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Posts Tagged ‘Eye Tracking’

We can think of visual images in different forms. Pictorial images like a painting, a photograph or a drawing often depict a congruous scene of figures, objects and background, telling a story, enclosed in a frame. An image in a marketing context may represent product objects, people (e.g., customers, sellers, models, endorsers), a view of the scene of a retail store, etc.. But we may also refer to the visual image of a print advertisement as a visual scene that displays a complex layout of pictorial images, brand logo, text and additional graphic elements of decoration. Rather frequently the ad would show portions of pictorial images (like ‘clip-arts’) embedded in the whole scene, and the spatial arrangement of its objects or elements appears as discontinuous. Visual images may further be related with product packages, website pages on the Internet, video, or the view of a store’s front window and its interior space when one is present on premises of the physical site. Viewing a visual image  is an experience that may be, for example, enjoyable, challenging, annoying or disturbing. If the image leaves us indifferent, however, we would not spend enough time to figure out what we experience.

Lindt ChocolateWhen the object of a researcher’s study is a visual marketing material like an ad or product packaging it is most sensible to show the actual material or a pictorial image of it to consumers participating in the study. It is essentially more reliable for measuring affective and cognitive responses going beyond elementary memory-based measures of awareness. As we try to measure consumers’ recall of detail in an ad’s scene, its accuracy tends to decrease sharply and therefore any further references to content asked from respondents are likely to be of low reliability. The same is true when studying response to a retail scene — we should bring the research participants to the brick-and-mortar site itself, show them photographic images of its scene (i.e., layout, design, merchandise display) or computer-simulated images for a store in planning. Presenting an image of the material or retail scene is likely to enable researchers to capture emotion-laden responses more varied in type and intensity, and reach greater depth in the thoughts and feelings evoked in consumers-viewers vis-a-vis reliance on memory or mental images re-constructed by participants in their minds.

Pictorial images may be used productively, nonetheless, also if they do not appear related to a focal product, brand or company. A visual image can be utilised as an implicit bridge that helps to connect consumers’ mindsets with a brand of interest and to open-up the respondents to engage in a dialogue with an interviewer about personal or more private aspects of their lives (e.g., how a brand may function in the relations between a parent and his or her children). Relevant pictures with respect to the topic of research may be introduced by the interviewer or the interviewee. Professor of marketing Gerald Zaltman (Harvard Business School / Olson Zaltman Associates consulting firm) advises that pictorial images can help consumers to reveal and reflect attributes of a focal brand or company even though on surface the image shows no relation to that brand; the image serves as a metaphor whereby figures or objects in the image substitute for the brand (e.g., a gorilla has been shown by purchasing agents to suggest that managers from the vendor company have been stiff and stubborn in negotiations with them or  have demonstrated insensitivity to their needs). In Zaltman’s technique of metaphor elicitation (ZMET) the consumers bring pictorial images of their choice to their interviews through which they may describe the brand or tell a story about the role it plays in their lives (1).

Advertisements compete eagerly for grabbing the attention of consumers against editorial content as well as other ads in their own product category or in any other domain. It is a tough and demanding competition. The methodology of eye tracking, enhanced by advanced technology for taking different measures of eye movement and fixations, is especially suited for studying what captures attention to the ad and how information is attended to and could be utilised within the ad scene. It is generally assumed that the longer the latency of fixation on an object or element, the more thought a viewer dedicates to it, though the technique cannot directly reveal much more about the nature of affective reactions or cognitive processes.

Important and useful insights have been gained through eye tracking research. An extensive research by Pieters and Wedel (2) shows, for example, that the power of text to capture attention is sensitive to the surface size of its text-body but a picture can capture attention fast almost regardless of its size. Hence it is unnecessary for advertisers to fill an ad copy with larger pictures in expectation that it would increase the chances of capturing attention to the picture and to the ad as a whole. For text, however, surface size, determined by amount of text or font size, is significant (e.g., consider magazine ads that combine a colourful and vivid picture on top and a body of text of some explanation beneath it for achieving maximum effect). Regarding brand logos, it is found that the surface size of the logo is likely to distract viewers from reading text. However, greater interest in a brand logo for any other quality (e.g., the brand itself) can increase interest in reading the text, and secondarily, watching the pictures in the print ad. Text is attended by viewers of print ads particularly more elaborately when viewers have a declared goal of buying a product of the type advertised (Rayner and Castelhano, 3); this is compared with a task when viewers are asked just to rate an ad — then pictures get to play a greater role in viewer attention (i.e., number of fixations and time spent observing and processing). Consumers are more interested in text portions of a print ad that provide information on a focal product relative to pictures when a purchase of product of that type is seen expected.

In order to characterise more concretely the processing of visual information and better understand the valence and content of feelings and thoughts, the investigation process of research has to continue with other methods (e.g., experiments, interviews with probing). The approach I put forward aims to provide such expansion of insights: the technique allows to attach additional information reported by viewers to objects or elements they choose and relate to in the visual material (e.g., a print ad, a photograph). Its starting point is based on visual thinking rather than verbal explications, therefore I named it Visual Impression Metrics. The following chart of a framework model of communication depicts plausible factors that may trigger the processing of ‘objects’ in a visual marketing material from the consumers’ point-of-view:

Two notes to the chart: (1) The combination of verbal and visual elements that correspond with each other is fundamental to encoding; (2) From an information processing perspective, consumers may go back and forth between attention to and processing of various elements or objects in the whole image.

A pivotal strength of eye tracking is the ability to trace when attention is awarded unconsciously to objects in the ad in addition to conscious attention — viewers transit between these processes as they move from bottom-up to top-down (and vice versa) processing of the information found in the visual material. A consequence of this, however, is that respondents are not likely to be able to comment on objects they attended to unconsciously. An approach as described above, while more reliant on conscious processes, may be used in conjunction with eye tracking so as to shed more light on how consumers-viewers utilise information from objects in the visual scene, their meanings or implications for them.

In the other realm of research using visual images, a pictorial image is utilised as an aid to enquiring on a topic or concept rather than being the subject of research. An interviewer may show the respondent a picture selected by the research team and invite him or her to discuss it (e.g., what they see in the picture, what it reminds them of, what associations it brings up about a product/brand). When showing the same picture to a group or sample of respondents, it is possible to compare and aggregate how various consumers relate and react to the same image. On the other hand, a picture retrieved and brought by each consumer-respondent is much more capable to entail an idea associated with a brand that is meaningful and relevant to that individual. Gerald Zaltman’s method for eliciting metaphors by visual images is most appropriate to that end — it is free of the assumptions or expectations of the marketers or researchers. But on looking at the interviewing process, it is apparent that separating the thoughts of the interviewer from those of the interviewee is not obvious. A main theme of the instructions of Zaltman to interviewers for probing, as demonstrated in his book “How Customers Think” (Chapter 4 Appendix), is to avoid offering an interviewee their own explanations or interpretations of a reply just given by him or her nor implying their own understanding of the picture. An effective probing approach is to follow-up on a last reply of the interviewee using his or her own words (4). The line between desired and flawed probing in examples given, however, is not always sharp and clear — one needs to carefully make the vital distinction between guiding the interviewee (right) and leading the interviewee (wrong).

Selecting a pictorial image as a stimulus to trigger an “enquiry” in a survey (i.e., quantitative research) needs to be done by careful screening and examination, guided by pre-tests and/or qualitative research techniques, in order to present a picture that conveys the target concepts one wishes to study or test. Vice versa, key constructs (e.g., emotions, thoughts or associations) revealed in a qualitative study by using visual images should be substantiated through quantitative methods for the relevant target population of consumers. Thus, researchers would choose for a survey a pictorial image they appraise, according to findings of the qualitative study, as the best representative or conveyor of the concept of interest shared by the consumers. The method of Visual Impression Metrics, for instance, is suitable for certifying whether focal figures or objects as portrayed in the image scene carry the expected meaning.

The possibilities for research with visual images are numerous; they offer some intriguing opportunities for enriching our consumer insights. Visual images evoke more quickly intuitive and emotional responses, they often succeed in encouraging people to share their thoughts and feelings, and may engage forms of visual thinking that differ from verbal thinking. Depending on context and purpose, visual images can be used in marketing research to enhance the quality, reliability and validity of our findings, and thereby improve the knowledge of marketers about their consumers.

Ron Ventura, Ph.D. (Marketing)

 

Notes:

(1) “How Customers Think: Essential Insights into the Mind of the Market”, Gerald Zaltman, 2003, Boston, MA: Harvard Business School Press.

(2) “Attention Capture and Transfer in Advertising: Brand, Pictorial and Text-Size Effects”, Rik Pieters and Michel Wedel, 2004, Journal of Marketing, 68 (Apr.), pp. 36-50.

(3) “Eye Movements During Reading, Scene Perception, Visual Search, and While Looking at Print Advertisements”, Keith Rayner and Monica S. Castelhano, 2008; In Visual Marketing: From Attention to Action, Michel Wedel and Rik Pieters (eds.)[pp. 9-42], London, New-York: Lawrence Erlbaum Associates.

(4) Ibid. 1.

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Models of consumer preferences and choice most often follow a rational paradigm of decision-making — the consumer weights the values of attributes of each alternative considered  to derive its total utility value, then compares those utilities and chooses the alternative achieving maximum utility. Assuming this well-defined and straightforward process, it is relatively easy to construct predictions of preference shares or market shares. Yet the decision rule hereby described  is not so trivial or easy for consumers to apply; it requires a relatively large amount of information and a non-negligible computation effort. In order to simplify the decision process, consumers may use any of a selection of short-cut rules or heuristics (alternative-based and attribute-based rules) that demand less information and less cognitive effort to make a choice. On many occasions the process may not even include the rational-optimizing weighted additive (WADD) rule. Hence, decision processes tend to be more diversified than normally assumed in marketing research.

When consumers apply rules other than the WADD rule, predicted preference shares based on a maximum utility criterion for choice may off-shoot consumer actual choices. Indeed, there are other factors that may be implicated in biasing predicted preference shares when measuring preferences in consumer surveys — factors such as product availability and the visual display of products in stores, accessibility of brand and product information in the real world, and time constraints. Preference shares may further deviate from actual market shares when quantity is a key factor (e.g., a consumer buys 1, 2 or 4 cups ofyoghurt on any single purchase occasion and when purchase frequency varies). However, I will focus in this post-article on the factor that resides within the consumers, that is, the decision rules they use. In fact, decision rules are elected by consumers in adaptation to situational and environmental conditions as noted above.

Different techniques have been developed since the 1950s for identifying and tracking the rules people utilise to reach a choice decision between alternatives (e.g., travel destinations, car brand and model). Not less important than identifying single rules is the task of mapping the sequence in which these are used in a complete decision process, and this is where the study of decision-making can become complex.

  • At the foundation of research in this field we find the “think-aloud” verbal protocol method for recording and mapping decision processes initiated by Simon and Newell. In this method, a consumer is given a choice problem and is requested to talk aloud  whatever thoughts come to his/her mind while (or right after) performing the task and reaching a decision (note: verbalising the thoughts aloud, not explaining them!) The content of protocols is later coded into procedures or mental operations that make up generic rules, and decision processes are mapped.
  • A related technique uses information display boards of brands and their associated product attributes. By tracking the sequences in which consumers retrieve information items (i.e., cells in a brand-attribute matrix) the researcher can infer what rules are likely to have been applied and map decision processes.  This approach is implemented in a software known as MouseLab for computer-aided data collection and analysis of the decision processes (now also available as an Internet-based application MouselabWEB). Researchers Payne, Bettmann and Johnson who developed the original software applied it for their ground-breaking work on the behaviour of the adaptive decision maker in selecting rules and constructing decision strategies.
  • Another interesting approach (Active Information Search [Brucks]) that allows decision makers to pose their own queries for product information (e.g., physical attributes, usage situations, price) may be seen as a semi-structured level between the protocol approach (least structured) and the approach of MouseLab (most structured).

More recently researchers in the area of decision-making have shown increased interest in the methodology of eye tracking for investigating search patterns for information. By measuring eye movements with specialised optical equipment, researchers capture locations where the eye fixates in a stimulus display (i.e., takes-in information) in-between rapid movements (saccades). This approach is already adopted often in studies of advertising effectiveness and shopper behaviour in retail scenes.

Researchers Reisen, Hoffrage, and Mast took the challenge of evaluating different techniques for tracking consumer decision processes (2008*). Primarily, they developed a multi-method framework approach called InterActive Process Tracing (IAPT). The investigative procedure has three stages:

  1. Active Information Search (selecting relevant attributes);
  2. Seeking specific attribute values of product alternatives (information acquisition) and choosing a preferred alternative;
  3. Verbal reporting of decision processes (i.e., retrospective but with assistance from a moderator in formalising descriptions of the decision rules applied).

Firstly, the IAPT approach allows to integrate three methods, one at each stage, and compare their contributions to our understanding of decision processes and predicting choices. Secondly, the researchers provide new methodological insights by comparing two alternative methods for the second stage: the more veteran method of MouseLab and the new method of eye tracking for recording search patterns.

  • In the second stage of information acquisition and choice, participants were shown several choice sets. Each choice set comprised a different selection of four alternative mobile phones (brands and their models) randomly drawn from a pool of phones available in the market. The form in which information on alternatives in a choice set is displayed is contingent on the method used for registering information acquisition: by mouse clicks or by eye fixations.

Let us consider first some key findings and insights obtained in the first study of Reisen and his colleagues, using MouseLab in the second stage for recording information acquisition:

  • Two major types of strategies were identified: additive strategies (alternative-based) with equal or varied weights assigned to attributes or elimination (by-attribute) simplifying strategies — the elimination strategies were used more frequently (30 out of 31 respondents) than additive strategies (23 out of 31) though most participants (71%) combined strategies of both types.
  • The difference in predictive ability of choices between additive (optimizing) strategies and elimination (simplifying) strategies is less dramatic as many might expect (55%-57% versus 47%-51%, respectively). Notably, predictive ability when using rules as described by participants in their individual protocols was much higher (73%) than in case of applying rules pre-defined according to literature as done for the comparison of strategies above.
  • When using an elimination strategy, logic suggests that one would not seek more information on an alternative after it had been eliminated. Interestingly, however, acquisition of specific attribute values was traced for an alternative that was supposedly already eliminated by respondents using an elimination-by-attribute type of strategy (67%). This  makes sense, according to the researchers, if consumers initially acquired information as they explored the alternatives available for choice and only in a second phase committed to a decision strategy (i.e., the strategy eventually described in the verbal protocol).

In the second study, Reisen, Hoffrage, and Mast tested two methods for tracking search and information acquisition in the second stage: MouseLab and eye tracking, the more recent entry into this field of research. The information acquisition of each participant in this study was measured using both techniques sequentially (i.e., first series of choice tasks with MouseLab, and the second series with eye tracking [or vice versa]; half of the choice sets overlapped between series of choice tasks).  The following are noteworthy findings and insights:

  • Frequencies of usage of additive and elimination strategies were similar in relative terms to those found in the first study.
  • Predictive ability in choice tasks applying MouseLab was a little higher (69%) than in choice tasks with eye tracking (63%), but not statistically significant. More importantly, in repeated choice tasks (i.e., the same choice set), when respondents remained consistent in their choice under both conditions, predictive ability of the choice of that same alternative was considerably higher (78%) than in inconsistent tasks (40%).
  • Respondents took more time overall to acquire information by mouse-click (MouseLab: 37 seconds) than by just eye gazing at the display (Eye Tracking: 20 seconds).
  • However, eye gazing appears as more time-efficient from a respondent’s perspective: when eye tracking respondents accessed items in cells about 42 times on average compared with 22 cell-accesses while employing MouseLab. It is noted that the number of different cells accessed was similar between conditions (15-17 cells). Put differently, eye tracking allows for a higher rate of re-acquiring or double-checking information items rather than inspecting more of the information available on alternatives.
  • In this study again it was observed that “regardless of the condition [MouseLab or Eye Tracking], participants accessed about 50% more information than prescribed by their strategy.” Yet, it is also reported that considering the information that is needed by a strategy, the greater part of it (82%) was accessed as expected. That is, the excessive information acquisition does not come necessarily at the expense of required information.
  • Note: Findings from the second study suggest enlightening clues as to how respondents tackle choice tasks in choice-based conjoint studies. In particular, it points at the selective manner in which respondents consider information on alternatives included in a choice task, looking at only some of the attribute values shown, and yet they may re-access those same values several times until making their choice. Apparently this process may take less than 30 seconds to complete.

Following their assessment of findings in the second study, Reisen and his colleagues seem unconvinced that using eye tracking in IAPT is advantageous over MouseLab. With eye tracking respondents can finish each a choice task more quickly and yet access more information. However, they tend to repeatedly access the same information for a purpose the researchers describe as “validating a tentative choice.” Further difficulties in using the eye tracking methodology for tracing decision processes are suspected inaccuracies in capturing information acquisition (i.e., a respondent accidentally fixates on a cell while thinking; when respondents voluntarily mouse-click cells, the search paths seem more systematic) and failed calibration of equipment that leads to loss of unusable data.   The researchers conclude (p. 655):

It appears that this methodology improves neither the exactness of the description of the cognitive processes nor the quality of the results concerning the information search. Although this method allows for a more natural way of searching for information, it does not provide more informative data than does Mouselab.

Eye tracking can teach us a lot about how consumers look at and attend to different portions of ads such as bodies of pictures, text and brand logos, their  appreciation of package designs, or how shoppers inspect product displays on supermarket shelves. It can be helpful also in studying the decision processes consumers follow, but it is not more appropriate and accurate than former methods known for a similar purpose like MouseLab. More importantly, each of the four methods considered for IAPT specialises in capturing different aspects of the decision process (e.g., characterising patterns of information acquisitions vis-a-vis identifying the decision rule applied). A primary lesson to be taken from this research is that using multiple complementary methods with different scopes of specialisations can contribute considerably to obtaining a better mapping of decision processes and building models with higher predictive ability.

Ron Ventura, Ph.D. (Marketing)

Reference:

Identifying Decision Strategies in a Consumer Choice Situation, Nils Reisen, Ulrich Offrage, and Fred W. Mast, 2008, Judgment and Decision Making, 3 (8), pp. 641-658.

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