Download - Mind Genomics: The science of everyday experience and its application to food

Mind Genomics: The Science of everyday experience and its application to food Howard Moskowitz

Michele Reisner [email protected]

Introduction – the world of the everyday As food science, food technology, and food design and development mature, we see an increasing emphasis on the mantra ‘understand the consumer.’ At the end of the day, of course, it’s what the consumer feels about what the industry offers, and how the industry ‘behaves’ in critical situations that’s going to make a difference. We can pride ourselves on technological and nutritional prowess, on hygiene and safety, on variety, but it’s really up to the consumer to tell us with his dollar vote whether our offerings and our behaviors are ok or not. Seventy years ago the food industry woke up to the need to please consumers. It was no longer a situation of ‘make it and they will come.’ People had to like what was offered. And the power of the consumer grew from there. It’s no surprise, then, that by the 1950’s the industry was deeply into describing the sensations produced by a food, with the hope of learning just what sensations would make a product a success. Nor is it a surprise that by 1960’s widespread acceptance testing was the norm; a food had to score well on a standard scale, e.g., the 9-point hedonic scale. And the story goes on, from description to testing, and now to getting insights about food using ethnography. The goal; understand the food more profoundly, acquire insights, create a better product, and succeed in the marketplace. And that’s where this new science, Mind Genomics, comes in. Simply stated, Mind Genomics is the science of everyday experience. Mind Genomics works by a research tool, RDE, rule developing experimentation. RDE creates vignettes about a food, vignettes written as if they were small, easy to read advertisements, like Figure 1. And, then presenting these vignettes to consumers, getting their reactions, and figuring out just what elements, or more specifically, what parts of these vignettes, drive interest. Figure 1: Example of a vignette

We could, of course, instruct our consumers to rate each of the elements in Figure 1, one

element at a time, but the reality is by doing so we allow the consumer to ‘game’ his answers, to adjust the ratings to what he or she believes the interviewer wants to hear. Mix and match the elements about a food experience, like we see in Figure 1, and the consumer respondent can no longer ‘game the interview.’ Faced with the demand to rate these vignettes, one vignette after another, the consumer quickly relaxes, more or less in the way many people relax while shopping for food, and gives intuitive answers, ratings from the ‘gut,’ ratings assigned without much mental editing.

The best way to understand Mind Genomics and its tool, RDE, is by examples. In the rest of

this article we’ll illustrate what Mind Genomics can teach us about products, about mind-sets, about responses to crises, and even about innovation. Our three illustrative case histories follow the similar paths, these five steps:

1. What is the problem? What are we looking for – what works in a product, identify a mind-set of

like minded consumers, or even invent a new product? The problems may sound different but in the world of Mind Genomics they can be answered with the same tool.

2. What’s the raw material? Mind Genomics works by mixing/matching ideas, presenting the

ideas, getting responses to the vignettes, and deconstructing the responses to the components. So the key here is the ideas. But, there’s another caveat. That caveat is – it’s better to be 75%

correct and on time then 100% right but late. Or, in simpler terms, just do the experiment. Don’t worry about being right – today’s internet-based tool, RDE, returns with data in 12-24 hours, so you can always redo the experiment.

3. What is the question that the respondent is instructed to answer? When a person reads a vignette, a combination of ideas, what’s he supposed to be thinking? Does he like what he reads? What about the emotion he feels when he reads? Or what’s the price he’d pay, or would he buy it at all? These are all valid responses.

4. What are we looking for in the data? Analysis is all important. Fortunately, Mind Genomics is based upon experimental design. It’s not necessary to be clever, to invent new methods. Follow experimental design to lay out the vignettes, use ordinary least squares regression, a true workhorse, to get the impacts of the elements, and you’re almost done!

5. What about segments? The hallmark of Mind Genomics is that people are different. We may

think they are the same, but people react to the same information quite differently. Mind Genomics looks for groups of people who are similar to each other in the way they respond to ideas. These are mind-set segments. Know the mind-set segment of a person, and you know what to create for that person, and how to communicate to that person.

Case history #1 – What works?

We begin with the simple question – when we describe a food, just what types of ideas resonate with consumers. Our topic – cheese. We might ask consumers to tell us, but then they tell us that brands are important, and so forth. Giving them the chance to rate the elements one at a time, and they’re likely to change their criterion as they move from brand to product feature to health feather, and so forth.

Let’s try something different. Let’s mix and match 36 elements, or bite size pieces of information as we like to refer to them, together to create 60 vignettes, similar in structure to Figure 1, with each respondent evaluating a unique set of 60 vignettes. These 60 vignettes comprise the same 36 elements, with the same element appearing in different combinations, against different backgrounds. That’s the beautiful thing of using a design of experiments; it does all the work of creating these unique vignettes for us. No two people see the same set of vignettes. The RDE tool will enable each respondent to ‘see and rate’ each vignette as a ‘totality,’ a ‘gestalt,’ one vignette at a time,. Then, to make interpreting easy for everyone concerned, we will transform our data. Instead of working with a graded scale that is often hard for managers to understand, we will work with two points; 0 (I don’t like the vignette, corresponding to ratings of 1-6), and 100 (I like the vignette, corresponding to ratings of 7-9). In practice the process is straightforward: 1. Assemble the elements, and allocate them to silos (groups of related ideas). For cheese, and for

this particular study, we will we will work with four silos, each with nine elements. The notion of silos and elements is a bookkeeping device, to ensure that elements of a similar type, but with contradictory messages, don’t appear together in the same vignette.

2. Invite the respondent to participate by a short email invitation, usually sent from a reputable ‘field service’ so the email doesn’t end up in the spam box.

3. Orient the respondent. Respondents aren’t necessarily accustomed to reading sets of 3-4 phrases and rating them as a gestalt. They need a moment’s instruction. They ‘get it’ pretty quickly, once they realize that they have to rate all of the elements in the vignette as a single entity, almost like a short concept or advertisement.

4. Present the vignettes, one at a time, get the ratings, and convert the ratings to the binary 0/100. Figure 1 showed us what a vignette looks like.

5. Use OLS (ordinary least squares) regression to create an equation showing how each of the 36 elements ‘drives’ the response (0=uninterested, 100=interested).

So what did we find? Mind Genomics emerges from these studies with results that are

blindingly clear. Let’s look at just a few of these results in Table 1. Table 1 shows the performance of the elements emerging after we related the presence/absence of the elements to the binary rating 0 (original rating 1-6) or 100 (original rating 7-9). Table 1: How 36 elements for ‘cheese’ perform, based upon a Mind Genomics study. Data from Healthy You!, Courtesy It! Ventures, LLC. Base size of 241

Additive constant (basic interest in cheese = 49 Strong performers

Irrelevant performers

The classic, traditional flavor of your favorite mozzarella, cheddar or American cheese 12

An important natural source of protein 2

The robust and zesty flavor of your favorite aged cheese 10

Endorsed by the American Heart Association 2

Builds and maintains strong bones 2

Endorsed by the American Dietetic Association 2

Modest performers All natural...no artificial flavors, no

preservatives 7

From Borden 1

Made with the freshest ingredients 6

Such pleasure ... knowing you're eating something healthy 1

Provides essential vitamins your body needs, including A, D, B12, and riboflavin 6

100% organic 1

From Kraft...Cracker Barrel brand 6

A quick and easy addition to any meal 1 An essential source of the nutrients that are important for heart health … like potassium, magnesium, and folic acid 5

A food you feel good about feeding your family 0

A naturally good source of calcium 5

Even better for you than you thought 0 May reduce your risk of high blood pressure and stroke 4

Recommended by your doctor 0

Healthy eating that tastes great 4

Recommended by nutritionists and dieticians 0

From Sargento 4 Soft, smooth, and velvety cheese 4

Poor performers Contains 13 vitamins and minerals your

body needs 3

Fills that empty spot in you…just when you want it -1

Endorsed by the American Diabetes Association 3

Lowfat…only 2g fat per serving -1

As part of a low fat, low cholesterol diet, 3

Dense, crumbly and firm -3

may reduce the risk of some forms of cancer Contains essential omega-3 fatty acids, which may reduce your risk of heart disease 3

With inulin … known to improve calcium absorption and improve digestion -3

From Land O'Lakes 3

Calms you down…just what you need when you're feeling stressed -4

With ingredients that restore and maintain a healthy balance in your digestive system 3

Made with plant sterol esters … clinically proven to lower cholesterol -9

6. We start with the additive constant, 49. This means that 49% of the 241 respondents, virtually

half, are willing to rate cheese 7-9, on the basis of the fact that the vignette or concept talks about cheese. Our consumers are ready to give cheese a ‘pass’ just because it’s cheese, a well known product. The additive constant factors in this basic, ingoing interest.

7. The impact value, the numbers in the body the table, show the additional percent of

respondents who would give a concept about cheese a rating of 7-9 when the element is incorporated. Look for big numbers, 10 or higher. These are big hitting elements. We only have two…and both talk about the basic food, the WiiFM, what’s in it for me. Here they are, the word pictures promising a sensory delight:

The classic, traditional flavor of your favorite mozzarella, cheddar or American cheese The robust and zesty flavor of your favorite aged cheese

8. It’s clear that some of the elements we chose are strong performers, some are weak performers,

that brand names don’t do much, most health messages don’t do much, and that the big opportunities are with product description.

Now that we know what’s happening with our 241 respondents, maybe we can get better news by breaking the data apart, into what the genders say, what the age groups say. Mind Genomics lets us create the ‘model’ for each person. Our 241 respondents can be classified as males versus females, under 40 years old versus 40 and older. . Let’s look at Table 2.

9. We see a little more promise when we look at the conventional breaks, by gender and by age.

There are a few more strong performers (Table 2.) The key here is that whereas there may be a few more strong performers, there’s no ‘big story,’ nothing to use for a new product thrust.

10. The real payout comes from dividing our 241 respondents, not by age or gender, nor even by

what they say is important. Rather, it’s by the pattern of how they react to cheese, i.e., the pattern of reactions to this limited world we are studying.

11. When we break apart the responses, using methods known generically as clustering, we end up with three radically different clusters, and some very strong performing elements. In fact, the mind-set segmentation points us to a group of respondents who are profoundly interested in the ‘healthful specifics’ of cheese. They didn’t come in wanting to tell us that they are so interested, and they couldn’t have ‘gamed’ the system. There is just too much happening in a test vignette, like Figure 1. But their subconscious took over. Our tool, RDE, rule developing experimentation, was able to tease out this group. Their responses ‘make sense.’ We get a sense

of their mind, just from the pattern of their reactions. We begin to see some powerful outcomes of the experiment. Cheese isn’t the same thing to all people.

12. Now that we know these segments, we realize that there is a substantial segment that is interested in cheese products with defined healthful characteristics. Table 2 tells us exactly what the ideas are that interest Segment #1 (it’s about health specifics), and therefore we end up with some direction about creating new products, designed specifically for this mind-set. Furthermore, we’re likely to be a lot more successful appealing to this ground, which is homogeneous in what they like about cheese. We don’t have to ‘boil the ocean’ to create a success.

13. The bottom line here – with Mind Genomics we’re able to get underneath what people say about

cheese, to get a sense of what really matters. And, we know that people cannot game the system; just too much is happening. At the end of the day, it’s all about product features for some, very little about the efforts of companies to enhance their brand images, and for a specific set of like-minded consumers an almost overwhelming attachment to health messages.

Table 2: Strong performing elements, by gender, by age, and by mind-set segment

Dividing the respondents by gender Total Male Female Base size 241 60 181 Additive constant (basic interest in cheese) 49 34 53 Males

The classic, traditional flavor of your favorite mozzarella, cheddar or American cheese 12 17 10

The robust and zesty flavor of your favorite aged cheese 10 12 9 Females


The robust and zesty flavor of your favorite aged cheese 10 12 9

Dividing the respondents by age (younger vs older) Total Under40 Over40 Base size 241 109 132 Additive constant (basic interest in cheese) 49 43 53 Age Under 40


The robust and zesty flavor of your favorite aged cheese 10 12 7 Age 40 or over


Dividing the respondents by mind-set Total Seg1 Seg2 Seg3

Base size 241 58 46 137

Additive constant (basic interest in cheese) 49 38 62 49

Mind-set segment 1: It's about health specifics An essential source of the nutrients that are important for

heart health … like potassium, magnesium, and folic acid 5 18 2 1

Contains 13 vitamins and minerals your body needs 3 14 2 -1 Provides essential vitamins your body needs, including A, D, B12, and riboflavin 6 13 4 3

May reduce your risk of high blood pressure and stroke 4 13 4 0 As part of a low fat, low cholesterol diet, may reduce the risk of some forms of cancer 3 13 0 0

Low fat…only 2g fat per serving -1 12 -9 -4 Contains essential omega-3 fatty acids, which may reduce your risk of heart disease 3 11 1 0 With inulin … known to improve calcium absorption and improve digestion -3 11 -1 -9

Endorsed by the American Heart Association 2 10 1 -1

Endorsed by the American Dietetic Association 2 10 -4 0

A naturally good source of calcium 5 10 6 3

Mind-set segment 2: It’s about healthy eating The classic, traditional flavor of your favorite mozzarella,

cheddar or American cheese 12 4 12 16

Healthy eating that tastes great 4 -1 10 4

Mind-set segment 3: It’s about brand & tradition The robust and zesty flavor of your favorite aged cheese 10 4 -1 16

The classic, traditional flavor of your favorite mozzarella, cheddar or American cheese 12 4 12 16

From Kraft...Cracker Barrel brand 6 -1 -4 12

Soft, smooth, and velvety cheese 4 -3 -11 11

Case History #2 – Discover the sensory mind-set for ‘texture’?

When people talk about foods some talk about what the food tastes like, others talk about the features of the food, still others talk about the food and health. At a meeting on texture we were confronted by the very simple, seductive question – is there a group of people who respond to texture? Can Mind Genomics discover this group? The question itself is intriguing. We know that there are person to person differences in what people like. Just walk down any supermarket aisle, to see dozens of different flavors of pasta sauces, coffees, teas, cookies, and so forth. We know from Mind Genomics that when we deal with a product such as coffee we can divide people into different segments, based upon the way the people respond to messages about flavor, about packaging, about brand and so forth. Just see Table 2. But what about using Mind Genomics in a far deeper way, to uncover the way people respond to the sensory input of products? We investigated this problem with our by-now (2011) standard web-tool, RDE. Our approach was fairly simple. We began with a hypothetical healthful yogurt. We created six silos, one for the way the snack looked, the second for the way it tasted, and so forth.

We then instructed the respondents to select a dollar value that they would pay for a pack of 24 4-oz yogurts at a club store. This rating, substituting dollars (an economic indicator) for liking (an attitudinal indicator) changes the task, makes it more stringent. We wanted to find out whether there was a group of people who would actually pay more for a positive texture experience. Our instructions appear in Figure 2. Figure 2: Screen shot of a concept about a healthy yogurt.

Our analysis was the same – for each respondent we related the presence/absence of the 36 elements to the dollars that the respondent would pay. We used OLS regression, but didn’t estimate the additive constant, since our assumption is that without any elements in the vignette no one would want to pay for the product. OLS regression returned with a dollar value for each element, for each respondent, 36 dollar values in all. We went further. Statisticians gave us tools such as clustering that allow us to divide the respondents into complementary groups. The respondents in a group, or cluster, are similar to each other in the pattern of the dollars they will pay for sensory experiences. This is a perfect way to look for our texture segment. Are there individuals who will pay for the texture experience? When it came time to divide the 205 participants, we ended up dividing the group into six clusters or segments. Only one cluster, with 41 out of the 205 respondents, was willing to pay more

money for good texture experiences. The remaining 164 respondents were not. We had found our texture segment in the population through Mind Genomics, as Table 3 shows. Table 3: Uncovering the ‘texture‘ segment (Segment 6, 41 respondents) and the complementary non-texture segment (segments 1-5, 164 respondents). The elements are sorted by the dollar value of the elements according to the texture segments. The table shows only those elements relevant to texture in the mouth and to the tactile experience when swallowing.

Texture (Seg 6)

Remainder Non- Texture

Base size 41 164 Average dollar value across all 36 elements $3.34 $3.05 Goes down like a regular yogurt $4.10 $3.11 Take your time swallowing... appreciate the comforting creamy texture $3.47 $2.99 Feels like you're eating soft ice-cream! $3.46 $3.07 So smooth and fresh... feels like silk in your mouth $3.44 $3.05 An absolute mouth-coating pleasure of yogurt will have you wanting more $3.22 $3.10 Easy to swallow... even little kids won't have trouble $3.22 $2.87 Fruit chunks pleasantly tickle your tongue $3.17 $3.12 Glides down your throat easily $3.14 $2.75 Velvety & moist... a refreshing feeling $3.08 $2.89 Cooling sensation while you are swallowing $3.03 $2.51 Glides down like mousse or pudding $3.01 $2.98

There was one thing left – to identify a person as a texture seeker. Just knowing that there is a ‘texture’ segment (texture-heads!) in the population isn’t enough. That’s scientific knowledge, to be sure. But how do we use this knowledge. What about creating a panel of these texture-heads (as it were), and have them guide product developers? All we need to do is have a way to find these texture-heads in the general population. The problem is that these people don’t know they are texture oriented, and there’s nothing about them, not who they are, not what they do, nor even what they feel about food, that tells us ‘here is a texture head.’ Fortunately, statisticians have given us another gift, DFA, discriminant function analysis, a statistical method that predicts membership in a group from certain key indicators about the person. DFA can be use to assign people to segments.

Food scientists reading the Journal of Food Science will recognize DFA; it’s commonly used to identify characteristics of foods that go along with certain behaviors, such as food spoiling quickly, and so forth. DFA ends up being a weighting system; get the right variables, give them the right weights using DFA’s classification functions, and you’re in business. You can assign new people to segments. Now apply the same statistical rigor to finding our ’texture heads.’ Using DFA, and the elements, we end up with a simple typing test in Figure 3. The respondent rates four questions on a 3-point scale. The underlying computations (see Table 4) quickly identify the mind-set segment to which the respondent belongs (non-texture head versus texture head). And with that, we’re off to develop products, not to whom a person is, not to what a person does, but rather

to develop products to please the inner person, the internal mind-set segment to which the person belongs. Figure 3: The mind-typing tool to discover whether a person is a ‘texture-head’. When combined with the classification function (Table 4), the mind-typing tool can assign a new person to one of the two mind-set segments, the Texture Segment, or the other segment.

Table 4: A worked table DFA (discriminant function analysis) to discover the texture segments. The table shows the four elements, the classification function, the response patterns from five hypothetical individuals, the values of both classification functions for each person, and then the segment assignment (shaded cell, bold type)

A. Classification Functions

B. How five hypothetical people might have assigned ratings using the typing tool

Segment 1 Non Texture

Segment 2 Texture Per1 Per2 Per3 Per4 Per5

Additive constant -5.855 -5.963 A delicious berry aroma... as if

you have a basket full of freshly-picked berries right in front of you 1.556 0.572 1 3 1 2 1

Has a comforting nutty 1.283 2.083 2 2 2 3 1

What do you think is a fair price to pay for

a package of 24 (4 ounces each) decadent

yogurts at a club store that…

have a delicious berry aroma... as if you have a basket full

of freshly-picked berries right in front of you?

has a comforting nutty aroma...made with real hazelnuts?

has a savory flavor... perfect for any time of day?

goes down like a regular yogurt?

1 = Less than $2.85

2 = Between $2.85 - $4.15

3 = More than $4.15

2

3

3

1

aroma...made with real hazelnuts Savory flavor... perfect for any time of day 1.562 0.849 3 2 3 1 1 Goes down like a regular yogurt 1.289 2.072 2 3 1 1 2

C. Value of the classification function for each segment, and segment

assignment based on the classification function showing the

higher positive value

Seg1 Texture 5.5 8.4 4.2 4.0 1.1 Seg2 Non Texture 5.5 7.8 3.4 4.3 1.7

Case History #3 – Beyond interest to emotion: Food from post-earthquake Japan Let’s move now beyond products, and deconstruct the reaction of people to major events in the food industry, specifically the March 2011 earthquake in Japan, and what that did to people’s feelings about food. We know that we can ask people to tell us whether they trust food from Japan, and that we will get answers, often politically correct ones. But what happens when we use RDE to probe more deeply. Can we attach ‘value’ to the different elements about the earthquake? And can Mind Genomics uncover linkages between these elements and emotions? We began our Mind Genomics study by identifying 36 elements, six silos, each with six elements. These are the factoids, the information, the elements that RDE combines into small, easy to understand vignettes. Respondents will read the vignettes about food from Japan, and rate their feelings on two attributes; interest in buying food from Japan (overall evaluation), and immediate feeling after reading the vignette, selected from a set of five alternatives. Figure 4 shows us the instructions that introduced the respondent to the vignettes. Again, it’s important to stress that our respondents each saw 48 different vignettes, and every respondent saw a unique set of vignettes. All 36 elements appeared, each five times. There’s no way the respondents could game the answer; too much is happening. Figure 5: Instructions to respondents for rating the vignettes about food from Japan

This time we did two analyses. First we looked at the degree to which each element drives one to feel comfortable about buying food from Japan. Table 5 shows us that no matter how hard we try, of course being honest, respondents feel that they are really not particularly interested in buying food from Japan. (The study was run March, 2011). The additive constant is low, 25, meaning that without elements, only one respondent in four would be prepared to assign a communication about food from Japan a rating of 7-9. It’s the elements which have to do the work, but no elements are capable of doing so, at least as of March, 2011. It’s not always what motivates people as the key take-away from a test, but that which doesn’t motivate them is equally as important. But there is more. The importance of our study is not necessarily in the column labeled interest, but rather in the columns 1-5, corresponding to the five feelings/emotions. Figure 5 shows us that the second question instructed the respondent to select the emotion felt after reading the vignette. We use OLS regression to link together the element and the selection of the emotion from the five alternatives. 1. Most of the elements are linked with ‘nervous,’ and then ‘suspicious.’

2. There is one element only that really links with ‘informed”. The US government has systems in place that protect us from unsafe imported foods...my food is safe

3. The elements do not link with ‘confused,’ nor do they link with ‘protected.’

4. The numbers in Table 5 (columns from suspicious to nervous) are the linkages each element, and the percent of times in a vignette of 3.75 elements that the particular feeling/emotion would be chosen when the element was presented in a vignette.

What we have just done is move from the product to the food situation, and introduce feelings/emotions to the RDE tool, and Mind Genomics. We didn’t have to limit ourselves to the Japan earthquake. We could just as easily have presented the respondent with vignettes about a food, with the elements being food features, brands, reassurance. Our linkages would then be between emotions and the more typical food elements that we encounter – whether product features, brand, or reassurance. Table 5: How different elements from RDE link to basic interest in buying food from Japan (column labeled interest), and how these key elements link with different emotions (columns 1-5).

Total Sample

Inte

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foo

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1 =

Ne

rvo

us

2 =

Su

spicio

us

3 =

Co

nfu

sed

4 =

Info

rme

d

5 =

Pro

tecte

d

Additive constant (basic interest in buying food from Japan) 25 na na na na Na The US government has systems in place that protect us from unsafe imported foods...my food is safe 1 2 5 6 11 2 Foods imported from Japan are allowed entry into the US after careful screening... I feel they are safe 0 1 11 6 7 2 Detectable radiation in food does not automatically mean the level is harmful -1 5 11 3 6 2 The authorities say the level of radiation is low and harmless... seems to make sense -1 4 10 6 5 4

If radiation can kill cancer cells...it might affect food -1 3 13 6 2 4 US laws require food importers to register... makes me feel safe about my food -1 5 12 -1 6 5 Japan lost a lot of money from the disasters...just for economic survival they might sell any food -2 9 11 1 3 2

I heard, read, or was told that all radiation could be bad -2 6 10 5 4 1 I believe that no food is absolutely safe...and our marketers do their best to sell only what is safe -3 4 13 1 5 3

It's hard to keep track of the safety of all imported foods -3 10 7 3 3 2 I don't trust marketers of imported foods...they might not care about my safety -3 10 9 2 3 2 Why should I eat foods from Japan ...when their own people are worried about their food safety? -3 10 9 5 2 1 Japan businesses might sell unsafe food to the rest of the world... to recover from huge business losses -4 11 11 1 0 1 I'm not sure importers measure the radiation of the food they sell to the US -4 10 6 3 4 2 To help Japan recover economically... the US government might allow unsafe foods to be sold -4 10 12 0 3 2

I don't want to die of cancer nor have children with abnormalities... radioactive foods are worrisome -5 12 9 7 0 -2

Summing up

In this rather whirlwind tour of Mind Genomics we’ve introduced a new concept that it’s possible to understand what’s important to people in their everyday lives. Whether this knowledge is about the features of products, about finding mind-sets in the population for directed development, or about specific general problems such as food quality from Japan and the way people feel emotionally, the newly emerging field of Mind Genomics may be in a position to make a contribution. It’s important to keep in mind that Mind Genomics emerges from induction, from uncovering patterns in nature at the level of ordinary experience, and then hypothesizing about the structure of the everyday experience.

But beyond the science are the applications. Of what value is it to know what’s important for a person as he thinks about cheese? Or are there texture heads, groups of people who pay attention to texture far more than do others? Or what factors frighten a person about a food safety problem, such as the radioactivity in Japanese food due to the earthquake? The value right now is knowledge; knowledge of the world, knowledge gained the old fashioned way, by experiment. The value in a year or two might well be more targeted development, more targeted sales, and hopefully more satisfaction with the foods we buy and consume. Acknowledgments The data from Study #1 on cheese came from the Healthy You! Mega study, courtesy of It! Ventures, LLC. The data from Study #2 on texture mind-sets was presented in part at the Research Chefs of America annual conference, Atlanta, GA, March, 2011. The data and the graphics will appear in a chapter entitled: Mind Genomics and texture - The experimental science of everyday life. (In: Food Texture Design and Optimization, ed. Y. Yadunadan, Wiley-Blackwell) The data from Study #3 on food concerns and the Japanese earthquake was presented in part on June 13, 2011 at the IFT Annual Conference & Food Expo in New Orleans, LA., by Aurora Saulo. . For further reading about Mind Genomics and RDE Website: www.SellingBlueElephants.com Experimental Design: Box, G.E.P., Hunter, J. & Hunter, S. (1978). Statistics for Experimenters. New York: John Wiley. Mind Genomics: Moskowitz H.R., German JB and Saguy IS (2005), ‘Unveiling health attitudes and creating good-for-you foods: The genomics metaphor, consumer innovative web based technologies’, CRC Critical Reviews in Food Science and Nutrition, 45, 165-191. Mind Genomics: Moskowitz H.R., Poretta S and Silcher M (2005), Concept Research In Food Product Design & Development, Ames, IA, Blackwell Publishing Professional.

http://www.sellingblueelephants.com/

Mind Genomics: Moskowitz, H.R., & Gofman, A. (2007). Selling Blue Elephants: How to Make Great Products that People Want Before They Even Know They Want Them. New Jersey, Wharton School Press. RDE Tool (Rule Developing Experimentation): Moskowitz H R, Gofman A, Itty B, Katz R, Manchaiah M and Ma Z (2001), ‘Rapid, inexpensive, actionable concept generation and optimization – the use and promise of self-authoring conjoint analysis for the foodservice industry’, Food Service Technology, 1, 149-168.