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New Consumer Research Technology for Food Behaviour: Overview and Validity

Garmt dijksterhuis.

1 Wageningen Food and Biobased Research, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands; [email protected]

René de Wijk

Marleen onwezen.

2 Wageningen Economic Research, Wageningen University and Research, P.O. Box 9101, 6700 HB Wageningen, The Netherlands; [email protected]

Background: the last decade has witnessed an explosion of new consumer behaviour research technology, and new methods are published almost monthly. To what extent are these methods applicable in the specific area of food consumer science, and if they are, are they any good? Methods: in this paper, we attempt to give an overview of the developments in this area. We distinguish between (‘input’) methods needed to shape the measurement context a consumer is brought in, e.g., by means of ‘immersive’ methods, and (‘output’) methods that perform measurements proper. Concerning the latter, we distinguish between methods focusing on neuro-science, on psychology, and on behaviour. In addition, we suggest a way to assess the validity of the methods, based on psychological theory, concerning biases resulting from consumer awareness of a measurement situation. The methods are evaluated on three summarising validity criteria; conclusions: the conclusion is that behavioural measures generally appear more valid than psychological or neuro-scientific methods. The main conclusion is that validity of a method should never be taken for granted, and it should be always be assessed in the context of the research question.

1. Introduction

In recent, years many new technological research methods have been proposed, tested, and published that enable the study of consumer food behaviour. The developments follow each other very rapidly, and by now, a very diverse palette of research methods has become available for consumer researchers to choose from. On the one hand, this results in new and promising possibilities to study consumer food behaviour; on the other hand, it may also result in unclarity for researchers. It can be difficult to decide which methods are available for specific research questions and how useful and valid they are. An overview of a wide range of methodologies is needed. For food consumer scientists specifically, it is of interest to find out whether new methods are applicable in their area and if they provide valid measurement results.

A main question regarding new technologies is how to rate the validity of the new technologies. In particular, some of the novel technologies may change the way consumers are aware of being part of a study. This paper concerns the validity of new consumer research technologies, as applied in a food behaviour context. Therefore, we introduce three validity criteria based on psychological theory concerning biases resulting from the awareness a consumer has of a measurement situation (see Appendix A ). The measurement methods introduced will be scored on these criteria in order to enable a view on the validity of the methods. First of all, the three criteria are introduced to spawn discussion concerning validity of consumer research. Such discussion on the validity of consumer research is needed (e.g., [ 1 ]), as was also pointed out by Dijksterhuis [ 2 ] in a critical review concerning the high failure rate of new food products.

1.1. Input and Output Technologies

The current study adds to the literature by providing an overview of novel technologies in the area of food consumer behaviour. We broadly differentiate between input and output technologies. Some technologies are used to bring the consumer into a certain situation in order to create a mind-set as close to real life as possible. Different contexts, operating at the input side of a study, are typically the independent variables of a study. Although we acknowledge the presence of different types of input, as noted, for example, by the recent so-called EPI-cube (Embodiment-Presence-Interactivity, [ 3 ]), we will not further differentiate between them.

Other new technologies are used to measure consumer related variables in order to get a grip on consumer behaviour or responses; these work at the output side of a study and typically are the dependent variables of a study.

Providing information to a consumer in an experiment (or to a respondent in a survey) can happen in many ways. The input side of consumer research consists of the experimental situation, which can include the screen of an online survey, the instruction by an experimenter, the physical surrounding during a measurement or any context provided by means of VR and related technologies.

At the output side of consumer behaviour, several types of new technologies can be used to measure behavioural outcomes. Here, we distinguish three types of methods aimed at collecting data on a neuroscience, a psychological, or a behavioural level.

Neuroscientific measurements, are measurements related to neural activity. They often refer to neurophysiological measurement, be they CNS (Central Nervous System) or ANS (Autonomic Nervous System) based. CNS-based measurements often employ such technologies as EEG, fMRI, or MEG, and they often point at some form of cognitive processing. Other psychophysiological measurements, i.e., of a wide range of ANS functions, often indicate the execution of tasks.

Psychological measurements inquire about psychological traits or states of a human subject—in our case, a ‘consumer’. Despite the seemingly broad range of methods that could fall under this heading, we define them as measurements of mental phenomena. Self-reported, past, or prospected behaviour is also considered a psychological measurement.

Behavioural measurements refer to the observable behaviour of a consumer. Any movement that is somehow monitored can constitute a ‘behavioural measurement’. Typically, these are motor measurements such as gait, movement, agitation, but hand movement, eye-movement, face movement, or chewing behaviour also fall under this heading. Response time measurements are also an example of a behavioural measurement.

One recent interesting innovation exists in voice assistants. Typically, one may think of voice assisted device operation (car navigation, phone number calling, (very) smart TV programming, [ 4 ]). One can also think of types of smart devices, such as Google Assistant or Siri. These devices can be used for consumer research by asking questions or giving instructions to consumers to operate their devices. At the same time, these smart devices may collect data in the form of the responses consumers give to their instructions or questions. Ethical problems obviously lurk, as it is not always clear if a consumer wants his/her voice commands and responses to be recorded for subsequent analysis, even if anonymously.

1.2. Implicit and Explicit Methodology

Explicit consumer research methods are those methods that require some form of answering from consumers, often using conscious reflection. Consumers have to make explicit what they mean, why they act as they do, why they make some choice, etc. Hereby, they are conscious of what they are answering, and they may ponder their answer before they give it. In contrast, implicit methods do not require consumers to do this. The implicitness, here, refers to the fact that information concerning consumer behaviour is inferred from a measurement without the consumer knowingly having control over the outcome (cf. [ 5 , 6 ]). As an example, the amount eaten and the speed with which food is consumed can be seen as implicit measures of acceptance of the food.

One may be tempted to think that implicit methods always require consumers being unaware of the measurement, but this is not true. It is possible to have consumers explicitly report on some matters but without the research question being addressed in this matter. The answers of the consumers can next be studied to provide evidence of their ideas, opinions, or attitudes concerning the matter. This type of measurement is implicit, although consumers are to explicitly answer some questions. In this case, the implicitness of a consumer measurement concerns the consumer being unaware of the underlying research question, rather than in the way consumers are to provide data (which appears explicit). In Section 4 , such implicitness will be coupled with a method’s validity.

In De Houwer and Moors [ 7 ], it is argued that a measurement procedure (what we may call a research method in our context of food related consumer studies) cannot be named ‘implicit’. They rather reserve the term ‘implicit’ for measurement outcomes. This means that we could not talk about ‘implicit methods’ but of ‘implicit measurement outcomes’. De Houwer [ 8 ] suggested to equate ‘implicit’, in this sense, to the term ‘automatic’. However, most of the context of their discussions of implicitness are done in the context of (implicit) attitudes. This means that the constructs-to-be-measured are ‘implicit’ to the subjects. This applies to the IAT (Implicit Association Test, ref. [ 9 ] revealing implicit stereotypes, often used in the context of racial prejudices. In the context of our current paper, being food related consumer behaviour, the constructs-to-be-measured, themselves, are not necessarily to remain unknown to the subjects. Subjects may e.g., know they are consuming too much unhealthy food, to name an example. What they do not know is the underlying motivation for their food choice, so this motivation remains implicit. The type of research methods we refer to aim to understand, to explain, and, ultimately, to predict consumer food related behaviour.

Some authors seem to equate implicit measurements with physiological measurements. Often, such methods are indeed implicit, but implicit measurement is by no means restricted to this. De Wijk and Noldus [ 10 ], in their overview of implicit and explicit measures of emotions, list four (implicit) measurement types:

• measures that reflect the activity of the central nervous system, such as EEG and fMRI,
• measures of activity of the autonomic nervous system such as skin conductance and heart rate,
• expressive measures, such as facial expressions,
• behavioural measures.

One could argue that specific facial expressions, such as surprise, can also be the result of emotional processes and as such, partly fall under the second type, an autonomous reaction. The same can be said about behavioural measures. Posture or walking speed can be the result of, or correlate to, autonomic activity. What De Wijk and Noldus [ 10 ] make clear is that there seems to be no sharp distinction between implicit and explicit measurement.

In this paper, we suggest not to define implicit measurements in terms of the measurement process itself but rather, in terms of the way a measurement situation is set up.

2. Input Side: Context Providing Technologies

Food products are seldom consumed in isolation. Instead, foods are typically consumed in specific consumption situations, such as at home, in a restaurant, or at a canteen. These situations tend to shape our food experiences. Consequently, food preferences measured in a sensory laboratory may be different from preferences for the same foods measured in a real-life situation, as demonstrated by numerous studies. Many of these studies report higher preference ratings in real-world situations [ 11 ] or differences between several contexts [ 12 , 13 ], even though there are also some studies that report no difference [ 14 ].

Testing consumers in real-life consumption situations is often difficult because these situations tend to be noisy and offer little control over conditions, which may have unwanted effects on the measurements. Instead, there is a growing trend to recreate consumption contexts in the laboratory. Using so-called ‘immersive technologies’, these recreated contexts offer a compromise between real-life consumer experiences and tightly controlled laboratory conditions. ‘Immersion’ is a term used for what is thought to happen to consumers in a research situation where they are provided with a situational context that totally captures their attention—ideally to the point that they forget that they are in an experimental context. As advantages of ‘immersive technologies’, some have been mentioned:

• overcoming the ‘respondent burden’ (the degree to which a consumer finds participation difficult, time consuming, or emotionally stressful [ 15 ],
• increasing some statistical quality (validity, reliability, power) of the collected data [ 16 ],
• adding control to an experimental situation, while keeping the situation ‘ecological’,
• adding context to (online) surveys to enable more truthful (and hence valid) answers or to increase commitment from respondents.

In a way, telling a good story to a consumer, in an otherwise quiet testing room, can also be seen as an immersive ‘technology’. Reading a story from a paper or listening to a story told is so low-tech that we will not cover this in more detail here [ 17 ] (p. 30 on ‘Story Telling’ and p. 31 on ‘Sketchy descriptions’). In short, anything to have the subject mentally ‘leave’ the testing surrounding and enter the realm of their imagination can be seen as an ‘immersive’ technology. Short, specialised surveys have been developed to probe how deep consumers felt ‘immersed’ while being in such a situation [ 16 ].

In the below, we will introduce some recent (input) context providing technologies, ordered from a relatively low level of ‘reality’ (or ‘immersiveness’) to a high level.

2.1. Tabletop Technology: A Tablet Computer as an Expensive Coaster

To provide consumers with a special eating context while dining, tablet computers have been used. They can provide a special context to the food, and it’s feasible to apply them in this manner in food testing ( Figure 1 , left panel). However, other ways may be possible that are less cumbersome or expensive. Other technology, e.g., sound, has been provided with food in a similar vein ( Figure 1 , right panel).

( Left panel ): a tablet computer as a plate to hold your food. ( Right panel ): sound provision during eating (from [ 18 ], p. 316 and 329, resp.).

2.2. Virtualised Food Products

Using photogrammetry, a food product is totally visually digitalised and digitally recreated. The recreation may follow a (factorial) design, systematically varying aspects of the food product (e.g., its size, shape, colour, surface roughness, etc.), to be presented to consumers. The consumers next assess sensory properties, of course restricted to visual aspects, but they could assess expectations of other aspects, as texture or flavour. Gouton et al. [ 19 ] present an application using chocolate chip cookies and a comparison of simulated cookies with real cookies. Some differences were found, which Gouton et al. [ 19 ] suggest may depend on specific photogrammetric software settings.

2.3. VR Technology

A plethora of VR-related technologies has seen the light in the last decade. VR-glasses have been around for a while, and they can now be obtained against relative low costs. Tools and devices exist where one can insert a cell phone in a device ( Figure 2 ), and dedicated apps on the cell phone will assure a VR-presentation when wearing the device. Cardboard -fold your own- versions exist for under €1. These lend themselves for being sent to large groups of consumers, for in home, online survey testing, providing context through dedicated apps. Dynamic context, the typical VR-experience where one can virtually move around inside a surrounding, can be provided through these means. Many tools include integrated sound and, often, spatial sound effects.

( Left panel ): VR glasses, wherein one can slide a cell phone; ( Middle panel ) (reprinted with permission from OVR technology (Copyright OVR technology)) and ( Right panel ) (reprinted with permission from Takuji Narumi (Copyright Takuji Narumi)): a VR system enabling olfactory stimulation.

Taufik, Kunz, and Onwezen [ 20 ] conclude that VR-provided contexts often lead to measurement results comparable to their real life counterparts. In addition, they conclude that the technology also seems promising to lead consumers to change their behaviour. Fang et al. [ 21 ] point out that VR methodology can also help reduce the hypothetical bias (the difference between a real experiment and an imagined one) by introducing a form of realism to the measurement situation.

Adding odour, relevant in a food context, to a virtual surrounding is more of a challenge. Several devices have been developed, but maybe some should be called contraptions instead (see Figure 2 , rightmost panel). Advertisements exist, boasting about their ability to produce 1 ms short odour pulses and a time to switch between odours of 20 ms. When one realises that it may take some 300 ms, depending on many circumstances, for an odourant to reach olfactory receptors [ 22 ], such numbers look a bit over-the-top.

Other VR applications exist that use vibratory devices to simulate felt textures on the hand [ 23 ]. Straightforward applications in food related consumer science may not be in sight, or it must be the possibility to deliver vibratory stimulation in-mouth to simulate oral texture. Although technically feasible at this moment, it’s probably currently restricted to laboratory environments [ 24 ].

Bone conducting devices have been applied to record auditory and/or vibratory stimulation. In particular, in specific food related studies, where one may want to record the chewing sound as perceived by the chewer her/himself, and we know that chewing sound is, to a large extent, bone conducted sound [ 25 , 26 ]. They could also be used to provide food related auditory or vibratory stimulation, e.g., to adapt the sound perceived (vibrations felt) by consumers when chewing food (although we have not found papers in this area). A vibrating straw technology exists where no food is sucked through the straw, but a vibratory device can deliver the illusion of food streaming into the mouth [ 18 ].

Another technology is the development of ‘Sensory Reality pods’ ( Figure 3 ). Inside something that is best described as a ‘phone booth’, a subject takes place, puts on VR-goggles, and can, from within the Pod, be stimulated with sound, smells, air flows, and heat radiation. It provides a multisensory immersive surrounding. Applications in food science may be a bit farfetched at the moment, but they are certainly feasible. At this moment, one person at a time can be immersed, but in theory, several pods can be used simultaneously, if only cost were no issue.

A subject sitting in a ‘Sensory Reality Pod’ (Reprinted with permission from SENSIKS. Copyright 2019 SENSIKS).

2.4. AR Technologies

In AR (Augmented Reality) parts of the real physical surrounding of a consumer is integrated into the virtual surrounding. VR knows limited application in eating research or other applications due to the fact that consumers cannot comfortably interact with a real food. Many VR devices do not allow smelling, eating or drinking, and will spoil the immersion. In AR the interaction is provided through integrating an object into the virtual surrounding. A fully natural interaction with ones surrounding, in particular with the (food) object, is still not possible. The interaction will likely still feel alienating.

In the development of packaging, AR applications are clearly envisionable, as they allow visual interaction of a consumer with a packaging. Coupling visual AR to a haptic vibratory device (providing manual package texture simulation), one can imagine that the interaction may reach a level close to reality.

2.5. (Serious) Gaming Applications

All above mentioned examples can also be used in gamification applications. A computer game aims to give a subject a lively sense of immersion by providing a virtual context. Reality is not implied in the type of context (the game can be about the weirdest of worlds), but it is provided by the interaction with the environment by the moving and handling virtual objects, reactions of other persons (or alien entities) in the game, etc. The original aim of computer games is entertainment, but a shift to consumer research applications is feasible. Gamification has also been applied to make surveys more engaging [ 27 ]. This is also an application area that can be of use to any type of consumer research.

Applications in (food) marketing exist in an application where visitors of an entertainment park can plan ahead their visit, enabling the park to optimally locate their services [ 28 ]. In particular, in the context of online purchasing, there may appear a future for such applications.

Giving smell or taste feedback in gaming applications has also been suggested [ 29 ], although this appears a farfetched application, as, at this moment, it is still at a distance from use in consumer research.

2.6. Wall Projections/CAVEs

The mentioned alienation in AR may result in it not yet being used much in food related applications or in typical consumer science applications where many consumers are tested. An alternative is the projection of a surrounding onto the walls of a room or in a ‘CAVE’. A CAVE (a recursive acronym: Cave Automatic Virtual Environment) is a (small) room, where, onto the walls, an environment is projected, either by back projection (where the walls need to be translucent), normal projections by means of beamers, or using very large LCD-screens. WFBR (Wageningen Food and Biobased Research, one of the research institutes of Wageningen Research, part of WUR.) employs a CAVE-like projection room, where eight beamers can project images onto the walls of a normal room ( Figure 4 a). In addition to the visual projections, sounds can be played, and an odour dispersing unit is installed as well.

( a ) WFBR ‘Experience room’. Lower: projections of a beach ( b ) and a sushi restaurant ( c ). Note that props are used in the room to add to the immersion. (Reprinted with permission from WFBR. Copyright 2019 WFBR).

Figure 4 b shows the ‘Experience Room’ in action in a recent study where a beach environment was created. Note that props (towels, sand-coloured flooring) are used to add to the level of immersion in the simulated surrounding. The (c) panel of Figure 4 shows a recreated sushi restaurant, including real tables with menus and some (plastic) plants to help increase the reality level of the simulation [ 30 , 31 ].

One can imagine that there is no limit to the possibilities of providing (near real) projections with sound and odour. Room temperature, air humidity, etc., could be added, provided the budget to develop the technology is high enough.

2.7. ‘Non-Virtual’ Reality

A special case is the immersion in a rebuilt environment, as was done by Holthuysen et al. [ 11 ], who rebuilt an airplane fuselage in a lab room, to test air catered food items, complete with airplane engine noise. It was immersion, but it is not the type of consumer technology we’re addressing here.

3. Output Side: Measurement Technologies

In the sections below, several ways of collecting consumer related data are introduced, based on them collecting variables from neurophysiological, psychological, or behavioural origin. We will present different methods in the three areas mentioned. Some will be mentioned only scantily, as they may be very new, hardly used, or are on the fringe of what we can see as promising new consumer measurement methods in the food area.

3.1. Neuro Scientific Measurement Technologies

Neuroscientific variables can be of an overwhelming complexity, in addition to them being plentiful. They have in common that they attempt to measure neuronal correlates of consumer behaviour relevant to the area at hand. We will not introduce such techniques as EEG, fMRI, MEG, ANS-measurements in some detail, nor psychophysiology in general, but we list specific ways in which some of these technologies have recently been put to use to understand consumer food related behaviour. All these measurement techniques have that they require a connection to the human body in common. Although they do often not have to penetrate the skin or require otherwise invasive medical procedures, their impact on normal functioning mostly makes them listed as potentially invasive techniques. They will, thus, require some form of Ethical Clearance.

Innovation in food consumer neuro science may not only lie in developing new technology, in addition to the many existing methods that exist, but also in their combination. According to Niedziela and Ambroze [ 32 ], these methods should be used in addition to, not instead of, established food consumer methodology. It is useless to employ an expensive and complex measurement that is equivalent to liking, when a simple liking question may provide the same result.

Another innovation is taking neuroscientific measurements outside the lab into the real world, which has to do with making the technology portable. EEG-caps have become, more-or-less, portable and allow for this. Ambient EEG measures may introduce additional noise, possibly rendering measurement results even more difficult to interpret. In addition, the validity of EEG brain activity assessments is not always known. A recent paper shows that an unequivocal interpretation of EEG measures is not always possible. Eijlers et al. [ 33 ] measure arousal, resulting from looking at magazine advertisements (including food ads) using EEG, and conclude that their findings cannot be taken to show ad effectivity.

A recent neurophysiological development is fNIRS (functional Near InfraRed Spectroscopy) applied to brain activity. It is a technology where NIR radiation is sent through the skull by an optode and picked up after it has been reflected by brain tissue. The hemodynamic response of the brain tissue affects what can be picked up, which is related to the activity of the brain tissue. This technology has been applied in consumer research. It is claimed that it is more portable and easier to handle than other neurophysiological measurements. However, it appears to be, to date, removed from practical (portable) applications in consumer science, and it is a laboratory tool still (but see [ 34 ].

Augmenting online measurements with neuro scientific measurements (including ANS) has also become feasible. Using the camera in respondents’ (laptop) computers, heart beats can be inferred from a colour change of the face or forehead. Other ANS-devices, or even EEG, can be coupled to respondents’ computers, but this will bring some additional complexity still and is not yet applicable for large consumer samples.

An example of an innovative ‘field’ application combining several above mentioned (near) portable technology can be found in Brouwer et al. [ 35 ]. They combined measuring EDA (Electro Dermal Activity), ECG (Electro CardioGraphy) and EEG, allowing for extraction of several parameters, while their subjects were cooking and tasting. The subjects were voice-instructed to follow a strict cooking and tasting protocol. They cooked a meal with either chicken or mealworms, which was only revealed to them during cooking. Brouwer et al. [ 35 ] report they can, from the neurophysiological data, predict, with 82% accuracy, what dish a subject was cooking (mealworms or chicken). The aim of this study was to provide an implicit measurement method of affect or emotion during an actual cooking experience. In a follow up study, Brouwer et al. [ 36 ] collected measurements of facial expressions and wrist accelerometry in addition. Figure 5 shows some subjects in the Brouwer et al. study [ 36 ], obviously in a laboratory setting, not in their own kitchens.

Subjects in the study of Brouwer et al. (2019): ( a ) subject cooking (photograph from [ 36 ], Figure 1, p. 5. Reprinted with permission from ref. [ 36 ]. Copyright 2019 A.-M. Brouwer); ( b ) dish with mealworms in the frying pan; ( c , d ) subjects’ faces on seeing the mealworms (Reprinted with permission from A.-M. Brouwer. Copyright 2019 A.-M. Brouwer).

Biological Measurement Technology

For sake of completeness, we’ll list some recent developments that may not strictly fit the moniker of ‘neurophysiology’ but are biological in their origin. One recent development is a portable glucose level sensor ( Figure 6 ). More of these types of sensors have become available, and they can link (via Bluetooth) to a smartphone app or to other devices. Although some say their device is ‘non-invasive’, a short needle is to penetrate the skin still. Nevertheless, consumers sometimes report that they forget they are wearing the device. Finding out if truly non-invasive versions [ 37 ] exist (and are reliable) will require additional literature search.

Wearable glucose sensor, to be worn on (and in) the arm. (Reprinted with permission. Copyright 2022 Marieke Ubachs).

Some ‘smart watches’ can provide biological or psychophysiological data as well. Obviously, wearers of these devices are not continuously aware of the measurements being made.

Breathalysers have also been deployed to non-invasively measure physiological parameters from subjects. To what extent this is applicable, or has been applied, in a food related consumer context is not known to the authors. Obviously, it can potentially provide relevant diet-related information, including that related to flavour release, or flavour retention, in mouth or throat.

Another recent development, of quite a different nature, is what some may call ‘consumer genomics’, as in the paper by Masih and Verbeke [ 38 ], which covers the relationship between the expression of certain opioid receptors and the results of individuals on the PANAS mood scale [ 39 ]. To what extent claims in this field can already be translated to real life consumer behaviour in the food area remains to be studied.

3.2. Psychological Measurement Technologies

Any survey, or set of questions, can be given to a group of consumers, thus constituting a ‘psychological measurement’. Even when new questionnaires or new psychological scales are developed, the technology is hardly to be called new. Perhaps, with adaptive on line surveys, e.g., of the conjoint type, one can speak of some (technological) innovation. Newer developments are found in Big Data and AI technology that enable ‘very’ interactive surveying, where a path through a set of items may depend on the answers of many other respondents.

In this section, we will present several rather different technologies that aim to obtain information of consumers’ attitudes towards, or reported choices of, food. Many of the newer technologies are often online extensions of earlier developments. Sensometrics, covering statistics, data collection, and experimental design, is a rapidly evolving field from which we list a few innovations. AI and Big Data oriented applications form such a vast and rapidly expanding field that we decided not to include this area.

3.2.1. Text and Web Scrape Technology

Automatic interpretation of texts, either from the web or otherwise, made available is a relatively new and currently expanding technology, also called text mining, or text analytics. It is defined by Hearst [ 40 ] as ‘the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources’. It has to be set apart from text search, as this refers to the finding of things you are looking for, so things that you already know something about (e.g., that they exist). Text mining aims to discover new things, previously unknown information, from any text source. It is already being applied in food consumer contexts [ 41 ]. In Figure 7 (taken from [ 41 ]), the size and quality of text sources is shown. It is presented here merely to provide an indication of the possibilities of the technology, as well as for consumer research in our field. The figure shows that the best quality is provided by the scientific publishers, and the lowest is by general social media sources. In between is the internet, where ‘anything’ can probably be found.

Text sources’ quality and size, for text mining purposes, in a food and nutrition context (Figure 2 from [ 41 ]).

These days, text mining will likely also be possible from recorded spoken text, enabling even greater ‘mining’ possibilities.

Web-scraping is becoming a standard tool, provided by several agencies in many different contexts. A relatively recent application is the automated mining of food and recipe related data on the web. Another related technology concerns the analysis of texts from consumers that have been asked to describe a certain product. This text can be automatically processed [ 42 ]. In this way, automatic analysis of consumer prompted, written comments on specific products may reveal underlying ideas and feelings that consumers have about specific products.

3.2.2. Surveys and Ecological Momentary Assessment (EMA)

Online survey research is not new. Every thinkable set of questions can be asked to consumers, both in offline and online surroundings. The easier it is to ask questions, the easier it is to answer them, and the more pregnant the following dictum becomes: “The biggest problem in asking consumers a question, is that you will get an answer” (after E.P. Köster, personal communication).

Additional measures of an implicit nature (mouse clicking, answering speed, etc.), can also be collected online, in addition to the answers given in the survey. These are behavioural/motor, rather than psychological, measurements.

Alerting consumers at certain moments to give an answer to a question, as is possible via smartphone technology, enables a new way of surveying. When consumers are required to list their activities or food consumption at random moments during the day, a bias that may arise from filling out questions at fixed moments on the day will be countered (e.g., using the Foodprofiler [ 43 ]) or the Traqq app, [ 44 ]). Another development is EMA (Ecological Momentary Assessment [ 45 , 46 ]), the sending of surveys to consumers’ smartphones when they are in a certain surrounding. Obviously, they have to first agree to their smartphone providing their location, and other parameters, to the investigators. If this is agreed, questions can be sent to consumers depending on a host of environmental parameters. Based on the location (in a supermarket, in a city, at a bus-station, etc.), time of day, weather conditions, previous activities (cycling, walking, shopping, relaxing), specific questions can be sent to the consumer. In this way, the investigators can ask questions appropriate to the real context the consumer is in. This technology also allows to interactively adapt a consumer’s set of questions, depending on the answers of another specific consumer or a consumer segment.

3.2.3. Online Interaction with Consumers

Many ways exist to interact with consumers that are not in physical proximity, but they are online. Currently, there are online versions of many consumer face-to-face research methods.

In Table 1 , traditional and online focus groups are compared (adapted from [ 47 ], Table 6.1, p. 137).

Comparison of traditional focus groups and their online version (adapted from [ 47 ], Table 6.1, p. 137).

Traditional focus groups are a well-established tool for inventorying consumer opinions in a range of areas. Online focus groups [ 47 ] are a relatively new development. The participants in such a focus group attend the meeting from behind their computer, as does the moderator. Nowadays many people have, at least, some experience with online meetings, so online focus groups will not be alienating to most consumers. It must be added that video/audio connections only cannot replace the full gamut of non-verbal communication that a moderator will use in face-to-face meetings. Validity issues—not uncommon in focus groups—remain, and they will have to be closely scrutinised. Special software has been developed to aid the interpretation of focus group transcriptions [ 48 ].

Netnography is ethnographical methodology applied to consumers’ internet behaviour. The online activities of consumers can be surveyed, or otherwise investigated, all within the limits of privacy legislation, which can be a complicating and contentious area. In addition to individual consumers’ net-behaviour, consumers subscribing to communities and services can be studied. When, say, a consumer is following recipe-sites that contain vegetarian recipes, in addition to traditional ones, a food producer may be interested in knowing the reaction of this consumer community on certain plant based products to replace dairy.

3.2.4. Sensometrical Methods

Sensometrics is the field that concerns itself with research methodology, statistics, and experimental design, especially aimed at collecting and analysing data in the sensory and consumer sciences ( www.sensometric.org , accessed on 4 January 2022). The remit of the field reaches beyond food to include all instances where humans (in trained panels, or as consumers) are used to collect data concerning their perception, or liking, of product-related stimulation. Originally, these products were limited to food products and food-related stimuli, and the panels typically were sensory panels. Over the years, the field included ‘sensory and consumer’ studies of non-food products, including a wide a range of products, spanning home and personal care to cars. The Sensometric Society organises a conference every second year, and the contributions are published in the Elsevier journal Food Quality and Preference. They are a rich source of methodology and statistics in the area of sensory and consumer science. New methods, both for data collection and for data analysis, are continuously published, too much to even attempt to summarise in this review.

An example of a typical new(ish) method from this area is the technology whereby consumers can sort items on a screen. The position of the items after the sorting can be totally free, in groups, or following a pre-specified criterion (‘Sort these bottles into three groups based on which you find belong together.’). The numerical position on the screen or the group structure can be analysed using the apparatus of multivariate methods [ 49 ]. In particular, MDS [ 50 ] or other methods, allowing mapping and matching of spatial configurations of stimuli [ 51 ], are apt for the analysis of this type of data.

Almost every week, a new AI-based method is published to find structure in large data sets. Additionally, consumer (food) related data is collected in ever increasing amounts and with increasing speeds. AI-methods may be necessary to enable analysis of these amounts [ 52 ], there’s simply too much data for human analysts to process. This also applies to neuro scientific data. The data sets collected during fMRI-experiments are so huge and complex that automated pre-processing has become necessary. Data sets collected from the combination of neuro scientific, psychophysiological, and other consumer (food) behaviour measurement methods are of the same ilk: huge and complex. Automated, AI-based analysis methodology may be needed: powerful, but for many a practitioner, beyond their control. This control appears to be transferred from the content based psychologist or neuro scientist to the statistician/AI-specialist who designs and develops the software needed to analyse the data. It is, therefore, becoming ever so important that the validity of the measurement method and the forthcoming data can be guaranteed.

3.3. Behavioural Measurement Technologies

Any behavioural output can be seen as a (muscle) motor reaction to external stimulation. We list very different types of behavioural measurement technologies, of which we found a few food consumer related applications. They cover measurements very close to an individual (oral sensing, food ingestion, chewing behaviour), somewhat more remote (online) measurements (clicking behaviour, face reading, eye-tracking, reaction times, voice analysis), and automatic tracking of consumers when navigating an area.

3.3.1. Food Ingestion and Automatic Eating Behaviour Assessment

Many innovations exist in the study of food consumption and food ingestion. This is the study of the amount, and types, of food that a consumer eats and digests. Food ingestion is notoriously hard to measure. See Willet [ 53 ] for the many different approaches in this area. When consumers report their own intake, it appears to be severely biased toward under reporting. Scientists in this field are always on the lookout for more objective, valid ways to measure actual intake. Many tools exist to measure ingestion, and they range from micro measurement instruments that may be inserted into a molar [ 54 ], to video registration of people eating, recording of muscle activity in the jaw, tongue, and/or throat, weighing plates while eating, or weighing the person after dinner.

A new development is the automatic recognition of what is on a plate, video capturing the plate, and submitting the images to software that is able to discern what is on the plate (and hopefully, indeed, will end up in the consumer). Software employing AI has been developed that can estimate the nutritional value of what is on the plate by processing the image [ 55 ].

The developers of the tooth mounted sensor [ 54 ] suggest it can intra-buccally monitor ingestion, to the point of sensing the nutritional quality of the material ingested.

Another innovation in this area is the extraction of additional, food consumption relevant parameters from the video capturing consumers’ faces. One of these parameters relates to the consumption duration, i.e., the time between putting the food in the mouth, and the last swallow. Other parameters that can be collected are chewing behaviour (duration of chewing, type (continuous or in bursts) of chewing, biting behaviour (estimated size of bite), etc. [ 56 , 57 ].

3.3.2. Consumer Food Choice

Any situation in which a consumer chooses an alternative from among a set of items constitutes the output of a choice process. In this review, we limit ourselves to relatively recent developments in measurement methods concerning food choice. Most valid food related consumer choice concerns real food. Many choices between food pictures can be made, or between descriptions of food, but such a reported choice would, in our view, be a psychological, rather than a behavioural, measurement.

Collected data about mouse clicks and their timing may also contain valuable information about the choice process. Many online survey tools allow for these types of measurements. Creative developments in food choice and ingestion measurement have been published recently. One such development is the computerised manipulation of portion size, by adjusting the portion on a plate, as it appears on a computer screen [ 58 , 59 ].

3.3.3. Face Reading

Automatic processing of facial expressions has become standard technology. Applications of this technology in food related consumer research are of a more recent nature. A consumer is seated in front of a camera, and often a computer screen. S/he can answer questions on the screen, look at pictures, or do other tasks, while the camera records the face of the consumer. Software is available to infer emotions from facial expressions. The emotion data can be used to study the reactions of consumers to the pictures or tasks provided on the screen.

A more recent innovation in this area is online face reading, where consumers are at home behind their own computers and perform the tasks, while the laptop camera is recording their faces [ 14 ]. Thomas et al. [ 60 ] summarised six points to take into account concerning automatic facial emotion reading. They refer to an earlier study by Mahieu et al. [ 61 ], where several items (perfumes, video advertisements, and chocolates) were studied using face reader technology. We have used the points put forward by Thomas et al. [ 60 ] to formulate five points of attention when performing online face reading studies:

• results depend on the type of product and product category,
• not all emotions show differences between products,
• an individual baseline emotion measurement is advised and appears stable,
• not all face reading software yields the same result,
• the face should not be obscured (e.g., by glasses).

In particular, the fourth point is troublesome. If different software systems for automatic facial emotion reading do not agree, one cannot be sure what it is that is assessed by the software. This is a serious threat of the validity of automatic emotional measurement through facial expression. It should be mentioned that the techniques are continuously evolving with regard to required lighting, interference by glasses/beards, and interference by movements during talking and eating. As a result of this, the differences between various techniques will likely become smaller.

3.3.4. Eye-Tracking

Eye-tracking is a well-established technology that is also in the consumer sciences. One of the assumptions often made, however, is that the object that is projected onto the fovea is also the object of greatest interest for the subject, as well as that this object has the greatest impact onto the behaviour (choice) of the subject. This assumption is not always granted, as parafoveal stimulation can also attract attention, and it can also affect perception outside awareness. Much research has been devoted to this in a reading context [ 62 ].

Eye-tracking can be carried out with a static subject, but the newest methodology enables eye-tracking, either from moving subjects [ 63 ] or from subjects in an immersive environment [ 64 ]. Developments in this area combine several measurements, such as eye-tracking and face reading [ 65 ].

3.3.5. Reaction Times

The time a subject takes before reacting on a stimulus, making a choice, or answering a question is since long known to carry information about the cognitive processes (‘elementary mental organisation’ [ 66 ]) intervening between the registration of the stimulus and the result of a behavioural reaction to it. Reaction times (RT’s) can be recorded, with high precision, in experimental laboratory settings. They can also be provided with online surveys, or online choice tasks. More noise will be present in such online RT’s than when lab-recorded, but provided the N is large enough, the RT’s can be as valuable as the lab-collected ones. Precautions will have to be taken in keeping the noise at acceptable levels by deleting very long and extremely short RT’s. This is no different from laboratory collected RT data. See Woods et al. [ 67 ] for an overview of the issues with collecting RT data over the internet. Kochari [ 68 ] mentions that the online RT’s collected, in numerical cognition studies, were comparable to those from lab-based studies.

3.3.6. Sentiment Analysis

Free text comments can be analysed to infer emotions of the provider of the comments [ 69 ]. More sources can be analysed for sentiment-content, using specially trained, machine learning, algorithms. An additional new field includes the automatic analysis of tone of voice, the speed of talking, and other speech properties. This allows for an emotion estimation based on voice utterings [ 70 ]. The authors could not find evidence that this is used in food consumer science, e.g., analysing the emotional connotation of consumers discussing food items or meals.

3.3.7. Tracking and Recording of Movement

Technology exists to track peoples’ position and movement while they navigate through a space. In consumer science this has been used in retail or mall environments. In addition, movement speed and gait tracking is possible while navigating in a retail environment (real or simulated), trying to find ones groceries or other products [ 71 ].

Relatively new behaviours are the movements one makes with a finger on a touch screen, or a mouse while pointing at positions on a computer screen, when navigating web-pages, or during online (food) ordering. Timing, trajectories followed, velocity of mouse or finger movements, and even pressure exerted, may reveal a lot about the interaction between the information on the computer or smartphone screen and the consumer doing the navigation.

3.3.8. Observation Technology

Observing consumers can be done in a host of circumstances, ranging from highly unnatural environments, where a consumer is in a lab to perform certain tasks, to very ‘ecological’, where consumers have no clue of being observed. The latter type of studies may run into ethical problems when consumers are filmed or photographed. When they are ‘just’ watched by observers, the impact onto their privacy may be limited. However, privacy legislation forbids the following of individuals to, e.g., find out what they choose in shops and how they may compare alternative products. New methodology exists where automatic tracking, more than just navigation through a shop or mall, is possible. The technology may even allow identification of subjects, by facial recognition and recognition of their gait, and monitor choice behaviour automatically.

4. Validity

New technologies clearly extend the possibilities to manipulate the testing environment and to measure consumer responses. With the increase in the amount of new measurement methods, another problem arises: viz., so the burden of the work shifts from the collection of the data to the analysis of the data and interpretation of the results. The dictum ‘Rubbish in, rubbish out’, still holds, so due attention to the measurement method and its validity is perhaps more needed than ever. We will follow the definition of validity as presented in Borsboom, Mellenbergh, and van Heerden [ 72 ], who state that a measurement of an attribute (an attribute being something in the real world that one desires to probe using a measurement method) is valid when the attribute exists in the real world and when variation in it causally affects variation in the measurement. The research problems in our area revolve around food choice, food consumption, sensory testing, consumer acceptance, etc. The question is when they can be expected to causally affect our measurements. In order to establish this, a theory is needed that is able to couple the attribute to the measurement outcome [ 72 ].

We will approach validity in its most simple guise, viz. ‘A measurement is valid when it measures what you intend it to measure’, i.e., when it clearly relates to the matter under study. We claim that our field aims to find research methods to address our main research questions, viz. to understand food related behaviour and perception, and we aim to address these in a valid manner. This means that, when a method is employed to ‘measure’ consumer behaviour, it should clearly relate to that behaviour and not just to what consumers self-report their behaviour to be. It may also mean that a brain imaging study may not be able to predict ultimate revealed preferences of consumers. We know that there are many external threats to valid measurements. In particular, as consumers never operate as automatons in an information vacuum, we know that there are many biases and unwanted influences affecting their responses. This is the reason that the three criteria that we reiterate below, introduced earlier by Dijksterhuis [ 73 ], explicitly address consumers in a measurement situation. These validity criteria distinguish between levels of awareness as being part of a measure. We argue that, especially these levels of awareness, may change due to the application of novel research tools, as they result in novel ways to engage with consumer. We add to the literature by applying these validity criteria on a range of novel technologies, thereby providing researchers with guidelines to support their choices on whether and how to use these technologies.

Three Criteria for Validity

The three criteria are not intended to disqualify any research method. They do not specify their strict application; rather, they are intended as a rule of thumb, enabling researchers to get an idea of the validity of a host of different methods. They may prompt them to find a method that best fits their specific behavioural research question. The criteria measure to what level of detail a consumer is aware of the measurement situation he or she is in. The idea being that such awareness may interfere with the result of the measurement. The psychological basis for this idea is briefly introduced in Appendix A .

The three criteria addressing validity are:

• Reflection: the research method requires the ‘person(a)’ of the consumer, i.e., he/she needs to think about his-/herself or his/her behaviour,
• Awareness: the method requires the consumer to know he or she is being tested,
• Informed: the method requires the consumer to know the underlying research question.

In Table 2 , we have listed several of the newer consumer science measurement (‘output’) technologies introduced in the above. For each of the presented research methods, a criterion applies or does not apply. This is shown in Table 2 , where a method is given a tick mark (✓) in the appropriate column when the criterion applies.

Selected ‘new’ consumer science technologies and an indication of their validity, based on the three criteria. When a criterion applies, a tick mark (✓) is shown.

Obviously, the scoring of the criteria is not an exact matter. It can be discussed, and it will depend on the exact way measurements are performed and research situations are constructed. This is also one of the main points in the whole exercise: that it should be discussed and that the validity of consumer measurements should never be taken for granted. The framework can, therefore, also be regarded as a guiding tool to reflect on and support decision making.

Some methods (criterion 1, ‘reflection’) require subjects to reflect on their own situation and past, future, or even hypothetical behaviour. This is the case in many survey or interview oriented methods. This is also underlying the well-known difference between stated and revealed preference in economics. In revealed preference theory, the measurement does not interfere with the consumer, as it only considers what is actually been bought or chosen.

In some methods, it is inescapable that the consumer knows he/she is being tested (criterion 2, ‘awareness’), e.g., it is hardly possible to measure psychophysiological parameters without the consumer knowing that a measurement is performed. Knowing to be in a test can influence the way consumers behave. However, the research question itself need not be disclosed in these measurements.

Regarding criterion 3 (‘informed’), there are types of methods, e.g., group discussions, in which consumers are directly interviewed about their view on the research question, so they have to know even the research question, or they can’t be part of the discussion.

Looking at Table 2 , a number of things may be concluded. Obviously, it is impossible to perform neuro scientific tests with consumers without them knowing that they are in a test situation. The only exception may be the possibility to assess heart rate from a visual image of the face. Other ANS measurements require an apparatus that is impossible to apply surreptitiously. However, recent developments enable sensors, such as smart watches, that need to be worn for longer periods but appear to not bother the subject, and they often even forget they are wearing a sensor.

Psychological methods are so ubiquitous that it is impossible to even attempt to summarise many of them. We limited ourselves to some of the newer methods. Finding novel information in available texts by means of ‘web-scraping’, typically texts available on the internet, does not need any consideration of the producers of the text. Netnography may also be employed without consumers knowing they are being tested. Surveys, focus groups, and other methods where questions are asked do require consumers to have at least some information about the question they are inquired about.

All behaviour based methods presented here do not require a consumer to think about him- or herself to enable a measurement of their responses, nor do they need to know the research question. For example, the way one eats (food ingestion, eating behaviour), or what one chooses to eat (food choice), can be assessed by video without the eating consumer knowing. Tracking consumers’ movement, e.g., through a retail environment, also does not necessarily require this. Face reading, eye-tracking, reaction time measurement, or choice outcomes do not require a consumer knowing about the measurement taking place, although with eye-tracking, it is probably difficult to perform measurements unobtrusively.

Overall, the tick marks in Table 2 seem to hint at the conclusion that behavioural measurements appear more valid than neuro scientific measurements and psychological measurements. We hasten to say that these numbers are based on our particular choice of consumer measurement technology and on our interpretation of the technology. The finding does not reflect any superiority of one method over another. Other authors may use other definitions of their specific research methodology, and they may be confronted with research questions that demand specific applications of methodology not taken into account in our scoring.

5. Conclusions

With the above mentioned provisos, we tentatively conclude that the behavioural based methods, in general, appear to enable valid results, concerning actual consumer behaviour. When behavioural research data are collected in order to predict future consumer behaviour, behaviour based data may be the preferred type to base predictive models on. Psychological research methods can back such models up with behavioural knowledge and knowledge about consumer segments, based on psychological traits. The neuro-science based research methods are probably best suited to study very specific research questions in a small group of consumers.

All in all, the validity of (food) consumer measurement methodology should never be taken for granted. The three criteria provide a means to suggest the validity of a method and, perhaps more importantly, they show that validity should be discussed and taken into account before the measurements proper take place.

Acknowledgments

The following colleagues have provided us with comments, answers on some of our questions, or have referred us to the right persons or locations for information: Anne-Marie Brouwer, Freek Daniels, Doris Dijksterhuis, Lonneke Janssen Duijghuijsen, Marvin Kunz, Marieke Meeusen, Saskia Meyboom, Görkem Simsek-Senel, Paul Smeets, Corrie Snijder, Marieke Ubachs, Shota Ushiama, Geertje van Bergen, Jos van de Puttelaar, Liesbeth Zandstra. Please note that acknowledgement does not imply endorsement of the viewpoints presented in this article.

Appendix A. A Theoretical Basis for the Biases Resulting from Consumer Awareness of a Measurement Situation

In the chapter called “The methods and snares of psychology.” William James writes about ‘the named state’ which should be distinguished from ‘the naming state’ [ 74 ]. This means that a verbal report of a certain psychological state, say a felt sadness (the ‘named state’), is made while the subject is in another state (the ‘naming state’). A subject has to interpret his/her bodily and/or psychological circumstances and internal state to be able to, after the fact, report something such as ‘I am sad’, or ‘I currently feel sadness’. The reporting is a certain psychological state in itself, which will exert an influence, e.g., via a memory, on the previous state which one tries to report. James’ conclusion is that the introspection and report of one’s own internal psychological states is a snare.

It has become more and more clear that behaviour is not always the result of consciously willing it. Wilson [ 75 ] writes about the ‘adaptive unconscious’, shaped by evolution, guiding us through a complicated environment, and helping us with decisions through intuition. This all happens without our explicit knowledge, and often even without the possibility of such knowledge. Many of the psychological processes responsible are inaccessible to conscious awareness. In this context Wilson [ 75 , 76 ] states “People can no more observe how they are unconsciously categorizing their environments, setting goals, and generating intuitions than they can observe how their kidneys work.”

Damasio [ 77 ] explains the difference between an emotion and a feeling, much as James [ 74 ] has introduced this distinction. The former is a bodily reaction to a stimulus, and the latter a conscious interpretation of that emotion by the subject. This means that the emotion is attributed a reason, but this does not mean that this attribution is the very cause of the emotion; reasons are not causes. The underlying, unknown, causes may provide a non-rational route to decision making, alongside the (often fabricated) reasons resulting from a rational route. Kahneman and Tversky [ 78 , 79 ] have amply illustrated that in many economical decision making contexts most people make decisions that can be shown to be sub-optimal from a strict rational, utilitarian, viewpoint. As a result subjects may actually gain less money in comparison with the alternative they do not choose. Damasio [ 80 ] suggests that this is the result of an ‘emotional route’ to decision making which may supersede the rational route.

Another line of work showing the importance of the unconscious in perception and appreciation is that of Zajonc [ 81 ]. Lazarus [ 82 ] argues that it is necessary first to analyse a situation, and to build some knowledge about it, in order for an affective response to occur, an approach known under the name of appraisal theory. Zajonc [ 83 ] argues the opposite, emotion/affect is primary, only afterwards may knowledge about a situation occur, but cognition is not a prerequisite for affective responses to occur. A respectable number of studies show that emotion has primacy over cognition [ 84 , 85 , 86 ]. In one telling experiment subjects were tachistoscopically presented with words, flashed too short to enable identification. However, this short presentation still allowed an appropriate affective response to the words. The affective value of the stimulus is somehow registered but the identity of the stimulus remained unknown to the subjects [ 81 ].

Author Contributions

Conceptualization, G.D.; methodology, G.D.; investigation, G.D., R.d.W., M.O.; writing-original draft preparation: G.D.; writing—review and editing, G.D., R.d.W., M.O.; visualisation, G.D.; supervision, G.D.; project administration, G.D.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.

The review is conducted in a funded project on behaviour and novel food contexts financed by the Dutch Ministry of Agriculture, Nature and Food Quality.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Exploring ethical consumer behavior: a comprehensive study using the ethically minded consumer behavior-scale (EMCB) among adult consumers

• Original Article
• Published: 20 May 2024

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• Paulo Duarte   ORCID: orcid.org/0000-0001-8449-5474 1 ,
• Susana Costa e Silva   ORCID: orcid.org/0000-0001-7979-3944 2 ,
• Isabella Mangei 3 &
• Joana Carmo Dias   ORCID: orcid.org/0000-0001-5900-2121 4 , 5  

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This research assesses how adult consumers perceive and behave concerning ethical practices, aiming to comprehend the obstacles that hinder ethical and sustainable consumption. Employing a modified version of the Theory of Planned Behavior (TPB) alongside the Ethically Minded Consumer Behavior-Scale (EMCB), a survey was conducted with 372 participants from Germany, young and educated, to reveal and compare the factors influencing ethical consumption, including both determinants and barriers. The results indicate positive correlations between attitudes toward ethical consumption, ethical obligation, self-identity, and the intention to engage in ethical consumption, while this intention is negatively associated with price. Additionally, the study validates the explanatory power of the modified TPB within the EMCB context. Understanding the drivers and hindrances of ethical consumption is crucial for companies and decision-makers, allowing them to prioritize these factors and refine strategies for promoting ethical consumption. This insight aids marketers in tailoring campaigns to reach this specific market effectively. Given the growing significance of ethical and sustainable consumption, this research provides valuable insights into the motivations and constraints shaping consumer behavior in this domain, contributing to both theoretical understanding and managerial decision-making for those targeting this consumer segment.

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The authors would like to thank NECE – Research Unit in Business Sciences, funded by the Multiannual Funding Programme of R&D Centres of FCT – Fundação para a Ciência e a Tecnologia, under the project UIDB/04630/2020, CEGE – Research Centre in Management and Economics, funded by the Multiannual Funding Programme of R&D Centres of FCT – Fundação para a Ciência e a Tecnologia, under the project UIDB/00731/2020, and COMEGI, funded by FCT – Fundação para a Ciência e Tecnologia, under the project UIDB/04005/2020.

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Duarte, P., e Silva, S.C., Mangei, I. et al. Exploring ethical consumer behavior: a comprehensive study using the ethically minded consumer behavior-scale (EMCB) among adult consumers. Int Rev Public Nonprofit Mark (2024). https://doi.org/10.1007/s12208-024-00404-x

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Realizing the full potential of behavioural science for climate change mitigation

• Kristian S. Nielsen   ORCID: orcid.org/0000-0002-8395-4007 1 ,
• Viktoria Cologna   ORCID: orcid.org/0000-0003-3706-8669 2 ,
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• Sebastian Berger   ORCID: orcid.org/0000-0003-2392-1024 3 ,
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• Laura Henn   ORCID: orcid.org/0000-0003-4036-1487 10 ,
• Florian Lange   ORCID: orcid.org/0000-0002-8336-5608 11 ,
• Paul C. Stern   ORCID: orcid.org/0000-0002-9481-3904 12 &
• Kimberly S. Wolske   ORCID: orcid.org/0000-0003-2295-115X 13  

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Behavioural science has yielded insights about the actions of individuals, particularly as consumers, that affect climate change. Behaviours in other spheres of life remain understudied. In this Perspective, we propose a collaborative research agenda that integrates behavioural science insights across multiple disciplines. To this end, we offer six recommendations for optimizing the quality and impact of research on individual climate behaviour. The recommendations are united by a shift towards more solutions-focused research that is directly useful to citizens, policymakers and other change agents. Achieving this vision will require overcoming challenges such as the limited funding for behavioural and social sciences and structural barriers within and beyond the academic system that impede collaborations across disciplines.

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Acknowledgements

K.S.N. gratefully acknowledges financial support from the Carlsberg Foundation, grant number CF22-1056. V.C. acknowledges support from the Swiss National Science Foundation Postdoc Mobility Fellowship (P500PS_202935). S.B. acknowledges support from the Swiss Federal Office of Energy (SI/502093-01). T.D. was supported in part by Michigan AgBio Research. F.L. was supported by an FWO postdoctoral fellowship (12U1221N).

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Are ‘10-grams of protein” better than ’ten grams of protein” how digits versus number words influence consumer judgments.

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Marisabel Romero, Adam W Craig, Milica Mormann, Anand Kumar, Are ‘10-Grams of Protein” Better than ’Ten Grams of Protein”? How Digits versus Number Words Influence Consumer Judgments, Journal of Consumer Research , 2024;, ucae030, https://doi.org/10.1093/jcr/ucae030

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Numerical information can be communicated using different number formats, such as digits (“5”) or number words (“five”). For example, a battery product may claim to last for “5 hours” or “five hours.” And while these two formats are used interchangeably in the marketplace, it is not clear how they influence consumer judgments and behavior. Via six experimental studies, two online ad campaigns, and one large secondary dataset analysis, we find that digits, compared to number words, positively affect consumer behavior. We refer to this phenomenon as the number format effect . We further show that the number format effect occurs because consumers feel that digits (vs. number words) are the right way to present numerical information: digits lead to a sense of feeling right that then affects consumer behavior. Finally, we show that the number format effect is amplified when credibility of the source of information is low, and attenuated when source credibility is high. The current research advances knowledge of how numerical information influences consumer judgments and behavior and carries important implications for marketers and policymakers as they communicate numerical information to consumers.

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PwC’s Voice of the Consumer Survey 2024

Shrinking the consumer trust deficit

• 30 minute read
• May 15, 2024

Companies can strengthen the confidence consumers have in them by executing on six key imperatives.

Listen to the Voice of the Consumer Survey report, and hear insights from our experts.

Trust is crucial for consumers and for the companies that sell products and services to them: as shoppers confront a set of overlapping and often mutually reinforcing disruptions—financial, ecological and technological—they are prioritising reassurance and reliability from the brands they engage with.

That’s a signal finding of our inaugural Voice of the Consumer Survey, which builds on insights amassed over 15 years of consumer research by collecting the perspectives of more than 20,000 consumers across 31 countries and territories on a wide range of issues, including caring for the environment, attending to their health, being open about data, finding value for money and embracing AI.

The good news for leaders of consumer-facing businesses: global consumer markets are set to continue expanding. The global consumer class, comprised of those spending US$12 or more per day, reached 4 billion last year, and is projected to reach 5 billion people by 2031 . The bad news: there’s a widening gap between the trust that executives think consumers place in their companies and the trust that consumers actually have in them. In order to maintain and grow market share, companies must figure out how to build trust in several dimensions.

In interviews conducted alongside our quantitative survey, senior executives confirmed the importance of trust and reputation to their strategy and growth—and described a landscape of both challenges and enormous opportunities. ‘In an era of wide distrust, business is still one of the more trusted institutions,’ says Esi Eggleston Bracey, Unilever’s Chief Growth and Marketing Officer. ‘People trust brands they love and companies that use their data responsibly. Employees trust companies that support them, have responsible practices and treat them as people. And investors trust performance and solid, continuous returns.’

Six consumer trust imperatives

• Forge bonds with eco-conscious consumers by connecting their intentions to positive environmental impacts.
• Create and promote a product portfolio that reflects consumers’ desires for wellness, nutrition and more sustainable food production.
• Strike a balance with social media use, recognising its significance as a platform for sales and engagement, while being mindful of consumer concerns about its credibility.
• Safeguard personal data, while continuing to use it to offer personalised services and elevated customer experiences.
• Navigate conflicting priorities in an economy with rising prices, meeting customers’ expectations of value while managing price increases effectively.
• Incorporate and experiment with AI tools in business operations while maintaining a human element, especially in more complex and personal services.

Forge greater bonds with eco-conscious consumers

A staggering 85% of survey respondents report experiencing firsthand the disruptive effects of climate change in their daily lives. A smaller but still considerable number (46%) also say that they are buying more sustainable products as a way to reduce their personal impact on the environment.

This gap presents a chance for consumer markets companies to better connect with environmentally aware consumers. It also calls for a deeper understanding of consumer behaviour, including that of the 43% of all surveyed consumers who report making more considered purchases to reduce their overall consumption.

Across the board, consumers tell us they would be willing to pay 9.7% above average price for sustainably produced or sourced goods, in line with last year’s pulse survey results of 9.8%. They say the sustainability incentives that would have the greatest impact on their purchasing are mainly tangible attributes, including production methods that emphasise waste reduction and recycling (40%), eco-friendly packaging (38%), and making a positive impact on nature and water conservation (34%). Messaging that promotes a company’s social responsibility programmes or community engagements (20% and 17%, respectively) is seen as less influential.

In interviews, executives from a wide range of consumer markets companies—representing grocery, health, home decor and more—shared similar experiences of their customers’ eco-minded behaviour. The expectation that companies will do the right thing for the environment is now seen as table stakes. Thus, companies must achieve a delicate balance between consumer affordability and environmental impact. This may involve switching from high-performance plastic packaging to biodegradable options, or giving customers a choice of using more costly sustainable aviation fuels for product delivery. ‘If reusable packaging was less expensive, not more, that would be a game changer,’ says Bálint Lévai, CEO of the dietary supplements company BioTechUSA, which has experimented with a range of alternative packaging options, with limited consumer uptake.

Cost increases associated with sustainability are a fundamental challenge for consumer-facing companies. The 2024 edition of PwC’s Annual Global CEO Survey showed that two-thirds of companies have efforts underway to improve energy efficiency, but only about half are creating innovative climate-friendly products or services. Many are also facing tough decisions on costs. ‘For companies, there is a real focus on operational challenges and minimising impacts on product pricing. How do they create systems and supply chains to meet their climate commitments? Where do they source raw materials at appropriate prices? How do they drive needed efficiencies in new processes?’ asks David Chavern, President and CEO of the Consumer Brands Association, a US trade group for manufacturers of consumer packaged goods (CPG). ‘Ultimately, climate risk will be priced into inputs and processes, and the necessary next question will be how to also deliver the right price to the consumer.’

• Expand the impact of compliance and regulatory work, like the Corporate Sustainability Reporting Directive (CSRD) , by using non-financial information to find bottom-line benefits.
• Build more resilient, more efficient and less energy-intensive supply chains through network optimisation, integrated visibility and technological innovation.
• Discover efficiencies in operations such as truck loading, warehouse operations, routing, and waste and inventory reduction through machine learning, AI, and analytics.
• Embrace the opportunity for premiumisation through product differentiation valued by consumers (e.g., products that commit to doing no harm).

Create a product portfolio that reflects wellness, nutrition and sustainable food production

More than half of consumers (52%) express intentions to boost their intake of fresh fruits and vegetables, while a smaller but important group (22%) plan to reduce their red meat consumption. Despite these health-orientated preferences, only 19% of consumers consider the environmental implications of their food choices. This disconnect presents a significant opportunity for food producers, retailers and wholesalers to bridge the gap between consumer intent and sustainable practice.

The growing interest in plant-based diets hints at a rising awareness of the environmental burdens posed by traditional meat production, particularly beef, which is a known contributor to greenhouse gas emissions. Explicitly addressing these consumer concerns may help companies integrate plant-based options into mainstream shopping habits, while being mindful that the main motivations behind these shifts are consumers’ considerations of general health (57%) and product cost (52%) when they make food and dietary choices.

Feeding the globe

The ambition to adopt healthier and more sustainable diets cannot rest on consumers alone; producers and retailers must also step up. Global population numbers are expected to surge from 8.1 billion today to 9.7 billion by 2050 , and the dual challenge of feeding more mouths and reducing food production’s ecological footprint is becoming increasingly urgent. Although the business model of selling in larger quantities is necessary and lucrative, it will require innovation to reduce potential risks to long-term environmental and social sustainability.

Food companies can leverage the willingness of consumers to pay a premium for sustainably produced goods as a competitive advantage. Effective strategies might also include comprehensive food packaging and presentation that not only guides consumers towards environmentally friendly choices but also builds trust through transparency in product design and the communication of clear sustainability information at point of sale. For example, six in ten consumers in our survey agree that an independent sustainability score on food products would be helpful and that incentives on the pricing of foods nearing expiry would drive likelihood to purchase these items.

• Support consumers’ ability to eat a healthy diet through clear category signposting, packaging information, and other targeted communication and marketing efforts.
• Expand portfolio strategies to incorporate a greater number of alternative meat products and proteins.
• Cater to a growing demand for health and wellness products, such as weight-loss prescription drugs and other over-the-counter solutions.
• Innovate to meet a new generation of informed consumers who are focused on their health, offering products such as wearables that incorporate health-tracking features.

Strike a balance between engagement with social media and caution over its credibility concerns

Consumers have mixed feelings about social media. They increasingly use it as a place for purchases; 46% of consumers report directly buying products through social media, a significant rise from 21% in 2019. And they really like it as a place for discovery and reviews: 67% of consumers use social media channels to discover new brands, and 70% of consumers seek reviews to validate a company before making a purchase. But at the same time, consumers are questioning its safety and reliability, ranking social media their least trusted industry.

Striking the right balance on social media is crucial for companies. Brands need to create engaging and authentic content that resonates with their target audience, while also being mindful of the concerns that consumers have on trust. Data protection was the leading factor that influenced consumer trust—83% of respondents consider it a top priority. Other important factors are the quality of goods and services (79%), companies’ treatment of employees (77%), and product affordability (75%).

of consumers report purchasing products directly through social media—up from 21% in 2019.

Businesses are engaged in a delicate dance of expressing their purpose and values while also navigating the need for caution in their communications with consumers. ‘Companies today are trusted more than governments and media. That gives companies an opportunity to play a larger and purposeful role in society,’ says Ronald den Elzen, Chief Digital and Technology Officer at Heineken. ‘At the same time, we see many companies becoming more careful and restrictive in advertising, based on the polarisation we are seeing around the world.’

Advertising’s new frontier

‘Five years ago, we were spending half of what we spend now on digital advertising,’ says Kyle Artz, Vice President of China Strategy at Coca-Cola, one of the world’s largest advertisers, stressing the importance of directing this spend towards innovative consumer engagement. Through personalised approaches, user-generated content initiatives, gamification and more, companies are competing in the digital space to create relationships that go beyond mere brand awareness. The impact of personalised social media advertisements is evident, with seven in ten consumers reporting that it would influence their purchasing decisions, followed by the influence of retailer websites (66%), email (54%) and text messaging (38%).

Global social media ad spending alone is projected to reach US$220 billion this year, up from the US$207 billion forecast for 2023. This includes a strong focus on social media influencers, both widely recognisable celebrities and aspiring individuals, who are now an established channel—our survey found they have influenced 41% of consumers to make a purchase. ‘Influencers are playing a much bigger role than what they were playing even a year ago,’ says Nitish Gupta, Managing Director in Vietnam of the personal care brand Kimberly-Clark. ‘Across medical, beauty, baby and childcare, and personal care categories, when I look at the social commerce landscape, it’s a sea change.’

• Emulate front-running CPG companies by building social ecosystems targeted to specific generations (Gen Z, millennials, etc.) across various social media platforms (YouTube, Instagram, Snapchat, Twitch, etc.).
• Invest in marketing and advertising to build brands instead of pursuing more transactional trade promotion spend.
• Take a digital-first approach to investing in advertising channels such as text and email, in addition to social media.

Safeguard personal data, while using it to elevate customer experiences

A large majority of consumers (83%) say that protection of their personal data is one of the most crucial factors in companies’ ability to earn their trust. When asked specifically about privacy, a significant majority of consumers (80%) also demand assurances that their personal information won’t be shared. But only around half feel confident that they understand how their data is stored and shared, and 71% express concerns about the security of their personal data on social media.

The acquisition and use of first-person data for personalisation has also become crucial to maintaining a competitive advantage in the marketplace. ‘Consumers expect that brands today understand them better than ever before,’ says Tien Yue Chen, Executive Director of the Malaysia-based pewter home decor and luxury gifting company Royal Selangor. As Coca-Cola’s Artz tells us: ‘First-person data for personalisation and advanced insights is a point of consideration in nearly every decision we make.’ With competition for valuable data intensifying, and new regulations coming into force in the  EU and several  Asia-Pacific countries , it’s imperative for companies to implement robust data protection measures and enact a strategy that engages with consumers without compromising the ethical use of data.

Use it, don’t abuse it

Industry executives describe a developing social contract that involves consumers willingly sharing their personal information in exchange for valuable incentives such as promotions, exclusives and other perks. Indeed, nearly 50% of consumers say they are happy for their data to be used to offer them personalised services and experiences. ‘When our customers notice that their data is being used to provide them with better products or treatments, or to achieve efficiencies in obtaining their prescriptions, we can see that they feel much more confident in sharing their data with us, and this reinforces our reputation,’ says Patriciana Rodrigues, Chairwoman of Pague Menos, a Brazilian pharmacy chain.

This trend is especially evident in loyalty programmes, which are becoming the primary engine of customer data for many companies. Given that an overwhelming 93% of executives in the United States believe that establishing and nurturing trust has a direct and positive impact on financial performance , this exchange of data and incentive has the potential to create a virtuous circle between trust and revenue.

• Responsibly scale your data strategy to realise the full benefits for the company and your consumers, given that many CPG companies have already made their foundational investments in data, tech and AI use cases.
• Build AI-enabled digital tools for testing early-stage ideas and for digital prototyping in order to create efficiencies, such as the shortening of innovation cycles.
• Elevate your ‘power brands’ by using insights from consumer data to narrow or expand the focus of your brand portfolio.

Meet customers’ expectations of value while managing price increases effectively

Inflation ranks overwhelmingly as the number-one risk that consumers think could impact their country over the next year; 64% put the issue within their top three concerns. That’s more than 20 percentage points ahead of other major threats, including slow economic growth, climate change and health issues, and it was the top concern consistently around the globe—despite lower rates of inflation and, in some regions, signs of deflation.

A consistent observation came to light in our interviews with executives in a variety of consumer goods sectors. After consumers largely accepted the price increases of the covid era, they have shown little tolerance for continued rises, especially as they turn their attention to mounting non-discretionary spending: 62% expect the most significant increase in spending in the next six months to be on groceries.

Cost-effective pricing is emerging as an important and complex factor in gaining consumer trust. Governments and regulators, acting out of a sense of duty to consumers, have already started taking action against price increases that they see as outside reasonable bounds. And consumers are searching for better value for their money: 40% would consider switching from their preferred name brands to more affordable options, such as discount brands and generic products. ‘It’s important to remember that value doesn’t equal price,’ says Noel Keeley, CEO of the Irish food retailer and wholesaler Musgrave Group. ‘It’s not necessarily the cheapest, it’s the brand they feel they are getting the best value from.’

Given consumers’ and lawmakers’ reactions to price increases, companies need to make other moves to not only manage pricing but secure their finances and reassure investors. ‘Globally, investors are asking about how we are going to approach volume growth in the future,’ says Artz. ‘There is huge pressure on [consumer goods] companies that enjoyed price increases during covid but are now finding it increasingly challenging to deliver volume growth on top of pricing. So we have to be prudent and continue to monitor the right balance of volume and price at both a global and local level.’

Focus on the purchase journey

Brands and retailers must embrace a more flexible omnichannel strategy to meet consumers’ evolving expectations for a dynamic mix of online and offline experiences. Marketers should also take note that the distribution of consumers’ preferred shopping locations—either in-store or via remote channels—has remained consistent post-pandemic. Since 2022, preference for shopping in-store has hovered at around 42%, via smartphone at 34% and via PC at 23%.

For some companies, increasing resources to deliver a consistent and personalised experience, regardless of the platform or location, is a source of competitive advantage. ‘Our investment in customer-centricity, alongside delivering exceptional customer experience regardless of channel, has paid off in terms of an uplift in brand loyalty across our offerings,’ says Marc Giroux, COO of Food for Canadian grocery chain Metro Inc.

Consumers are seeking personal connection, particularly in their discovery of new brands; 55% of survey respondents say they choose to visit physical stores and engage with salespeople, compared with 49% who seek out recommendations from family and friends, and 46% who turn to online browsing. Indeed, many executives stressed the importance of empowering their sales staff through access to more personalised consumer data and offering consumers more meaningful in-store services and experiences.

Modest technological empowerment is also key to building consumer trust and satisfaction in the in-store experience. Nearly 40% of consumers indicated that the availability of mobile or contactless payment solutions would encourage them to shop in-store. Additionally, more than one-third of consumers expressed interest in smart tags that provide product details on smartphones, as well as self-checkout systems.

• Secure profitability (as prices normalise in the second half of 2024) by investing in consumer demand, restoring volume through marketing and advertising, and continuing cost-saving initiatives.
• Attract customers back into stores through targeted investments in talented staff and technology, to improve the shopping experience.
• Consider mergers and acquisitions as part of a disciplined capital allocation. Lagging brands may be ripe for pruning.

Incorporate AI while maintaining the human element

Companies face the challenge of responsibly aligning consumer sentiment towards emerging technology, like generative AI (GenAI ), with the technology’s current and potential capabilities. A substantial 80% of consumers express concerns about GenAI’s future developments. Although more than half of consumers trust GenAI for simpler tasks, such as aggregating product information or providing recommendations, consumers are less confident about its usage in higher-risk, more personal services such as healthcare. This means that companies must tread carefully in integrating technology that can reduce operating costs, addressing consumer concerns and maintaining ethical standards.

Unilever’s Eggleston Bracey, for example, notes that acceptance of AI among employees and consumers has grown significantly in the past 18 months. ‘I’m amazed at the receptivity towards AI from January 2023 to today,’ she told us. ‘Internally, we like to think of AI as an opportunity for Augmented Intelligence, the blend of artificial and human intelligence. Our responsible AI strategies dictate we always have a person in the loop.’

‘We’re also seeing more adoption as large language models have made AI so accessible, making it a lot less scary for people. People are identifying with it now because it’s making life easier.’

Take the percentage of consumers who trust AI to provide product recommendations: 50%. This number will continue to climb as familiarity with ChatGPT and other AI applications increases, and if consumers turn away from incumbent search engines and towards AI platforms. ‘AI search, with its propensity to “choose” products for consumers, will be a massive change for CPG companies,’ says Chavern of the Consumer Brands Association. ‘But that won’t be the only way that AI could turn marketing on its head. It is easy to see how it could be used for massively improved ad efficiency and content individualisation.’

Early-stage adoption

Today, adoption of machine learning and GenAI applications, such as LLMs (large language models) and text-to-image tools, varies among brands. Companies have used these tools for internal improvements such as supply chain optimisation, company information management and pricing strategies. Some brands have progressed further—and done so quickly—experimenting with consumer engagement and marketing personalisation through design-led tools in controlled environments or ‘sandbox’ settings. ‘Six months ago, we were just starting out in terms of our GenAI journey,’ says Kavindra Mishra, CEO of Shoppers Stop, a chain of department stores in 40 cities across India. ‘Now, we are using it to interact with more than 9 million loyalty customers, enabling us to personalise our engagement with them, and supercharging our understanding of their attitudes and behaviours.’

Despite high interest among respondents in the use of chatbots for providing detailed responses (42%) and to solve complex problems (44%), nearly half (49%) of consumers demand direct connection with a sales representative if the chatbot is unable to answer the consumer’s query effectively. This, again, highlights the crucial balance that companies need to strike between technological innovation and the human touch.

• Develop a responsible strategy for the regulatory landscape that will emerge around AI in the next few years.
• Scale successful machine learning, AI and digital use cases to optimise sales and demand planning.
• Explore the potential of AI tools to increase supply chain efficiency, from new product development to waste and inventory reduction.
• Test and learn with the producing capabilities of LLMs on ad campaign development and production.

Unlocking the trust premium

As global consumer markets continue to expand, companies must move beyond their own perceptions of customer trust and learn what their clients actually think. Senior executives recognise this, and though challenges exist, there are also significant opportunities for those companies that prioritise trust-building efforts rooted in brand building, responsible practices and solid performance. Trust is an increasingly valuable currency in consumer markets, so companies must commit to building and maintaining long-term integrity.

In January and February 2024, PwC surveyed 20,662 consumers across 31 countries and territories: Australia; Brazil; Canada; China; Czechia (Czech Republic); Egypt; France; Germany; Hong Kong, SAR; Hungary; India; Indonesia; Ireland; Malaysia; Mexico; the Netherlands; the Philippines; Poland; Qatar; Romania; Saudi Arabia; Singapore; Slovakia; South Africa; South Korea; Spain; Thailand; United Arab Emirates; Ukraine; the United States; and Vietnam. The respondents were at least 18 years old and were asked about a range of topics relating to consumer trends, including shopping behaviours, emerging technology and social media.

Interviews with industry executives took place in March and April 2024.

PwC Research , PwC’s global centre of excellence for market research and insight, conducted this survey.

Sabine Durand-Hayes , Global Consumer Markets Leader, is a partner with PwC France.

Myles Gooding , Global Consumer Markets Advisory Leader, is a partner with PwC Canada.

Brian Crane , Global Consumer Markets Assurance Leader, is a partner with PwC US.

Rakesh Mani , a specialist in the Southeast Asia Consumer Markets advisory practice, is a partner with PwC Malaysia.

Kelly Pedersen , a leading practitioner in global consumer markets transformation, is a principal with PwC US.

PwC’s 27th Annual Global CEO Survey: Thriving in an age of continuous reinvention

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PwC’s consumer markets practice provides support in areas such as disruption, the impacts of e-commerce, supply chain management, compliance and regulatory pressures, data analytics, and changing customer demands.

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How to understand consumer behavior through marketing data and analytics

Understanding consumer behavior is crucial for effective marketing strategies. It allows marketers to tailor their campaigns, improve customer satisfaction, and drive higher sales. This is where marketing data analytics comes into play. By leveraging data analytics, marketers can gain valuable insights into consumer preferences, behaviors, and trends, leading to more informed decision-making and successful marketing campaigns.

In fact, according to a recent study, 73% of companies that utilize data analytics in their marketing strategies report significant improvements in their customer acquisition and retention rates. Additionally, businesses that use data-driven marketing are six times more likely to be profitable year-over-year. In this blog, we’ll explore various aspects of marketing data analytics, including its definition, importance, tools, and practical examples.

What is data analytics in marketing?

Data analytics in marketing refers to the process of examining raw data to conclude consumer behavior and market trends. It involves collecting, processing, and analyzing large sets of data to identify patterns and insights that can inform marketing strategies. For businesses seeking to leverage these insights effectively, partnering with a reputable Digital Marketing Agency can significantly enhance their marketing efforts.

Importance of data analytics for marketers

Data analytics is essential for marketers because it provides a data-driven approach to understanding consumer behavior. By analyzing various data points, marketers can:

• Identify target audiences more accurately.
• Optimize marketing campaigns for better ROI.
• Enhance customer experience by understanding their preferences.
• Predict future trends and behaviors.

Key metrics and data points used in marketing analytics

Several key metrics and data points are crucial in marketing analytics, including:

• Click-through Rates (CTR): Measures the number of clicks on an ad divided by the number of times the ad is shown.
• Conversion Rates: The percentage of users who complete a desired action, such as making a purchase.
• Customer Lifetime Value (CLV): The total revenue a business can expect from a single customer account.
• Bounce Rate: The percentage of visitors who leave a website after viewing only one page.
• Engagement Metrics: Includes likes, shares, comments, and other forms of interaction on social media and other platforms.

What is the role of advertising data analytics?

Advertising data analytics involves analyzing data from advertising campaigns, including those managed by specialized Instagram Ads Agencies , to understand their effectiveness and impact on consumer behavior. This type of analytics helps marketers optimize their ad spend and improve campaign performance.

How advertising data provides insights into consumer behavior

Advertising data analytics provides insights into:

• Ad Performance: Which ads are performing well and which are not.
• Audience Segmentation: Understanding different segments of the audience and their responses to ads.
• Consumer Journey: Tracking the consumer journey from ad exposure to conversion.

Examples of advertising metrics

• Click-through Rates (CTR): Indicates how well an ad captures the audience’s attention.
• Conversion Rates: Measures the effectiveness of an ad in driving desired actions.
• Cost Per Acquisition (CPA): The cost associated with acquiring a new customer through an ad campaign.
• Return on Ad Spend (ROAS): The revenue generated for every dollar spent on advertising.

Marketing data analytics tools

Overview of essential marketing data analytics tools.

Several tools are essential for effective marketing data analytics, including:

• Google Analytics: A web analytics service that tracks and reports website traffic.
• Adobe Analytics: A comprehensive analytics solution for tracking and analyzing customer interactions across various channels.
• HubSpot: A marketing, sales, and service software that provides analytics for inbound marketing.
• Tableau: A data visualization tool that helps in creating interactive and shareable dashboards.

Features and benefits of popular tools

Google analytics.

• Features: Tracks website traffic, user behavior, and conversion rates.
• Benefits: Provides comprehensive insights into website performance and user interactions.

Adobe Analytics

• Features: Offers real-time analytics, segmentation, and cross-channel data integration.
• Benefits: Helps in understanding customer behavior across different channels and devices.
• Features: Includes tools for email marketing, social media, and content management.
• Benefits: Integrates marketing data with sales and service data for a holistic view of customer interactions.
• Features: Allows for the creation of interactive dashboards and visualizations.
• Benefits: Makes complex data easier to understand and share.

How these tools help in understanding consumer behavior

These tools help marketers understand consumer behavior by:

• Tracking User Interactions: These tools keep an eye on how people click around websites, react to ads, and engage with different types of content.
• Analyzing Trends: They delve deep into the data to uncover recurring patterns and trends in consumer behavior.
• Segmenting Audiences: Instead of treating all consumers the same, these tools help marketers divide them into smaller groups based on their behaviors, preferences, and characteristics.
• Optimizing Campaigns: Armed with insights from tracking user interactions, analyzing trends, and segmenting audiences, marketers can fine-tune their campaigns to be more effective.

What are analytics in marketing?

Analytics in marketing involves the systematic analysis of data to gain insights and make informed decisions. It encompasses various types of analytics, including descriptive, predictive, and prescriptive analytics.

Marketing data analysis example

Step-by-step marketing data analysis example, step 1: define objectives.

Set clear objectives for the analysis. For example, a company wants to increase its email campaign’s open rate.

Step 2: Collect data

Gather relevant data, such as email open rates, click-through rates, and subscriber demographics.

Step 3: Analyze data

Use tools like Google Analytics and Excel to analyze the data. Identify patterns and trends.

Step 4: Generate insights

Generate insights from the data analysis. For instance, emails sent on weekdays have higher open rates.

Step 5: Implement changes

Implement changes based on the insights. Send emails during peak times identified in the analysis.

Step 6: Monitor results

Monitor the results to see if the changes have a positive impact on the open rates.

Tools and methodologies used in the analysis

Common Tools used in the analysis include:

• Google Analytics: This tool helps us track and understand what people are doing on our website and with our emails.
• Excel: We use Excel to organize and play around with the data we get from Google Analytics. It helps us make sense of the numbers by putting them into tables and graphs that are easier to understand.
• A/B Testing Tools: These tools are used to try out different versions of our emails to see which ones work better. For example, we might send one email at one time of day and another email at a different time to see which one gets more clicks or opens.

Recap of the key points discussed in the article

In this blog, we discussed the importance of understanding consumer behavior through marketing data analytics. We covered various aspects, including what is data analytics in marketing, the role of advertising data analytics, essential marketing data analytics tools, real-world marketing data examples, and a detailed explanation of what are analytics in marketing. We also provided a step-by-step marketing data analysis example to demonstrate how to extract consumer insights.

The importance of leveraging marketing data analytics for understanding consumer behavior

Leveraging marketing data analytics is crucial for understanding consumer behavior. It enables marketers to make data-driven decisions, optimize marketing strategies, and improve customer experiences.

Future trends in marketing data analytics

Future trends in marketing data analytics include the increasing use of artificial intelligence and machine learning, the integration of cross-channel data, and the growing importance of real-time analytics.

Marketers are encouraged to adopt data analytics practices to stay competitive in the ever-evolving marketing landscape. For further reading and resources on data analytics and marketing, consider exploring courses on platforms like Coursera or reading books on the subject.

By embracing marketing data analytics, marketers can gain a deeper understanding of their consumers, leading to more effective and impactful marketing strategies.

Featured image credit : benzoix/Freepik

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