DOI: 10.31038/AWHC.2021454

Abstract

The study explores the mind of the shopper from the inside out, focusing on motives and interests emerging from the respondent’s own perception of herself with respect to an ideal skincare shopping experience. A Mind Genomics cartography (experiment) investigated the phrases that a female cosmetic shopper would use to describe herself in terms of the ultimate skincare shopping experience. Respondents evaluated sixty-three unique vignettes, created from 35 different phases, vignettes created according to a permuted experimental design. The analysis focused on the discovery of ‘mind-sets’, groups of respondents who showed similar patterns in the element which they felt best escribed them. Three mind-sets emerged: Ebullient, Insecure, and Perfectionist, respectively. A second analysis (scenario analysis) looked at the way five different emotional outcomes (e.g., pleasurable, informative) interacted with the remaining elements. Four of the five emotional outcomes (pleasurable, informative, glamorizing, and therapeutic) interacted with other element, but only among two mind-sets (Ebullient, Perfectionists). There were no interactions for the third mind-set, Insecure, or interactions when the emotional outcome was stated as ‘transformative.’ The paper shows the potential of deep analysis when the data are collected in a systematized fashion, using permutable experimental designs, and individual-level modeling.

Introduction

If we were to go back 70 years ago, to the beginning of the 1950’s, visiting companies manufacturing and marketing cosmetics, we might find an interesting, albeit strange world. It would be a world where there were people whom today we call ‘giants,’ people whose names are on the door, and who are revered for their vision, their inventiveness, their marketing prowess, and for the fact that they are no longer around to prove the opposite. The 1950’s, and the war period just before, was the era of the great person. These early giants ‘knew’ at an intuitive level what the customer wanted, and how to approach the customer. The head of the company might not know how to formulate the product but knew what the customer would like One might do research on customers, perhaps to see who buys, but not for creative purposes. The research would be labelled as sales research, the recitation of ‘what happened,’ and perhaps ‘why’.

At the same time, the advertising industry was promoting the cult of expert as well, not in the creation of the vision for the product, and certainly not the product itself, but rather in the presentation of the product to the public. What to say about the product, what to show about the product, how to communicate the hard-to-communicate emotions and benefits of the cosmetic were left up to the brilliant advertisers of the 1950’s, so-called creative geniuses.

The foregoing is by way of introducing our study, something from the middle of the second decade of this 21^st century, 60 years later, the span of two-three professional lifetimes, after many of the great cosmetic founds and the legendary advertising genius, built the business, and retired. The focus of this paper is not the past, but the knowledge of today’s cosmetic consumer, the ‘she’ who buys in these still early years of the 21^st century.

Asking a Respondent about Herself

Consumer researcher have realized that people differ dramatically from each, not necessarily in who they are as defined by conventional demographics, but by what they do, and in a much deeper way by who they are. What people do in the world of shopping for cosmetics can be further broken down into where they shop, their self-described motives and shopping behavior, and what they end up buying. For many products, this knowledge suffices. Whether cosmetics enjoy their greatest success at the counter, and should be sold that way, is hard to answer. The success of selling high end cosmetics on the Internet may address the fact that one does not need a profoundly deep understanding of people’s mind.

There are papers addressing cosmetic sales at the store counter, and in some case contrasting the sales process with that occurring online. The issue is that the papers give a sense of general differences, but they do not give the specificity, or the insight needed to be translated into business [1,2]. For example, we know from the published literature that people define themselves by the products they buy, and in the case of cosmetics, the products that women purchase have symbolic meanings, with these meanings transferred to the purchaser when she uses the product. For example, a superior cosmetic product may enhance a woman’s self-esteem when she uses it [3]. Furthermore, as Wu & Lee (2016) wrote in their paper on impulse buying in cosmetics marketing “Cosmetics differ from other retail goods in so far as the ‘consumption situation’ must influence consumers’ ‘impulse buying behaviour’ through ‘experiential marketing’ [4]. “ In other words, for at least one group (female, unmarried, age 30-35, university degree), it is the experience at the cometic counter in a store which often leads to an impulse purchase.

The foregoing discoveries tell us that it is important to give the cosmetic customer the ‘right experience’ at the cosmetic counter. That information is helpful. It is in the form of a sociological report or anthropological report. We now know the behavior, observing from the outside in. We know what happens; we know that there is a regular pattern. What we do not know is the specifics, the words, the phrases which address the external behaviors, and perhaps even drive them.

It was towards the goal of a profound understood of the high-end shopper of cosmetics and fragrances that this study was addressed. The reality was that a great deal about how women shop for cosmetics and fragrances were already known, but the different activities, appearing to be similar to each in other when looked at against the vast array of behaviors, were actually radically different. The study was to answer the very practical question of what a high-end shopping experience should be like in the mind of the customer. The approach, Mind Genomics [5] had already been used to explore the ‘High End’ of semi-luxury items [6], as well as High End perfumes [7].

Mind Genomics

Mind Genomics is an emerging branch of science focusing on the experience of the everyday, a topic that has not been well explored, despite its ubiquity. The topics of everyday, such as the purchase of cosmetics, are often topics left to business (recording what people buy), to advertising (what persuades), to formulation (what works), and the trade (how to move the product into the hands of the customer). These different groups, business, advertising, and so forth, are not oriented towards developing systematic knowledge of an archival sort, shareable with others, simply because cosmetics are sold for the benefit of the company.

Mind Genomics moves on a different path. With part of its history traceable to experimental psychology, the goal of Mind Genomics is to relate aspects of a topic such as cosmetics to the way people respond. The research strategy is experimentation, where the independent variable is a description of the one’s experience with cosmetics, and the dependent variable is a rating [8,9]. In this project the focus is on the way the cosmetic experience is described, and the response of people as to whether the description applies to them.

Beyond the experimentation is the use of statistical methods to create ecologically valid test stimuli, viz. combinations, and vignettes. In the ordinary research world, the respondent would be presented with statements about the cosmetic experience, especially the purchasing experience. The statements would be presented one statement at a time The respondent would then rate each phase, each statement about the shopping experience, using a scale to show the degree to which the ‘statement applies to me’. The problem with the one-at-a-time stimulus is that it forces the respondent to intellectualize the evaluation. Each phrase or test stimulus must be evaluated on the same scale, although the phrases might be of different types (e.g., how I feel when i put on makeup vs what type of experience do i want to when i go shopping). Respondents have a very difficult time maintaining the same criterion for different types of elements. An easier way is to mix the different statements, create small combinations, vignettes, acquire the reactions to the vignettes and deconstruct the reactions to the contributions of the individual elements. This activity might seem convoluted, but it gets around the problem of forcing the respondent to maintain a constant evaluation criterion with radically different elements. The reason the vignette approach works is because the compound description defies simple classification. The respondent ends up using the same criterion for all vignettes, and generally stops trying to outwit the system [10].

Learning ‘Who I am as a Cosmetic Shopper’ within the Design and Analytic Framework of Mind Genomics

At the time of the research, qualitative studies with high end shoppers emerged with the obvious finding that shoppers go into stores with different objectives. The earlier work had focused on things under the store’s control, and under the manufacturer’s control. The focus was on what was being sold, and the messages communicated to different types of customers. The typing of customers was based on then standard psychographics thinking, viz., that there are a limited number of basic ‘minds or ‘mind-set’ who do the shopping. The objective was to identify these basic groups, and to assign each woman shopper to one of these basic groups. Bringing the topic forward, the objective of the study reported here is to understand how the respondent defines herself as a cosmetic shopper, but a shopper who goes to buy cosmetics for different reasons. So, we are interested in the combination WHO she is, and the emotional OUTCOME.

Given the foregoing issues, it appeared possible to apply the Mind Genomics approach with a slight change. The world view of Mind Genomics is the analysis of decisions made about the world of the everyday. The standard Mind Genomics process defines the topic, creates a set of questions which tell a story about the topic, and then generate sets of answers to each question. In most studies the Mind Genomics procedure creates short descriptions of a product or service by combining the answers or elements, doing according to an experimental design. The respondent reads the set of descriptions, the offerings, and responds by separately rating each vignette in the set. The evaluation is usually ‘good/bad’, ‘go/no go’, etc. The analysis of Mind Genomics deconstructs the evaluative rating into the part-worth contributions of the different answers, the different elements. The process is simple, all of the elements are of the same type, and there is no ambiguity.

When all of elements are ‘external’ there is no issue. The respondent would be presented with the different combinations messages about the cosmetic purchase situation and instructed to rate the degree to which the combination fits the respondent. The deconstruction of the ratings show how strongly element fits the respondent. The e research ‘twist’ in this paper involves the measurement of statements about the respondent’s predilections, the nature of the behavior at the cosmetic counter, and a phrase about one off five internalized states of feeling about what the purchase experience should create.

During the early phases of the project, it became increasingly clear that the same person could shop for different reasons at different times. The five different end uses emerged as a range of alternative ‘psychological states’ that on person might have, albeit at different times. Whether these five states of mind could be separately experienced by one shopper was not of interest. It was sufficient to find out what messages described a person who was in one of the five states. That information was new to the marketing team. The five states about the ultimate skincare shopping experience were pleasurable, informative, glamorizing, therapeutic, or transformative, respectively.

The additional requirement was that the research should not call direct attention to any overall feeling about the shopping experience. The overall feeling should be an element in the study, on par with the other elements. The concern was that in a standard approach using today’s tools of market research, the researcher might simply create a matrix, the columns corresponding to the five states of ultimate shopping experience (viz., pleasurable .. transformative), the rows corresponding to different statements about the experience, and then for each column (state of experience), instruct the respondent to check every element which applies, or rate the fit of each element to the each of the five ultimate states of shopping experience. That approach would provide data, it always does. The question was whether the data would be meaningful. Simply asking the respondent to do something, having the respondent fulfill the request, and analyzing the data does not necessarily make the results meaningful.

Research and Analytic Steps Applied by Mind Genomics

Step 1: Define the Raw Material, Specifically Topic, Questions, and Elements (Answers to the Questions)

Mind Genomics works by presenting combinations of messages to the respondent and getting the answer. The steps involve the topic, questions which ‘tell a story’, and a variety of stand-alone phrases which answer questions.

The topic is ‘What describes ME’. Table 1 shows the seven questions, and the five answers to each question. These questions attempt to tell a story. The requirement to ‘tell a story’ Is not an absolute requirement. Rather, the idea of telling a story is to provide a framework wherein information can be presented to the respondent in a meaningful and seemingly rational format.

Table 1: The raw material for the study, comprising seven questions and five elements (answers) for each question.

Mind Genomics is flexible. Occasionally, ‘stray elements’ with no home find themselves inserted into a question. Thus, Question 1 (Describe your skin – what you have, what you want), has four elements about the skin (A1-A4), and room for a fifth element. In that case, A5 was put in (A5: For me it’ about staying sexy).It makes no difference as long as the element does not clearly contradict elements from other questions. The structure of questions and answers is done for bookkeeping purposes, and as an aid to the underlying experimental design. The respondents never see the questions. They only see answers, or more accurately, they only see combinations of answers.

The seventh question is the key to the study because it presents five ways of thinking about the ideal experience. There are five such ways of thinking, which will play an important role in the analysis. However, at this time, at the start of the study, when the elements are being assembled, Question 7 (What would you say is your ultimate skincare shopping experience) is simply a question, and the answers are simply elements.

Step 2: Create 63 Vignettes or Combinations of Elements Using an Experimental Design

It is at this point that Mind Genomics departs from more conventional methods. It will be these small combinations of 2-5 elements each that will be evaluated by the respondent, rather than the single element. Figure 1 shows an example of a vignette.

Figure 1: Example of a four-element vignette.

The experimental design ensures that each of the 63 vignettes comprises the appropriate number of elements and the specific combination of elements. The experimental design is nothing more than a prescription for what elements will be combined. The experimental design is created to allow the 63 ratings, one per vignette, from each respondent to be analyzed by OLS (ordinary least-squares) regression at the level of each individual respondent. At the level of the individual respondent all elements appear equally often, no vignette comprises fewer than two elements nor more than five elements, and each respondent evaluates different combinations, because the elements are permuted. That is, the permutation simply changes the code, so that A1 might become A3, A2 become A4, A5 becomes A2 etc. [11].

Figure 1 shows an example of a four-element vignette. The elements are put together without any connectives. The structure of the vignette itself is a set of texts put one below the other, all centered It is easy to graze across the text and assign a rating. The structure of the vignette prevents it from become a densely worded concept. The respondent has no trouble le ‘grazing’ through the vignette, assigning a rating, and then going on to the next vignette.

Step 3: Create a Rating Scale, and an Orientation Page

Figure 2 shows the orientation page, and the rating scale. The respondent does not need an introduction to the topic, other than knowing the name of the study, the rating scale (How well does this concept describe YOU?), and some additional house-keeping information. The vignette gives away a little as possible about the nature of the design.

Figure 2: The orientation page with the rating scale.

Step 4: Invite Respondents to Participate

The respondents comprise individuals who sign up for so-called ‘online panels.’ The individuals provide information about which they are their interests, etc. and ‘opt in’ to participate. With the increasing number of online surveys, working with these panelists has become the preferred method for research. The respondents do the surveys for compensation, but the specific agreement remains a matter between the individual and the online panel company. As a cautionary note, it is usually easier to work with these online panel providers than to source panelists oneself.

Step 5: Acquire the Data and Transform the Data into a Form Usable for Subsequent Analyses

The actual interview lasts about 10 minutes, with the respondent reading the orientation, and rating the 63 vignettes, followed by a self-profiling questionnaire.

The respondents rated each of the vignettes on a 9-point scale. Managers who use the data from these types of studies often express difficulty understanding what the ratings mean. Indeed, such difficulties are more widespread than one would like to believe. It is easy to work with an anchored Likert Scale, such as our 1-9 scale, but what does a rating of 4 or 6 or 7 mean? The question is profound. S.S. Stevens, legendary psychophysicist at Harvard University in Experimental Psychology during the years 1938 to 1973, often stated as much, when he averred that one of the hardest problems in science is to convert a continuum to a yes/no (Stevens, 1968, personal communication to author) The issue of the ‘best’ conversation is deceptively simple until the researcher is faced with a practical issue such as communicating with managers.

The common practice by consumer researchers is to divide this anchored Likert or category scale into two parts, corresponding to NO and YES, respectively. The division point is a matter of personal preference. For this study, the focus was on a stringent definition of ‘fits me’. The stringent criterion led to this division: Ratings 1-7 transformed to 0, and Ratings 8-9 transformed to 100. Following the transformation, a vanishingly small random number was added to the transformed ratings. The magnitude of the number (<10-5) is such that it adds the requisite variation to the rating in case all ratings from a respondent would end up being 1-7 (all transformed to 0) or 8-9 (all transformed to 100). In that case the regression program would simply crash without the miniscule variation introduced by the random number.

Figure 3 shows a preview of the data that will be used for rest of the analysis. A total of 251 women, cosmetic shoppers participated, each evaluated 63 different vignettes. Each respondent generates an average transformed value, which shows us the degree to which the respondent feels that the vignette describes her. The distribution of this average is shown by graph. The average ranges from 0 to 100, again with each circle corresponding to a respondent.

Figure 3: Distribution of average Top2 ratings, by vignettes comprising 2, 3, 4 and 5 elements, respectively. Each point in the graph corresponds to one of the 251 respondents.

The figure is broken out into the averages of each of the 251 respondent for those vignettes comprising two elements, three elements, four elements, and five elements, respectively. As the number of elements in the vignette increases, there is a sense conveyed by the graph that a greater number of respondents feel on average that the vignette DOES NOT DESCRIBE THEM (viz., the distribution skews to the left, and the lower averages). As yet, however, we do not know anything about the ‘internals’ of the vignettes, viz., which elements drive a feeling of ‘describes me’.

Step 6: Create a Data Matrix Ready for OLS (Ordinary Least-squares) Regression Analysis

The data matrix comprises 63 rows for each respondent or 15,813 rows for the total panel of 251 respondents. Each row corresponds to a specific vignette, an a specific respone.t

The columns are set up as following:

Column 1 = Column order in the matrix. This is very important, when the researcher wishes to sort the data, and do analyses on certain parts of the matrix. Giving each row an order number allows the researcher to sort the data at the end of the analysis, so the matrix can be returned to its original form.

Column 2 = Respondent identification number (101-351). The respondent identification number is repeated 63 times, once for each vignette.

Column 3 = Order of testing for that panelist (1-63).

Column 4-38 = One column for each element (A1-G5). There are 35 columns for the elements. For each row, the cells in columns 4-38 either have the number ‘0’ when the element is missing from the vignette corresponding to the row, or the number ‘1’ when the element is present in the vignette corresponding to the row.

Column 39 -Rating assigned by the respondent on the 9-point scale

Column 40 – The transformed rating from column 39, being either 0 or 100 added to the vanishingly small random number. For ratings of 1-7 the transformed value is 0. For ratings of 8-9 the transformed value is 100.

Column 41 – Membership of the respondent in a two-mind-set solution, explained below

Column 42 – Membership of the respondent in a three-mind-set solution, explained below

Remaining columns – classification information about the respondent (age, products used, stores shopped, education, income, etc.).

Step 6: Create a Grand Model for the Full Set of Respondents

Recall that the variable TOP2 takes on the value 0 when the vignette was assigned the rating 1-7 and takes on the value of 100 when the vignette was assigned the rating 8-9. The model using all the data is expressed as: TOP2 = k1(A1) + k2(A2) … k35(G5).

The foregoing equation comprises 35 terms, one term for each of the 35 elements. The coefficients are the weighting factors. The model does not use an additive constant, the reason being that the model will be used in several different ways, and the elements must have coefficients that are directly comparable to each other, without the contribution of an additive constant. In this way there is no other influence on the magnitude of the coefficients. It is important to note that the coefficients estimated with an additive constant show very similar patterns to the coefficients estimated without an additive constant, as Figure 4 shows.

Figure 4: The 35 coefficients for the total panel estimated with an additive constant in the model (abscissa) versus without an additive constant (ordinate).

Table 2 shows the strong performing elements for the total panel. For these models or equations without the additive constant, coefficients of 15 or higher are ‘meaningful’ from previous observations. Surprisingly, out of 35 elements selected by professionals in the cosmetic business, only three elements emerge as strong performers, strong definers of oneself. This is a remarkable finding. One would have thought that there would be many more strong-performing elements. As the data will suggest, the paucity of strong performing elements may be the consequence of the existence of underlying mind-sets, with different points of view, which end up neutralizing each other in the data from the total panel.

Table 2: Strong performing elements for the total panel.

Step 6: Create 251 Individual-level Models, Cluster the Individuals Using the Models, Extract Two and then Three Clusters (Mind-Sets)

A hallmark of Mind Genomics is the use of the data to extract mind-sets, groups of individuals with similar patterns of coefficients. The coefficients, in turn, show how the respondent ‘weights’ each of the 35 elements to drive the rating of TOP2 (viz., the rating of 100 after the transformation).

A key benefit of the underlying experimental design is that each respondent from the 251 respondents evaluated the precise elements so that the researcher can apply OLS regression to the data from each respondent. This approach produces a matrix of 251 rows, one per respondent, and 35 columns, one per element.

The matrix becomes the basis for clustering, to identify basic groups. Before the clustering, however, the matrix was further subject to statistical analysis, using principal components factor analysis. The 35 variables, viz. the coefficients, were reduced to five independent factors. Each respondent was assigned by the factor analysis to a location in the new five-dimensional space. The locations are defined by the ‘factor scores’ which differ by respondent, and map to the original 35 coefficients.

The final step in the clustering was to apply k-means clustering to the newly created data matrix comprising 251 rows (one row per respondent) and five columns (one column for each newly created factor). The clustering computed a distance between each pair of the 251 respondents, and located the respondents first into two groups, and then into three groups [12]. The two groups (clusters, mind-sets) could not be easily interpreted because there were too many ‘stories’ intertwined. The three groups were far more easily to interpret.

Table 3 suggests three different and easy to name mind-sets, each again showing fewer than 35 elements which perform strongly, viz., with a coefficient of 15 or higher. The three mind-sets are distributed across age and income (Table 4).

Table 3: Strong performing elements for three emergent mind-sets.

Table 4: Age and income of the total panel and the three mind-set.

MS 1 (Exuberant) – A sense of a woman who loves life, and wants to look it, and live it.

MS2 (Insecure) – A person who wants to feel secure. Surprisingly, this mind-set reacts strongly to only one element.

MS3 (Perfectionist) – A person who wants to know what she is doing, and ‘get it right’.

Interaction – How end uses acts as ‘directors’ of the performance of other elements

Ewald & Moskowitz (2007) introduced the of scenario analysis to understand the interactions among variables [13]. The idea is that elements may interact with each other, affecting the way that respondents respond to the vignette. For example, when the item can have one of several different brands, having one brand in the vignette can set an expectation, whereas having a different brand in the vignette will set a different expectation. The way to discern the effect of the brand on the performance of the elements is to separate the vignettes by brand, thus creating strata, and run the study for each stratum separately in that way it is possible to see how the coefficients of all of the non-brand elements change when the brand changes.

In our study on cosmetics, we have one group of elements, those in Question G, on one’s ideal skincare shopping experience. There are five different statements about ultimate experience, ranging from Pleasurable (G1) to Transformative (G5). Tables 2 and 3 suggest that these are not important elements in the mind of the respondent to describe oneself, a perfectly plausible result. The elements deal with the state of mind. Perhaps one does not feel that the ultimate skincare shopping experience is relevant as a descriptor of oneself.

In this final analysis of the data, we revisit the ultimate skincare shopping experience, not as an element which varies in competition with four other elements, but rather as a constant, present in all vignettes in a stratum. The process is straightforward. W first creates six strata of vignettes from the raw data. A stratum comprises all vignettes containing one specific elements from Question G on ideal experience This first step in the scenario analysis is means creating one stratum each for vignettes comprising G1, a second stratum for vignettes comprising G2, etc., and finally a sixth stratum for vignettes absent an element from G, by design.

We run the six regression equations, with only 30 elements (A1-F5). The elements G1-G5 are fixed in a stratum. We look at the strong performing elements, operationally defined as 30 or higher. When we do this analysis, we find the following:

1. For each of the five described ultimate skin care shopping experiences, no element reaches 30 when we look at the total panel across the six experiences (G0 and G1-G5).
2. When we look at mind-sets, one experience, ‘transformative’, fails to produce any element with coefficient of 30 above.
3. When we look at the mind-sets, each mind-set shows specific strong-performing elements.
4. We conclude that there is more to creating mind-sets about what elements drive strong responses. There is the distinct possibility that the focus must be on the combination of topic, mind-set and situation, as shown by Table 5, specifically by strong performing elements for a mind-set which change according to the stated ultimate skincare shopping experience.

Table 5: Scenario analysis, showing how the ultimate skincare shopping experience, when directly stated in the vignette, can increase the likelihood of a respondent saying, ‘it describes me’.

Discussion and Conclusions

Mind Genomics cartographies were designed for rapid scans of a product or service category, at first to identify what ideas as messages work, but then as way to understand the topic of how a person makes a decision within a specific, granular aspect of life. The early studies, of which this is an example, having been run about ten years ago, in 2012, required the managers, marketers, researchers and sales individuals to structure their thinking, and forced a systematized approach onto what had previously been the domain of the artist marketer or creative advertising professional.

It became clear over time with this study and with others that the experts had a great more knowledge than they were even aware of. There were ideas about what words and phrases worked, and senses of how these strong words and phrases were appropriate or not appropriate for given situations. What became also increasingly obvious was that the knowledge about the desired cosmetic experience was unorganized in the minds of the experts The knowledge was there, as well as the realization that there were profound differences among women in the way they shopped. Knowledge of this profound knowledge emerged as anecdotal, for the simple reason that the world of cosmetics (and fragrances) operated at two levels. At the very concrete level, there were product tests, and attitude and usage questions about brands, and feelings. The product tests were done on an as-needed basis, with technical reporting needed for product design and development. At a higher level was the tracking studies, about products used, feelings toward products, towards the category, and so forth. The results of these high-level studies emerged as charts, with a lot of trends, but very little specific information that could be used ‘as needed’, in an off-the-shelf format.

The data tables in this paper suggest immediately that there is a fertile field to be planted and tilled. This field comprises the systematic analysis of messaging, not simply to show to the ‘client’ that one’s creative ‘works, but rather a database which can drive new and important insights. The time has now arrived for the business community to invest in the systematic data basing of communications, phrases, not at the level of 20,000 feet, couched in generalities and endless tables, but rather in simple-to-use data created at the level of granular experience. The contribution of Mind Genomics to that prospect is a simple system, template (see www.BimiLeap.com), with rapid turnaround (hours), and of low cost and low risk. The study on cosmetics is simply one more example of what can be accomplished in a short time, with little effort.

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DOI: 10.31038/AWHC.2021453

Abstract

We present a meta-analysis of five Mind Genomics cartographies, done over a 20 year period, all dealing with exercise; purchasing exercise equipment (Buy It!, 2002), joining a branded, well know exercise spa franchised around the US (Curves, 2010), what to say to entice a person to join an exercise club or a gym (2012, student projects at Queens College), and, at the tail-end of a Covid-stricken 2019, how to lure customers back to using exercise equipment either at home and/or the same equipment in a gym (2020). We present the strong performing messages for each cartography, show the power of mind-set segmentation, the power of doing simple background research (student work in 2012), and introduce new foci as well, emotions linked with elements and engagement with messages revealed by response times. These five case histories, the paper shows how Mind Genomics was used by different people in the same ‘general space,’ over a 20-year period, and how Mind Genomics evolved to incorporate new measures alongside its basic measure.

Introduction

We live in a society focused on beauty, whether beauty of the face or of the body, occasionally of the spirit and even of the mind. Whereas among the ancients and their successors in the medieval and modern worlds the search for physical beauty was both artistic and philosophical, today it manifests itself in the world of cosmetics and the world of fitness. One can barely drive through a town, a city, even a rural area without seeing stores devoted to making people more beautiful.

Our focus in this paper is on the world of exercise, a world perhaps not as glamorous as the world of facial beauty and the world of fashion, but a world important, nonetheless. With increasing prosperity and with increasingly caloric intake, coupled with lessened demand on physical activity for work, there is the natural result of increased weight, of lessened body tone. Add that to the oft-feared factor of aging, and one has created a perfect storm for people to focus on what can turn back the clock of time.

The topic of gyms and fitness clubs has enjoyed a moderate amount of published research, and undoubtedly a great deal more one-off business studies deposited after use in the corporate files, presumably hidden away forever, or until the issue has been forgotten along with the research effort. The topics involved in gyms range from a focus on the trajectory of human development [1] to the emotions and motives for joining gyms [2], to health, both physical and psychological [3,4]. At the same time, there is the business aspect of clubs, the need to convince people that certain clubs are worth paying for [5-7].

During the past two decades, as Mind Genomics evolved into the science it is today, a variety of research efforts generated some interesting data on exercise, fitness, and related topics. We look at the salient results from five studies, one run 20 years ago, three run about 10 years ago, and one run a year ago. One study was run to understand how people wanted to shop for exercise equipment. The remaining four studies were run to understand what messages makes people want to join health spas and exercise gyms.

A Short Introduction to Mind Genomics

In 1964, mathematical psychologists R. Duncan Luce and John Tukey had been involved in creating a strong, new, axiom-based foundation for mathematical psychology, and particularly for powerful measurement without using numbers. The approach they developed was called conjoint measurement, the measurement of quantities by the measurement of combinations of such quantities. The approach sounds perfectly ordinary today; measurement mixtures of ideas and from the measurement of the mixtures deduce the measure of the components. The mathematics would appear in a daunting first paper in a new journal, the Journal of Mathematical Psychology, volume 1, number 1, first paper. IN other words, the premier new journal, and the lead article [8].

Conjoint measurement would have remained a stunning intellectual contribution, albeit an esoteric one, except for the efforts of Wharton business school professors Paul Green, Abba Krieger, and Yoram Wind, who would take it, make it practical, and apply it to various problems [9-11]. The literature using conjoint measurement would grow, until the approach would be used for products, for public policy, and so forth [12,13]. One needs only Google(r) the academic literature to get a sense of its applications.

Despite its popularity, most published papers, and indeed most likely research reports buried in corporate offices are one-off studies, executed to solve a particular problem. Conjoint measurement required knowledge of the variables, a painful creation of the combinations, a painful execution, and an analysis, not to mention an equal painful explanation of the method. In other words, the system was expensive, slow, and clunky, reserved for the most important (better read better-funded) project [14].

Mind Genomics emerged out of conjoint measurement, propelled by three key goals:

1. Create a system which, like Conjoint Measurement, would be able to measure the strength of ideas by measuring combinations of ideas, so-called vignettes. There was recognition that responses to vignettes could not be easily ‘faked’ as well as the fact that compound messages were more typical in the everyday world than single messages comprising one idea.
2. Make sure that each respondent evaluated a unique set of combinations of the same set of elements. This notion of different sets of the same elements emerged from the world of medicine, and the MRI, which takes pictures of the same tissue from different angles, and then recombines them in the analysis phase to come up with a single, 3-dimensional picture [15].
3. Create a system which could generate information that would be databased, with the data comparable within a study, and across studies [16].

The studies reported here were run in the same way, following these steps:

1. Raw Materials: Create a topic, create a set of questions which ‘tell a story’, and for each question provide a set of ‘answers’ which give different facts. The number of questions can vary but the number of answers for each question is always equal. The Mind Genomics method allows for a variety of such options, such as four questions with nine answers, six questions with six answers, four questions with four answers, etc. The most common study as of this writing (2021) is the design comprising four questions, each with four answers (16 elements).
2. Test Combinations: Create a fixed set of combinations, specified as an experimental design The experimental design prescribes the precise set of combinations, doing so by specifying which elements are put together. The experimental design is set up so that the variables are statistically independent, allowing methods such as OLS (ordinary least-squares) regression to reveal how each element or message contributes to the rating assigned to the vignette, viz., to the combination. Respondents do not rate the components; they rate the vignettes, the combinations, which is more natural to them [17].
3. Permute the Combination: There are a fixed set of combinations, but the combinations are permuted [15]. For example, one design comprised four questions, nine answers per question, 60 combinations, and many different variations of the underlying design with 60 combinations. This approach lets the ‘experiment’ cover much of the range. Thus, Mind Genomic trades off precision of measuring one small region of the possible combinations of messaging, and instead opts to measure a great deal of the region, albeit with less precision.
4. Transform the Ratings in a Way Which Permits Managers to Understand the Results More Easily: In these five studies, four used a 1-9 scale anchored at each end; one used a 5-point scale. Managers who work with the data derived from scale often ask ‘what does a 6 mean’ or a ’4’ mean, etc. To make the data easier for managers to use, consumer researchers and political pollsters have learned transform a scale with many points to a binary scale, no/yes. Following this practice, the researcher transformed the ratings on the 9-point scale to a binary scale (1-6 transformed to 0, ratings of 7-9 transformed to 100). In the case of the 5-point scale, the conversion was 1-3 transformed to 0, 4-5 transformed to 100. In each case, a vanishingly small random number was added to every data point, whether transformed to 0 or to 100, respectively. The random number ensured that no dependent variable would ever be all 0’s or all 100’s for any single individual. This slight variation ensured that the OLS regression always worked for each individual respondent
5. Create Equations Relating the Presence/Absence of the Elements to the Newly Created Binary Variables: The experimental design makes it possible to create the equation even with the data of one respondent. Whether the equation is created for a group of respondents, or even for a single individual, the equation is expressed in the same way:

For those experiment designs using the 4×9 structure (four questions, nine answers for each question): Binary Variable (TOP3) = k₀ + k₁(A1) + k₂(A2) … k₃₆(D9)

For those experimental; designs using the 6×6 structure (six questions, six answers for each question):

Binary Variable (TOP3) = k₀ + k₁(A1) + k₂(A2)… k₃₆ (F6)

For those experimental design using the 4×4 structure (four questions, four answers for each question): Binary Variable (Top2) = k₀ + k₁(A1) + k₂(A2) … k₁₆(D4)

6. Uncover Mind-sets: We often divide people by factors that we can easily measure. The easiest of course are WHO a person is, and in today’s digital world, what a person DOES. One can also divide people by the patterns of their answers to sets of questions, the pattern of answers to these questions assigning a person to a group based on attitude. These groups are large, and not particularly actionable. That, knows what a person buys do not tell us what messages move the respondent to buy, and what messages are turnoffs. We don’t typically think like that – viz., having details information about how the world of people’s minds divide for a topic. Usually, the topic is too small, too irrelevant for a deep, detailed investigation.

Mind Genomics create different groups of people, not based on who they are, but rather on the pattern of their reactions to limited types of information. That is, the division of people is not based on the way the person thinks about large (and important) problems, buts divides people on the pattern of responses to any topic, in ways which make sense. The method is called clustering [18]. Clustering is based upon mathematical criterion. However, the choice of the number of clusters to use is based upon two non-mathematical criteria. The first is parsimony – fewer clusters are better than more clusters. The second is interpretability – the clusters must tell a coherent story. Parsimony and interpretability are opposed; more clusters mean easier to tell a story, but only the truly relevant elements need be included in the cluster.

7. Relate the Elements to the Transformed Rating Scale to Show the Impact of Each Element: The OLS (ordinary least-squares) regression analysis uses the data defined by the researcher Once members of the different groups have been identified (viz., respondents belonging to Total, to Males vs Females; to mind-sets 1 vs 2 vs 3), etc. The subgroups are defined either by how the respondent describes himself or herself (done in the context of the study, through self-profiling classification), or the mind-sets are created through clustering and the data from all respondents in a specific mind-set or cluster are combined to create one dataset, and the OLS regression run using all data from that dataset.

Cartography 1 – Buying Exercise Equipment

Study # 1 was done in 2002, just about 20 years ago. The study was part of a large group of studies which focused on the nature of messaging which represent one’s idea shopping experience [13]. Figure 1 shows the wall of studies. All studies were identical except for the name of the product, and certain features and stores. The respondent selected the study and was led to the introduction for that study shown in Figure 2.



Figure 1: The wall of studies for Buy It! the respondent selected the study.



Figure 2: Respondent instructions for the Buy It! Study. The introduction comes from the study dealing with exercise equipment.

The different studies in the Buy It! project comprised four questions or silos, each with nine answers or elements. We will use the term element instead of answer. The 4×9 design (four questions, nine answers) generate 36 elements in total, combind according to an underlying experimental design into 60 vignettes. Each element appear an equal numbr of times across the 60 vignettes. Furthermore, the vignettes comprised 2-4 elements, so by design many of the vignettes were ‘incomplete,’ viz.,lacking an answer from one of the four questions. As noted above, each respondent evaluated a unique set of combinations, permutations of the original design.

Table 1 shows only those elements which exhibit at least one strong performing element. The element had to have an estimated coefficient of +8 or higher when the dependent variable was defined as a binary scale (1-6 → 0; 7=9 → 100). Table 1 thus shows the highlights. We show the elements first in terms of total panel, then in terms of three mind-sets to emerge, and then in terms of gender.

Table 1: Strong performing element for the 2002 Buy It! study on exercise equipment. (Table courtesy of It! Ventures).



1. The additive constant is a measure of the closeness of the vignette to one’s ideal shopping experience, in the absence of elements. The additive constant is purely theoretical, an estimated parameter. It does tell us, however, how positive the respondent is to the shopping experience. The additive constants are all low, between 26 and 34. It will be the elements which will make a difference.
2. In most Mind Genomics studies we end up with a few elements which do well. The total panel shows two elements, C2 (Let’s you get your shopping done quickly), and B1 (The price is JUST RIGHT … ALL OF THE TIME). They score 9 and 8, weaker than we will see when we turn to the three mind-sets which emerged from clustering the respondents based upon the similarities among the set of 36 coefficients.
3. It will be the mind-sets which show the big differences, differences which suggest three patterns:

a. Mind-Set 1 responds to the stores

b. Mind-Set 2 convenience

c. Mind-Set 3 wants price and choice.

4. It is mind-sets, not genders, which show the strong responses to the elements, a pattern which shows the power of Mind Genomics to uncover these basic groups in what would seem to be a population which is indifferent to the messages because the first data column. There is no indifference, but rather strong albeit different preference patterns.

Cartography 2 – What Messages Drive Women to Say that They Will Join Curves

Study # 2 was run in December 6-7, 2010, at the behest of Queens College, in collaboration with author HRM, and the mathematics department of Queens College. The study was part of the ‘vetting processes that Queens College used to create a mathematics course, Math 110, offered 2011-2013. Two of the owners of a local gymnasium were interested in signing up with Curves. They approached Queens College and funded the study, which was otherwise done on a pro bono basis with permission to publish the results of the study in two years.

The ingoing brief for the study was the following:

To prosper in the present economy Curves Owners must always be on the look-out for new ways to accommodate and engage their members. Curves owners need faster access to useful consumer insights in developing marketing messaging and programs, products and services. The Vision – Increase lasting memberships at Curves. The marketing and sales strategy to emerge from Mind Genomics was set forth at the set-up meeting:

Attract prospects to come into your club by using optimum message appearing to the general marketplace through: Web coupons, Mailing Value Coupon packs, Web site landing page, E-mails, In person

When the prospect comes in, lead the discussion with those Curves features that appeal most to each individual: Use a simple approach to tell you EXACTLY what to say to each individual

Study # 2 was run on December 6-7, 2010, with a population of women of all ages across the US. The raw material for the study came from current (2010 basis) marketing and advertising messaging from Curves website as well as from websites of peer fitness clubs: Butterflylife, Contours express, Fitness club Forwomen, and Lady of America, respectively.

Table 2 shows the strong performing elements for the study. There were 36 elements. Surprisingly, most of the elements fared quite poorly. That is, despite their use in the promotional literature, their actual performance was poor using the criterion of consumer reactions. This is often the finding of a Mind Genomics study, perhaps because the elements used have not been established ‘effective’, in a rigorous, unbiased manner. That is, most elements used in the study may well have been legacy elements, the origin and usefulness of lists lost if, in fact, they are really existed.

Table 2: Strong performing elements for the ‘Curves’ study.



A key benefit of the Mind Genomics approach is the ability to assign new people to the appropriate mind-set. This is done with the PVI (Personal Viewpoint Identifier). The PVI uses statistical methods such as DFA (Discriminant Function Analysis), and Decision Trees to create a limited set of questions emerging from the elements or answers of the study. These are the elements which best differentiate the mind-sets from each other. Figure 3 shows an example of how the PVI appears in 2010, summarizing the process. The figure shows the objective, the respondent introduction (left column), and then one of the three questions and one of the three outputs (right column). The new respondent completes a short set of questions. The pattern of responses to the questions suffices to assign that respondent to one of the three mind-sets just uncovered. The process lasts about 30-45 seconds.



Figure 3: The PVI (Personal Viewpoint Identifier), showing the introduction. one question (of three) from the PVI, and the feedback when the respondent is assigned to Mind-Set (Segment) 1. The figure shows the version of the PVI from 2010.

Cartography 3 – Joining a Fitness Club

Study #3 (as well as Study #4 on joining a gym) was done in 2012 at Queens College by a cadre of four students in the Math 110 course. The course was an experiment run for five semesters at Queens College of City University of New York. The idea was to teach the students a combination of critical/creative thinking with a dose of mathematics and mathematical thinking. The students were divided into groups of four individuals, instructed on the basics of Mind Genomics (at that time called Addressable Minds for business), and selected a topic. The two studies reported here, chosen by two separate groups of students, show the power of creative thinking, and the strong performance of the elements when the students were engaged in research, and challenged to do their best.

Figure 4 shows the orientation page to the study and is similar to the orientation pages of previous studies. The student was interested both in what drives interest in joining a fit club (question #1), as well as the emotional reaction to after reaching each vignette (question #2). This presentation of data from Study # 4 focuses only on the data from Question #1 (joining) to demonstrate the richness of the results. Emotions will be shown in the next study on joining a gym (Cartography #4).



Figure 4: The orientation page to the study on joining a fitness club.

The actual design was a so-called 4×6 (four questions, six answers). Table 3 shows the strong performing elements by total panel, by gender, and by four emergent mind-sets. One mind-set, MS1, is very small, and should be discarded, but we leave it here for completeness. What emerges as remarkable in light of the previous two studies in the richness of the results, something that will be seen in the next study as well on joining a gym? The reason for the richness can be principally attributes to good up-front thinking by the four students who participated. The students took the project seriously, looked at the different messaging on the Internet, selected what seemed to be reasonable, and put that messaging into the study. The results, with 50 respondents, are no less than spectacular, with the number of very strong performing elements.

Table 3: Performance of elements for joining a fitness club.



Mind-Set 1: Very modestly interested (additive constant 29), but attracted by the ‘shock’ value of services and prices

Spa: full body massages, facials, waxing, sauna

Dollar a day trial membership (3 months max))

Mind-Set 2: Barely interested (additive constant 9) but attracted by some outstanding features

Spa: full body massages, facials, waxing, sauna

Child-care available with an indoor playground, recreational activities and professional supervision

Ultra clean environment with towels for all members

Olympic size pool with over ten swimming lanes.

Mind-Set 3: Basically disinterested (additive constant -8) but exceptionally interested in cardio and fitness, as well as making it part of an easy daily schedule

Achieve your ideal weight

Free shuttle bus within a 15-mile radius of the gym

Gymnastic classes that help improve flexibility

Dollar a day trial membership (3 months max)

Spinning classes offer an innovative alternative for cardio training.

Mind-Set 4: Modestly interested (additive constant 25), and want machines for self-training

Two floors of free weights and machines.

The words of the four students are especially relevant here to summarize the results, and to show how new-to-the-approach students can learn to think more deeply about the topic.

“Addressable Minds was able to help identify the immense importance the therapeutic effect can provide to its members in a fitness center. Mostly overlooked, due to cardio and weightlifting but equally important to fitness and health is the state of the mind and spirit. The high response rate for the “Spa” and “Organic Food Court” elements demonstrate members are seeking more than just a weight room. Eating the right food along with properly relaxing the mind and body allow members to perform better in the gym thus maximizing the results. Providing these added qualities are crucial to health inside and out but also allow members to get more out of it than a traditional fitness center. Furthermore “Child Care” allows the member to escape responsibilities for a time, to truly focus on strengthening the spirit and mind.”

Cartography #4: Joining a Gym

Study # 4 was done at the same time as Study # joining a health club, but by a different team of students in the same class, Math 110 in Queens College. The process was the same and the instructions were virtually the same except that the work ‘gym’ replaced the phrase ‘fitness club’.

The data for this fourth cartography once again shows the power of doing one’s homework, of taking messages from competition. Table 4 shows low additive constant (-6) suggesting that it is the specifics of the gym which make a difference, not the basic interest in the gym. Table 4 also shows that the additive constant is higher for males (+24) and vanishing low for females (-16). For females, it will be the elements which must do the hard work to convince.

Table 4: Performance of elements for joining a gym.





The data becomes, more interesting when we look at the three mind-sets which emerge.

Mind-Set 1 is basically uninterested in the gym but strongly differentiates among the messages, and actually loves most of the messages except for those dealing with children. The elements interesting Mind-Set 1 are:

No enrollment fee

Feeling some pain…come get a massage from our wonderful masseuses

One on one time with private fitness trainer

Take part in one of our 50 different classes (yoga, spinning, Zumba etc.)

Not sure if you want to join…sign up for a week free membership

Come workout with our brand new 200 + fitness equipment

Diet plans to help and encourage your health and well- being.

Mind-Set 2 likes the notion of joining a gym, although again it is what the gym offers (additive constant 20). The key elements interesting Mind-Set 2 are:

Take part in one of our 50 different classes (yoga, spinning, Zumba etc.)

Relax in the Sauna and hot tub after a tough workout.

Mind-Set 3 has no predisposition to joining the gym (additive constant 8) but like a low price. Their response show that they are the opposite of Mind-Set 1, viz., interested in an activity with their children. The elements interesting Mind-Set 3 are:

Low monthly cost just 30 dollars a month

Full time student? …Show your school ID and only pay 25 dollars a month

Fun summer camps to allow children to stay fit and meet friends

Looking for competition…Weekly contests with prizes offered

Free childcare while you work out

Different types of sports available for all ages

Mommy and me classes with a variety of times to suit working parents

Keep your child active after school with enjoyable activities.

Table 5 shows how the elements link with the choice of emotion. The analysis of the emotion responses was slightly different from the analysis of the ratings for joining. Recall that for the reactions about joining, the 9-point rating scale was converted to one binary variable, taking on the value ‘0 when the original rating was 1-6, and taking on the value ‘100’ when the original rating was 7-9.

Table 5: Strong linkages (>=8) between elements and selected emotion for the cartography on selecting a gym.





This type of transformation does not work for the emotion scale, known as a ‘nominal scale.’ The numbers are simply placeholders for different, not necessarily related emotions. The solution, simple and in the same spirit, was to create FIVE new binary variables, one binary variable for each of the five emotions. A selection of an emotion for a vignette would result in the value ‘100’ for the newly created binary variable corresponding to that emotion, and the value ‘0’ for the four new created binary variables corresponding to the emotions not selected. For example, when the respondent selected the emotion ‘5’ (Interested), the newly created binary variable ‘INTERESTED’ was assigned a value of ‘100’ and the remaining four binary variables (EXCITED, WEARY, CERTAIN, APPREHENSIVE) were all assigned a value of ‘0’. The vanishingly small random number (<10^-5) was added to each newly assigned value, whether ‘0’ or ‘100’, respectively.

The regression analysis relating the presence/absence of the elements to the selection of the emotion was run separately five times, once for each of the five newly created vignettes. The equation was the now familiar regression equation, but the equation was absent the additive constant. The rationale is that the additive constant would be the same for the five newly created binary variables and provides no additional information.

The two emotions selected most often are confident and curious. Only the strong linkages are show, 8 or higher. The emotion confident links with elements giving the respondent control and choice:

No hidden fees

Don’t worry about not finding a spot with our free underground parking garage

Come workout and feel better about yourself

Take a dip in the clean and refreshing Olympic sized swimming pool

Do a lap(s) on the indoor and outdoor running tracks

Don’t wanna go to the gym alone… 4 free guest passes a month

Take part in one of our 50 different classes (yoga, spinning, Zumba etc.)

Different types of sports available for all ages

Take a class with friends that are offered all day

Top of the line staff to help you with any questions.

The emotion curious links with diversion (movie), children, easy to access, and a personal trainer

Spin away while watching a movie in our luxurious fitness theater

Free childcare while you workout

Keep your child active after school with enjoyable activities

Easily accessible from public transportation

One on one time with private fitness trainer.

The new learning here is that by linking emotion with the elements, one begins to get a deeper sense of why the elements seem to do well. The respondent may not be able to articulate the reason for choice, but the nature of the linked emotion may provide that insight.

Cartography 5: Gym-Two-Ways (2020)

Study #5 was done in late 2020, during the waning period, after the height of the lockdown due to Covid-19. The objective was to see what type of basic messages would attract prospective customers, who had just been through the lockdown, and might be interested in return to a gymnasium, or having a home trainer work with them using the same equipment in their home.

By 2020 Mind Genomics had transitioned to the much easier to use 4×4 design, comprising 16 elements (four answers each to four questions) The design generated 24 vignettes, which were permuted by the standard permutation approach, so that across the 106 respondents many of the possible vignettes were estimated. The rating scale was also reduced from 9 points to 5 points. The total time for the Mind Genomics exercise went from about 15 minutes to 3 minutes

Table 6 shows a much-reduced set of strong performing elements. There are still themes emerging, but what is important is the reduced set of strong performers, and the much lower coefficients. The reason for this may be the emergence of a society whose ability to concentrate on messages and to become excited has diminished and continues to diminish. It may well be that over the period of a decade the prospective audience has been saturated, the so-called paradox of choice [19]. That paradox may reveal itself in what might be called a customer-based ennui, and a growing indifferent to messaging.

Table 6: Performance of elements for GYM-TWO-WAYS.



Our final analysis concerns response time. The Mind Genomics program recorded the interval between the presentation of the vignette and the respondent’s rating. The interval is called the response-time, and presumably co-varies with internal psychological processes, including reading and decision-making [20,21]. We can look at the estimated response time of each element, with the estimation coming from the OLS (ordinary least-squares regression). Once again there is NO additive constant.

Table 7 shows the longest and the shortest response times. Most response times are between 0.5 and up to, but not including one second. Only a few elements are processed very quickly or processed more slowly than the half-second range between 0.5 and 1.0 seconds.

Table 7: Estimated response time for elements. Only the ‘short’ and the ‘long’ response times are shown.



Elements that are quickly (estimated response time <= 0.4 seconds)

No preparation…just be willing (males)

Choose intensity that intrigues you (male)

Share inspiration with GYM-TWO-WAYS community. (MS2 – Expertise)

Support transition from at-home to gym experience. (MS2 – Expertise)

Coaches understand YOUR fitness journey. (MS2 – Expertise)

Elements processed slowly (estimated response time >= 1.0 seconds).

Expert instructors.(MS1 – Experience)

Lifelong results through education and access to science. (MS1 – Experience)

Learn at home. (MS2 – Expertise)

Learn at your own pace. ( MS2 – Expertise).

Discussion and Conclusion

During the past six decades, since the early and middle 1960’s, researchers have focused on obtain increasing amounts of information from customers. Sixty years ago, it was sufficient to measure general attitudes towards products, desires for certain features, and perhaps the ‘gap’ between what was being offered and what was desired by consumers. This so-called ‘gap-analysis’ was satisfactory but over time the world of consumer goods and services would evolve to cut-throat competition. New methods were needed to understand the competitive frame.

The heyday of consumer research saw the development of new ways to understand the mind of the consumer. In terms of goods and services, Professors Paul Green and Yoram Wind at Wharton pioneered the methods of experimental design to study the trade-offs that consumers would make when considering a service or a product. The notion of trade-off was not new, but the zeitgeist of the 1970’s was moving towards the study of mixtures as being the natural stimuli.

It is in the spirit of this movement towards studying mixtures that Mind Genomics was born. The objectives were no longer to do single, difficult-to-execute studies, but rather to create a system that would be able to produce knowledge on demand for verticals such as buying products (Buy It!, source of the study on exercise equipment), or solutions to practical problems at the time, at low cost, with cycle times per iteration of a day or less (Curves, GYM-TWO-WAYS), or teaching tools, forcing first-year, non-quantitatively oriented college students to research the competitive frame, and to run a study which both taught them how to structure their inquiry, and how mathematics could provide important information.

The studies here represent a collection of different issues, done by different researchers, at different times. The numbers are comparable. The coefficient is the percent of respondents saying ‘yes’. Thus, there is ongoing learning, revealed by the meta-analysis, the learning transcending the specific elements, and in addition showing how nature of the up-front research may provide information of higher value.

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