market research and forecasting, chatbots and virtual moderators, and conducting secondary research.

Virtual market research & forecasting

A common issue in conventional market research frequently pertains to the adequacy and representativeness of the sample size, which is the total count of participants or data points collected in the research sample. AI is poised to address this issue through the concept of virtual panels. Instead of relying solely on large and expensive respondent samples, AI can cluster behavioral traits from smaller samples and create a larger virtual pool of respondents. This approach enables more accurate predictions of consumer behavior. While there may be limitations, such as initial binary answers, the ability to run numerous experiments simultaneously is a significant advantage. This can be particularly useful for determining optimal product pricing or assessing the impact of attribute changes on sales.

Chatbots & virtual moderators

AI-driven chatbots and virtual moderators are evolving in the realm of qualitative research. Currently, they are limited to delivering pre-programmed questions in a conversational format. However, as AI advances, there’s potential for these tools to interpret respondent answers and engage in dynamic, tailored conversations. This would represent a shift from a question delivery format to a virtual moderator capable of probing interesting points and tailoring follow-up questions. This development could change the scalability of conversational qualitative research.

Conducting secondary research

Secondary or desk research involves gathering and analyzing existing data and information. AI has a significant role to play in automating this process. AI algorithms can efficiently sift through vast amounts of data from online databases, reports, and articles. They can extract relevant insights, identify market trends, assess competitor performance, and help in decision-making processes. AI-driven secondary research saves time and ensures that organizations have access to up-to-date and comprehensive information, making it an essential tool for market analysis and strategy development.

AI is poised to transform market research by enabling more efficient and cost-effective data collection, analysis, and interpretation approaches. These advancements will lead to more accurate predictions, greater scalability, and enhanced decision-making capabilities for businesses across various industries. As AI continues to evolve, its impact on market research will likely grow significantly in the years ahead.

The importance of AI in market research cannot be overstated. It has liberated people involved in market research from the constraints of traditional data-gathering methods, opening up a world of real-time insights and predictive capabilities. The ability to monitor market trends, analyze sentiment, and track social media dynamics has become indispensable for businesses seeking a competitive edge.

Launching a successful product or service in the fast-paced business world is akin to navigating a complex maze where AI serves as a guiding light. It empowers companies to make informed decisions based on a wealth of data, transforming raw information into actionable insights with remarkable speed and precision.

In this rapidly changing landscape, AI in market research is not just an advantage; it is indispensable for those who seek to thrive and remain competitive. As we move forward, the fusion of AI and market research will continue to shape the business world, providing a clearer path through the intricate maze of the market and enabling companies to navigate it with confidence and precision.

Transform data into strategy today! Leverage LeewayHertz’s cutting-edge AI solutions to elevate your market research operations.

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What does market research entail?

Market research involves the systematic gathering, analysis, and interpretation of data related to a target market. It aims to understand consumer behavior, preferences, and market dynamics, providing valuable insights for informed business decisions.

What is the role of AI in market research, and what are its benefits?

AI plays a crucial role in market research by automating data analysis, pattern recognition, and predictive modeling. It enhances the speed and accuracy of extracting meaningful insights from vast datasets, enabling highly informed decision-making.

The benefits of AI in market research include improved speed and accuracy, deeper insights through advanced analytics, cost savings, and the ability to process large datasets efficiently, leading to more strategic decision-making.

In what ways can AI be applied in market research?

AI is applied in market research for tasks like sentiment analysis, predictive analytics, trend identification, and automated survey analysis. These applications streamline processes, uncover hidden patterns, and provide a deeper understanding of market dynamics.

What distinguishes traditional market research from AI-based approaches?

Traditional market research relies on manual data collection and analysis, which can be time-consuming and subjective. AI-based market research leverages automation, machine learning, and data processing to deliver faster, more objective, and data-driven insights.

What are the use cases of AI in market research?

AI is integral to various market research applications, including automated sentiment analysis to gauge consumer opinions, predictive analytics for forecasting market trends, competitor analysis using advanced algorithms, and social media listening to understand brand perception. AI enhances efficiency by automating survey analysis, uncovering patterns in consumer behavior, and delivering actionable insights from vast datasets. These diverse use cases showcase AI’s transformative role in making market research more accurate, timely, and strategically impactful for businesses.

How can LeewayHertz help integrate AI solutions into our market research strategies?

LeewayHertz specializes in developing customized AI solutions tailored to your market research needs, ensuring seamless integration with your existing strategies.

Can LeewayHertz customize AI-powered solutions to meet my specific market research needs, such as consumer behavior analysis or trend forecasting?

Yes, LeewayHertz excels in creating tailored AI solutions. Whether it’s consumer behavior analysis, trend forecasting, or other specific requirements, our team can design and implement solutions that align with your unique market research goals.

What AI technologies does LeewayHertz specialize in for market research applications, and how can these technologies provide a competitive advantage in the industry?

LeewayHertz excels in various AI technologies, encompassing natural language processing, machine learning, and computer vision. These technologies offer a competitive edge by enabling advanced analysis, predictive modeling, and automation in market research.

What kind of data security measures does LeewayHertz employ to ensure the confidentiality and privacy of sensitive market research data processed through AI systems?

LeewayHertz prioritizes data security. We implement robust encryption, access controls, and compliance with industry regulations to safeguard the confidentiality and privacy of your sensitive market research data.

Can LeewayHertz assist in integrating AI solutions with existing market research tools and platforms that my company may already be using?

Yes, LeewayHertz has expertise in seamless integrations. Our team can work with your existing tools and platforms to ensure a smooth integration of AI solutions into your current market research workflows.

How does LeewayHertz approach the implementation and deployment of AI-driven market research solutions, and what is the expected timeline for seeing tangible results?

LeewayHertz follows a systematic implementation process. The timeline for tangible results depends on the complexity of the solution, data availability, and specific requirements. Our team will work closely with you to establish realistic timelines.

What level of technical support and maintenance does LeewayHertz offer after the implementation of AI solutions for market research, and how quickly can issues be addressed?

LeewayHertz provides comprehensive technical support and maintenance post-implementation. Our team is committed to addressing issues promptly, ensuring the smooth operation of AI solutions in your market research processes.

What steps does LeewayHertz take to ensure compliance with relevant regulations and ethical considerations when utilizing AI in market research?

LeewayHertz is committed to ethical AI practices and compliance with relevant regulations. We ensure that our AI solutions for market research adhere to industry standards and legal requirements, prioritizing transparency and responsible use of data.

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Artificial intelligence marketing software companies provide faster and more cost-efficient market research data by using AI to streamline processes. These companies employ algorithms that process large amounts of data in far less time than traditional market research methods. Artificial intelligence marketing software is quickly becoming the market research tool of choice for firms that want to scan billions of information touchpoints and generate substantial research reports.

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• Posted on March 14, 2023

Top 5 OpenAI-Powered Market Research Tools

OpenAI-powered market research tools are software applications that leverage machine learning and artificial intelligence algorithms developed by OpenAI to analyze data and generate insights about markets, customers, and competitors. These tools use natural language processing, image recognition, predictive analytics, and other AI technologies to help businesses make data-driven decisions, identify emerging trends, and stay ahead of the competition.

Importance Of OpenAI-Powered Market Research Tools

The importance of OpenAI-powered market research tools lies in their ability to provide businesses with timely and accurate insights into market trends, consumer behavior, and competitive landscape. By leveraging AI technologies analytics and automation, these tools can help businesses make informed decisions faster and with greater confidence, while reducing costs and minimizing the risk of errors.

For example, OpenAI-powered market research tools can help businesses:

• Identify new market opportunities and emerging trends based on large volumes of data and social media analytics.
• Generate custom reports and insights on consumer behavior and preferences, helping businesses tailor their marketing messages and product offerings.
• Automate data analysis and report generation, freeing up time and resources that can be better spent on other strategic initiatives.

In essence, OpenAI-powered market research tools are essential tools for businesses looking to stay ahead of the competition and make data-driven decisions in a fast-paced, ever-changing market environment.

Now we know that in today’s fast-paced market environment, businesses need to stay ahead of the competition by making data-driven decisions, let’s discuss some of the OpenAI-powered market research tools that businesses should consider to gain a competitive advantage. 

These tools use natural language processing, image recognition, predictive analytics, and other AI technologies to help businesses make data-driven decisions, identify emerging trends, and stay ahead of the competition.

The top 5 OpenAI-powered market research tools are: 

1. Poll the People

Poll the People is an OpenAI-powered market research tool that combines the power of AI and human intelligence to help analyze and understand data quickly and efficiently. It provides analysis tools to understand which design resonates with the users through the use of AI-backed word clouds , audience demographics, and data. The platform also provides an insights tab that instantly summarizes the results in a test report, giving users all the feedback they need to decide. This helps publishers and authors easily conduct book testing and efficiently find the design that will connect best with the intended audience, leading to a higher chance of sales.

• Cost-effective: Poll the People is a cost-effective market research tool that allows users to get actionable insights in under an hour. This makes it an ideal solution for companies on a budget.
• Easy to use: The platform is easy to use and provides templates for testing various things like titles, covers, blurbs, ads, brand names, logos, and more. This makes it easy for marketers to quickly deploy an ad test within minutes.
• AI-backed: Poll the People is powered by OpenAI, which provides a comprehensive and efficient solution for testing and optimizing digital marketing assets. This helps marketers become superheroes by eliminating roadblocks, driving conversions, and improving overall marketing effectiveness .
• Limited customization: The platform does not offer much customization options, which may limit its usefulness for certain types of research.

Appen is an OpenAI-powered market research tool that offers a range of data annotation and collection services for market research purposes. Appen’s platform uses AI and machine learning algorithms to automate some tasks, such as data categorization and sentiment analysis and relies on a global workforce of human annotators for other tasks, such as transcription services. The combination of AI and human-powered services allows Appen to handle large-scale data annotation and collection projects with high accuracy and efficiency.

• Customizable: Appen offers customizable data annotation and collection services, allowing businesses to tailor their research projects to their specific needs.
• Large-scale Data Collection: Appen can handle large-scale data collection and annotation projects, making it suitable for businesses that require extensive data sets.
• Quality Assurance: Appen provides quality assurance services to ensure accuracy and consistency in data annotation and collection, which can help improve the reliability of research results.
• Fast Turnaround: Appen has a global workforce and can provide fast turnaround times for research projects, making it ideal for time-sensitive market research needs.
• Cost: Appen’s services can be costly, making it less accessible for smaller businesses or those with limited budgets.
• Dependence on Human Workers: While Appen uses AI-powered algorithms for some tasks, the platform relies heavily on human workers for data annotation and collection, which can introduce biases and errors.
• Limited Control: Appen’s services rely on the skills and availability of its workers, which can limit businesses’ control over the data collection and annotation process.
• Security Concerns: Appen collects and stores sensitive data, which can raise security concerns for businesses and consumers.

Pecan is an OpenAI-powered market research tool that provides automated analysis and insights for market research purposes. It uses advanced natural language processing (NLP) algorithms powered by OpenAI’s GPT language models to analyze large volumes of unstructured data, such as customer feedback, social media posts, and online reviews. The platform’s AI-powered analysis provides businesses with actionable insights and recommendations to improve their products, services, and customer experience. Pecan’s key features include sentiment analysis, topic modeling, intent analysis, and summarization.

• Automated Analysis: Pecan automates the analysis process, which saves businesses time and resources that would otherwise be spent on manual analysis.
• Accurate Insights: Pecan’s AI-powered analysis is highly accurate and can identify patterns and trends that may not be apparent through manual analysis.
• Easy-to-Use: Pecan’s platform is user-friendly and requires minimal technical 4. expertise, making it accessible to businesses of all sizes.
• Continuous Improvement: Pecan’s algorithms adapt and improve over time based on feedback and data inputs, ensuring continuous improvement and accuracy.
• Limited Customization: Pecan’s analysis is automated, which limits businesses’ ability to customize the analysis to their specific needs.
• Dependence on Data Quality: Pecan’s analysis relies on the quality of the data inputs, which can introduce biases and errors if the data is inaccurate or incomplete.
• Lack of Human Interpretation: Pecan’s analysis is automated, which means that it may not capture the nuances of human language and behavior that may be important for certain types of research.
• Data Security Concerns: Pecan collects and stores sensitive data, which can raise security concerns for businesses and consumers.

Remesh is an AI-powered market research tool that uses natural language processing (NLP) to facilitate real-time conversations between businesses and their customers or stakeholders. The platform uses OpenAI’s GPT language models to analyze and interpret customer feedback and provide real-time insights to businesses. Remesh’s conversational AI platform allows businesses to engage with large groups of participants simultaneously, providing a cost-effective and efficient way to gather insights and feedback.

• Real-time Conversations: Remesh’s conversational AI platform enables real-time conversations between businesses and customers, providing instant feedback and insights.
• Cost-effective: Remesh’s platform allows businesses to engage with large groups of participants simultaneously, providing a cost-effective way to gather insights and feedback.
• Scalable: Remesh’s platform is highly scalable and can accommodate large groups of participants, making it suitable for businesses of all sizes.
• AI -Powered Analysis: Remesh uses OpenAI’s GPT language models to analyze and interpret customer feedback, providing businesses with actionable insights and recommendations.
• Limited Customization: Remesh’s conversational AI platform is highly automated, which limits businesses’ ability to customize the conversations to their specific needs.
• Dependence on Data Quality: Remesh’s analysis relies on the quality of the data inputs, which can introduce biases and errors if the data is inaccurate or incomplete.
• Limited Depth: Remesh’s platform may not capture the nuances of human language and behavior that may be important for certain types of research, as it relies on pre-defined responses and questions.
• Limited Use Cases: Remesh’s platform is best suited for certain types of research, such as customer feedback and stakeholder engagement, and may not be suitable for other types of research.

Crayon is an AI-powered market intelligence platform that uses OpenAI’s natural language processing (NLP) technology to track and analyze market trends, customer feedback, and competitor activity in real time. It provides businesses with actionable insights and recommendations to help them make data-driven decisions and stay ahead of the competition.

The platform enables businesses to track their competitors’ activity across various channels, including social media, news, and websites. Crayon uses machine learning to analyze and categorize this data, identifying trends and changes that may affect the market.

• Real-time Insights: Crayon uses AI-powered NLP technology to track and analyze market trends and competitor activity in real time, providing businesses with up-to-date insights and recommendations.
• Comprehensive Coverage: Crayon provides businesses with a comprehensive view of the market landscape, including customer feedback, pricing trends, and competitor activity across multiple channels and platforms.
• Customizable Dashboards: Crayon’s platform offers customizable dashboards that allow businesses to track and monitor specific metrics and KPIs, enabling them to focus on what matters most to them.
• Collaboration and Integration: Crayon’s platform integrates with other business tools, such as Salesforce and Slack, and allows for collaboration and knowledge sharing among team members.
• Learning Curve: Crayon’s platform can have a steep learning curve for businesses that are new to AI-powered market intelligence tools, which may require additional training and resources.
• Limited Depth: Crayon’s platform may not capture the nuances of human language and behavior that may be important for certain types of research, as it relies on pre-defined responses and questions.
• Dependence on Data Quality: Crayon’s analysis relies on the quality of the data inputs, which can introduce biases and errors if the data is inaccurate or incomplete.
• Cost: Crayon’s platform can be expensive for small businesses or startups, as it is primarily geared towards larger enterprises.

Frequently Asked Questions

• What Are the Advantages of Using AI in Market Research? AI in market research offers numerous advantages. It allows for the analysis of large volumes of data in a short time, providing businesses with timely and accurate insights into market trends, consumer behavior, and the competitive landscape. AI-powered tools can identify new market opportunities and emerging trends, helping businesses stay ahead of the competition.
• How Do AI Tools Enhance Market Research Processes? AI tools enhance market research processes by automating data analysis and report generation. This not only saves time and resources but also minimizes the risk of errors. AI tools use technologies like natural language processing, image recognition, and predictive analytics to provide businesses with data-driven insights, enabling them to make informed decisions faster and with greater confidence.
• What Are Some Examples of Market Research Apps and How Do They Utilize AI? Examples of market research apps that utilize AI include Poll the People, Appen, Pecan, Remesh, and Crayon. These apps use AI to gather and analyze data, enabling businesses to understand customer preferences and behaviors better. For instance, Poll the People uses AI to ensure the accuracy and reliability of the data it collects.
• What Is the Role of Artificial Intelligence in Consumer Research? Artificial Intelligence plays a crucial role in consumer research. It helps businesses understand customer preferences and behaviors, enabling them to tailor their marketing messages and product offerings. AI can analyze consumer behavior patterns and predict future trends, helping businesses to stay ahead of the competition.
• What Are the Best Practices for Using Automated Tools in Market Research? Best practices for using automated tools in market research include setting clear objectives for what you want to achieve with the tool, ensuring the data you feed into the tool is clean and accurate, regularly reviewing and updating the tool’s settings to ensure it’s providing the insights you need, and integrating the tool with other systems to maximize its value.

In conclusion, OpenAI-powered market research tools offer businesses a powerful way to gather and analyze data, enabling them to make data-driven decisions and stay ahead of the competition. The top 5 OpenAI-powered market research tools we discussed are Poll the People, Appen, Pecan, Remesh, and Crayon.

If you’re looking to gain insights into your target market and better understand customer preferences and behaviors, Poll the People is a great option. With its AI-powered capabilities, you can rest assured that you’re getting accurate and reliable data. Consider trying Poll the People today to see how it can help you better understand your customers and grow your business.

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The state of AI in 2022—and a half decade in review

Adoption has more than doubled since 2017, though the proportion of organizations using AI 1 In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example, natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work). has plateaued between 50 and 60 percent for the past few years. A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development , and showing signs of faring better in the tight market for AI talent. On talent, for the first time, we looked closely at AI hiring and upskilling. The data show that there is significant room to improve diversity on AI teams, and, consistent with other studies, diverse teams correlate with outstanding performance.

Table of Contents

• Five years in review: AI adoption, impact, and spend
• Mind the gap: AI leaders pulling ahead
• AI talent tales: New hot roles, continued diversity woes

About the research

1. five years in review: ai adoption, impact, and spend.

This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we have seen shifts over this period.

2. Mind the gap: AI leaders pulling ahead

Over the past five years we have tracked the leaders in AI—we refer to them as AI high performers—and examined what they do differently. We see more indications that these leaders are expanding their competitive advantage than we find evidence that others are catching up.

First, we haven’t seen an expansion in the size of the leader group. For the past three years, we have defined AI high performers as those organizations that respondents say are seeing the biggest bottom-line impact from AI adoption—that is, 20 percent or more of EBIT from AI use. The proportion of respondents falling into that group has remained steady at about 8 percent. The findings indicate that this group is achieving its superior results mainly from AI boosting top-line gains, as they’re more likely to report that AI is driving revenues rather than reducing costs, though they do report AI decreasing costs as well.

Next, high performers are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes  (Exhibit 1). 2 All questions about AI-related strengths and practices were asked only of the 744 respondents who said their organizations had adopted AI in at least one function, n = 744. Also important, they are engaging more often in “frontier” practices that enable AI development and deployment at scale , or what some call the “ industrialization of AI .” For example, leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly. They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms. And AI high performers are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI applications by using emerging low-code or no-code programs , which allow companies to speed up the creation of AI applications. In the past year, high performers have become even more likely than other organizations to follow certain advanced scaling practices, such as using standardized tool sets to create production-ready data pipelines and using an end-to-end platform for AI-related data science, data engineering, and application development that they’ve developed in-house.

High performers might also have a head start on managing potential AI-related risks, such as personal privacy and equity and fairness, that other organizations have not addressed yet. While overall, we have seen little change in organizations reporting recognition and mitigation of AI-related risks since we began asking about them four years ago, respondents from AI high performers are more likely than others to report that they engage in practices that are known to help mitigate risk . These include ensuring AI and data governance , standardizing processes and protocols , automating processes such as data quality control to remove errors introduced through manual work, and testing the validity of models and monitoring them over time for potential issues.

AI use and sustainability efforts

The survey findings suggest that many organizations that have adopted AI are integrating AI capabilities into their sustainability efforts and are also actively seeking ways to reduce the environmental impact of their AI use (exhibit). Of respondents from organizations that have adopted AI, 43 percent say their organizations are using AI to assist in sustainability efforts, and 40 percent say their organizations are working to reduce the environmental impact of their AI use by minimizing the energy used to train and run AI models. As companies that have invested more in AI and have more mature AI efforts than others, high performers are 1.4 times more likely than others to report AI-enabled sustainability efforts as well as to say their organizations are working to decrease AI-related emissions. Both efforts are more commonly seen at organizations based in Greater China, Asia–Pacific, and developing markets, while respondents in North America are least likely to report them.

When asked about the types of sustainability efforts using AI, respondents most often mention initiatives to improve environmental impact, such as optimization of energy efficiency or waste reduction. AI use is least common in efforts to improve organizations’ social impact (for example, sourcing of ethically made products), though respondents working for North American organizations are more likely than their peers to report that use.

Investment is yet another area that could contribute to the widening of the gap: AI high performers are poised to continue outspending other organizations on AI efforts. Even though respondents at those leading organizations are just as likely as others to say they’ll increase investments in the future, they’re spending more than others now, meaning they’ll be increasing from a base that is a higher percentage of revenues. Respondents at AI high performers are nearly eight times more likely than their peers to say their organizations spend at least 20 percent of their digital-technology budgets on AI-related technologies. And these digital budgets make up a much larger proportion of their enterprise spend: respondents at AI high performers are over five times more likely than other respondents to report that their organizations spend more than 20 percent of their enterprise-wide revenue on digital technologies.

Finally, all of this may be giving AI high performers a leg up in attracting AI talent. There are indications that these organizations have less difficulty hiring for roles such as AI data scientist and data engineer. Respondents from organizations that are not AI high performers say filling those roles has been “very difficult” much more often than respondents from AI high performers do.

The bottom line: high performers are already well positioned for sustained AI success, improved efficiency in new AI development, and a resultingly more attractive environment for talent. The good news for organizations outside the leader group is that there’s a clear blueprint of best practices for success.

3. AI talent tales: New hot roles, continued diversity woes

Our first detailed look at the AI talent picture signals the maturation of AI, surfaces the most common strategies organizations employ for talent sourcing and upskilling, and shines a light on AI’s diversity problem—while showing yet again a link between diversity and success.

Hiring is a challenge, but less so for high performers

All organizations report that hiring AI talent, particularly data scientists, remains difficult. AI high performers report slightly less difficulty and hired some roles, like machine learning engineers, more often than other organizations.

Reskilling and upskilling are common alternatives to hiring

When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half are doing so. Recruiting from top-tier universities as well as from technology companies that aren’t in the top tier, such as regional leaders, are also common strategies. But a look at the strategies of high performers suggests organizations might be best served by tapping as many recruiting channels as possible (Exhibit 2). These companies are doing more than others to recruit AI-related talent from various sources. The findings show that while they’re more likely to recruit from top-tier technical universities and tech companies, they’re also more likely to source talent from other universities, training academies, and diversity-focused programs or professional organizations.

Responses suggest that both AI high performers and other organizations are upskilling technical and nontechnical employees on AI, with nearly half of respondents at both AI high performers and other organizations saying they are reskilling as a way of gaining more AI talent. However, high performers are taking more steps than other organizations to build employees’ AI-related skills.

Respondents at high performers are nearly three times more likely than other respondents to say their organizations have capability-building programs to develop technology personnel’s AI skills. The most common approaches they use are experiential learning , self-directed online courses, and certification programs, whereas other organizations most often lean on self-directed online courses.

High performers are also much more likely than other organizations to go beyond providing access to self-directed online course work to upskill nontechnical employees on AI. Respondents at high performers are nearly twice as likely as others to report offering peer-to-peer learning and certification programs to nontechnical personnel.

Increasing diversity on AI teams is a work in progress

We also explored the level of diversity within organizations’ AI-focused teams, and we see that there is significant room for improvement at most organizations. The average share of employees on these teams at respondents’ organizations who identify as women is just 27 percent (Exhibit 3). The share is similar when looking at the average proportion of racial or ethnic minorities developing AI solutions: just 25 percent. What’s more, 29 percent of respondents say their organizations have no minority employees working on their AI solutions.

Some companies are working to improve the diversity of their AI talent, though there’s more being done to improve gender diversity than ethnic diversity. Forty-six percent of respondents say their organizations have active programs to increase gender diversity within the teams that are developing AI solutions, through steps such as partnering with diversity-focused professional associations to recruit candidates. One-third say their organizations have programs to increase racial and ethnic diversity. We also see that organizations with women or minorities working on AI solutions often have programs in place to address these employees’ experiences.

In line with previous McKinsey studies , the research shows a correlation between diversity and outperformance. Organizations at which respondents say at least 25 percent of AI development employees identify as women are 3.2 times more likely than others to be AI high performers. Those at which at least one-quarter of AI development employees are racial or ethnic minorities are more than twice as likely to be AI high performers.

The online survey was in the field from May 3 to May 27, 2022, and from August 15 to August 17, 2022, and garnered responses from 1,492 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 744 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office; Bryce Hall , an associate partner in the Washington, DC, office; Helen Mayhew , a partner in the Sydney office; and Alex Singla , a senior partner in the Chicago office, and Alex Sukharevsky , a senior partner in the London office, global leaders of QuantumBlack, AI by McKinsey.

The authors wish to thank Sanath Angalakudati, Medha Bankhwal, David DeLallo, Heather Hanselman, Vishan Patel, and Wilbur Wang for their contributions to this work.

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Can AI make market research easier? DHS is trying to find out

Scott Simpson, the digital transformation lead in the Office of the Chief Procurement Officer at DHS, said about 200 contracting officers are testing three AI c...

Homeland Security Department contracting officers had a problem — conducting market research is a constant struggle.

And with the Biden administration’s push to expand the industrial base, particularly with more small and disadvantaged businesses, acquisition workers needed some extra help.

Scott Simpson, the digital transformation lead in the Office of the Chief Procurement Officer at DHS, said the Procurement Innovation Lab’s new artificial intelligence for market research tool is improving and accelerating the search for new contractors.

“The acquisition workforce kept coming to us and saying, ‘Hey, we need help with market research. We don’t know where to start. We’ve had a lot of tough customers out there that also don’t know where to start. What can you do for us?’” Simpson said after speaking at a recent ATARC procurement event. “We had a couple of vendors on contract, and we said they’re doing AI work for us already. Why don’t we talk with them? Our users went through a discovery session with those vendors, and that led into development. The users were on board the whole time looking at mockups and wireframes, and all that kind of stuff. Users went into development and testing, and they came up with these three tools. The AI for market research tools, by the three companies, went live in September. We awarded three governmentwide contracts that anyone across federal government can use.”

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The AI for market research tools scrape public databases like SAM.gov, USASpending.gov and the Federal Procurement Data System-Next Generation looking for companies who have done work in a specific sector before such as cybersecurity or software development.

Simpson said user receives not just the company’s names, but information about the agencies they worked for previously, whether they are a large or small business as well as socioeconomic class and a host of other data to help contracting officers see who is available.

“The most relevant vendors are at the top and the least relevant are at the bottom. It doesn’t show all of the vendors in the whole world because that would be a lot of work. But it samples them and shows you like a good 20 to 100, or something like that,” he said. “You can flip through and see which ones you think are capable or not. It shows you the exact record for why they think they are relevant.”

Pilot includes 200 users

From that list contracting officers can perform more targeted market research, limiting their searches to specific governmentwide acquisition contracts or calling a handful of companies asking them to respond to a request for information.

“We’re really hoping that this market research tool helps to speed along the process , and give people that starting point to start their market research,” Simpson said. “This is that starting point showing them trends about where people have bought this in the past from other governmentwide agency contracts or from agencies specific contracts, as well as the other big question that we got was, should this be set aside for small business? This helps to show you people who has set this aside for small business before and people who have not, and gives you that information at the start.”

DHS launched the pilot with about 200 users last fall from every component. Simpson said the PIL is measuring progress and impact through a qualitative survey.

He said DHS conducted a survey before the pilot kicked off last fall asking how contracting officers performed market research at that time and how they rated the quality of their efforts to include how much competition they received.

AI tool available to non-DHS users

“We’ll do that again after our six-to-nine month pilot to measure what’s going to be that return on investment if we keep going down this path. We were hoping that the time spent doing market research goes down, and the quality of that market research goes up,” Simpson said. “The reaction so far has been positive. One of the reasons we wanted to do the pilot is just to work out the kinks. This is really the first time that they’re seeing these systems in a live setting. You can beta test with dummy data all you want, but until you get it out in the field and start doing this with live data, you never really know what you want in a system. This is that time to say, ‘Well, hey, I liked this, but I don’t like this, or this needs to be refined.’”

         Read more: Artificial Intelligence

The AI for market research tool isn’t just for DHS and its component agencies. Simpson said any agency can take advantage of the tools. DHS has briefed the Chief Acquisition Officer’s Council and the Acquisition Innovation Advocates Council about the technologies.

“There are other agencies out there that are definitely looking at doing their own pilots. There’s one agency that’s potentially looking at using the beta licenses to start their market research pilot,” he said. “There’s interest out there because people are hungry for these things that helped to reduce some of that time spent on these mundane or low value things so you can shift your work and your effort to the high value things. We all know that the 1102 field is both a tough one to hire for and a tough one to keep people as they’re definitely overworked, especially in that fourth quarter. So anything we can do to get them more time to focus on that high value work. That’s what we’re looking to do. And I think all the agencies are hungry for that.”

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How One Marketing Team Made AI Part of Its Daily Work

• Michelle Taite

Nine out of ten marketers agree: Organizations must increase their use of AI if they want to stay competitive.

In today’s always-on environment, AI tools can help marketers optimize and personalize their campaigns quickly and efficiently. But AI alone won’t yield meaningful campaigns. Impact-driving work requires both human ingenuity and machine speed — a combination marketers can’t fully embrace without daily practice. This article discusses how one team experimented with used AI to complement their creative marketers on various tasks, and how it resulted in their most impactful campaign to date.

A marketer’s job is never done. Marketing leaders increasingly recognize that their teams need more support to square consumers’ around-the-clock expectations with the bandwidth of human marketers. According to a survey conducted by Forrester, nearly nine out of ten marketers believe their organization must increase its use of AI to stay competitive, especially as resources remain flat or decrease. Yet only half of marketers feel that they have adequately adopted AI, a discrepancy that isn’t necessarily surprising.

• Michelle Taite is the CMO of Mailchimp at Intuit. She is responsible for the business’s end-to-end brand, acquisition, performance, product, and lifecycle marketing activities globally. Previously, Michelle was the VP of Global Marketing for QuickBooks at Intuit, and she has also held roles at Unilever and New Balance. Michelle currently serves on the Association of National Advertisers Global CMO Growth Council and was named one of Insider’s Most Innovative CMOs in 2023.

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Our next-generation Meta Training and Inference Accelerator

April 10, 2024 · 8 min read

• We’re sharing details about the next generation of the Meta Training and Inference Accelerator (MTIA), our family of custom-made chips designed for Meta’s AI workloads.
• This latest version shows significant performance improvements over MTIA v1 and helps power our ranking and recommendation ads models.
• MTIA is part of our growing investment in our AI infrastructure and will complement our existing and future AI infrastructure to deliver new and better experiences across our products and services.

The next generation of Meta’s large-scale infrastructure is being built with AI in mind , including supporting new generative AI (GenAI) products and services, recommendation systems, and advanced AI research. It’s an investment we expect will grow in the years ahead as the compute requirements to support AI models increase alongside the models’ sophistication.

Last year, we unveiled the Meta Training and Inference Accelerator (MTIA) v1 , our first-generation AI inference accelerator that we designed in-house with Meta’s AI workloads in mind – specifically our deep learning recommendation models that are improving a variety of experiences across our products.

MTIA is a long-term venture to provide the most efficient architecture for Meta’s unique workloads. As AI workloads become increasingly important to our products and services, this efficiency will improve our ability to provide the best experiences for our users around the world. MTIA v1 was an important step in improving the compute efficiency of our infrastructure and better supporting our software developers as they build AI models that will facilitate new and better user experiences.

Now, we’re sharing details about the next generation of MTIA.

Next generation MTIA chip model. Drag to rotate.

This inference accelerator is part of our broader full-stack development program for custom, domain-specific silicon that addresses our unique workloads and systems. This new version of MTIA more than doubles the compute and memory bandwidth of our previous solution while maintaining our close tie-in to our workloads. It is designed to efficiently serve the ranking and recommendation models that provide high-quality recommendations to users.

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Under the hood

This chip’s architecture is fundamentally focused on providing the right balance of compute, memory bandwidth, and memory capacity for serving ranking and recommendation models. In inference we need to be able to provide relatively high utilization, even when our batch sizes are relatively low. By focusing on providing outsized SRAM capacity, relative to typical GPUs, we can provide high utilization in cases where batch sizes are limited and provide enough compute when we experience larger amounts of potential concurrent work.

This accelerator consists of an 8x8 grid of processing elements (PEs). These PEs provide significantly increased dense compute performance (3.5x over MTIA v1) and sparse compute performance (7x improvement). This comes partly from improvements in the architecture associated with pipelining of sparse compute. It also comes from how we feed the PE grid: We have tripled the size of the local PE storage, doubled the on-chip SRAM and increased its bandwidth by 3.5X, and doubled the capacity of LPDDR5.

Our new MTIA design also features an improved network on chip (NoC) architecture that doubles the bandwidth and allows us to coordinate between different PEs at low latency. These and other new functions in the PEs form the key technologies that are vital to our long-term roadmap to scale MTIA to a wider variety of more challenging workloads.

First Gen MTIA

1.12B gates, 65M flops

19.34mm x 19.1mm, 373mm2

43mm x 43mm

0.67V logic, 0.75V memory

Host Connection

8x PCIe Gen4 (16 GB/s)

102.4 TFLOPS/s (INT8) 51.2 TFLOPS/s (FP16/BF16)

Vector core: 3.2 TFLOPS/s (INT8), 1.6 TFLOPS/s (FP16/BF16), 0.8 TFLOPS/s (FP32) SIMD: 3.2 TFLOPS/s (INT8/FP16/BF16), 1.6 TFLOPS/s (FP32)

Memory Capacity

Local memory: 128 KB per PE On-chip memory: 128 MB Off-chip LPDDR5: 64 GB

Memory Bandwidth

Local memory: 400 GB/s per PE On-chip memory: 800 GB/s Off-chip LPDDR5: 176 GB/s

Next Gen MTIA

2.35B gates, 103M flops

25.6mm x 16.4mm, 421mm2

50mm x 40mm

8x PCIe Gen5 (32 GB/s)

708 TFLOPS/s (INT8) (sparsity) 354 TFLOPS/s (INT8) 354 TFLOPS/s (FP16/BF16) (sparsity) 177 TFLOPS/s (FP16/BF16)

Vector core: 11.06 TFLOPS/s (INT8), 5.53 TFLOPS/s (FP16/BF16), 2.76 TFLOPS/s (FP32) SIMD: 5.53 TFLOPS/s (INT8/FP16/BF16), 2.76 TFLOPS/s (FP32)

Local memory: 384 KB per PE On-chip memory: 256 MB Off-chip LPDDR5: 128 GB

Local memory: 1 TB/s per PE On-chip memory: 2.7 TB/s Off-chip LPDDR5: 204.8 GB/s

The hardware

Serving our workloads effectively is not simply a silicon challenge. Co-designing the hardware system and the software stack along with the silicon is essential for the success of the overall inference solution.

To support the next-generation silicon we have developed a large, rack-based system that holds up to 72 accelerators. This consists of three chassis, each containing 12 boards that house two accelerators each. We specifically designed the system so that we could clock the chip at 1.35GHz (up from 800 MHz) and run it at 90 watts compared to 25 watts for our first-generation design. Our design ensures we provide denser capabilities with higher compute, memory bandwidth, and memory capacity. This density allows us to more easily accommodate a broad range of model complexities and sizes.

Beyond this, we have upgraded the fabric between the accelerators and between the host and accelerators to PCIe Gen5 to increase the bandwidth and scalability of our system. There is also the option to add an RDMA NIC if we choose to scale out beyond the rack.

The software stack

Software has been one of our key areas of focus from the start of our investment in MTIA. As the initial developers of PyTorch , we value programmability and developer efficiency. Our MTIA stack is designed to fully integrate with PyTorch 2.0 and features like TorchDynamo and TorchInductor. Frontend graph-level capturing, analysis, transformation, and extraction mechanisms (such as TorchDynamo, torch.export, etc.) are agnostic to MTIA and are being reused. The lower level compiler for MTIA takes the outputs from the frontend and produces highly efficient and device-specific code. This lower level compiler itself consists of a few components that are responsible for generating executable code for models and kernels.

Below this sits the runtime stack responsible for interfacing with the driver/firmware. The MTIA Streaming interface abstraction provides the basic and essential operations that both inference and (in the future) training software require to manage the device memory, as well as run operators and execute compiled graphs on the device. Finally, the runtime interacts with the driver, which sits in user space – a decision we made to enable us to iterate faster on the driver and firmware within our production stack.

In many ways this new chip system runs the software stack similarly to MTIA v1, which made it much faster for the team to deploy since we had already done much of the necessary integration and development work needed to be able to run our applications on this architecture. The new MTIA is designed to be compatible with code developed for MTIA v1. Since we had already integrated the full software stack to the silicon, we were up and running our traffic with this new chip in a matter of days. This allowed us to land this next-generation MTIA silicon rapidly, going from first silicon to production models running in 16 regions in less than nine months.

Triton-MTIA

We’ve further optimized the software stack by creating the Triton-MTIA compiler backend to generate high-performance code for the MTIA hardware. Triton is an open source language and compiler for writing highly efficient ML compute kernels. It improves developer productivity for writing GPU code and we have found that the Triton language is sufficiently hardware-agnostic to be applicable to non-GPU hardware architectures like MTIA.

The Triton-MTIA backend performs optimizations to maximize hardware utilization and support high-performance kernels. It also exposes key knobs to leverage Triton and MTIA auto-tuning infrastructures to explore the kernel configuration and optimization space.

We have implemented support for the features of the Triton language and integration into PyTorch 2, providing extensive coverage for PyTorch operators. Thanks to TorchInductor, for example, our developers can leverage Triton-MTIA in both ahead-of-time (AOT) and just-in-time (JIT) workflows.

We observed dramatically improved developer efficiency with Triton-MTIA, which allowed us to scale up compute kernel authoring and significantly expand the support of PyTorch operators.

Performance Results

The results so far show that this MTIA chip can handle both the low complexity (LC) and high complexity (HC) ranking and recommendation models that are components of Meta’s products. Across these models, there can be a ~10x-100x difference in model size and the amount of compute per input sample. Because we control the whole stack, we can achieve greater efficiency compared to commercially available GPUs. Realizing these gains is an ongoing effort and we continue to improve performance per watt as we build up and deploy MTIA chips in our systems.

Early results show that this next generation silicon has already improved performance by 3x over our first generation chip across four key models we evaluated. At the platform level, with 2x the number of devices and a powerful 2-socket CPU, we are able to achieve 6x model serving throughput and a 1.5x performance per watt improvement over the first generation MTIA system. To achieve this, we have made significant progress optimizing kernels, compiler, runtime, and host serving stack. The time to optimize models is going down as the developer ecosystem matures, yet there is more headroom to improve efficiency in the future.

improved performance over our first gen chip

MTIA has been deployed in the data center and is now serving models in production. We are already seeing the positive results of this program as it's allowing us to dedicate and invest in more compute power for our more intensive AI workloads. It is proving to be highly complementary to commercially available GPUs in delivering the optimal mix of performance and efficiency on Meta-specific workloads.

Meta’s ongoing investment in custom silicon

MTIA will be an important piece of our long-term roadmap to build and scale the most powerful and efficient infrastructure possible for Meta’s unique AI workloads.

We’re designing our custom silicon to work in cooperation with our existing infrastructure as well as with new, more advanced hardware (including next-generation GPUs) that we may leverage in the future. Meeting our ambitions for our custom silicon means investing not only in compute silicon but also in memory bandwidth, networking and capacity as well as other next-generation hardware systems.

We currently have several programs underway aimed at expanding the scope of MTIA, including support for GenAI workloads.

We’re only at the beginning of this journey, and we’re inviting people who want to be a part of it to visit Meta Careers to learn about our open positions.

Acknowledgements

We would like to thank Eugene Burmako, Kaustubh Gondkar, Adam Hutchin, Olivia Wu, and everyone involved in the development and productionization of the next-generation MTIA solution

Written by:

Eran tal, nicolaas viljoen, joel coburn, and roman levenstein.

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Global generative artificial intelligence (ai) in healthcare market size to exceed usd 29.8 billion by 2033 | cagr of 34.84%.

The Global Generative Artificial Intelligence (AI) in Healthcare Market Size was Valued at USD 1.5 Billion in 2023 and the Worldwide Generative Artificial Intelligence (AI) in Healthcare Market Size is Expected to Reach USD 29.8 Billion by 2033, according to a research report published by Spherical Insights & Consulting. Companies covered: IBM Watson, Open AI, Google LLC, Microsoft Corporation, Johnson & Johnson, Neuralink Corporation, NioyaTech, Oracle, Saxon, Syntegra, Tencent Holdings Ltd., Epic Systems Corporation, Insilico Medicine, Nvidia Corporation, Abridge AI Inc., and Others Key Vendors.

New York, United States , April 22, 2024 (GLOBE NEWSWIRE) -- The   Global Generative Artificial Intelligence (AI) in Healthcare Market  Size is to Grow from USD 1.5 Billion in 2023 to USD 29.8 Billion by 2033, at a Compound Annual Growth Rate (CAGR) of 34.84% during the projected period.

Get a Sample PDF Brochure: https://www.sphericalinsights.com/request-sample/4173

Generative AI plays a pivotal role in revolutionizing healthcare and life sciences, transforming patient care and research field, capability to enhance diagnostics, treatment plans, and drug discovery processes, leading to more personalized patient care and efficient healthcare solutions with an improved advancement in research, optimizing resource allocation and improving patient outcomes. Generative AI has the capability to transform healthcare (automated system), via enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. It is also used in billing, diagnosis, treatment, and research in the healthcare system. The growing demand for personalized and targeted healthcare interventions with a rising focus on growing medical imaging technologies for accurate and efficient and efficient diagnosis of disease are responsible for uplifting the market. According to the data shown by Crunchbase, more than a fifth of all venture funding in February went to AI companies, with $4.7 billion invested in the sector. On the contrary, the rising concerns regarding data privacy and security and exposure of sensitive information have restrained the market. Regulatory compliance on gen AI is also responsible for restraining the market.

Browse key industry insights spread across 200 pages with 110 Market data tables and figures & charts from the report on the  " Global Generative artificial intelligence (AI) in healthcare Market  Size, Share, and COVID-19 Impact Analysis, By   Component (Solutions and Services), By Application (Personalized Treatment, Patient Assistance, Patient Monitoring & Predictive Analytics, Medical Image Analysis & Diagnostics, Drug Discovery & Development and Others), By End-Use (Healthcare Providers, Pharmaceutical and Life Science Companies, and Healthcare Payers), and By Region (North America, Europe, Asia-Pacific, Latin America, Middle East, and Africa), Analysis and Forecast 2023 – 2033."

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The solution segment dominates the market with the largest revenue share through the forecast period. Based on component, the global generative artificial intelligence (AI) in healthcare market is segmented into solutions and services. Among these, the solution segment is dominating the market with the largest revenue share through the forecast period. Generative AI solution in the healthcare market leads to superior outcomes in diagnostics, accelerating drug development, and precise patient care in the healthcare system. These factors lead to upgrading the existing healthcare infrastructure into more cost-effective, regulatory compliance and seamless integration.

The medical image analysis & diagnostics segment dominates the market with the largest revenue share through the forecast period. Based on application, the global generative artificial intelligence (AI) in healthcare market is segmented into personalized treatment, patient assistance, patient monitoring & predictive analytics, medical image analysis & diagnostics, drug discovery & development and others. Among these, the medical image analysis & diagnostics segment is dominating the market with the largest revenue share through the forecast period. The medical imaging analysis and diagnostic segment has major role for advancing and shaping healthcare industry. The remarkable progress of medical imaging analysis has found in diagnostic and treatment approaches.

The healthcare providers segment is expected to grow at the fastest CAGR during the forecast period.     Based on the end-use, the global generative artificial intelligence (AI) in healthcare market is categorized into healthcare providers, pharmaceutical and life science companies, and healthcare payers. Among these, the healthcare providers segment is expected to grow at the fastest CAGR during the forecast period. The healthcare provider segment includes hospitals, clinics, and diagnostic centers. Gen AI has propelled medical processes via enhancing diagnostics, and optimization of patient care that leads to advanced healthcare delivery.

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North America dominates the market with the largest market share over the forecast period.

North America dominates the market with the largest market share over the forecast period. The presence of well-established healthcare infrastructure in the region is responsible for promoting the market growth. Further, technological advancement and growing investment in R&D facilities are majorly responsible for propelling the generative artificial intelligence (AI) in healthcare market in the region. These factors are emphasizing efficiency of healthcare system with precise medicine system.

Asia-Pacific region is expected to grow the fastest during the forecast period. The rapid digital transformation and growing tech ecosystem in countries like China, India, Japan, and Singapore leads to the adoption of advanced AI technologies in healthcare sector. Further, revolutionizing healthcare outcomes in the region is promoting the market.

Competitive Analysis:

The report offers the appropriate analysis of the key organizations/companies involved within the global market along with a comparative evaluation primarily based on their product offering, business overviews, geographic presence, enterprise strategies, segment market share, and SWOT analysis. The report also provides an elaborative analysis focusing on the current news and developments of the companies, which includes product development, innovations, joint ventures, partnerships, mergers & acquisitions, strategic alliances, and others. This allows for the evaluation of the overall competition within the market. Major vendors in the global Generative artificial intelligence (AI) in healthcare market are IBM Watson, Open AI, Google LLC, Microsoft Corporation, Johnson & Johnson, Neuralink Corporation, NioyaTech, Oracle, Saxon, Syntegra, Tencent Holdings Ltd., Epic Systems Corporation, Insilico Medicine, Nvidia Corporation, Abridge AI Inc., and Other Key Players.

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Recent Developments

In August 2023,  Cognizant expanded collaboration with Google Cloud, utilizing generative AI to enhance administrative processes, aiming for cost optimization and improved user experiences. This collaborative partnership aimed to strengthen healthcare solutions, improve business efficiencies, and enhance user experiences.

In September 2023 , HARMAN launched a new private large language model for the healthcare industry- HARMAN HealthGPT. HealthGPT represents a leap in healthcare solutions by leveraging generative AI to empower healthcare professionals, researchers, and institutions through advanced patient care, medical research, and decision-making.

Global Generative Artificial Intelligence (AI) in Healthcare Market, Component Analysis

Global Generative Artificial Intelligence (AI) in Healthcare Market, Application Analysis

Personalized Treatment

Patient Assistance

Patient Monitoring & Predictive Analytics

Medical Image Analysis & Diagnostics

Drug Discovery & Development

Global Generative Artificial Intelligence (AI) in Healthcare Market, End-Use Analysis

Healthcare Providers

Pharmaceutical and Life Science Companies

Healthcare Payers

Global Generative artificial intelligence (AI) in healthcare Market, Regional Analysis

North America

Rest of Europe

Asia Pacific

South Korea

Rest of Asia Pacific

South America

Rest of South America

Middle East & Africa

Saudi Arabia

South Africa

Rest of Middle East & Africa

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AI Technology to Propel Global PC Market Growth in 2024

Published 2024-04-22 08:31 by Hilbert Hagedoorn

The year 2024 is viewed as a pivotal period for AI PCs, including both desktops and laptops, with projections indicating that 45% of new laptops shipped during the year will be AI-enabled. William Li, a senior analyst, noted that the shipment and deployment of generative AI laptops are expected to accelerate significantly between 2025 and 2026. This growth will be supported by new processor platforms from chip vendors, alongside the development of new generative AI functions and applications.

The stabilization of inventory levels and the conclusion of a replacement cycle, which peaked during the COVID-19 pandemic, sets the stage for AI laptops to significantly contribute to the recovery of overall PC shipments in 2024. In the latter half of the year, manufacturers are expected to increasingly promote AI PCs as their flagship offerings, in anticipation of new system-on-chip (SoC) releases designed to enhance computational performance.

Source:  Counterpoint Research

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ASUS Introduces 39-Inch UWQHD Curved Gaming Display with Some Serious Specifications

A new experimental legal framework in Russia shows the perils and promise of future AI regulation

A new experimental legal regime for the development of artificial intelligence (AI) projects is unfolding in Moscow and subsequently throughout Russia. The first initiative was introduced in a draft bill earlier this year, which underwent very few changes after its first publication. As a result, Russia's Federal Law No. 123-FZ came into force on July 1, 2020, introducing a special legal framework for "digital sandboxes" in Moscow.

Digital sandboxes are territories where technologies may be developed and tested even if such technologies fall outside of the current legislation. And as a result of the issues posed by this new law, a more expansive law - Federal Law No. 258-FZ - will come into effect in 2021.

Federal Law No. 123-FZ

This experimental regime allows companies to work on innovative AI technologies that are not regulated under existing legislation. It creates opportunities for seeing just how AI functions under real-life conditions.

Additionally, the new law is a first step in the legal regulation of AI in the Russian Federation, since it introduces definitions for artificial intelligence and artificial intelligence technology that may be used in future regulations. The duration of the experimental regime is five years, and it started in July 2020.

The experimental regime will also facilitate the realization of the Moscow government's project, "Moscow - the Smart City 2030," which focuses on the development of smart living, smart mobility, a smart economy, and a smart environment in Russia's capital. Although the project aims to fundamentally digitize the city by 2030, some AI technologies already form an integral part of the daily life of Moscow's citizens. For example, self-driven cars have been tested in Moscow since 2019, and several companies are currently examining their use on public roads.

By launching digital sandboxes, the authorities can closely monitor which legal issues arise from the daily use of AI and which of those must be urgently regulated. The clearest red-flag issues going forward are a liability in cases of AI malfunction or fault, and the accidental disclosure or erasure of personal data by AI.

Fundamental concerns

The provisions of 123-FZ regarding the processing of personal data have already caused significant concern among citizens. For example, the law states that no personal data involved in the project can be transferred to persons not related to the experiment, nor can that data be stored outside of Moscow. This seems obscure, given the boundless nature of the Internet and the fact that cloud services are commonly used for data storage and access. Moreover, the personal data will still be at the disposal of the Moscow city government, as well as the entrepreneurs and legal entities entered into a special register of operators.

The update also amends the Law on Personal Data. Part 1 of Article 6 is supplemented by Clause 9.1, according to which the processing of personal data obtained as a result of depersonalization is carried out in order to increase the efficiency of state or municipal administration, as well as for other purposes provided by 123-FZ.

The amended Article 10 of the "Law on Personal Data" indicates that "the processing of personal data regarding the state of health obtained as a result of depersonalization is allowed in order to increase the effectiveness of state or municipal administration."

At the same time, the law does not address such fundamental questions as:

• where the data of our digital identities will be stored and how it will be erased;
• how exactly the privacy of citizens will be ensured; and
• who will be liable in the cases of failure to depersonalize data.

Such regulations over personal data also raise questions about the primary aim of the new law, as well as generating concerns that participants in the experimental regime will have access to and can process the personal data of Moscow's citizens.

To minimize the negative effect of the law, a draft bill was submitted to the State Duma proposing to delay the introduction of the legislation until July 2025 in order to provide more clarity on the processing of anonymized personal data. The explanatory note related to the bill indicates that the use and implementation of AI technologies, including tracking and facial recognition systems, cause serious and legitimate concerns among citizens.

During the proposed postponement period, legal gaps related to the responsibility of officials for possible violations of the proper use of personal data can be eliminated. The draft received negative comments during the first reading, and the second reading of a revised version will take place during the autumn session of the State Duma.

Overall, Russia has the resources to implement innovative technologies into the country's operations, but the legislation is still lagging significantly. This could lead to an abuse of power by both the authorities and the accredited operators with access to the data.

Federal Law No. 258-FZ

As a result, comprehensive regulation of AI in Russia is urgently needed. First steps in this regulation, however, have already been taken, including the recent enactment of the Federal Law No. 258-FZ "On Experimental Legal Regimes in the Sphere of Digital Innovations in the Russian Federation. " This law comes into force in January 2021. Other corresponding laws are expected in the years ahead.

This new law will allow the creation of digital sandboxes throughout the whole country. Unlike 123-FZ, 258-FZ establishes the maximum duration of the regime for three years, with a possible extension.

The spheres allowed to participate in this new regime include the medical sector, transportation and logistics, architecture and construction, finance, the remote sales of goods and services, municipal services, industrial production, and agriculture. Other sectors may be added later.

Under the new law, the government will have the right to establish exemptions from certain legislative requirements that prevent the implementation of digital innovations. However, in order for digital sandboxes to begin immediately after the implementation of 258-FZ, the sectoral federal laws regulating areas of respective activity must also be adjusted accordingly. Moreover, the hierarchy of overlapping provisions between 123-FZ applying to Moscow and 258-FZ extending to the whole country presents issues that must be resolved in the course of the implementation of the new laws.

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Companies Linked to Russian Ransomware Hide in Plain Sight

Cybersecurity experts tracing money paid by American businesses to Russian ransomware gangs found it led to one of Moscow’s most prestigious addresses.

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By Andrew E. Kramer

MOSCOW — When cybersleuths traced the millions of dollars American companies, hospitals and city governments have paid to online extortionists in ransom money, they made a telling discovery: At least some of it passed through one of the most prestigious business addresses in Moscow.

The Biden administration has also zeroed in on the building, Federation Tower East, the tallest skyscraper in the Russian capital. The United States has targeted several companies in the tower as it seeks to penalize Russian ransomware gangs, which encrypt their victims’ digital data and then demand payments to unscramble it.

Those payments are typically made in cryptocurrencies, virtual currencies like Bitcoin, which the gangs then need to convert to standard currencies, like dollars, euros and rubles.

That this high-rise in Moscow’s financial district has emerged as an apparent hub of such money laundering has convinced many security experts that the Russian authorities tolerate ransomware operators. The targets are almost exclusively outside Russia, they point out, and in at least one case documented in a U.S. sanctions announcement, the suspect was assisting a Russian espionage agency.

“It says a lot,” said Dmitry Smilyanets, a threat intelligence expert with the Massachusetts-based cybersecurity firm Recorded Future. “Russian law enforcement usually has an answer: ‘There is no case open in Russian jurisdiction. There are no victims. How do you expect us to prosecute these honorable people?’”

Recorded Future has counted about 50 cryptocurrency exchanges in Moscow City, a financial district in the capital, that in its assessment are engaged in illicit activity. Other exchanges in the district are not suspected of accepting cryptocurrencies linked to crime.

Cybercrime is just one of many issues fueling tensions between Russia and the United States, along with the Russian military buildup near Ukraine and a recent migrant crisis on the Belarus-Polish border.

The Treasury Department has estimated that Americans have paid $1.6 billion in ransoms since 2011. One Russian ransomware strain, Ryuk, made an estimated $162 million last year encrypting the computer systems of American hospitals during the pandemic and demanding fees to release the data, according to Chainalysis, a company tracking cryptocurrency transactions.

The hospital attacks cast a spotlight on the rapidly expanding criminal industry of ransomware, which is based primarily in Russia. Criminal syndicates have become more efficient, and brazen, in what has become a conveyor-belt-like process of hacking, encrypting and then negotiating for ransom in cryptocurrencies, which can be owned anonymously.

At a summit meeting in June, President Biden pressed President Vladimir V. Putin of Russia to crack down on ransomware after a Russian gang, DarkSide, attacked a major gasoline pipeline on the East Coast, Colonial Pipeline , disrupting supplies and creating lines at gas stations.

American officials point to people like Maksim Yakubets, a skinny 34-year-old with a pompadour haircut whom the United States has identified as a kingpin of a major cybercrime operation calling itself Evil Corp. Cybersecurity analysts have linked his group to a series of ransomware attacks, including one last year targeting the National Rifle Association. A U.S. sanctions announcement accused Mr. Yakubets of also assisting Russia’s Federal Security Service, the main successor to the K.G.B.

But after the State Department announced a $5 million bounty for information leading to his arrest, Mr. Yakubets seemed only to flaunt his impunity in Russia: He was photographed driving in Moscow in a Lamborghini partially painted fluorescent yellow.

The cluster of suspected cryptocurrency exchanges in Federation Tower East, first reported last month by Bloomberg News, further illustrates how the Russian ransomware industry hides in plain sight.

The 97-floor, glass-and-steel high-rise resting on a bend in the Moscow River stands within sight of several government ministries in the financial district, including the Russian Ministry of Digital Development, Signals and Mass Communications .

Two of the Biden administration’s most forceful actions to date targeting ransomware are linked to the tower. In September, the Treasury Department imposed sanctions on a cryptocurrency exchange called Suex, which has offices on the 31st floor. It accused the company of laundering $160 million in illicit funds.

In an interview at the time, a founder of Suex, Vasily Zhabykin, denied any illegal activity.

And last month, Russian news media outlets reported that Dutch police, using a U.S. extradition warrant, had detained the owner, Denis Dubnikov, of another firm called EggChange, with an office on the 22nd floor. In a statement issued by one of his companies, Mr. Dubnikov denied any wrongdoing.

Ransomware is attractive to criminals, cybersecurity experts say, because the attacks take place mostly anonymously and online, minimizing the chances of getting caught. It has mushroomed into a sprawling, highly compartmentalized industry in Russia known to cybersecurity researchers as “ransomware as a service.”

The organizational structure mimics franchises, like McDonald’s or Hertz, that lower barriers to entry, allowing less sophisticated hackers to use established business practices to get into the business. Several high-level gangs develop software and promote fearsome-sounding brands, such as DarkSide or Maze, to intimidate businesses and other organizations that are targets. Other groups that are only loosely related hack into computer systems using the brand and franchised software.

The industry’s growth has been abetted by the rise of cryptocurrencies. That has made old-school money mules, who sometimes had to smuggle cash across borders, practically obsolete.

Laundering the cryptocurrency through exchanges is the final step, and also the most vulnerable, because criminals must exit the anonymous online world to appear at a physical location, where they trade Bitcoin for cash or deposit it in a bank.

The exchange offices are “the end of the Bitcoin and ransomware rainbow,” said Gurvais Grigg, a former F.B.I. agent who is a researcher with Chainalysis, the cryptocurrency tracking company.

The computer codes in virtual currencies allow transactions to be tracked from one user to another, even if the owners’ identities are anonymous, until the cryptocurrency reaches an exchange. There, in theory, records should link the cryptocurrency with a real person or company.

“They are really one of the key points in the whole ransomware strain,” Mr. Grigg said of the exchange offices. Ransomware gangs, he said, “want to make money. And until you cash it out, and you get it through an exchange at a cash-out point, you cannot spend it.”

It is at this point, cybersecurity experts say, that criminals should be identified and apprehended. But the Russian government has allowed the exchanges to flourish, saying that it only investigates cybercrime if Russian laws are violated. Regulations are a gray area in Russia, as elsewhere, in the nascent industry of cryptocurrency trading.

Russian cryptocurrency traders say the United States is imposing an unfair burden of due diligence on their companies, given the quickly evolving nature of regulations.

“The people who are real criminals, who create ransomware, and the people working in Moscow City are completely different people,” Sergei Mendeleyev, a founder of one trader based in Federation Tower East, Garantex, said in an interview. The Russian crypto exchanges, he said, were blamed for crimes they are unaware of.

Mr. Mendeleyev, who no longer works at the company, said American cryptocurrency tracking services provide data to non-Russian exchanges to help them avoid illicit transactions but have refused to work with Russian traders — in part because they suspect the traders might use the information to tip off criminals. That complicates the Russian companies’ efforts to root out illegal activity.

He conceded that not all Russian exchanges tried very hard. Some based in Moscow’s financial district were little more than an office, a safe full of cash and a computer, he said.

At least 15 cryptocurrency exchanges are based in Federation Tower East, according to a list of businesses in the building compiled by Yandex, a Russian mapping service.

In addition to Suex and EggChange, the companies targeted by the Biden administration, cyberresearchers and an international cryptocurrency exchange company have flagged two other building tenants that they suspect of illegal activity involving Bitcoin.

The building manager, Aeon Corp., did not respond to inquiries about the exchanges in its offices.

Like the banks and insurance companies they share space with, those firms are likely to have chosen the site for its status and its stringent building security, said Mr. Smilyanets, the researcher at Recorded Future.

“The Moscow City skyscrapers are very fancy,” he said. “They can post on Instagram with these beautiful sights, beautiful skyscrapers. It boosts their legitimacy.”

An earlier version of a picture caption with this article misstated the year in which Colonial Pipeline was hacked. It was 2021, not 2020.

How we handle corrections

Andrew E. Kramer is a reporter based in the Moscow bureau. He was part of a team that won the 2017 Pulitzer Prize in International Reporting for a series on Russia’s covert projection of power. More about Andrew E. Kramer

Inside the World of Cryptocurrencies

Tigran Gambaryan, an American compliance official for the giant cryptocurrency exchange Binance, flew to Nigeria in February for a planned two-day business trip. Here’s how he ended up in a Nigerian prison .

Two years after the cryptocurrency market crashed, there are signs that crypto is booming again in the Philippines, long a center of crypto activity .

Pushed by a nonprofit with ties to the Trump administration, Arkansas became the first state to shield noisy cryptocurrency operators from unhappy neighbors. A furious backlash has some lawmakers considering a statewide ban .

Ben Armstrong, better known as BitBoy, was once the most popular cryptocurrency YouTuber in the world. Then his empire collapsed .

Federal judges are weighing whether digital currencies should be subject to the same rules as stocks and bonds. The outcome could shape crypto’s future in the United States .

New investment funds that hold Bitcoin have begun trading , and it might be tempting to invest in them. Should you ?

To revisit this article, visit My Profile, then View saved stories .

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Masha Borak

Inside Safe City, Moscow’s AI Surveillance Dystopia

Sergey Vyborov was on his way to the Moscow Metro’s Aeroport station last September when police officers stopped him. The 49-year-old knew that taking the metro could spell trouble. During a protest against Russia’s invasion of Ukraine, police had fingerprinted and photographed him. He’d already been detained four times in 2022. But he was rushing to his daughter’s birthday, so he took a chance.

Vyborov wasn’t arrested that day, but the police informed him that he was under surveillance through Sfera, one of Moscow’s face recognition systems, for participating in unsanctioned rallies. Considered one of the most efficient surveillance systems, Sfera led to the detention of  141 people last year. “Facial recognition, and video cameras in general in a totalitarian state, are an absolute evil,” Vyborov says. 

Vyborov finds himself at the bottom of a slippery slope that privacy advocates have long warned about . Under the guise of smart city technology, authoritarian and democratic governments have rolled out huge networks of security cameras and used artificial intelligence to try to ensure there is no place to hide. Cities have touted the ability of such systems to tackle crime, manage crowds, and better respond to emergencies. Privacy campaigners say such systems could be used as tools of oppression. In Moscow, Vyborov and countless others now face that oppression on a daily basis.

The Russian capital is now the  seventh -most-surveilled city in the world. Across Russia, there are an  estimated 21 million surveillance cameras, and the country ranks among the top in the world in terms of the number of connected surveillance cameras. The system created by Moscow’s government, dubbed Safe City, was touted by city officials as a way to streamline its public safety systems. In recent years, however, its  217,000 surveillance cameras, designed to catch criminals and terrorists, have been turned against protestors, political rivals, and journalists. 

“Facial recognition was supposed to be the ‘cherry on top,’ the reason why all of this was built,” says a former employee of NTechLab, one of the principal companies building Safe City’s face recognition system.

Following Russia’s invasion of Ukraine, Safe City’s data collection practices have become increasingly opaque. The project is now seen as a tool of rising digital repression as Russia wages war against Ukraine and dissenting voices within its own borders. It is an example of the danger smart city technologies pose. And for the engineers and programmers who built such systems, its transformation into a tool of oppression has led to a moment of reckoning. 

Founded in 2015, NTechLab  caught the attention of the global press with the February 2016 launch of FindFace, an app that allowed anyone to identify faces by matching them with images gathered from social network  VKontakte , Russia’s Facebook equivalent. Met with warnings of the  “end to public anonymity,” the app was reportedly downloaded by 500,000 people within two months of its launch. But for NTechLab, it was primarily a proof of concept for its nascent face recognition algorithm.

NTechLab still felt like a startup when one former employee, who asked not to be named for privacy reasons, joined the company. And he was drawn in by the complexity of the work.

Matt Burgess

Steven Levy

“From [an] engineering point of view, it’s very interesting to work with: It’s very difficult,” he says. 

After the release of FindFace, NTechLab began selling its face recognition tech to small businesses, such as shopping malls that could use it to catch shoplifters or see how many people return to certain stores. But NTechLab was also working with the Moscow Department of IT Technology (DIT), the government department tasked with building Moscow’s digital infrastructure. In 2018, when Russia hosted the FIFA World Cup, NTechLab’s face recognition tech was connected to more than 450 security cameras around Moscow, and its tech  reportedly helped police detain 180 people whom the state deemed “wanted criminals.”

At its inception, Moscow’s face recognition system was fed official watchlists, like the database of wanted people. The system uses these lists to notify the police once a person on the list is detected, but law enforcement can also upload an image and search for where a person has appeared. Over the years, security and law enforcement agencies have compiled a database of the leaders of the political opposition and prominent activists, according to Sarkis Darbinyan, cofounder of digital rights group Roskomsvoboda, which has been campaigning for a suspension of the technology. It remains unclear who is in charge of adding activists and protesters to watchlists.

In March 2019, following the success of the World Cup trial—some of Russia’s “most wanted” people were arrested while trying to attend matches—the Moscow Department of Transportation, which operates the city’s metro, launched its own surveillance system, Sfera. By October 2019, 3,000 of the city’s 160,000 cameras were enabled with face recognition tech, according to interior minister Vladimir Kolokoltsev.

NTechLab was one of many companies building the slew of systems that would later be branded Safe City. International companies, from US tech firms such as Nvidia, Intel, and Broadcom to South Korea’s Samsung and Chinese camera maker Hikvision, worked alongside local firms such as  HeadPoint ,  Netris , and  Rostelecom  that have developed various components of the surveillance systems. According to procurement documents cited by the UK’s BBC, three companies besides NTechLab created face recognition tech for Moscow’s growing surveillance apparatus, including Tevian, and Kipod, and VisionLabs. Moscow's Transportation Department said in social media posts that Sfera was built using VisionLabs technology, although the company downplays its involvement.

NtechLab says it operates in compliance with local laws and does not have access to customer data or camera video streams. Nvidia and Intel say they left Russia in 2022, with Nvidia adding that it does not create software or algorithms for surveillance. Broadcom and Samsung also say they stopped doing business in Russia following the invasion. VisionLabs says it only provides the Moscow Metro with its face recognition payment system. Other companies did not respond to requests for comment. The DIT and the Moscow Department of Transportation did not respond to requests for comment.

At the end of 2018, as Russia cracked down harder on political dissent online and in the streets, the DIT started to change, says a former employee who asked to remain anonymous for safety reasons. The department used to just be the “technical guys” providing assistance to security services, with the Moscow government recruiting highly paid IT specialists to make the most efficient systems possible, according to Andrey Soldatov, an investigative journalist and Russian security services expert. But according to the former employee, the DIT was beginning to reflect the Kremlin’s authoritarian bent.

Then came Covid. 

Safe City launched in 2020, at the height of the Covid-19 pandemic. Russia, like some other countries, seemingly used the pandemic as grounds to expand its surveillance systems to catch people breaking self-isolation rules. By mid-March 2020, Safe City’s face recognition system had  caught 200 people breaking lockdown restrictions. At the same time, Moscow  introduced a regulatory sandbox for the development of AI applications with the participation of large IT companies, exempting authorities from the country’s already lax data protection requirements. “With Covid, [the DIT] essentially became a part of the repressive apparatus,” says Soldatov.

In addition to its network of more than 200,000 cameras, Safe City also incorporates data from  169  information systems, managing data on citizens, public services, transportation, and nearly everything else that makes up Moscow’s infrastructure. This includes anonymized cell phone geolocation data collection, vehicle license plate recognition, data from ride-hailing services, and voice recognition devices. As Safe City was still rolling out in 2020, the Russian government  announced  plans to spend $1.3 billion deploying similar Safe City systems across Russia. From the outside, the potential for the system to be abused seemed obvious. But for those involved in its development, it looked like many other smart city projects. “No one expected that the country would turn into hell in two years,” says one former NTechLab employee, who asked to remain anonymous for safety reasons.

Attempts to break open Moscow’s digital black box have been stonewalled. Alena Popova, whose image was captured during a protest against  politician Leonid Eduardovich Slutsky in April 2018, filed the first lawsuit against Moscow’s DIT for allegedly violating her privacy, seeking a ban on face recognition tech. The case was  thrown out , but Popova has continued to file lawsuits, including one at the European Court of Human Rights—which Russia is  no longer a part of. 

While Moscow operates one of the world’s most pervasive surveillance systems, Russian law does not safeguard individual privacy. With seemingly no hope of recourse, some activists have been forced to leave Russia altogether. Popova is now on the list of foreign agents and is living in an undisclosed overseas location. “I will not apply to any political asylum in any country because I would like to go back to my own country and fight back,” she says.

A key concern is that Moscow’s surveillance system was designed to conceal its data collection from Moscow’s 12 million residents, says Sergey Ross, founder of the Collective Action Center think tank and a former Moscow politician. Although the system is run by the Moscow government, elected members of the Moscow City Duma  say  they are excluded from regulating face recognition systems and have little insight into how it is being used. “It’s a complete black box,” says Ross.

“It was clear that sooner or later the technology would be used to catch activists and dissenters,” says Roskomsvoboda’s Darbinyan. 

Russia made  almost 20,500 political arrests in 2022 , according to data from human rights media organization OVD-Info, which characterizes the number as “unprecedented.” The arrests have sparked fears that Safe City will be expanded to catch draft dodgers—although former NTechLab employees say that doing so would be technically difficult to pull it off because of too many false positives. Still, Moscow police appear to be using face recognition to aid Russia’s war efforts in other ways.

In September 2022, just after Putin announced additional mobilization for the war against Ukraine, Viktor Kapitonov, a 27-year-old activist who’d protested regularly since 2013, was stopped by two police officers after being flagged by face recognition surveillance while he approached the turnstiles in Moscow’s marble-covered Avtozavdodskaya metro station. The officers took him to the military recruitment office, where around 15 people were waiting to enlist in Putin’s newly announced draft. 

“They let me in without waiting in line as if I were some sort of VIP person,” he says. The recruiters wanted to force Kapitonov to enlist, but he ended up escaping the draft. “I explained that I am not fit, I have a disability.”

From 2017 to 2020, NTechLab became one of Russia’s  fastest-growing companies. Other face recognition firms have cashed in as well: The revenue of Russian face recognition developers  grew between 30 and 35 percent in 2022, thanks in part to deals struck in the Middle East, Southeast Asia, India, and South America. Russia’s national AI strategy has supported such firms with grants, tax exemptions, and subsidies, which have benefited both startups and state corporations, including tech and finance giant Sber , telecom provider  Rostelecom , and defense firm Rostec, which previously owned a minority stake in NTechLab. While NTechLab continues to work globally, reporting a revenue increase of 35 percent in 2022, it has also faced a backlash against its work with the Russian state.

In June of last year, a “name-and-shame” list of NTechLab employees was  published [in Russian] with information collected from social media. The project went viral, and some employees reported being harassed online. Artem Zinnatullin, a software engineer now based in the US, says he published the list after NTechLab  sold its new  silhouette recognition technology to the Moscow government in June 2022. To him, it signaled support for Russia’s war in Ukraine. In the post, he called NTechLab “the blacksmith of the Digital Gulag.” Zinnatullin, who says he knew people arrested with the help of face recognition technology, believes publishing the list of NTechLab employees was only fair. “You recognize people on the street, it’s only fair if we use public data to recognize who you are,” he says.

Unlike many face recognition companies that keep a low profile, NTechLab’s splash with FindFace has turned it into a recognized brand. Employees say this high profile has made them into scapegoats. 

As arrests of activists and politicians mounted, the ethics of NTechLab’s technology became a recurring topic at company meetings. NTechLab staff have resisted the use of the company’s face recognition in rallies and refused to sell the technology to the military, according to people familiar with these discussions. Still, the NTechLab leadership concluded that the technology was ultimately positive—even if the occasional dissenting voice was arrested because of it. 

“We all saw these positive examples, we saw how it really catches criminals,” says one former NTechLab employee. “Most people in NTechLab would say they were doing something very good, technologies that can help and save people’s lives. It really did.”

As Russia furthered its march toward authoritarianism in 2021, NTechLab leadership began talking about moving the company abroad, according to people familiar with internal company discussions. But with lucrative government contracts abounding—NTechLab  received a $13 million investment from the Russian Direct Investment Fund, the country’s sovereign wealth fund, in September 2020—its investors resisted the idea. The company was also changing. Its founders, Alexander Kabakov and Artem Kukharenko, stepped down from NTechLab—and both left Russia in December 2021 and February 2022, respectively, declaring   their  anti-war stance on social media. 

Other employees left amid an exodus of IT talent from Russia. The war changed how they viewed their work. “Looking back, we realize that we shouldn’t have done it,” says an NTechLab employee. “But even in 2017 and 2018, it was a completely different country. At least, that’s how it seemed to those who weren’t very immersed in politics.”

Russia’s Safe City projects show no sign of slowing. As more surveillance systems are deployed across the country, Moscow’s DIT is planning to centralize video streams collected across all regions into its own system. And new projects to digitize public services may make it even easier for the government to eventually create large databases where everyone can be found, according to Popova. “It is really scary,” she says. “If they digitalize all the databases and combine them to make this joint database, they can find everybody.” In July, Putin  signed a federal law that funnels personal biometric data collected in the country into a single system—an effort to obtain an “almost unlimited monopoly” on the collection and storage of biometrics, says Roskomsvoboda’s Darbinyan. 

In a further expansion of the Safe City project, Rostec is also  reportedly  developing software that will help authorities predict riots and prevent their escalation by analyzing media reports, data from social networks, video cameras, and other sources. Rostec did not respond to a request for comment on its development of these systems.

Similar systems have been developed in some Chinese cities, and Russia is now playing catch-up. “The Russian government would probably like to move toward China, but they do not yet have the necessary technology,” says Kiril Koroteev, head of international practice at the Russia-based Agora International Human Rights Group.

For now, many activists in Russia are left to do whatever they can to skirt the country’s growing surveillance apparatus, including avoiding the Moscow Metro. Kapitonov hopes that a balaclava will keep him safe, while Vyborov aims to ride the metro early in the morning, when there are fewer police around to detain him. 

“I think that it was inevitable that such a system would be made sooner or later,” says one former NTechLab employee. Face recognition is like a knife, he says: It can be used to cut food, but it can also be used to cut innocent people. He now regrets that NTechLab played a key role in building Moscow’s Safe City project. He has left Russia and doesn’t think he will work on face recognition again. “I do not want to mess with it anymore,” he says.

Update 9:25 am ET, February 6, 2023: Clarified the role of VisionLabs in the Sfera system and that NTechLab's founders have since left the company.

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Don't buy the dip in stocks just yet as a wave of selling is about to bring the market to a bottom, according to one of the biggest bulls on Wall Street

• Investors shouldn't rush to buy the latest dip in the stock market, Fundstrat's Tom Lee said.
• That's because volatility is rising, which could bring near-term pressure to stocks.
• The market could be less than a month away from hitting a bottom, Lee predicted.

Don't buy the dip in stocks just yet — there's a wave of selling that could see the market bottom out in the coming weeks, according to one of Wall Street's biggest bulls.

Tom Lee, Fundstrat's head of research and one of the most bullish stock forecasters this year, issued a word of caution for investors looking for opportunities amid the market sell-off. Stocks have slumped after taking in a hot inflation report for March, escalating tensions in the Middle East, and hawkish guidance on Fed rate cuts, causing the S&P 500 to notch four straight days of losses .

But opportunistic investors shouldn't rush into stocks just yet, Lee said, pointing to a surge in the VIX , the market's volatility gauge. Higher volatility typically triggers selling among investors, he warned, which could lead to near-term pressure for stocks.

"While we normally like to buy dips, as we said earlier this week, the surge in the VIX says we gotta take buying the dip extra slowly," Lee said in a video sent to clients on Thursday.

A buying opportunity could come soon, as the market looks poised to bottom, Lee said. That's largely because the positive catalysts for stocks are still in play, like strong corporate earnings growth. The S&P 500 is on track to report earnings growth of over 7% for the first quarter, per estimates from FactSet .

The Fed also looks poised to cut interest rates sometime this year, even if rate cuts could be delayed further than investors are expecting. Markets are now pricing in one or two rate cuts by December, according to the CME FedWatch tool .  

Lee predicted markets could hit a trough within the next month or possibly sooner, assuming that Middle East conflict does not escalate further, volatility eases, and investors show signs that they're slowing their pace of selling.

"This pullback, I think, is very good because it's providing good entry points," Lee said. "All the things that are supporting stocks are still in place."

Lee predicted the S&P 500 could hit 5,200 by the end of the year, but has noted the index could notch 5,500 or higher in the best-case scenario . He was spot-on in his 2023 stock forecast, correctly calling a 20% gain in the benchmark index .

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