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Big Data in Retail: A Revolution [Use Cases and Examples]


  • Dec 13,2023

The retail industry is an ever-changing landscape. How do big data and retail combine? The global big data analytics in retail market size was valued at $4,854 million in 2020 and is projected to reach $25,560 million by 2028. This shows us the importance of big data in retail. 

In this article, we will dive deep into the world of big data in retail. Let’s see: 

What is Big Data?

Use of big data in the retail industry .

  • The Process of Data Collection in the Retail 
  • Big Data Benefits  
  • Real-world Applications: Big Data Use Cases in Retail 

How Big Data is Shaping the Future of Retail?

Tackling challenges: the reality of using big data in retail.

Big data refers to extremely large and complex datasets that cannot be easily managed, processed, or analyzed using traditional data processing tools. These datasets are characterized by the three Vs:

  • Volume: Big data involves large amounts of data. This could be in terms of terabytes, petabytes, or even exabytes of information.
  • Velocity: Data is generated at a high speed and must be processed quickly. For example, social media posts, sensor data, and online transactions are generated rapidly and require real-time or near-real-time processing.
  • Variety: Big data comes in various formats and types, including structured data (like databases), unstructured data (such as text, images, and videos), and semi-structured data (like XML files). The diversity of data sources adds complexity to the analysis.

Big data has become increasingly valuable in the retail industry, offering a range of applications and benefits.

big data in retail industry case study

 Here are some key ways in which big data is used in the retail sector:

Customer Analytics:

  • Customer Segmentation: Retailers can use big data to segment their customer base based on various factors such as purchasing behavior, demographics, and preferences. This allows for targeted marketing strategies tailored to specific customer segments.
  • Personalized Marketing: Big data enables retailers to personalize marketing efforts by analyzing customer data to understand individual preferences. This can include personalized recommendations, promotions, and targeted advertising.

Inventory Management:

  • Demand Forecasting: Retailers can use big data analytics to predict future demand for products. By analyzing historical sales data, market trends, and external factors, retailers can optimize inventory levels, reduce stockouts, and minimize overstock situations.
  • Supply Chain Optimization: Big data helps in optimizing the supply chain by providing real-time insights into the movement of products. This ensures that the right products are in the right place at the right time, minimizing delays and improving efficiency.

Price Optimization:

  • Dynamic Pricing: Retailers can adjust prices dynamically based on real-time market conditions, competitor pricing, and customer demand. This allows for competitive pricing strategies and maximizes revenue.

Customer Experience Enhancement:

  • In-Store Analytics: Big data technologies, such as sensors and cameras, can be used to analyze customer behavior in physical stores. Retailers can gain insights into foot traffic patterns, popular product areas, and overall store layout effectiveness to enhance the in-store experience.
  • Omni-Channel Experience: Big data enables a seamless shopping experience across various channels (online and offline). Retailers can integrate data from different touchpoints to create a unified customer experience.

Fraud Detection and Security:

  • Fraud Prevention: Big data analytics can identify patterns indicative of fraudulent activities, helping retailers detect and prevent fraud in online transactions.
  • Security Enhancement: Retailers can use big data to enhance cybersecurity measures, protecting sensitive customer data and maintaining the integrity of their systems.

Market Intelligence:

  • Competitor Analysis: Retailers can analyze data to understand market trends, monitor competitor activities, and identify opportunities for growth or improvement.
  • Social Media Monitoring: Big data analytics can be used to track and analyze social media sentiments, helping retailers understand public opinion about their brand and products.

Operational Efficiency:

  • Optimizing Store Operations : Big data helps retailers optimize various operational aspects, such as staffing levels, store layout, and checkout processes, leading to increased efficiency.
  • Energy Management: Retailers can use big data to monitor and optimize energy usage in stores, reducing costs and promoting sustainability.

The process of data collection in retail

The process of data collection in the retail industry involves gathering information from various sources to understand customer behavior, optimize operations, and make informed business decisions. Here is an overview of the key steps in the data collection process in retail:

Identifying Data Sources

  • Point of Sale (POS) Systems: Transaction data from POS systems provides information about customer purchases, product sales, and transaction details.
  • Customer Relationship Management (CRM) Systems: CRM systems store customer information, including contact details, purchase history, and preferences.
  • E-commerce Platforms: For retailers with an online presence, data from e-commerce platforms includes website traffic, online transactions, and customer interactions.
  • Sensors and IoT Devices: In-store sensors, beacons, and other IoT devices can collect data on customer movements, dwell times, and interactions with products.
  • Social Media: Monitoring social media platforms provides insights into customer sentiments, preferences, and trends.

Data Capture

  • Structured Data: This includes data with a defined format, such as transaction records, customer profiles, and inventory lists.
  • Unstructured Data: This type of data, like customer reviews, social media posts, and images, doesn't fit neatly into a database and requires advanced analytics for interpretation.

Data Integration

Integrating data from various sources is crucial for creating a comprehensive view of customer behavior and overall business performance. Tools and platforms for data integration help consolidate data from different systems, ensuring consistency and accuracy.

Data Cleaning and Transformation

Raw data often requires cleaning to remove errors, inconsistencies, and duplicates. Data transformation involves converting data into a usable format and preparing it for analysis.

Retailers store vast amounts of data, and choosing the right storage solutions is essential. Options include traditional relational databases, data warehouses, and big data storage systems like Hadoop or cloud-based platforms.

Data Security

Retailers must prioritize data security to protect sensitive customer information and comply with privacy regulations. Encryption, access controls, and regular security audits are essential components of data security.

Data Analysis

Analytical tools and techniques are applied to extract meaningful insights from the collected data. Analysis may include customer segmentation, trend identification, and performance evaluation.

Reporting and Visualization

Data findings are often communicated through reports and visualizations, making it easier for stakeholders to understand and act upon insights. Dashboards and visual analytics tools are commonly used for this purpose.

Continuous Monitoring and Iteration

Data collection is an ongoing process, and retailers need to continually monitor and adapt their data collection strategies. Feedback loops and regular assessments help refine data collection methods over time.

By following these steps, retailers can build a robust data collection process that supports informed decision-making, enhances customer experiences, and improves overall business performance.

Big Data Benefits 

Big data offers a multitude of benefits across various industries, enabling organizations to make informed decisions, optimize operations, and gain a competitive edge. Here are some key benefits of leveraging big data:

Informed Decision-Making

Big data analytics provides valuable insights derived from vast and diverse datasets, enabling organizations to make data-driven decisions. This helps in understanding customer preferences, market trends, and operational efficiency.

Improved Customer Understanding

Big data allows businesses to analyze customer behavior, preferences, and feedback. This understanding helps in personalizing products, services, and marketing strategies, ultimately enhancing customer satisfaction and loyalty.

Optimized Operations

Through the analysis of large datasets, organizations can identify inefficiencies and streamline their processes. This includes optimizing supply chain management, inventory levels, and resource allocation to improve overall operational efficiency.

Enhanced Product and Service Development

Big data insights help organizations identify market needs and trends, leading to the development of products and services that better meet customer demands. This can result in increased innovation and competitiveness.

Cost Reduction

By identifying areas of inefficiency and optimizing processes, big data can contribute to cost reduction. For example, improved inventory management can minimize overstock and stockouts, reducing carrying costs and potential revenue loss.

Predictive Analytics

Big data analytics enables organizations to forecast future trends and outcomes using predictive modeling. This capability is particularly valuable for demand forecasting, risk management, and proactive decision-making.

Fraud Detection and Security

Big data analytics helps in detecting unusual patterns and anomalies that may indicate fraudulent activities. This is particularly crucial in industries such as finance and healthcare where security is a top priority.

Real-Time Insights

Big data technologies enable real-time data processing and analysis. This is essential for industries like e-commerce, finance, and healthcare where timely decisions can have a significant impact on outcomes.

Competitive Advantage

Organizations that effectively harness big data gain a competitive advantage by staying ahead of market trends, understanding customer needs, and making quicker and more informed decisions than competitors.

Environmental Sustainability

Big data analytics can be applied to monitor and optimize resource usage, energy consumption, and waste management, contributing to more sustainable and eco-friendly practices.

Real-world Applications: Big Data Use Cases in Retail

The integration of big data analytics has become instrumental for businesses seeking to stay competitive and enhance their operational efficiency. In this section, we explore real-world use cases where retailers and FMCG brands leverage big data to drive innovation, optimize processes, and improve customer experiences. 

These examples highlight the diverse applications of big data in areas such as customer personalization, inventory management, dynamic pricing, and supply chain optimization, showcasing how data-driven insights are reshaping the retail industry.

Customer Personalization and Recommendations

Example: Amazon

amazon big data in retail

Amazon is a pioneer in using big data for customer personalization. The e-commerce giant analyzes customer browsing history, purchase behavior, and search patterns to provide personalized product recommendations on its website and through email notifications.

How it Works: Amazon's recommendation engine uses machine learning algorithms to analyze vast amounts of customer data. The system continually learns and adapts to user preferences, offering a highly personalized shopping experience .

Inventory Management and Demand Forecasting

Example: Walmart

walmart big data in retail

Walmart is known for its sophisticated supply chain management and inventory optimization. The retail giant uses big data to forecast demand accurately, manage inventory levels, and minimize stockouts and overstock situations.

How it Works: Walmart's inventory management system analyzes historical sales data, seasonal trends, and external factors (such as weather events) to make real-time decisions about product replenishment and distribution.

Supply Chain Optimization and Demand Forecasting

Example: Procter & Gamble (P&G)

Procter-Gamble big data in retail

Procter & Gamble, a multinational consumer goods company, leverages big data for supply chain optimization and demand forecasting. By analyzing vast amounts of data, including historical sales, market trends, and social media signals, P&G aims to anticipate consumer demand accurately and ensure that products are available when and where they are needed.

How it Works : P&G employs advanced analytics and machine learning algorithms to process data from various sources. This allows the company to make data-driven decisions about production schedules, inventory levels, and distribution, ultimately minimizing stockouts, reducing excess inventory, and enhancing overall supply chain efficiency.

In-Store Analytics for Physical Retailers

Example: Macy's

macys big data in retail

Macy's, a large department store chain, uses in-store analytics to enhance the shopping experience. The retailer employs sensors and cameras to monitor customer movement, identify popular product areas, and optimize store layouts.

How it Works: Macy's utilizes in-store analytics to gather data on customer behavior, such as foot traffic patterns and product interactions. This data informs decisions about store layout, product placement, and promotions to improve the overall in-store experience.

Big data is revolutionizing the retail landscape, offering retailers unprecedented insights that span inventory management, logistics, and customer interactions. This data-driven approach not only improves operating margins by up to 60% but also transforms every facet of the retail experience.

Big data analytics allows retailers to plan inventory, stock levels, and logistics with unparalleled precision. By understanding customer habits, retailers optimize stock levels, streamline logistics, and anticipate demand fluctuations, enhancing overall supply chain efficiency.

From personalized product recommendations to smoother payment options, big data analytics enhances the customer-facing aspects of retail. This data-driven approach improves sales processes, creating a seamless and personalized shopping experience. Enhanced customer service, powered by data insights, boosts customer satisfaction and brand loyalty.

Big data identifies potential bottlenecks and issues within the supply chain and sales processes, enabling retailers to proactively address challenges before they escalate. This approach minimizes downtime, disruptions, and associated costs, ensuring smooth operations and business resilience.

Leveraging comprehensive data insights enables retailers to manage inventory effectively, leading to improved customer satisfaction, increased brand loyalty, and enhanced revenue generation. By aligning with customer expectations through data-driven strategies, retailers position themselves for success in a competitive marketplace.

While big data offers transformative benefits for the retail industry, its implementation comes with a set of challenges that retailers must navigate. Tackling these challenges is crucial for maximizing the potential of big data analytics. Here's an exploration of the reality of using big data in retail and how industry players address these hurdles:

Data Security and Privacy Concerns:

Challenge: The vast amount of customer data collected raises concerns about privacy and security. Retailers must adhere to data protection regulations and safeguard sensitive customer information.

Solution: Implementing robust cybersecurity measures, encryption protocols, and compliance with privacy laws help mitigate these concerns. Transparent communication with customers about data usage builds trust.

Integration of Disparate Data Sources:

Challenge: Retailers often deal with data silos, where information is stored in separate systems. Integrating these disparate data sources can be complex.

Solution: Employing data integration platforms and technologies helps unify data from various sources. This enables a comprehensive view of customer behavior, inventory levels, and other critical insights.

Skill Shortages and Training:

Challenge: There is a shortage of skilled professionals who can effectively manage and analyze big data. Retailers may struggle to find or develop the talent needed.

Solution: Investing in employee training programs, hiring skilled data scientists, and partnering with external experts or data analytics firms can address the skills gap.

Infrastructure and Scalability:

Challenge: Managing the infrastructure required for big data processing and storage can be costly and challenging to scale.

Solution: Cloud computing services provide scalable and cost-effective solutions. Many retailers are migrating their data operations to the cloud to leverage its flexibility and efficiency.

Costs of Implementation:

Challenge: The initial investment in big data infrastructure, analytics tools, and skilled personnel can be significant.

Solution: Retailers often adopt a phased approach, starting with targeted use cases that offer quick returns on investment. Cloud-based solutions can also provide a more cost-effective entry into big data analytics.

Ensuring Data Quality:

Challenge: Inaccurate or incomplete data can lead to flawed insights and decisions.

Solution: Implementing data quality assurance processes, regular audits, and leveraging data cleansing tools help ensure the accuracy and reliability of the data being analyzed.

Big Data for Retail

Big data is reshaping retail by providing opportunities to enhance customer experiences, streamline operations, and drive innovation. Retailers can leverage data analytics for personalized customer interactions, optimized inventory management, and dynamic pricing strategies, leading to improved efficiency and increased satisfaction.

The benefits of big data in retail are significant, offering the potential to boost operating margins and stay competitive in the market. Despite challenges like data security and skill shortages, retailers can overcome them by investing in infrastructure and training while maintaining ethical data practices.

As retailers embrace big data, the future holds the promise of a more streamlined, customer-centric industry, where data-driven decision-making plays a central role in driving growth and success.

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Big Data in Retail: Common Benefits and 7 Real-Life Examples

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In an industry where brands face the challenge of e-commerce giants like Amazon, dynamic pricing, and the growing thrift shopping trend, retailers need all the help they can get to stay competitive. Big data has become so prevalent and accessible that more retail brands than ever are relying on data-driven insights to optimize pricing, streamline operations, and improve customer experience.

The first step to using big data to stay competitive? Understanding how big data is working in the retail industry and gleaning insights from how other brands have already put it into practice.

Big data in retail: An overview

To stay competitive, retailers make better buying decisions, must offer relevant discounts, convince customers to hop on new trends, and remember their customers’ birthdays—all while making the business run behind the scenes. How do they keep up? Big data in retail is essential to target and retain customers, streamline operations, optimize supply chain, improve business decisions, and ultimately, save money.

Before the cloud was readily available, companies were limited to tracking what a person bought and when. With more sophisticated technology, companies can capture a wealth of data about their customers, like their age, geographical location, gender, favorite restaurants, other stores they shop at, what books or news they read—the list goes on and on. Retailers have now turned to cloud-based big data solutions to aggregate and manage that data.

So how exactly do these large data sets help retailers make critical business decisions?

4 big data benefits for retail

Big data analysis can predict emerging trends, target the right customer at the right time, decrease marketing costs, and increase the quality of customer service. Common benefits of using big data in retail include:

  • Maintaining a 360-degree view of each customer — Create the kind of personal engagement that customers have come to expect by knowing each individual, at scale.
  • Optimize pricing — Get the most value out of upcoming trends and know when, and how much, to decrease off-trend product prices.
  • Streamline back-office operations — Imaging maintaining perfect stock levels throughout the year and gathering data from registered products in real-time.
  • Enhanced customer service — Unlock the customer service data hiding in recorded calls, in-store security footage, and social media comment. 

1. 360-degree view of the customer

The “360-degree view” term gets thrown around a lot, but what does that mean? It all boils down to a comprehensive picture of a customer that is as accurate as possible. Retailers need to know a customer’s likes and dislikes, their likelihood of using coupons, their gender, their location, their social media presence, etc.

Blending just a few of these data points can lead to sophisticated marketing strategies. For example, fashion retailers typically hire expensive celebrity brand ambassadors. But by paying attention to customer gender, likes, and social media presence, fashion brands can find more affordable and effective micro-influencers to represent their brands on Instagram

2. Price optimization

Big data gives businesses an advantage when pricing products. Consistently monitoring relevant search words can enable companies to forecast trends before they happen. Retailers can prepare new products and can anticipate an effective dynamic pricing strategy.

Pricing can leverage the 360-degree view of the customer as well. This is because pricing is largely based on a customer’s geographical location and purchasing habits. Companies can run beta tests for segments of their customer population to see which pricing fits best. Understanding what a customer expects can inform the retailer of ways they can stand out against their competition. 

3. Streamlined back office operations

Anyone who has worked in retail has experienced that sinking feeling when their stock is depleted. For the rest of their shift, that manager will be dealing with angry customers. Ideally, companies would eliminate this situation entirely. While that may not always be possible, big data can help companies manage supply chain and product distribution.

Product logs and server data can give retailers clues as to how their operations are running upstream. The products themselves can expose bugs, too. Customers that register their wearables, for example, can show the product performance over time.

4. Enhanced quality of service

Think about the last time you called a toll-free number. Usually, there is a warning that your call will be, “recorded for quality purposes.” Big data analysis can bring top issues from those recorded calls to light, and then measure the success of company-led quality changes over time.

Some retail companies scrutinize in-store video footage and motion sensors to improve customer experience. Retailers measure how often customers gravitate towards an area in the store, and strategically place items they want to sell first. This is not a new concept—grocery stores deliberately design their layout, causing you to come out with more food than intended.

There are insights waiting to be uncovered in customer reviews and comments as well. Analyzing these reviews can allow retailers to notify customers that particular garments may run small or large. “Sentiment analysis” can also be used to identify whether customers are talking positively or negatively about certain products and companies at large.

7 real-world examples of big data in retail

Now that we know big data is essential to maintain a competitive edge in retail, it’s important to understand how to leverage this information in the real-world. The following big-name retail companies use big data platforms to make decisions that drive revenue and boost customer satisfaction.

1. Aldo uses big data to survive Black Friday

Without a doubt, Black Friday and Cyber Monday are the most stressful days for retail businesses, and the most exciting days for consumers. In fact, the National Retail Federation estimates that sales in November and December are responsible for as much as 30% of retail annual sales .

Aldo is a shoe and accessory company based in Canada that uses big data to address this crazy time of year. The company operates on a service-oriented big data architecture, integrating multiple data sources involved in payment, billing, and fraud detection. This integration project enables Aldo to deliver a seamless ecommerce experience—even on Black Friday.

2. Office Depot integrated offline and online big data

Office Depot Europe operates two brands (Office Depot and Viking) in 13 countries. As the leading office supply retailer, Office Depot Europe stays ahead by integrating online and offline efforts. That’s a lot of disparate data.

The organization uses a big data platform to link data from their offline catalog, online website, customer call centers, ERP s, and fulfillment systems. Office Depot Europe outcompetes other office supply companies by targeting particular customer segments and allocating internal spending to positively affect the productivity of various departments. 

3. Groupon analyzes a terabyte of data per day

Groupon is an e-commerce site that connects subscribers to discounts on activities, travel, and other goods and services. To serve that range of customers, Groupon must process over one terabyte of raw data every day.

That dataset is too extensive to store and study without a big data platform. Groupon uses a major IT framework to import, integrate, transform, and analyze data in real time. Key stakeholders are able to run reports and visualize data from millions of customers in bite-sized formats.

4. Big data made PriceMinister flexible and agile

PriceMinister (now Rakuten ) is a French retail group with a third-party pricing model. They put their customers in touch with sellers, and ensure that all transactions between the parties are successful by collecting massive data sets monitoring buyer and seller activity.

To alleviate strain on their IT department, PriceMinister adopted a big data platform to integrate buyer and seller datasets with an Oracle database containing all 100 million PriceMinister products. This technology enables PriceMinister to amplify their flexibility and reactivity. Updates to their buyer and seller information post almost immediately.

5. myWorld Solutions AG uses big data to augment Salesforce

myWorld Solutions AG is a shopping network that allows their customers to collect points and cashback on their purchases at more than 70,000 merchants in 47 countries. Managing all that data is a laborious undertaking.

The brand uses a big data platform with a Salesforce connector to merge, clean, and transform their customer and merchant data before deploying into Salesforce Sales and Marketing clouds. This integration allows myWorld Solutions AG to readily access customer information, track marketing performance, and course correct, if need be. 

6. Disrupting the fashion industry with big data

Groupe Zannier (now Kidiliz Group) is a French retailer with a brand portfolio covering all segments of children and adult fashion. Some famous Zannier Group brands include: Kenzo, Levi’s, and Marc Jacobs.

To maintain their status as a fashion industry leader, Zannier Group needs to be an expert in the changing desires of children, teenagers, and adults in and outside of France. To do this, Zannier Group consolidates data from two major ERPs. With this integrated dataset, the business can distill retail activity into significant customer purchasing patterns to influence real-time sales and inventory decisions.

7. Big data bringing diverse business units together

Naville is a Swiss company that primarily markets and distributes press products. Besides distributing 3,000 publications, the retailer is responsible for other business units: a candy and chocolate chain, a tourist guide, a comic store chain, and a string of outlet malls.

Naville embraces a service-oriented big data architecture to ensure data flow between all four business units. On top of this architecture, Naville develops business applications that enable them to scale and refine communication and connectivity across business units.

Getting started with big data in retail

Today, customers expect a certain amount of guided selling. They want to know about products that interest and appeal specifically to them. Retailers need to present their consumers with products and promotions uniquely tailored to their preferences and habits. Catering to that individuality increases revenue, customer satisfaction, and brand loyalty.

At the same time, these retailers have to be mindful of their operations, marketing budget, and pricing optimization. They must develop and market products that are trending. They must be prepared with enough stock on Black Friday. They must price their products appropriately. Aggregating, normalizing, and interpreting big data allows retailers to achieve all of these objectives and more.

How do you get started? The first step is to choose a suite of apps that can link data from nearly any cloud or on-premises system while maintaining data integrity . Talend Data Fabric is a comprehensive suite of apps with 900+ connectors to facilitate seamless cross-communication with any number of CRMs, ERPs, and other customer data. Demonstrate the power of big data retail today — try Talend Data Fabric.

Ready to get started with Talend?

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Unleashing the Power of Big Data in the Retail Industry in 2024

Table of contents:.

Imagine you're at the helm of a big fashion retail chain and have big data analytics as your secret weapon. It's like a conversation with your customers without saying a word, tracking every glance they make at those trendy jeans or retro sneakers online and in-store. Your system sends spot-on personalized offers, nailing their style preferences, and they're eating it up. Behind the scenes, it's all about playing chess with your inventory, moving the right pieces, like those suddenly in-vogue vintage tees, to where they're most wanted.

Market Size of the Big Data Industry

The table displays the big data market size over the past years.

What is Big Data Analytics in Retail?

In the retail sector, big data analytics is diving deep into vast oceans of data — from what customers click online to every item they pick up in stores. This isn't just counting beans; it's about understanding patterns, predicting trends, and getting a 360-degree view of what your customers want, even before they know it themselves. It's about harnessing the power of data from multiple sources: social media buzz, website traffic, in-store interactions, and even external factors like weather or economic shifts.

The Modern Retail Industry Landscape

The retail landscape is a speed freeway, constantly evolving and highly competitive. It's no longer just brick-and-mortar versus online stores; it's a complex web of omnichannel experiences where digital and physical realms intertwine. Consumers are more informed and empowered than ever, with expectations for personalized experiences. Retailers are under pressure to keep up and stay ahead, making sense of consumer behaviors, managing inventories smartly, and tailoring marketing strategies that resonate with diverse market segments.

Optimize store layouts, product placements, and merchandising strategies!

The retail industry in the global economy.

The retail industry is multi-trillion-dollar, touching everything from mammoth supercenters to quaint corner stores. This industry is a vital cog in the wheel of economies, driving employment, influencing consumer prices, and shaping life quality. It's a barometer of economic well-being, where retail spending shifts signal economic trends. Big data analytics has become the linchpin, empowering retailers to make smarter decisions, stay competitive, and meet the demands of consumers worldwide.

Traditional Methods in Retail

  • Retailers relied heavily on sales data collected at the cash register. This process was often manual, with sales associates recording sales in ledgers.
  • Understanding customer preferences and experiences was done mainly through physical feedback forms and surveys.
  • Retailers conducted market research through direct observation. It meant physically watching customer behaviors and noting which products attracted more attention.
  • Decision-making regarding stock levels, pricing, and promotions was primarily based on historical sales data.
  • Store associates were crucial in gathering information about customer preferences and complaints.
  • Decisions about store expansions, new product lines, and marketing strategies were based on broader economic indicators and fundamental financial analysis .
  • Relationships with suppliers and decisions about product sourcing were often based on personal experience and intuition.
  • Marketing strategies in traditional retail were more generalized. They were based on broad demographics and used standard channels: print ads, TV commercials, and flyers.

Introduction to Big Data Analytics in Retail

Big data in the retail industry has always been about understanding customers and markets, but big data analytics has changed it, bringing a new depth and precision. Today's retail landscape is about harnessing a deluge of data to make smarter decisions, tailor customer experiences, and drive strategic growth.

Big Data Analytics vs. Traditional Data Analysis in Retail

Big data analysis significantly differs from traditional data analysis in several fundamental ways, fundamentally altering how data is collected, processed, and used for decision-making.

Big data analytics represents a paradigm shift in understanding and utilizing data.

Big Data Analytics for Retailers Is a Dynamic Process

Big data in the retail sector involves an intricate process that spans collecting, processing, and analyzing vast amounts of data from many sources.

Collecting Data

It includes traditional sources like sales records and customer databases and more modern sources: social media interactions, website traffic, customer reviews, and IoT devices in stores. The volume of data collected is massive , often reaching petabytes, and it includes a variety of data types — structured data (sales figures), semi-structured data (web logs), and unstructured data — social media posts and video content.

Processing Data

Given the volume and variety, this data requires storage solutions, often utilizing cloud-based platforms for scalability and flexibility. Technologies like Hadoop and NoSQL databases come into play here, managing large datasets' storage and quick retrieval. Data from various sources must be normalized (formatted consistently) and cleaned (removing inaccuracies or duplicates) to ensure reliable analysis.

Analyzing Data

Retailers use advanced AI analytical tools and techniques for processing this data. This includes machine learning algorithms, predictive analytics, and data mining techniques to extract meaningful patterns and insights. One of the critical aspects of big data analytics in the retail market is the ability to process data in real-time. Big data retailers gain deep insights into customer preferences and behaviors by analyzing this info.

Actionable Insights

It requires optimizing supply chains, tailoring marketing campaigns, designing store layouts based on customer traffic patterns, or developing new products based on customer feedback and trends. The process is cyclical. Insights gained from analytics lead to actions, the results of which are again captured as data, feeding back into the system for continuous improvement and refinement.

Tailor product offerings and deliver targeted promotions.

Big data in retail: the source of benefits.

The benefit of big data in retail stems from its ability to provide insights into customer behaviors and preferences, enabling personalized marketing and efficient inventory management. Retailers optimize their operations, predict trends, and create tailored shopping experiences by harnessing data from diverse sources.

Big Data Analytics in Retail — Efficient Business Practices

  • Enhanced customer personalization
  • Improved inventory management
  • Effective pricing strategies
  • Insightful customer analytics
  • Streamlined supply chain operations
  • Data-driven decision making
  • Targeted marketing and promotions
  • Fraud detection and prevention
  • Enhanced customer experience
  • Competitive advantage

Three Real-World Big Data Use Cases in Retail

  • Walmart 's Data-Driven Supply Chain

One of the world's largest retailers uses predictive analytics to forecast demand, optimize inventory levels, and manage logistics across its massive network of stores. This approach has enabled Walmart to reduce overstock and stockouts.

  • Starbucks ' Personalized Marketing

Starbucks provides personalized offers and recommendations by analyzing customer data through its loyalty program and mobile app. This strategy has increased customer engagement, higher sales per visit, and boosted loyalty program sign-ups.

  • Amazon 's Recommendation Engine

Amazon provides highly personalized product recommendations by analyzing past purchases, browsing history, and customer reviews. This system accounts for a significant portion of sales, showcasing the power of personalized marketing and cross-selling.

Big Data Analytics Solutions

Big data analytics has become a cornerstone in the retail industry, offering many applications that change how retailers manage their operations and interact with customers.

Inventory Management

Retailers use big data analytics to predict which products will be in high demand. They can optimize stock levels by analyzing sales patterns, seasonal trends, and social media trends. It leads to cost savings, improved customer satisfaction, and increased sales.

Customer Segmentation

Big data allows retailers to segment customers more accurately based on their shopping behaviors, preferences, and demographics. Retailers create more effective marketing campaigns and build stronger customer relationships, leading to higher conversion rates.

Pricing Optimization

Retailers adjust prices in real-time by analyzing customer demand, competitor pricing, market conditions, and weather forecasts. It maximizes profits and ensures competitiveness, as prices are always in tune with market dynamics.

Supply Chain Management

By analyzing data from the entire supply chain, retailers identify inefficiencies and optimize predictive maintenance of equipment, route optimization for deliveries, and better supplier management for improved efficiency, reduced costs, and more reliable delivery.

Predictive Analytics for Sales Forecasting

It involves analyzing historical sales data, current market trends, and other external factors like economic indicators. It allows for better strategic planning, more effective marketing, and more efficient inventory management.

Big Data Analytics in Retail Cases

You can see several big data applications in retail:

  • Walmart’s analytics hub, known as the Data Café, processes over 2.5 petabytes of data every hour to make real-time decisions on inventory and operations across its stores.
  • Target uses big data to predict customer needs, famously identifying expecting mothers based on their shopping habits and targeting them with relevant offers.
  • Nike leverages data from its online platforms and apps to personalize marketing and create products that align with customer preferences, enhancing customer experience.

Challenges and Risks of Big Data Analytics in Retail

While big data analytics offers numerous advantages to the retail industry, it also brings challenges and risks that need careful consideration and management. To address these challenges, retailers must adopt a robust data governance framework, ensure compliance with data protection laws, invest in the right technology infrastructure, and focus on building a skilled team with expertise in data analytics.

Ethical and Privacy Considerations

With increasing volumes of personal data being collected, there's a heightened risk of privacy breaches and misuse of information. If not handled correctly, it can lead to legal repercussions under data protection laws like GDPR and CCPA and damage to the retailer's reputation. Customers are increasingly aware of their data rights and are concerned about how their information is used and stored.

Integration and Management of Big Data in Retail Systems

Retailers need to ensure that their infrastructure handles large volumes of varied data and seamlessly integrates it from multiple sources . Poor integration leads to data silos, inefficiencies, and inaccurate analytics, which in turn can result in misguided strategies and decisions. There's also the technological challenge of ensuring data quality and the reliability of analytics outputs.

Staffing and Training Needs for Successful Implementation

Finding and retaining qualified staff can be challenging, especially given the competitive market for these skills. A retail organization might struggle to extract meaningful insights from the data without the right team. Moreover, there's a need for ongoing training and development to keep up with evolving technologies and analytical techniques.

Future Trends in Big Data for Retailers

As we look ahead, big data analytics in the retail sector is poised for further transformative innovations and trends.

  • Advanced AI and machine learning algorithms are becoming more sophisticated, enabling more profound and accurate insights into customer behavior, demand forecasting , and inventory optimization. AI-driven personalization will reach new heights.

Demand forecasting

forecasting accuracy

out-of-stock reduced

Andrew M. photo

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  • The integration of IoT with big data analytics is a game-changer. Smart shelves, IoT-enabled inventory management, and in-store sensors will provide real-time data streams, enhancing the efficiency of operations and customer engagement.
  • Augmented and Virtual Reality (AR and VR) technologies will integrate with big data to offer immersive shopping experiences. Virtual try-ons, interactive 3D product views, and AR-based navigation in physical stores will become more prevalent.
  • With the rise of intelligent assistants and visual search technologies, big data analytics will play a crucial role in interpreting and responding to voice and image-based queries, further blurring the lines between online and offline retail experiences.
  • Predictive analytics will evolve to offer hyper-personalization in marketing and sales, predicting customer needs and preferences with remarkable accuracy and enhancing the effectiveness of targeted marketing campaigns.

The future of big data analytics in retail is dynamic, with emerging technologies and trends continually reshaping the landscape. The ongoing evolution of these analytics will make big data transforming operations to redefine the shopping experience.

Impact Of Big Data Analytics in Retail

Several more retailers have harnessed the power of big data analytics to drive significant improvements in their operations, sales, and customer experiences. Here are some big data in retail examples.

Home Depot’s Omnichannel Strategy

The home improvement retail giant Home Depot integrated big data analytics into its omnichannel strategy, combining online and offline customer data for a seamless shopping experience. This approach resulted in a 6.7% increase in year-over-year sales and a significant improvement in customer satisfaction scores, as shoppers enjoyed a more personalized and efficient shopping experience across all channels.

Zara's Fast Fashion Powered by Big Data

Zara , a part of the Inditex group, is renowned for its fast fashion model. Big data analytics is pivotal, enabling rapid response to fashion trends. By analyzing sales data, customer feedback, and fashion trends in real-time, Zara reduced its design-to-sale cycle to just two weeks. This agility contributed to a 20% annual increase in sales and helped Zara maintain its position as a leading fashion retailer.

Best Buy’s Customer-Centric Strategy

Best Buy 's large chain of electronics stores implemented big data analytics to enhance its customer-centric approach and compete effectively with online retail giants. They optimized product offerings and store layouts by analyzing customer purchasing patterns and feedback. This led to a 5% increase in domestic sales. The analytics also enabled more effective inventory management, reducing carrying costs and improving profitability.

A Retailer's Guide to Adopting Big Data Analytics

Step 1: define objectives and scope.

Determine what you want to achieve with big data analytics. Establish the scope of your big data initiative. Before scaling up, decide whether to start with a specific area (like sales or customer feedback).

Step 2: Assess Current Data Infrastructure

Review your current data collection and storage systems. Understand the limitations and potential of your existing infrastructure. Identify gaps in your current data setup, including data types not being captured that could be valuable.

Step 3: Plan Data Collection and Integration

Pinpoint various data sources relevant to your objectives: transaction data, online customer interactions, supply chain data, etc. Develop a strategy for integrating disparate data sources. It may require cloud-based solutions or data warehousing techniques.

Step 4: Select Appropriate Big Data Analytics Tools

Explore big data tools and platforms that align with your objectives and existing systems. Options include Hadoop, Apache Spark, or cloud-based services. Ensure the tools you select are customized to your needs and are scalable as your data requirements grow.

Step 5: Talent Acquisition and Training

Acquire talent with expertise in big data analytics: data scientists, analysts, and IT professionals with relevant experience. Invest in training for your existing staff. Familiarize them with big data concepts and tools to ensure a smooth integration.

Step 6: Implementation and Integration

Start with a pilot program focusing on a specific aspect of your retail operations. It helps in understanding the practical challenges and potential benefits. Gradually expand the implementation to other business areas, integrating insights from the pilot phase.

Step 7: Integral Data Analysis and Insight Generation

Use your analytics tools to continuously analyze the collected data. Look for patterns, trends, and insights that align with your predefined objectives. Translate these insights into actionable strategies. It modifies marketing campaigns and adjusts inventory levels.

Step 8: Ongoing Review and Adaptation

Regularly monitor the outcomes of your big data analytics initiatives against your set goals. Be prepared to adapt your strategy based on the insights gained and evolving market conditions. Big data retail analytics is a dynamic process that requires continuous refinement.

Areas where companies plan to increase their big data analysis investment

Areas where companies plan to increase their big data analysis investment

The Integral Role of Provider in Big Data Management

A data engineering provider is the mastermind behind managing and making sense of big data. They set up robust systems to collect and store vast amounts of data from varied sources, ensuring stability and efficiency. They organize this complex data, preparing it for analysis. Then, as data scientists, they dive deep into this data, using advanced analytics and machine learning to uncover hidden patterns and insights. They translate these findings into understandable, actionable businesses’ strategies for businesses. Please complete the form , and let's try it in your practice.

How is big data used in retail?

Big data in retail is used to analyze customer behavior, preferences, and purchase history to enable personalized marketing technology strategies and optimize inventory management. Additionally, it aids in supply chain optimization by providing insights into demand forecasting and improving operational efficiency for retail companies.

How are big data problems solved in the retail sector?

Big data problems in the retail sector are often solved through AI analytics and machine learning algorithms, enabling retailers to extract meaningful insights from vast datasets, optimize inventory management, and personalize customer experiences. Additionally, implementing robust data infrastructure, cloud computing, and data integration solutions helps retailers handle and process large volumes of information efficiently.

What is big data analytics, and how does it apply to my retail business?

Big data analytics involves examining large, diverse data sets to uncover hidden patterns, customer preferences, and market trends. In your retail business, it can help you understand customer behavior, optimize your supply chain, personalize marketing strategies, and ultimately enhance customer experience and boost sales.

What are the key benefits of implementing big data analytics in the retail sector, and how can it improve my bottom line?

Implementing big data analytics in retail offers precise customer targeting, efficient inventory management, and enhanced shopping experiences, leading to increased customer loyalty and sales. By leveraging these insights, retailers streamline operations, reduce costs, and improve their bottom line through more effective marketing strategies and customer-centric decision-making.

Are there any real-world examples of big data in retail that have successfully implemented big data analytics to drive growth and profitability?

Major retailers like Walmart and Amazon have successfully used big data analytics to drive growth and profitability, optimizing everything from supply chain logistics to personalized customer recommendations. Their strategic use of data has enabled them to better understand customer needs, predict market trends, and enhance operational efficiency, setting new standards for the use of big data in the retail industry.

What big data in the retail industry case study can you suggest as significant?

One significant big data implementation in the retail industry involves Amazon, which leverages big data to analyze customer behavior, predict purchasing patterns, and optimize its vast product inventory. Amazon enhances customer recommendations through AI analytics and machine learning, streamlines its supply chain, and continuously improves the shopping experience.

How can I get started with big data analytics in my retail business, mainly if I have limited technical expertise?

To start with big data analytics in your retail business, consider partnering with a data analytics service provider who can tailor solutions to your specific needs and guide you through the process. Additionally, investing in user-friendly analytics software designed for non-technical users helps you gradually build your in-house capabilities and better understand your customers and business. It improves the use of big data in retail.

What are the common challenges or barriers businesses face when adopting big data analytics, and how can I address them?

Common challenges in adopting big data analytics include integrating diverse data sources, ensuring data quality and security, and facing a shortage of skilled data professionals. To address it, businesses invest in robust data integration tools, prioritize data security measures, and seek partnerships or training to build expertise in data analytics.

How does big data analytics impact customer experience and satisfaction in the retail industry?

Big data analytics significantly enhances customer experience in retail by enabling personalized shopping experiences and recommendations tailored to individual preferences and behaviors. This increases customer satisfaction, as shoppers feel understood and valued and are more likely to find products that meet their needs and expectations.

What types of data should I collect, and how can I ensure data privacy and security for my customers?

In your retail business, focus on collecting customer demographics, purchase history, online browsing behaviors, and feedback, which provide valuable insights into customer preferences and trends. To ensure data privacy and security, adhere to data protection regulations like GDPR, implement cybersecurity measures, and be transparent with customers about how their data is used and protected. It's true for all applications of big data in retail.

Can big data analytics help me with inventory management and supply chain optimization, and if so, how?

Big data analytics significantly aids in inventory management and supply chain optimization by analyzing sales patterns, demand forecasting, and supplier performance, allowing for more accurate stock levels and efficient logistics. It leads to reduced costs, minimized stockouts or overstock situations, and a more responsive and streamlined supply chain that aligns closely with customer demand and market trends.

What role does artificial intelligence and machine learning play in the future of big data for retail?

Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize big data analytics in the retail industry by enabling more advanced, predictive insights and automating complex decision-making processes . These technologies will allow retailers to anticipate customer needs, optimize operations, and create more personalized shopping experiences, driving innovation and competitive advantage through big data analytics in the retail sector. It also makes handling big data and knowledge management in retail easier.

How can I measure the ROI of my big data analytics initiatives in the retail sector, and what key performance indicators should I be tracking?

To measure the ROI of your big data analytics initiatives in retail, track key performance indicators: increased sales revenue, improved customer retention rates, reduced operational costs, and enhanced marketing campaign effectiveness. Additionally, monitor metrics like inventory turnover, customer satisfaction scores, and supply chain efficiency to gauge the direct impact of your data-driven strategies on business performance and growth.

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The Impact of Big Data on The Retail Sector: Examples And Use-Cases

Big data in retail is changing the rules of the game - don't miss out on its opportunities

"Information is the oil of the 21st century, and analytics is the combustion engine.” - Peter Sondergaard , Gartner

In today's hyper-connected digital world, big data is everywhere.  We all have a digital footprint and believe it or not, almost everything we do online can be analyzed, quantified and used to help track consumer trends , behaviours and insights that help brands reach out to us on an engaging, personal level.

That said, should you be a retailer and reading this: if you're not at present, you need to start using big data to your advantage, by mastering and understand it namely thanks to a retail analytics software for instance.  And to connect with your target audience with valuable, personalized content or deals tailored to their personal needs, you need big data.

Big data offers in-depth information about the people your brand is targeting and it’s changing the face of the retail world in a colossal way.

To help you understand the impact of big data in retail , we’re going to look at the reasons why big data is important to the sector. We’re also going to delve into some valuable big data retail use cases to paint a vivid picture on the value of these metrics in the consumer world.

Let’s get going.

Why Is Big Data In Retail Important?

As mentioned, we live in a time where the average consumer is not only incredibly tech-savvy, but they crave intimacy with the brands in which they're looking to invest, both on financial and emotional level.

Nowadays, a one size fits all mentality just won’t do.

Big data is helping retailers to understand their prospects on a deeper level and with a host of metrics including social media preferences, browsing behaviours, devices preferences, geographical demographics and much more readily available, brands are branching out in more meaningful ways than ever before.

And the value of big data isn’t just exclusive to digital-only retailers.  Cisco discovered that in-store analytics alone offers a $61 billion value stake for retail. What this means is that older, more traditional brick-and-mortar stores have been given a new lease of life.

By leveraging highly-precise insights on how shoppers engage with their products or displays while in-store, high street businesses have been able to tailor their merchandising, deals and marketing campaigns to meet the needs of their shoppers. It’s also helped physical retailers measure their ROI far more accurately, helping with growth, development and commercial progression. The best tool that can help you with that is a retail dashboard , that lets you visualize all your analytics easily.

It’s clear that big data is an essential component of any modern retailer’s arsenal and without using such information, insights and metrics to your advantage, you could risk seriously falling behind the competition. 

Using Big Data To Your Advantage

Besides the fact that big data analytics is helping those in the retail sector understand their customers far better, making the experience more personal, engaging and initiative at every stage of the buying journey, it also has notable benefits in other areas:

  • Demand: By understanding data-based insights on customer habits, retailers can understand which of their products and services are most in-demand and which ones they should potentially stop offering. Not only can these insights serve to save money and where to place investment, but it will also help brands to give the consumer exactly what they want.
  • Prediction: Trend fore casting algorith ms in big data can help brands make key market predictions and forecast consumer trends. Thanks to professional data alerts , retailers monitor the demand fluctuation in real-time, and can develop products that will provide them with the best return on investment.
  • Pricing: By gaining access to insights on real-time customer transactions, retailers can gain a better understanding which prices yield the best results on particular products. Big data technology can also be utilized for 'markdown optimization' - an understanding of when prices on particular items should be dropped. Retail giant Walmart has reaped the rewards of  real-time merchandising , and as a result of its success, the brand is now in the process of building the world's biggest private cloud in a big to dig even deeper into the behaviour of its customers.
  • Cross-channel: In today's world, the omni-channel experience is a big deal. Google research suggests that 98% of Americans switch between devices in the same day. As mobile technology and social media become all the more sophisticated, consumer craves a retail experience that offers value across a host of mediums and devices. Retail big data gives brands the power to harness insights extracted from these various devices and mediums to create campaigns, initiatives and offers that create a buying journey that works seamlessly both in a digital and physical sense. And as retailers that adopt omni-channel strategies earn 91% greater year-over-year customer retention rates compared to companies that don’t, this is an area that you can't afford to ignore.

In summary, by using big data analytics to your advantage, you will be able to understand the wants, needs and desires of your customer base, understand demand, predict priceless market trends, make smarter pricing decisions and create valuable cross-channel shopping experiences. In turn, these efforts will boost your brand awareness, customer loyalty and conversion rates exponentially.

Now you understand how you can use big data to gain an all-important competitive edge, let’s look at some big data in retail examples.

Brands Benefiting From Big Data And Analytics

In the world of retail, many innovative brands and businesses have already seen great results from using big retail data to their advantage. These big data retail use cases will show you how.

This colossal coffee brand needs little introduction - and there's a reason this Seattle-based brand has not only survived but thrived over its many decades of existence. In a nutshell, that reason is innovation.

Starbucks has the uncanny ability to open a number of branches on the same block and enjoy a healthy level of profit from each. By using big data analytics to its advantage, Starbucks can predict the growth potential of each new store by looking at metrics such as location, traffic, area demographics and customer behaviour.

Moreover, Starbucks gathered insights from their 90-plus million transactions per week and used this data to deliver a personalized experience to its customers, sparking innovations including its tailored digital rewards scheme that becomes more intuitive the more data it gathers on a customers buying habits and purchase history.

With reported revenue of $22.39 billion last year alone, it’s fair to say that Starbucks is a real retail winner.

The Weather Channel

Despite being a weather channel, The Weather Channel is one of the most forward-thinking broadcasters in the world of modern entertainment.

Through its data platforms, Location FX and Weather FX, the broadcaster studies the weather’s impact on the emotions of its viewers. By doing so, The Weather Channel harnesses the power of predictive analytics to steer its advertising partners’ campaigns in the right direction by spotting valuable locational trends.

Take the channel's partnership with Pantene and Walgreens. By using the metrics collected by The Weather Channel, Pantene and Walgreens gained the power to anticipate when air humidity would be at its highest, launching a targeted campaign to prompt women to grab their products in a bid to prevent “embarrassing” (or so they say) seasonal frizz.

The result? A 10% increase in sales of Pantene at Walgreens for two months around the campaign's launch.

Among the many consumer innovations Costco has launched as a result of harnessing digital metrics and insights, an incident involving a batch of contaminated fruit is perhaps the brand’s the most striking big data in retail examples.

Like many modern-day wholesalers, Costco tracks what you buy and when. A California-based fruit packing business warned Costco about the potential of listeria contamination in its stone fruits. Instead of sending out a blanket warning to all who shopped at Costco in recent weeks, the company was able to notify the specific customers that bought those particular fruits, first notifying them by phone, followed by a letter.

This particular example is a testament to the unrivalled power of big data analytics in the retail sector.

Ignore This At Your Own Peril

Contrary to the big data retail use cases detailed above, there have also been some infamous cases of commercial failures as a result of ignoring digital data and emerging technologies.

One of the most recent is the liquidation of the longstanding toy brand, Toys’R’Us.  Not long ago, the children's retailer announced that it would close around 180 stores in the USA alone as it begins bankruptcy proceedings.

Among a range of issues that led to its commercial doom, an obvious failure to undergo a comprehensive data-led digital transformation contributed to the brand’s downfall. By failing to use the wealth of available digital data to its advantage, Toys’R’Us failed to offer the kind of innovative omni-channel experience that helped set it apart from online retail giants like Amazon.

Had the company delved deeper into big data, it would have stood a tangible chance of retaining its established customer base and created a tailored shopping experience that would have helped it thrive in the digital age.

How Do Modern Dashboards Help You Make Sense of Your Big Data?

As we have seen all along this article, leveraging your big data analytics will lead to an increased business success. Hereafter we present you an actual way to use these analytics by visualizing important retail KPIs it in an understandable manner: professional real-time dashboards.

Sales & Order Dashboard

sales and order retail dashboard - to help you leverage big data analytics

**click to enlarge**

With the advent and generalization of internet, consumption ways have changed, and today 96% of Americans now shop online against 22% back in 2000 . This drastic increase also brings its load of analytics for you to track and understand consumer behavior better. As an online retailer, data collection is easier and abundant – so you better track the right metrics in an organized way to avoid drowning in information, and to make the most out of it.

With such a retail dashboard as the one above, you can evaluate how you perform in terms of orders and sales. Tracking the total orders you have and the average order per customer lets you manage the inventory to avoid product shortage or on the contrary, overflow.

Likewise, this dashboard shows you the perfect order rate, that gives insights on how efficient your business is when it comes to delivering without incident – from the fulfilment of the order to the shipping and delivering. This is precisely the type of metric you want to track as it directly impacts the customer retention rate and ultimately, the likelihood that they recommend your services to friends and relatives.

Finally, you can also see the returns, that need to be tracked and understood: why do customer send back their item? That will greatly help you improve your products and services in the future.

Retail Store Dashboard

big data in retail: a retail store dashboard to manage your analytics

Now, not every retailer is online, even though it is less and less the case. But in many cases, the offline, in-reality presence is stronger than the online one. It is as important and needs even more tracking, as the data isn’t as easy to collect and compile as it can be online. Many different entities are involved, from the warehouse management to the sales store itself and its inventories.

By tracking what is happening in your retail store, you can identify some customers’ patterns and adapt your strategies accordingly to the results. Knowing what your best-seller articles are will give you insights on what is trendy at the moment and what customers like. You can order similar articles but also prevent stockout by identifying the top-10 items, and monitoring the OOS (Out of Stock) rate. Stockouts is a situation you clearly want to avoid because it will turn your unsatisfied customers to the competition, and harm your image.

Likewise, evaluating your total sales and breaking it down per category and per location will let you know who and where is your main point of revenue. That way, you can create customized campaigns that will have a higher impact on the audience, and better ROI.

The point is, big data is the future of retail and if you want to succeed in today’s and indeed tomorrow’s world, leveraging the wealth of consumer insights available to you is essential. If you don’t, you stand to fall behind the rest of the pack, rendering your brand as well as your products obsolete sooner than you might think. On the flipside, use big data to your advantage and the rewards could be endless - and that’s a beautiful thing.  To enhance your big data knowledge further, you can check out our article on how big data in logistics is transforming the supply chain.

If you want to see how a retail analytics software can help your organization, register now for a  14-day free trial , or  get in touch  with us – we look forward to hearing from you!

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Retail and Big Data: Transforming the Industry to Become a Leader

The three v’s of big data in retail, key benefits of using big data in the retail industry, the big data in company development, 7 retail companies, the pioneers of using big data, big data use cases in the retail industry, epam's experience in using big data in retail software development.

Since 2011, Dmitri has been helping business readers navigate the technology market through expert analysis and editorial work. At EPAM Startups & SMBs, Dmitri shows startups and SMBs across industries how to drive business value from their software engineering investments.

Optimize your software development capabilities by adding top talents from one of the leading software engineering companies in the world

The retail industry is a complex and volatile field that has been increasingly growing over recent years. The projections state that global retail sales are likely to reach $31.3 trillion by 2025. Staying afloat and managing to steadily increase profits require in-depth data analytics and a smart application of data-driven insights. Big data analytics has entered and revolutionized every industry, and retail is not an exception. But why do some companies manage to thrive and use technologies to advance and some still fail? In this article, we will go over the benefits of big data and knowledge management in retail, discover the most prominent examples, and take a look at successful applications of the technology.

Big data is a broad term that can confuse non-tech people. Looking at the definition through the main concepts allows you to better grasp how big data in eCommerce can enrich retail and optimize your business. The three V’s stand for volume, velocity, and variety that shape the world of big data.

According to the projections, we will collectively generate 149 zettabytes in 2024. If a zettabyte is an unfamiliar concept to you, one zettabyte equals a billion terabytes. The volume of data created every day is growing and will continue to grow in the future. Obviously, data volume is an essential part of big data. Retailers also require large amounts of information to be able to analyze it and draw meaningful conclusions.

Velocity accounts for how fast data is retrieved and collected, which usually depends on the type of business. To some businesses, analyzing real-time data is crucial for decision-making, whereas other companies collect data in batches. The colossal volumes of data need to be timely collected, processed, cleaned, stored, and analyzed.

Data nowadays comes in different shapes and forms such as video, PDF, text, graphics, and a lot more. Although having a variety is great for business, processing different types of data takes a lot more resources. This aspect of big data requires understanding the types of data, their format and usage, and the ability to process it in a meaningful way.

Big data analytics is essential for almost every business, especially in the retailing industry. But how exactly can your organization benefit from applying this technology? Simply storing vast amounts of data will not change your business for the better, but if utilized properly, it can give valuable insights. In this section, we will go over the most significant advantages of big data implementation in the retail sector.

Data accessibility

In the abundance of various devices that can access data nowadays, being able to collect information from all of them becomes crucial. Retailers need to observe customer behavior and purchase history from their computers, mobile phones, tablets, and other internet-connected devices. Retail and big data analytics allow us to consolidate and analyze info from any device, including wearables, and make data-driven decisions.


Customers prefer personalized interactions and suggestions from brands and businesses as opposed to generic responses from the past. Knowing what your customer is looking for and their preferences allow companies to generate personalized messages, emails, discounts, special offers, and loyalty programs. Instead of randomly suggesting the most profitable or attractive products, big data and analytics for retail help us to create offers that would speak to the particular customer.

Customer segmentation

The costs of customer acquisition are notoriously high and unfeasible. Segmenting your customer base allows you to target clients that are more likely to complete a purchase. Attracting new customers and turning them into paying clients plays an important role in any marketing strategy; however, persuading existing customers to continue shopping with your company is more financially efficient.

The Internet of Things allows companies to produce wearable devices that generate even more helpful data. IoT devices are connected to the internet and capture customer behavior and actions data and send it to a centralized server. Using big data analytics for retail, companies have even more insightful information to sift through and adjust their marketing campaigns. You may also be interested in the article about IoT in retail .

Predictive maintenance

In the ever-changing world of retail, the ability to predict market shifts and customer behavior is a game-changer. Based on collected historical data, companies can make accurate predictions and determine how certain trends and events might affect customers. For example, what they will be likely to buy in case of a lockdown or sudden change of weather conditions. Knowing what your customer base wants and needs allows you to plan out the inventory and obtain a competitive edge.

Improved customer experience

Having access to customer data allows organizations to see user journeys and identify where customers get confused about the navigation and abandon the app or website. It could be small and easy to fix details that hinder users from completing the purchase such as bulky shopping cart previews, cumbersome payment gateways or unclear address form. Big data analytics helps determine which steps of the way make users abandon the shopping cart and solve this issue in the future. For example, autofill for personal information such as name, address and phone number in a food delivery app can significantly improve customer satisfaction and consequently sales revenue.

Price optimization

Finding the ultimate price for the product that will maximize the profits is not an easy task. The price will also fluctuate depending on the season and overall demand. Big data analytics can help you locate the perfect time to increase or drop the price and ultimately allows you to drive up the sales revenue.

Enhanced customer retention

Customer retention drops when users do not receive what they want and need and look for alternative solutions. In order to slow down churn, it is important to identify disengaged users and change their minds. You can ask for their feedback, offer personalized solutions and discounts, and come up with other tactics to make sure they stay with you.

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The market of retail includes various directions that can all benefit from big data tools. Retailers plan to focus their big data initiatives on predominantly merchandising, marketing, eCommerce, and supply chain. But which issues does big data solve when it comes to these elements?

Supply chain efficiency

Supply chain management refers to actions that bring raw materials down the supply chain and turn them into final products. Establishing and nurturing good relationships with suppliers, partners, and customers is a vital part of supply chain management. By conducting retail data analysis using big data projects, companies can learn more about their vendors and evaluate their financial and performance metrics. Besides suppliers, it’s important to get to know your customers and their relationship with the brand. Data-driven technologies allow us to conduct sentiment analysis and learn what customers say about the product on various platforms.

Another benefit of applying big data analytics to supply chain management is inventory management optimization. Learning customer behavior and identifying market trends allows us to predict how much inventory will be required in a certain period. This way, you can avoid overstocking and understocking and prevent monetary losses.

Marketing and Promos

How big data is changing retail marketing analytics is by unlocking endless opportunities for improving and fine-tuning marketing strategies. First of all, you can break your customer base down into demographic groups that allow you to target specific customers. For example, you can separate your user base by age, location, gender, and more. Further, you can determine which types of content speak to your audience the most and move them down the sales pipeline. Customer data can also help you identify why customers choose to use or not to use your product. Finally, big data tools help marketers evaluate the success of marketing campaigns to improve them in the future.

Omnichannel sales

Omnichannel shopping experience has become an essential part of most brands. The majority of shoppers admit that they check the prices and discounts across various channels to find the best deal. Omnichannel shoppers are also more likely to spend more money on your products compared to single-channel customers. Research from CMO Council shows that 85% of consumers prefer having access to both online and offline experiences, or in other words, digital and brick-and-mortar.

Big data analytics can also help you manage inventory across all channels and make sure it’s balanced out and sensible. Omnichannel experiences allow you to offer cross-promotions and drive both channels through one another.

Financial analysis

Since the early days of eCommerce, the biggest concern has been fraud detection and prevention. Making sure the application is not hacked and not stealing personal customer data is the focal point of the application development process. Luckily, machine learning algorithms based on historical data allow us to recognize fraudulent behavior and detect patterns.

Data science also helps organizations analyze their financial data and identify the roadblocks that prevent them from exploiting potential opportunities. Retail business intelligence tools allow us to generate financial reports with valuable insights that can be utilized to help the company grow. Finally, the market itself fluctuates and fuels different trends, which can also impact your company's performance. Knowing these trends in advance facilitates early actions that can help you explore new opportunities and avoid bad outcomes.

Research and development

R&D is about coming up with new ideas that expand the company and improve the brand. However, some of these scenarios can have detrimental effects on your performance. Testing these ideas in advance without wasting resources and making sure they are a good investment is something you can do using big data analytics. Instead of relying solely on historical data, companies can utilize predictive analytics to build models and evaluate their R&D initiatives.

Risk management

Every business holds many risks that need to be continuously assessed and analyzed. For example, customer churn is one of the most significant risks that will definitely occur in some capacity but can be mitigated. Using big data, companies can forecast the risk of customer churning and take action to retain them.

Another common risk is untested third-party relationships that can severely jeopardize your reputation and finances. Data analytics can help you monitor the vendors and their performance and identify early signs of malicious activities. Finally, there might be some issues that drain your profits without you even knowing. Defects in production, unoptimized supply chain and other operational risks that heavily impact the end product can be detected and mitigated.

Utilize big data to take your company to the next level

Optimize your supply chain, conduct efficient financial analysis, and take risk management in your company to the next level. The team of EPAM Startups & SMBs will help you implement all the mentioned ideas and many more.

More and more companies recognize the potential and impact of big data and utilize this technology to benefit their business. Especially big players with excess resources to invest in technology have shown impressive results from implementing big data analytics. Let’s check out seven retail companies that have successfully applied big data to enhance their performance.

The CMO of Home Depot, Kevin Hofmann, said: “ Our data allows us to know our customer at a more individual level. Our targeting capability will help us get to them at the right place at the right time, and we aspire to have all of our messages be tailored to the audience”.

Home Depot’s big data investment allowed them to target their ads so precisely that even people from the same street will see different ads whenever they visit their website. They also experiment with weather-triggered advertisements that are only deployed under certain weather conditions. Additionally, Home Depot generates inventory-driven ads that are pushed forward depending on the inventory availability in certain stores. As a result, their online shop grew by $1 billion over the past four years, and the overall growth measured 20%.

Walmart is one of the retail companies that use big data to identify the busiest times in their stores and pharmacies and optimize staff scheduling to accommodate their customers. The company also uses simulations to build optimized routes from the dock to the store and cut the duration of deliveries. Being a large store with millions of products, Walmart also decided to rethink merchandising to offer items that are sought after as well as new and discounted products. Finally, the retail company uses customer data to create a personalized shopping experience and anticipate the needs of its customers. Walmart has experienced a 15% increase in online sales which translates into an additional $1 billion in revenue.

Andy Kettlewell, the Vice President of Inventory and Analytics, said: “Data is critical for everything that we do at Walgreens, and with that data the customers are telling us what they buy and what they need.”

Walgreens is a drugstore chain that handles eight million customers in online and offline stores every day. By capturing and analyzing vast amounts of customer data, they can accurately manage their inventory and provide a better overall experience.

Philippe Benivay, the Head of Experimental Data Intelligence, said: "Data and artificial intelligence allow us to move faster to create cosmetic products that meet the infinite diversity of beauty needs and desires of consumers around the world.”

L’Oreal is one of the retail companies using big data to drive their R&D and develop new formulas that appeal to their audience and market trends. They utilize data analytics to create new products and measure their performance among their customer base. The company has used the technology to also improve their KPIs and boost sales revenue.

PetCube is an innovative pet tech company that develops software that captures and analyzes pets’ behavior and uses it to create an interactive video experience. Their inventions allow pet owners to play and talk to their pets and even give them treats using their platform and evaluate how the pet is acting and feeling to provide helpful insights to their owners. The product has already gained traction and attracted half a million dollars from Kickstarter as well as raised $14 million in seed, venture, and Series A.

McDonald’s is a famous case study that utilized data analytical technologies to enhance customer experience. For example, they measure when large groups of people are likely to show up at the drive-through to accommodate them better. The company has also equipped their drive-throughs and restaurants with digital menus that allow customers to preorder and complete their purchases a lot faster. The digital menus display special offers and promotions based on the weather, time of the day and season, local events, and purchase history. The company has also implemented big data applications in retail to optimize inventory management , which is exceedingly more important for low-margin businesses like food service.

Nike collects customer data through their app, including additional solutions like Nike Training Club and Nike Run Club apps that analyze their fitness data and offer personalized guides for their workout sessions. The added value facilitates a stronger bond between the company and clients and encourages them to purchase fitness equipment and clothes that are tailored to their goals and experiences.

Nike went even further and invested in a product Nike Fit that scans customers’ feet to identify the perfect shoe style, type, and size. The app then stores this data to make personalized suggestions whenever the user shops online.

Netflix was at the forefront of using big data to attract new customers and retain the existing ones. The company offers personalized movie and series recommendations based on what the user watched for longer periods. The algorithm can also predict which shows will become the new hit and which will remain fairly unnoticed. They also tailor the thumbnails and trailers depending on the person’s watch history and reviews.

The streaming service company also applies big data insights for its movie production strategies. For example, they can calculate the costs of shooting in one location versus another and identify how to streamline the entire process to minimize the expenses.

Adopt the lessons of the best retail companies

Experts of EPAM Startups & SMBs use big data to create robust personalization strategies for retail businesses. We provide big data software and application development services that help you enter the space of digital transformation.

If you are not as big as Netflix or Mcdonald's, you might be wondering how you can utilize big data. Let’s explore the most prominent applications and examples of big data in the retail industry that you will benefit from.

360° View of the customer

This is one of the most prominent big data use cases in retail that allows companies to create dashboards that offer a detailed customer profile using data from different sources. The profile may include demographics, CRM data, and communication history with the company. The data may even include recent social media posts, search history on the company’s site and other important details. Customer service representatives can immediately access this information to provide a tailored experience and save their time. The data can even predict why the customer is getting in touch and offer solutions.

Fraud prevention

Big data in fintech allows credit card companies to make more accurate predictions, prevent fraudulent transactions, and improve their precision so that customers do not get calls every time they make a seemingly unexpected purchase. Since the company knows more about the client’s purchase history, they are more likely to accurately evaluate the purchase.

For example, instead of calling up the client because of a sudden purchase of an airline ticket, the company looks at the full history to determine the legitimacy of the purchase. If the client also booked a hotel or bought sunscreen or a suitcase, the ticket purchase does not look suspicious anymore.

Security Intelligence

Unfortunately, the development of newer and more sophisticated technologies does not stop hackers from successfully gaining access to company and user data. Big data analytics in retail delivers tools that can detect anomalies and alert the company and the customer about potentially fraudulent activity. Working in real-time, such tools use analytics and machine learning to identify unusual behavior and prevent people with bad intentions from stealing personal data.

Big data in retail helps organizations find the best price that generates the highest revenue for the company. Some models even allow companies to segment customers and analyze how much each customer is willing to pay for the product to adjust the prices accordingly. This strategy is not overwhelmingly successful in the B2C market but has become a standard in the B2B environment.

Operational efficiency

Operational inefficiency incorporates a set of potential issues that hinder the profits from going up. For example, a company tracks the sales of a new product at each point of sales and realizes that one store has not sold any items. Upon getting in touch with the store manager, they learn that they forgot to put the product on the shelf. Without big data analytics in the retail industry, the company would not have learned this information in time to swiftly fix the problem and would have lost money and product visibility.

Personalized recommendations

As we discussed in the Netflix example, the use of big data in the retail market allows companies to offer personalized recommendations that are more likely to appeal to the customer. This technology has become so commonplace that customers nowadays expect to see items based on their search and purchase history. Retailers that do not offer personalized recommendations will lose to their competition in the long run.

Internet of Things

The Nike example demonstrates a successful application of IoT to improve customer experience and drive up sales. These can entail big data solutions for retail firms that monitor customer movement or track the weather conditions and collect valuable data that is vital for customers’ purchase behavior. Using these insights helps companies to push forward the products that are most useful to the customer under the current circumstances.

EPAM Startups & SMBs is a team of experienced technologists, data scientists, and strategists that delivers digital transformation to businesses. Our expertise and acute knowledge of both business and technology allow us to take companies to the next level and unlock new exciting opportunities.

Take a look at one of EPAM’s top case studies below.

Edmunds Minsk Hackathon 2015: EPAM Big Data Innovation

Edmunds is a car-shopping guide that helps individuals choose the right vehicle. EPAM has organized a hackathon to brainstorm ideas on how to improve the car purchasing experience as well as increase the sales revenue for the car dealer. As a result, the team developed an app that tracks the prices and incentivizes customers to make a purchase whenever the price is down. Another achievement was a tool that compares the dealer's performance to their competition in their area. Finally, EPAM delivered a new feature to the site that allows customers to share their own posts about their cars with the important vehicle data that stays in the system and can be used to generate maintenance offers.

Epic Games is a game developer that produced one of the most popular games: Fortnite. Working together, EPAM and Epic Games created a digital platform for millions of gamers where they can communicate, track their experiences, generate friend lists and customize privacy settings. The game has become a large community of players that can enjoy the game and find like-minded people.

Telefónica Germany

Telefónica Germany is one of the largest telecommunication companies that came to EPAM to enable growth and insights for the future. EPAM developed solutions that help the company process large amounts of data while also drastically increasing the speed. The team also delivered a BI blueprint that allows the company to enter the big data analytics space.

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The ways big data and retail industry solutions can benefit your company, drive up the sales, and enhance customer experience are vast. However, the adoption of data analytics is not only about data storing and processing but also about how retailers use big data, extract valuable information and recognize the insights. If you would like to enter the digital transformation era and need assistance, get in touch with EPAM Startups & SMBs . We are a team of all-around experts who will help you go through a technological transformation and utilize the gained data to better your organization.

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Mastering Big Data in Retail: Strategies, Metrics, and More

Table of Contents

You stroll into your favorite store, and it’s like they’ve read your mind. The perfect pair of shoes, the ideal shade of lipstick, and that gadget you’ve been secretly coveting — all right there, waiting for you. Ever wondered how they do it?

It’s time to peel back the curtain and discover the technology behind it all — big data!

What Is Big Data?

Big data isn’t just data. It’s about collecting a mind-boggling amount of information and turning it into a mechanism. It makes retailers know what you want before you even do it.

Big Data’s Five V’s

We usually break down big data with the five V’s that perfectly illustrate the core ideas behind its collection and use:

Volume: Every time you click, tap, or swipe online, it’s recorded. Retailers use these volumes of data to decode trends and crack shopping behavior.

Variety: Retailers scoop up data from everywhere — your purchases, social media posts, online reviews, and even the weather forecast.

Velocity: Big data isn’t a history book; it’s more like a live broadcast. Retailers change prices, restock shelves, and tailor ads in real time. Amazon is legendary for this — it adjusts prices every 10 minutes to stay ahead of the game.

Veracity: Data accuracy is the name of the game. Imagine if your online order said “in stock” but really wasn’t. Stores like Macy’s invest in tools to keep their data rock-solid.

Value: It’s all about the bottom line. Big data lets retailers craft personalized experiences and super-targeted ads to generate bigger profits.

The convergence of big data and the retail industry is a long story, covering the use cases, success metrics, innovations, examples, and more. Let’s first explore the most common applications and see what employees don’t have to analyze and adjust manually anymore.

Why and How Big Data and Retail Converge

The idea behind using big data in the retail industry is pictured by how small data points have a significant impact on shopper or retailer decisions. Some of the most common applications include personalizing shopping experiences, managing inventory, building pricing strategies, and detecting fraud — all with data.


You know that feeling when Amazon suggests the perfect book you didn’t even know you wanted? Here, big data crunches your past purchases, browsing history, and even what others like you are buying to adjust recommendations just for you. All with the help of artificial intelligence and machine learning.

Sometimes you may also receive an email with discounts for products you’ve been eyeing. Big data is behind that, too. It helps retailers send personalized promotions, increasing the chances that you’ll find something you love.

Inventory Management

Retailers no longer rely on gut feelings to stock their shelves. Big data analyzes historical sales data, market trends, and even external factors like weather to predict what customers want, ensuring products are available when needed. Walmart, for instance, uses big data to predict product demand with a 95% accuracy rate .

Another thing is stock optimization. It’s a delicate balance between having enough in stock without overloading your warehouse. Big data fine-tunes inventory levels, saving retailers money while making sure you can still grab that must-have item.

The mobile solutions for warehouse management make the picture even brighter. Workers can record inventory data on the go with no need to return to stationary kiosks. This amps up, cutting down on errors and costs, and ultimately improving productivity.

Read more about how Worker Activity Logs Give Valuable Insights in Inventory Management

Pricing Strategies

A good example is how retail flight prices change with big data depending on when you book. Big data analytics enables dynamic pricing, where product prices adjust in real time based on demand, competition, and even your browsing history.

Consequently, retailers keep an eagle eye on competitors. It helps them spot opportunities to price competitively or offer better deals to win your business.

Fraud Detection

Big data is like a digital detective, monitoring transactions for unusual patterns. If it detects something fishy — like an unusually large purchase or a transaction from a different country — it can flag it for further review.

Retailers can use big data to build models that predict which transactions are most likely to result in chargebacks. This proactive approach saves them money and helps keep prices lower for you.

Now that we’ve covered applications, let’s get technical. We’ll discuss where big data comes from, the numbers it produces, the cool innovations, and the tech that makes it happen.

And prior to this, we need to distinguish retail from e-commerce, which are very similar things but do differ in one main aspect.

E-Commerce vs. Traditional Retail

Simply, traditional retail happens offline, and e-commerce happens digitally. E-commerce is retail, but offline retail is not e-commerce.

In the e-commerce corner, we have a virtual shopping universe, where the stores never close, and your favorite products are just a click away. It’s a realm of digital transactions, algorithms, and personalized recommendations that shape your shopping journey.

Traditional retail, on the other hand, thrives in the physical world, where storefronts beckon you with tangible products to touch, try on, and take home. It’s about store layouts, foot traffic, and the art of engaging with customers face-to-face.

Some retailers operate in both worlds. Thus, if you are a case, consider the differences in success metrics tracked with big data for retail and e-commerce .

Success Metrics of Big Data in E-Commerce & Retail

To appreciate the synergy of big data and retail, let’s truly recognize the unique metrics that both offline retail and e-commerce utilize. In reality, tracking these metrics and sticking to their best values lets retailers build successful strategies regarding inventory management, pricing, and other areas mentioned before.

E-Commerce Metrics

Traditional retail metrics.

The data composing these metrics comes from a variety of specific sources, such as web analytics tools, sales records, POS systems, financial statements, and more. The ability to combine diverse data sources and track metrics effectively has brought many leaders to really innovative solutions.

How Big Data Analytics in Retail Market Drives Innovation

Alright, let’s get cozy with a quick and captivating story.

Imagine you’re in the heart of Silicon Valley, where technology innovation blooms like wildflowers in spring. Here, startups are reimagining retail using big data.

DreamAgility is rewriting the playbook on product data. Using Visual AI, they swiftly generate precise, multilingual product data from images, saving time and money that would otherwise be spent on manual data entry. Retailers are experiencing triple-digit sales growth, all while keeping costs in check, a rare feat in today’s competitive market.

Then there’s Obsess , an experiential e-commerce platform. They’re turning websites into immersive 3D virtual stores. It’s like stepping into a real shop, all from the comfort of your screen.

But here’s the mind-bender Spark Neuro . They’re decoding your brainwaves to understand your response to content. It’s not science fiction; it’s science-meets-commerce. They measure your attention span and emotional reactions, giving retailers unprecedented insights into what keeps you engaged.

These startups are creating more engaging shopping experiences, saving time and money, and even diving into the depths of our brains to understand what truly captivates us.

But what is the tech behind all this?

Big Data Tools for Retail

Let’s take a quick look at how retailer tools handle, store, and make the most of the data, with a nod to some key technologies in the mix:

Data Warehousing: Retailers turn to data warehousing solutions like Amazon Redshift and Snowflake. These platforms are like digital vaults, expertly organizing vast amounts of data, both structured and unstructured, with impressive efficiency.

Data Analytics Platforms: Tools such as Tableau and Power BI are the retail detectives, extracting precious insights from heaps of data. They’re the secret behind informed decision-making, helping retailers stay ahead of the curve and startups borrow the best practices and templates.

Machine Learning and AI: Retailers use machine learning and AI to forecast consumer behavior, fine-tune pricing strategies, and create personalized shopping experiences. It’s like having a crystal ball for retail.

Customer Relationship Management (CRM): Think of CRM software like Salesforce and Creatio as the retail memory banks. They manage and organize customer data, enabling retailers to tailor their marketing efforts for maximum impact.

Inventory Management Systems: Systems like SAP Integrated Business Planning and Oracle Retail are the backstage heroes. They provide real-time inventory data, ensuring that products are always in the right place at the right time, simplifying stock management.

Web Scraping: This is where the secret police come into play. Technologies like web scraping let retailers gather data from external sources, such as competitor prices and customer reviews without huge manual effort.

We hope this gives a rough overview of big data tools and techniques that deliver exceptional customer experiences and propel business growth in today’s retail.

Learn more about Retail Data Analytics Techniques, Software, and Advantages

Case Studies: Netflix, Tesco, Walmart

While it’s true that well-known industry giants like Netflix, Tesco, and Walmart often dominate the spotlight in retail and e-commerce success stories, it’s essential to remember that the power of big data extends to SMBs as well.

Any retail business, regardless of its size, can produce successful use cases of big data by implementing a solid data strategy in its workflow.

Netflix: Personalized Content Pioneer

Netflix, although not your typical online store, has completely changed the way we enjoy entertainment. At its heart is a data-powered approach that customizes the viewing experience for millions of subscribers worldwide.

Netflix employs advanced algorithms that dig into your watch history, your likes and dislikes, and even the selections of folks who share your taste. This data forms the bedrock of its uncanny ability to suggest movies and TV shows tailor-made just for you. It’s not just about what’s trending; it’s about what’s perfect for you in the moment.

This high level of personalization keeps viewers hooked and happy, ultimately driving Netflix’s impressive growth and customer loyalty.

Tesco: Reducing Food Waste with Supply Chain Optimization

Tesco, one of the world’s retail giants, showcases the tremendous influence of data analytics in supply chain management. To cut down on food waste and streamline its supply chain, Tesco places great emphasis on data analysis.

By tapping into sources such as point-of-sale systems and inventory tracking, Tesco obtains up-to-the-minute information on product demand. This allows them to manage their inventory smartly, lowering the chances of perishable items getting wasted.

Through data-driven inventory choices, Tesco not only reduces its environmental impact but also delivers fresher products to customers, elevating their shopping experience.

Walmart: Data-Driven Inventory Management and In-Store Experience

Walmart, another retail giant with a vast physical presence, is a prime example of how big data can transform traditional supermarkets. Walmart uses data analytics extensively for inventory management and demand forecasting.

Through the analysis of historical sales data, weather forecasts, and even social media trends, Walmart ensures that its stores are well-stocked with the right products at the right time. This approach minimizes stockouts and overstocking, saving costs and enhancing the customer experience.

But their journey doesn’t stop there. Walmart also utilizes data to optimize its in-store experience. For instance, it uses the Check Out With Me program, which equips employees with mobile apps to provide speedy checkout assistance to customers anywhere in the store.

Whether it’s personalizing content at Netflix, reducing food waste at Tesco, or optimizing inventory management and in-store experiences at Walmart, big-data retail examples are shaping the future of these businesses and their interactions with customers.

Now, let’s dive into the less exciting but important subjects of security, ethics, and risks tied to using big data in retail. It’s like the “vegetables” of our retail data discussion — not the most fun, but necessary!

Ethics and Risks of Big Data in E-Commerce

In fact, these issues matter in any data-driven initiative. We just once again remind you that having all perks comes with great responsibility and potential hurdles.

Data Privacy and Security

In the era of big data, safeguarding customers’ personal information defines the brand’s reputation and user real safety. E-commerce and retail companies must navigate the ethical concerns surrounding data privacy and security, ensuring that sensitive data is protected from breaches and misuse.

As we checked out those innovative startups earlier, you might’ve noticed they’re diving into some pretty private space, like analyzing brainwaves. The rules and regs on such things are usually lagging behind. Technology zooms ahead, and the rule-makers are still catching up. So, it’s a bit of a Wild West when it comes to ethics and privacy.

Data Integration

As retailers collect vast amounts of data from various sources, the challenge lies in integrating this data effectively. Ensuring that data is accurate, consistent, and accessible across different systems is essential for informed decision-making.


The scalability of online retail platforms to handle increasing data volumes is crucial. Rapid growth can strain existing infrastructure, affecting website performance and customer experiences. Ethical concerns arise when businesses struggle to meet demand due to scalability limitations.

The field of big data requires specialized skills. Retailers may face challenges in finding and retaining talent with the necessary expertise in data analytics, machine learning, and data security, creating an ethical responsibility to invest in skill development.

Lack of Developers

Find out how to deal with the lack of IT talents without compromising project delivery.

Still, securing big data and keeping it ethical is worthwhile as its importance continues to grow.

Now, as we move on from the not-so-fun stuff, let’s wrap up with a final word on the promising future of big data analytics in retail. It’s all about staying confident and innovative as we step into this exciting landscape!

Trends in Big Data and Retail

Imagine the shopping life in the upcoming years. You enter the world of IoT and e-commerce. Your smart fridge might reorder groceries for you, and your wearable might suggest new workout gear when it senses you’ve been hitting the gym regularly. IoT is all about making tech feel more human, bridging that gap between the digital and the personal.

Now, let’s talk trust. With all these online transactions, blockchain changes the way they happen. It adds layers of security and transparency, making you feel even safer when you click that Buy Now button.

Have you ever considered shopping in a virtual store? Well, that’s where v-commerce comes into play. It’s like stepping into a digital mall where you can interact with products before you buy them.

And speaking of interacting, imagine trying on clothes virtually with AR/VR. You can see how that shirt looks on you without ever leaving your room. It’s changing the way we shop for fashion.

When it comes to supply chains, predictive analytics is making waves. Retailers are using data to predict what and when you’ll want to buy next, ensuring products are in stock when you need them.

But it’s not just about convenience; it’s about going green too. Green e-commerce is all about sustainable practices, from eco-friendly packaging to reducing emissions in delivery.

And let’s not forget about crypto. Some forward-thinking retailers are even accepting cryptocurrencies as payment, opening up a whole new world of possibilities.

There you go — a glimpse of the trends that will shape the retail and e-commerce setup with big data in the coming years. Globally, it’s a real transformation of our ordinary routines and personal lifestyles.

So, we’ve explored the incredible analytics potential, innovations, and ethical considerations that come with big data in retail. From personalized shopping experiences to supply chain optimization, it’s clear that big data is reshaping how we shop and do business.

Stay tuned for more fascinating insights, and if you’re hungry to integrate big data in your retail project, don’t forget to drop us a line, so you get an extensive consultation on how big data fits you. See you in the next chapter!

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Explore how retail data analytics improves the shopping experience, optimizes inventory, safeguards data privacy, and more.

Explore the potential of retail business intelligence, learn how to enhance customer experience, and streamline business operations to gain a competitive edge.

Unleash the power of business intelligence and discover key BI tools to transform your big data analytics and gain a competitive edge.

Read up on the key differences between data warehouses and data lakes so that you can determine which might best suit your company.

Discover why data science is a must for modern-day enterprises and what pitfalls leaders need to be aware of prior to starting this kind of software project.

Dive into the data warehouse architecture topic so you are prepared for the implementation of new data storage systems.

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Information and Communication Technology for Intelligent Systems pp 469–477 Cite as

Big Data Analytics in Retail

  • Shubham Lekhwar 5 ,
  • Shweta Yadav 5 &
  • Archana Singh 5  
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  • First Online: 15 December 2018

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 107))

The main objective of this paper is to examine the application and benefits of big data analytics in retail industry and to explore the opportunities and possibilities ushered by big data in retail industry. Retail has its own share of data tsunami to grapple with. As in any other field, big data in retail presents both challenges and opportunities. Today, analyzing the data and relying on retrieved information more than ever to inform, test, and design strategies have become a key to smart retailing. The study is based on different sources of secondary data and thus is descriptive and qualitative in nature. The paper also arrays various challenges and issues that the major part of retail industry is facing. In the end, the paper discusses the possible ways in which these challenges and issues can be minimized and how retail can start with big data project and can make effective use of big data to increase customer involvement and improve bottom line.

  • Big data analytics
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Shubham Lekhwar, Shweta Yadav & Archana Singh

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Lekhwar, S., Yadav, S., Singh, A. (2019). Big Data Analytics in Retail. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-13-1747-7_45

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The importance of big data in retail

by Nick Schwalbach | Dec 14, 2022 | Data Management , Experts

Here are the top things you should know when it comes to the importance of big data in retail:

What is big data?

  • How big data is being used in retail?

How do retailers collect data?

Real-life examples of retailers using big data, how big data is transforming retail.

We all know that data-driven research and development is the key to success in an ever-growing digital age. And this is no different in retail.

Since the start of the online retail boom, brick-and-mortar stores have often found it difficult to keep pace with the speed and convenience of online deliveries.

However, big data analytics gives physical stores a unique springboard to success, opening the door to improved customer experience and upselling opportunities that even digital competitors can’t match.

Now, both business models are thriving and enjoying greater efficiency and profitability – namely because of the many retail analytics solutions now available.

Put simply, big data analytics is the collection and interpretation of information on a grand scale.

Computer algorithms identify patterns and trends in retail data, which can then be used in conjunction with qualitative data on typical human behavior, interactions and experiences.

This gives individuals and companies tangible data that—with the right software, resources and knowledge—can be used effectively to reveal more about the habits of their customers.

Big data can also be defined as a mass increase in the volume, variety and velocity of data coming in. This is known as “the three Vs” of big data:

Volume – Retail data is often vast and unstructured. Without relevant resources, staff can be left to draw findings from this data manual, which is often inefficient and can be inaccurate. Big data analytics solutions automate this responsibility, generating quick and accessible findings that drive actions.

Variety – With great strides in technology in recent decades in how and where we can collect information, retail data takes many more shapes and forms than ever before, so businesses must be wary.

Velocity – The speed at which data arrives is also faster than ever before. This means dedicated teams need to react quickly to extract value from that data and act on it in real time.

How big data is being used in retail

Big data analytics provides retailers with so much valuable and actionable information that it’s now critical for companies in almost every decision.

To start, big data analytics help retailers understand customers. In brick-and-mortar stores, this means everything from which POS displays are selling the best to the directional shopping habits of customers.

Online, big data analytics helps predict upcoming trends and which SKUs each regional store will need to stock to remain competitive year-round.

Whether it’s monitoring social media trends for the latest “buzz” or making sure stock matches seasonal demand, big data analytics reveals the exact stock businesses need, and how much, ahead of time.

As well as helping businesses to improve the customer experience, big data analytics in retail is used to drastically boost efficiency. Many companies use cloud data solutions to track inventory levels and sales figures in real time, and they also use these solutions to predict future demand more accurately.

Big data is increasingly used to personalize the online shopping experience, too. For example, online retailers use data-driven algorithms to provide shoppers with product recommendations – based on their purchase history – to add to their baskets pre- and post-checkout.

With so much data now available to retailers, it needs to be collected in many ways. Retailers can either ask for data directly – via email address and phone number forms for marketing purposes – or go through more indirect channels.

When consumers click on a website, they’ll be asked to accept tracking cookies. These are chunks of data that attach themselves to the user’s unique browsing ID, giving websites an idea of how long they’re browsing, which pages and products they’re looking at and what they buy. This information then helps companies tailor their marketing efforts.

Retailers can also tap into third-party data from suppliers. This providesinformation on consumer habits to streamline the online experience.

Brick-and-mortar stores can also collect internal data. Point-of-sale data collection is key in managing which products need to be in specific regions to match consumer demand year-round.

Big data analytics can be used to streamline the order process as well, primarily in EDI systems to provide more data points to shipping teams throughout the supply chains.

EDI software helps keep everything from orders and invoices to shipping notices in one easy-to-use hub. Big data analytics provides more data on any external factors that could interfere with anyone of these processes, letting companies respond quicker.

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Big data in retail underpins the growth of every successful modern business and we’re seeing it being integrated into businesses’ strategies in real-time.

For example, retail giant Walmart is developing the “world’s largest private cloud,” with algorithms built to track data on inventory, transactions, and competitor activity. This allows them to respond to market changes almost instantly.

Some companies even reap the rewards of mutual collaboration. In a seemingly confusing collaboration between Pantene, The Weather Channel, and supermarket giant Walgreens, Pantene saw its sales skyrocket over 10% in Walgreens stores through its data-driven “haircast” project.

With the help of forecast data from The Weather Channel, retailers could market selected products based on seasonal changes and the weather forecast that week, driving increased sales.

Global giant Amazon has also perfected the art of data collection and application in its user recommendations. In fact, Amazon is so successful in using big data marketing and sales tactics, that 35% of its sales are generated from its customer recommendations algorithm.

It’s no secret that increased data unlocks a wealth of customer insight. More than ever, retailers can plan for inventory, stock, logistics and customer expectations with greater precision.

Big data analytics in retail not only has the potential to improve the operating margins of companies by 60% but revolutionize all areas of retail.

Big data analytics also shapes inventory management and logistics and provides detailed insights into customer habits. These are being used to drive sales, streamline the sales process with product recommendations and slicker payment options and to improve customer service across the board.

The role of big data in retail is also to identify potential bottlenecks and find work-around solutions before they have a chance to evolve into more significant issues, saving retailers the costs of downtime and disruptions.

Using as many data insights as possible helps retailers and supply chains manage inventory issues and potential disruptions, thereby improving customer satisfaction, brand loyalty, and revenue generation.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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A portrait of Molly Fulton, who sits in the waiting room of one of the urgent care centers she runs. She wears a blazer over a black blouse with her hands folded in her lap.

By Reed Abelson and Julie Creswell

An urgent care chain in Ohio may be forced to stop paying rent and other bills to cover salaries. In Florida, a cancer center is racing to find money for chemotherapy drugs to avoid delaying critical treatments for its patients. And in Pennsylvania, a primary care doctor is slashing expenses and pooling all of her cash — including her personal bank stash — in the hopes of staying afloat for the next two months.

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These are just a few examples of the severe cash squeeze facing medical care providers — from large hospital networks to the smallest of clinics — in the aftermath of a cyberattack two weeks ago that paralyzed the largest U.S. billing and payment system in the country. The attack forced the shutdown of parts of the electronic system operated by Change Healthcare, a sizable unit of UnitedHealth Group, leaving hundreds, if not thousands, of providers without the ability to obtain insurance approval for services ranging from a drug prescription to a mastectomy — or to be paid for those services.

In recent days, the chaotic nature of this sprawling breakdown in daily, often invisible transactions led top lawmakers, powerful hospital industry executives and patient groups to pressure the U.S. government for relief. On Tuesday, the Health and Human Services Department announced that it would take steps to try to alleviate the financial pressures on some of those affected: Hospitals and doctors who receive Medicare reimbursements would mainly benefit from the new measures.

U.S. health officials said they would allow providers to apply to Medicare for accelerated payments, similar to the advanced funding made available during the pandemic, to tide them over. They also urged health insurers to waive or relax the much-criticized rules imposing prior authorization that have become impediments to receiving care. And they recommended that insurers offering private Medicare plans also supply advanced funding.

H.H.S. said it was trying to coordinate efforts to avoid disruptions, but it remained unclear whether these initial government efforts would bridge the gaps left by the still-offline mega-operations of Change Healthcare, which acts as a digital clearinghouse linking doctors, hospitals and pharmacies to insurers. It handles as many as one of every three patient records in the country.

The hospital industry was critical of the response, describing the measures as inadequate.

Beyond the news of the damage caused by another health care cyberattack, the shutdown of parts of Change Healthcare cast renewed attention on the consolidation of medical companies, doctors’ groups and other entities under UnitedHealth Group. The acquisition of Change by United in a $13 billion deal in 2022 was initially challenged by federal prosecutors but went through after the government lost its case.

So far, United has not provided any timetable for reconnecting this critical network. “Patient care is our top priority, and we have multiple workarounds to ensure people have access to the medications and the care they need,” United said in an update on its website .

But on March 1, a bitcoin address connected to the alleged hackers, a group known as AlphV or BlackCat, received a $22 million transaction that some security firms say was probably a ransom payment made by United to the group, according to a news article in Wired . United declined to comment, as did the security firm that initially spotted the payment.

Still, the prolonged effects of the attack have once again exposed the vast interconnected webs of electronic health information and the vulnerability of patient data. Change handles some 15 billion transactions a year.

The shutdown of some of Change’s operations has severed its digital role connecting providers with insurers in submitting bills and receiving payments. That has delayed tens of millions of dollars in insurance payments to providers. Pharmacies were initially unable to fill many patients’ medications because they could not verify their insurance, and providers have amassed large sums of unpaid claims in the two weeks since the cyberattack occurred.

“It absolutely highlights the fragility of our health care system,” said Ryan S. Higgins, a lawyer for McDermott Will & Emery who advises health care organizations on cybersecurity. The same entity that was said to be responsible for the cyberattack on Colonial Pipeline, a pipeline from Texas to New York that carried 45 percent of the East Coast’s fuel supplies, in 2021 is thought to be behind the Change assault. “They have historically targeted critical infrastructure,” he said.

In the initial days after the attack on Feb. 21, pharmacies were the first to struggle with filling prescriptions when they could not verify a person’s insurance coverage. In some cases, patients could not get medicine or vaccinations unless they paid in cash. But they have apparently resolved these snags by turning to other companies or developing workarounds.

“Almost two weeks in now, the operational crisis is done and is pretty much over,” said Patrick Berryman, a senior vice president for the National Community Pharmacists Association.

But with the shutdown growing longer, doctors, hospitals and other providers are wrestling with paying expenses because the steady revenue streams from private insurers, Medicare and Medicaid are simply not flowing in.

Arlington Urgent Care, a chain of five urgent care centers around Columbus, Ohio, has about $650,000 in unpaid insurance reimbursements. Worried about cash, the chain’s owners are weighing how to pay bills — including rent and other expenses. They’ve taken lines of credit from banks and used their personal savings to set aside enough money to pay employees for about two months, said Molly Fulton, the chief operating officer.

“This is worse than when Covid hit because even though we didn’t get paid for a while then either, at least we knew there was going to be a fix,” Ms. Fulton said. “Here, there is just no end in sight. I have no idea when Change is going to come back up.”

The hospital industry has labeled the infiltration of Change “the most significant cyberattack on the U.S. health care system in American history,” and urged the federal government and United to provide emergency funding. The American Hospital Association, a trade group, has been sharply critical of United’s efforts so far and the latest initiative that offered a loan program.

“It falls far short of plugging the gaping holes in funding,” Richard J. Pollack, the trade group’s president, said on Monday in a letter to Dirk McMahon, the president of United.

“We need real solutions — not programs that sound good when they are announced but are fundamentally inadequate when you read the fine print,” Mr. Pollack said.

The loan program has not been well received out in the country.

Diana Holmes, a therapist in Attleboro, Mass., received an offer from Optum to lend her $20 a week when she says she has been unable to submit roughly $4,000 in claims for her work since Feb. 21. “It’s not like we have reserves,” she said.

She says there has been virtually no communication from Change or the main insurer for her patients, Blue Cross of Massachusetts. “It’s just been maddening,” she said. She has been forced to find a new payment clearinghouse with an upfront fee and a year’s contract. “You’ve had to pivot quickly with no information,” she said.

Blue Cross said it was working with providers to find different workarounds.

Florida Cancer Specialists and Research Institute in Gainesville resorted to new contracts with two competing clearinghouses because it spends $300 million a month on chemotherapy and other drugs for patients whose treatments cannot be delayed.

“We don’t have that sort of money sitting around in a bank,” said Dr. Lucio Gordan, the institute’s president. “We’re not sure how we’re going to retrieve or collect the double expenses we’re going to have by having multiple clearinghouses.”

Dr. Christine Meyer, who owns and operates a primary care practice with 20 clinicians in Exton, Pa., west of Philadelphia, has piled “hundreds and hundreds” of pages of Medicare claims in a FedEx box and sent them to the agency. Dr. Meyer said she was weighing how to conserve cash by cutting expenses, such as possibly reducing the supply of vaccines the clinic has on hand. She said if she pulled together all of her cash and her line of credit, her practice could survive for about two and a half months.

Through Optum’s temporary funding assistance program, Dr. Meyer said she received a loan of $4,000, compared with the roughly half-million dollars she typically submits through Change. “That is less than 1 percent of my monthly claims and, adding insult to injury, the notice came with this big red font that said, you have to pay all of this back when this is resolved,” Dr. Meyer said. “It is all a joke.”

The hospital industry has been pushing Medicare officials and lawmakers to address the situation by freeing up cash to hospitals. Senator Chuck Schumer, Democrat of New York and the chamber’s majority leader, wrote a letter on Friday, urging federal health officials to make accelerated payments available. “The longer this disruption persists, the more difficult it will be for hospitals to continue to provide comprehensive health care services to patients,” he said.

In a statement, Senator Schumer said he was pleased by the H.H.S. announcement because it “will get cash flowing to providers as our health care system continues to reel from this cyberattack.” He added, “The work cannot stop until all affected providers have sufficient financial stability to weather this storm and continue serving their patients.”

Audio produced by Jack D’Isidoro .

Reed Abelson covers the business of health care, focusing on how financial incentives are affecting the delivery of care, from the costs to consumers to the profits to providers. More about Reed Abelson

Julie Creswell is a business reporter covering the food industry for The TImes, writing about all aspects of food, including farming, food inflation, supply-chain disruptions and climate change. More about Julie Creswell


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