The Myth of Segmentation or How to Move Beyond

November 28, 2017 | Author: Jenny Clark | Category: Market Segmentation, Quantitative Research, Insight, Value (Ethics), Brand
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The Myth of Segmentation or How to Move Beyond Jochum Stienstra ESOMAR Congress Odyssey, Athens, September 2010



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The Myth of Segmentation or How to Move Beyond Jochum Stienstra ESOMAR Congress Odyssey, Athens, September 2010


The Myth of Segmentation or How to Move Beyond Jochum Stienstra

INTRODUCTION The Dutch daily NRC Handelsblad published a hilarious coverage of'Motor-way Man'. The English marketing agency'Experian'had postulated this group as a new segment: living in a new housing estate, near the motorway, about 30 years of age, hard-working, spending a lot of time in the car and living together (without children) with a comparable partner. The point was that this group was supposed to switch from the labour party to the conservative party. So the political party that is able to win (conservatives) or keep (labour) the hearts of this group, would – in the agency's view – hold the key to the victory. The hilarious part of the coverage is the quest for Motor-way Man. The journalist takes considerable pains to find and talk to actual specimens of this breed. The journalist (Peter van Straaten) does succeed in speaking to several people who share some of the aspects of Motor-way Man (not politically interested, travel a lot, work hard), but none of them actually turn out to be spot on: they are either too rich, or too old, or have children. As the journalist wishes to speak to the'real Motor-way man', he turns to Professor Richard Webber, who came up with the new segment in the political landscape, for directions. Professor Webber explains his system, consisting of 67 different groups, all to be found by postal code. With his system he could pinpoint the Motor-way man to the exact location. Armed with the'right postal code'the journalist travelled full of hope to Horwich, one of the supposed hotspots for Motor-way man. The first house within the given postal codes was inhabited by an elderly woman, almost twice the age of the target group.'Oh no, you will not find many people around 30 here'answered the lady when asked where to find Motor-way Man. Peter van Straaten ended up talking to another professor, Stuart Wilks-Heeg, who laughed away the fuss about Motor-way Man as an ever-recurring phenomenon of the'doorway to election groups'('in 1997 they were referred to as'Worcester woman'). Only a few weeks after I read this article, it became apparent that the political battle had become a completely different game – not the traditional tug-of-war between Labour and Conservative, but a new three party battle. A new player had come into the field: Clegg and his Liberal Party. This little history is interesting because it explains something about the phenomenon of segmentation. Finding groups with a common motivation, with common needs that can be addressed using a targeted approach, is a potentially extremely powerful tool. There seem to be several'messages'behind the NRC coverage. The first is the fact that you can postulate a group with special needs, but the needs of this group are there on an aggregated level. These are characteristics of a group and not of Downloaded from



people within that group. The characteristics of the group can be viewed, counted and calculated, but as soon as you turn to one of the'members'of the group you seem to lose focus. This is a fact that any qualitative researcher is aware of. Whenever you have to speak to consumers from a specific segmentation, you encounter the same problems Peter van Straaten had trying to find his Motor-way Man. And if you are unlucky enough to have viewers who are not completely satisfied with the results you will hear'this man/woman is not really at the heart of my target group'. The second message is that segmentation is a simplification by nature. Segmenting means: focussing on aspects that a group shares and not on the differences between them. The more you zoom in on individuals the more apparent the differences between them become. Depending on your perspective, the dimensions you use to compare the consumers, you will find different groups. The third message is that segmentation always has and probably will have this magical reputation. The marketing community is always on the lookout for the new'killer'group, a gold mine for consultants. Every now and then a new group is postulated. Sometimes these new groups provide us a new insight, sometimes they are nothing but a temporary fad. This reflects the fundamental question I had as a researcher: we know that segmentation can be effective (it has often proved to be in scientific studies) but what are the boundaries of the success? What are the dangers of using segmentation models? When does the simplification become a barrier to effective marketing rather than an important success factor? And more importantly, what is conceivable beyond segmentation? I decided to launch a study regarding this subject – a study in the classical'layman'sense: not aimed at getting scientific valid research, but aimed at gaining understanding the current situation and gaining a sense of what we can expect in the near future. I started reading a multitude of studies, formed hypothesis, talked it over with colleagues and business partners. In time I expanded this a little, and formalized the qualitative method into an expert interview session. I interviewed 12 experts, three from science, three from the research industry, three international clients (all multinationals with a different attitude regarding segmentation varying from using one international segmentation model to dealing with segmentation issues on a more ad hoc basis) and three marketing consultants dealing with big clients. Often these interviews turned into interesting, enlightening discussions. I can say that during this discussion my ideas about segmentation changed; it was a trip in that sense. In this paper, I would like to take the readers along on my trip. I will do that by introducing five themes, each of which represent a myth followed by a reality call. After this, I will define a few'Rules'about segmentation and beyond. In order to get a discussion going, I am writing a provocative piece. Therefore, I have chosen not to reveal the names of the experts I have talked with, so that they will not be associated with some of the more controversial aspects of my thesis.

THE FIVE MYTHS OF SEGMENTATION In the course of my trip, I encountered five myths about segmentation. I would like to present each of them as a short statement, each with a brief explanation of why these statements are actually myths. This is a sort of'demolition'work because much of the foundation of segmentation will now be presented as quicksand. This is not to advocate the end of segmentation as such, or to suggest that segmentation is a myth in itself. However, it is important to understand that if segmentation works (which I do not deny), the reasons for its success are not the ones we believe. In the second sector of this paper, I will explain why segmentation can be a powerful tool despite the five myths that I came across.

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MYTH 1: YOU CAN PUT CONSUMERS IN BOXES If there is one myth about segmentation, it is the idea that consumer or business-to-business clients can be put in a box. Segmentation is categorization, the art of creating boxes. To put people in a box is what all marketers dream about, and often demand. Not only should the task of putting your target group into boxes be simple and straightforward (a set of questions that is initially extensive, but is subsequently whittled down to a few questions that appear to have the power to explain'with 76% certainty'whether a person belongs to the one or to the other segment). It is vital that each box is connected to a simple set of rules that apply to all members of the box. This simplification has a very strong appeal because it is – or seems to be – so extremely manageable. In a large organization it really eliminates complicated discussions. There are two important reasons why it is actually not true that you can put a consumer in a box: one is statistical of nature, the other philosophical. Statistical In the traditional clustering methods, clusters of people are sought in the data cloud. You could visualize these as'gravity points': similarities in the answering patterns. Of course, each respondent has their own'combination of answers', but some combinations are similar to a certain extent. The cluster analysis calculates an'optimum'. In this optimum, each person is put in a cluster. The solution that explains the highest degree of variance is the best solution. Simply put: there are differences and similarities, but the clusters offer a solution with the greatest number of possible similarities. For the model, you are either'in'or'out'. In reality, the data explain that you are further away from or closer to the centre of the cluster. So actually, there are no boxes, there are'data gravity points'with unclear boundary zones. The segmentation scientists have left behind the idea that'one person fits in one category'. New segmentation techniques – such as latent class – allow some fuzziness. They allow people to be partly in one segment and partly in another. These techniques are not yet common in marketing research, but are beginning to be used more and more. Philosophical The statistical operations are'manipulations in a data set'. As you calculate a segment, you calculate similarities in answering patterns. What do these patterns actually mean? What do they represent? After the calculation, you can make a valid assumption on the quantitative side. This is all about processes: syntax. But the assumption in all segmentation is that these patterns represent something: a way of life, a tendency to do certain things in a certain way. In order to be able to communicate about the segments in a company, you actually have to'forget'about all the mathematical operations of the data set. You would not be able to communicate about that in a comprehensible way. So instead of talking about the only certain part of the process (the syntax, the operations on the data set) you would talk about the semantics. The semantics are basically interpretation. We look at the response patterns and interpret them. For instance,'respondents with these response patterns can be viewed as more conservative; they spend less and have a tendency to look at the past'. Descriptions like this are easy to find. But is this actually a box? Does it exist? No. It is a simplified label, a way that helps you to make sense of the data. It only has a loose connection with the so-called'box'(in reality a gravity point in a data set with unclear boundaries). This is the point where the scientist has to assume. The assumption is often taken as fact and reshaped in a clear box that we all can believe in, and that we all can give our own interpretation.

MYTH 2: CONSUMER FITS IN ONLY ONE BOX The second myth is strongly related to the first. The first myth stated that the concept of a'box'is actually wrong. However even Downloaded from



if statistics and philosophy tell us this is a simplification, we cannot escape the attraction of postulating the existence of boxes. If we do, and if there is at least a partly valid justification for that, we feel that consumers should be in only one box, at least in only one box at a one time – that the population can actually be viewed as a large pile of marbles. You can separate them, sort them by colours and at the end of the day you have several piles of different marbles with different colours, shapes and sizes. Even if you can argue about which pile some of the marbles should be placed in, you will not ultimately be able to deny that the different piles are distinct in colour and size. And you cannot dispute the fact that a marble is in either one pile or another. This could be true for marbles. However, for human beings it is not. The main reason is that a person is not – like a marble – a given set of characteristics. In the first place, people change. They respond differently in different circumstances. They react to their environment in a way that can only partly be predicted. There is a'systematic'way of change: as society is changing in a constant way, consumer acts and the laws behind these actions are changing; as the consumer play different roles in different contexts their personality is changing as well. MacCrackens'Transformations offers a good example of how this works. He describes how, over time, consumers have learned to adopt more roles, more'persona'. In MacCracken's view, we can no longer speak about consumers with a certain identity; we have to speak of consumers who manage several identities and play with these identities. Apart from this'systematic'change, we see a fluctuation of changing characteristics in the human mind. How does that compare to marbles? Instead of a marble, with a specific colour, size and dimensions, a better comparison would be something with a set of changing colours, changing sizes and measurements that offer the extra option of displaying different looks in different circumstances or even displaying two forms at the same time. Although the changes are not random, they cannot easily be predicted, because – over time – the rules that define change are being changed as well. So the statement that consumers cannot be seen as'easy to categorize'is actually an understatement.

MYTH 3:'QUANTITATIVE RULES QUALITATIVE' The third myth is also associated with the first one. The segmentation market is dominated by quantitative research. This is because we need mass to make segments. You could argue that a segment represents characteristics of a large group that does not necessarily appear in each individual in the group. You need calculations that will be stable only in fairly large groups – all quantitative. Yet I will explain that the quantitative supremacy is a myth; in fact, qualitative aspects rule in segmentation. Semantics rule statistics Where the calculations that define the'gravity points'are statistics and therefore quantitative, in fact the most powerful aspect of segmentation is'the label': the meaning we attribute to the clusters. As a consequence, the qualitative part of the model – the semantics – rules. Whereas in the formation of the segments quantitative played the primary role, in communication the qualitative wins. First you will see the qualitative description, then you will hear'how big the segment is'. To emphasize the qualitative supremacy, the segments will be given names with a qualitative touch. One of my clients (whom I did not interview for this project) collects names and gave me a few sheets with over 50 segment names. It is interesting to see that there is a'language', a'system'or even a'grammar'defining how the names are to be given. Typically you will find a blend of several segmenting principles hidden in the name. There is often an'action'part that refers to something a segment does ('the spender') combined with a'socio-demographic part (such as age category:'the elderly spender') and/or attitude parts ('the thrill seeking spender','the cautious spender'). Two axis models Downloaded from



Typically you will see a model behind the segments. The model typically represents a two axis system of'sense-making'. This is a qualitative model representing attitudes or values and therefore an instrument for distributing meaning. The axis needs to be abstract enough to be able to make coherent groups. You couldn't, for instance, have one axis with'likes brown bottle more'vs,'likes green bottle more'if you wanted to make a segmentation for people who buy bottled beer. A more abstract value, for instance: more ego-oriented vs. more'friendship-oriented'could work. The talk is not about the math behind the model. The talk is about the abstract meaning that the axis has been given. Thus, the model is primarily qualitative. The qualitative aspect of the model helps you to communicate. Humans are familiar with'meaning'. The model creates a common language, enabling the company to share meaning and to communicate. In the beer market, for instance, the contrasting attributes'for me'vs.'with friends'created a clear brand distinction. Some of the brands in Holland clearly defined themselves as'ego'(in the past: Grolsch), whereas others, like Amstel, clearly communicate the message'with friends'(Amstel. Our beer). The qualitative axis created a language we can speak in. The segments can be easily understood in qualitative terms. So the model does not actually create segments; it creates meaning as a means to be able to describe segments. In this respect, segmentation models often become'thinking models'. Many of my informants felt that this aspect is, indeed, important and positive. It can help you with product or service creation. For instance, you can think of products that are even more'me'than the current ones. The danger of using models like this is that they tend to become self-explanatory. They create a reality that is hard to deny. The models tend to become indisputable and they prompt you to keep on looking at the world from the dimensions the model offers you. In this respect, they can create a false sense of certainty.

MYTH 4: THE ULTIMATE SET OR'VALUE AS THE FINAL FRONTIER' The fourth myth I encountered is the myth of value-based systems as'the final frontier'. It is a fact that value-based systems have been a productive answer to segmentation problems in the past, and it is a fact that looking at values is a productive way to understand needs. But will this answer last forever as a basis for segmentation? To make my case that this is a myth, I would like to come up with a brief look at the history of value-based systems. The rise of value-based segmentation 'Value-based systems'appeared in the late 1880s, early 1890s of the last century. In the epoch before that, the traditional base for segmentation, the socio-demographic measures, had lost predictability. You could no longer rely on the predictably of the factors'age, gender, region, education and income'. That is to say: the patterns of consumer behaviour became too complex for this simple system. Society had grown away from'simple'and'fixed'structures. Basically this was a sign of'markets growing up'. Look at the car market, for instance. In the early times of'available cars'you had to be glad just to get one. So Ford could afford to offer the T-Ford in all colours, as long as you chose black. He had done the trick of making a car as such affordable for the higher middle class. In the 1960s more and more models became available, and also became available to the'Average Joe'. With the growing choice of functional comparable models, the emotional level became more important. Here the market became mature: choice in every'functional'subcategory, and emotional factors as an important motivational driver. You could choose Citroen if you wanted to emphasize your creative lifestyle. You could choose German cars for a sense of high quality or Italian cars to show off. So the traditional segmentation would no longer predict behaviour. It was a logical step to turn to the theory of values, as values represent stable ideas or motivation in what people strive for in life. Value-based segmentation systems are all funded on the profound value research by Rokeach, for example, (who postulated 15 end values and 15 instrumental values as leading principles) and Schwartz (who worked on value clusters). The value systems give depth to attitudes. A deeper insight into the values behind the attitudes can help understand how the attitudes came into existence. At Downloaded from



the moment, there is hardly a segmentation model imaginable that does not at least incorporate value aspects. Many models build on a theory regarding value clusters, postulating four to six basic groups that hold an comparable value system, offering a ready-made two-axis model that enables you to differentiate the value segments. Built-in simplicity The value theory offers very powerful insights and a way to cluster needs in a'simple'structure. However, all of the valuebased segmentation systems have a built-in weakness. A value cluster has to be abstract by nature. The very fact that you can make a selection of four to six value groups is a guarantee of simplification. So even if the value clusters are stable and do represent consumers who have a tendency to react in a comparable way, we cannot expect them to be too much alike. There have to be differences as well as similarities. This is reflected by the fact that the human race is capable of very flexible use of values; so flexible that we can even twist them to'double speech'(for instance: Arbeit macht frei'designating the concentration camps as places that make freedom possible). But apart from these more extreme changes in the meaning of values, we see that over time the meaning of values is constantly changing. You can observe this in the evolution of the meaning of words: for instance the Dutch word'stout'meant'brave'in the 17th century; in the current age it means'naughty'. And the word'naughty'in English now has a sexual connotation that reflects a different approach to values like'decency'over the last 50 years. Therefore the meaning of values or value clusters will continually vary.'Safe'in the context of financials was different before 2007 than it was thereafter. And it was different in the 1890s and most certainly it was different in the 1950s. And even if we are stable in the sense that we do have a strong tendency to embrace'safety'as an important value, and even if we do, as a rule, like to choose more familiar solutions (the same destination on holidays, the same employee), we will inevitably step away from this pattern every now and then. Circumstances or even coincidence can lead us to the try out a different type of behaviour. In a sense, the value theory is no different from socio-economic segmentation. It offers us a grip on consumers. It helps us think about different groups and ways to look at them, but neither gives us a final solution to segmentation. By nature, both have to employ simplification. I have been speaking to clients who had very powerful segmentation based on socio-economics. Another client had a very productive segmentation based on behaviour and attitude (no values included). Some of the experts I talked with gave us examples of powerful value-based segmentations. Thus, it looks as if all these'methods'are instruments. But none of the instruments has huge value in itself; it can have value under certain circumstances.

MYTH 5: WE CAN ASSUME STABILITY In'myth 3'I stated that segmentation models are a language to speak in; they are a'meaning system', providing a direction to the meaning that can be attributed to a product category. The fourth myth states that'value-based meaning systems'will not be the final answer to our problems, or even worse: their value will decline. The fifth myth that I would like to address goes even a bit further. It is the myth of stability in any meaning system, or rather, the myth of stability in the usage of the meaning system. Wedel mentions stability as one of the six conditions for segmentation. But how certain can we be regarding stability? Every now and then, currently valid laws seem to be washed away. After the introduction of EasyJet, the flight industry was no longer the same. Not only did prices change; the whole look of the sector changed. The whole category was transformed from an exclusive, non-transparent, expensive category to a rather transparent, not too expensive and all but exclusive market. After a transformation like this, any efforts based on the previous landscape will have become less effective. Even if the segment'Motor-way Man'had been a productive one (I have no opinion Downloaded from



regarding this. The fact that Van Straaten couldn't find the Motor-way Man is not proof that the segment does not exist), it could still have failed, because overnight the political landscape had changed as a third party entered the stage. Experian was not only shooting at a moving target; the very ground on which the canons were standing was moving as well! We tend to assume that'normally'we can expect a rather stable landscape. We tend to assume that change is a slow process. But how certain can we be regarding that? And this question relates directly to the first issue regarding segmentation: because if segmentation is an abstraction and a simplification, if segmentation is by nature only partly able to explain behaviour and thus is only partly efficient, how efficient will it be if the rules change during the operation? I would like to stress that this is not a theoretical point. Radical changes occur more often than we realize. And the point is: we notice the change only after it had occurred because we looked at the world and interpret the world from the perspective we had before the change. In order to be able to make sense of the reality blur, we just ignore the facts that do not support our current line of thinking, until we are forced to face them (and even then we sometimes just keep on walking the old path).

THE POWER OF SEGMENTATION: LAW OF FOCUS All together, the trip brought us five myths of segmentation, some of them undermining the very fundaments segmentation is built on. How is it possible that it is so popular? Are the five'laws'mentioned above completely false perhaps? And how about the fact that there is so much scientific work on segmentation? They can't be all wrong, can they? Wouldn't that argue against the five myths? In this part of my paper I would like to explain why segmentation can work, even if my five myths are valid. The gap between science and marketing practice In my journey I have talked with and read papers and books from both scientists and practitioners. The first thing that will come to mind if you read scientific articles and you talk to companies and brands about their practice in segmentation or segmentation models, is the big gap between the two worlds if you look at the way they talk about the subject. It is as if you are hearing two completely different languages. In scientific literature, segmentation is treated as a tool for finding valid segments. The debate is often about statistical methods and the validity of certain segmentation dimensions. For instance, how valid and predictable is a cluster of questions about values as a basis to describe coherent differences in answering other questions or coherent differences in behaviour? The debate may be about the set of questions used, or the validity of a scientific model used, say, a model about the dimensions that are relevant in brand experience. Or the debate is about how to merge data, how to combine different sets of data. Or, if you are reading more'high level'literature, looking at segmentation using a helicopter view, about the usability of several types of segmentation and the conditions that have to be fulfilled for valid segmentation. The best overview, if you are interested in this kind of literature, is provided by Wedel (1999), who gives a systematic overview of the conditions needed for fruitful segmentation. The latter part of his book is only for those with a mathematical background, but the first chapters are very accessible. So the debate in this scientific literature is primarily about conditions and techniques. However, the benefits of segmentation for clients who use segmentation models are on a completely different level than'how accurate, valid, and real'the segments are. As a rule, those aspects are not questioned at all; they are taken for granted. The specialists are expected to deliver the right techniques and discover valid segments. You may think this to be a truism, but that is not completely the case. The reason the end users do not question the methods or techniques is that the main benefit to them, which I can clearly deduct from our interviews, is what I would like to describe as the'law of focus and language'. An important value of using segmentation is disguised in the very fact that the segmentation is a simplification. Human cannot deal easily with complexity. If there are a thousand possible angles from which to view the consumer, we get frightened. The Downloaded from



endless possibilities frighten us; they are paralyzing. Creating a simple framework makes it much easier to act. Even if the segments are not (completely) valid, the fact that you have a shared vision of the market is big plus. The segmentation creates a language within the company, creates a story with both internal ('we are working as a company to better understand our target group') and external ('there are four types of consumers') aspects. Since the language is simple, it is easy to communicate. No Babylonian misunderstandings, since there is a clear idea about how to talk about your target group. Since it is all about consumers, this helps to stimulate thinking about your consumers and their needs. It creates a platform for innovation: what new offerings can we create for the segments? How can we improve the products, services, communication if we look at the different segments? So if the entire scientific discussion regarding method is – as a rule – about conditions, validity, techniques and quantitative measures, the demands of the organizations using segmentation are at the qualitative level of: being able to interpret the complex reality, being able to focus. This leads me to the following laws which explain why segmentation can work, even though some of the assumptions that are taken for granted are in fact quicksand. The laws I would like to state: 1. The law of focus 2. The law of partial effect 3. The law of mass 4. The law of topology vs. segmentation I will explain these briefly: The law of focus In this demand, the very'trip'to a segmentation model is in itself useful: the fact that as a corporation you start the search for a model, you dive into your target group, you look for a way of clustering those, you start a conversation in the company about your clients. The fact that this is an expensive operation only helps: the importance of the project is clear; it is sanctioned by a budget; there is external help from a segmentation expert. Then, after the segmentation model has been created, a new period starts, using the segmentation as a tool to start acting, sharing it in the company, creating new policy, new products, new services around it. This asset is robust for'segmentation'mistakes: even if the model is not completely right, even if the world around you is changing a bit (or a lot), the energy that is freed by the shared purpose creates stability on its own. This is what I would like to call'the law of focus'. The law of partial effect Humans tend to think in terms of'either/or'. If segmentation works, it is because it offers some improvement over not using any model. Even if you cannot put consumer in boxes, even if your calculations do not cover reality in full, if you can improve uncertainty a little bit you might be more successful with a segmentation. If a postulated segment helps you to target 10% better in a group than without the segments, it can mean the difference between failure and success. So even with false assumptions, you can win. The law of mass Downloaded from



The fact that segmentation can work, even if the assumption of one person in one box is not right, is because of the mass. Even if you would have to admit that consumers can fit in several boxes at the same time, and even if you do not (yet) use the new, fuzzy segmentation techniques that allow for that fact, someone might partially fit in a box for a while. Because in marketing we communicate with a mass, we do not care if the (only partially existing) boxes are only temporarily inhabited by a changing public. So, even with the wrong assumptions that it is possible to segment consumers in a simple way, we can go ahead with the all-too-simple techniques. The law of topology vs. segmentation Segmentation models often work as a topology to find needs as opposed to finding the'consumer'. The two-axis model can be viewed as a'topology', a map for locating the needs that are relevant for the category. I have often seen that models created as'segmentation models', to locate'types of consumers'actually function as'need maps', helping the client to define coherent clusters of need. If you are able to create a product that is consistent in the kind of needs it reflects, you will be successful. In qualitative research I have often seen that offers like that are robust for segmentation. That is to say: consumers who are supposed to fall into segment 1, are absolutely interested in a product designed for segment 2, be it that they may use it for different reasons or talk about it in a different way. A beautiful example is the Renault Twingo. This was designed for young urban types. In Holland it became popular amongst elderly people, because it was small, easy to handle, not too expensive and looked cute. If your segmentation model creates a language that helps you to find consistent needs, you will be successful. Instead of segment-oriented marketing, you make'self-segmenting products', products that will'find'their own segments. The value of segmentation as it is currently used Our statement is thus that segmentation is primarily qualitative, rather than quantitative. We need to acknowledge the point that in practice segmentation is a sort of'insight distribution'method. It is a method for distributing knowledge and understanding of the consumer and his needs throughout the company. It is a method for sharing the way a brand makes sense of the variety of consumers and needs it is targeting. And to be more precise: it is a way to bring focus to the way this sense-making process is dealt with within the company. This statement is supported by the fact that most segmentation models lack a sound method for actually finding the people who belong to the segments. This is made clear by Wedel in his book: most systems that are easy to use represent a model where you can describe segments that are valid, stable and big enough to be worth the trouble. But there is no method for finding the people in the segment. Because of that, the segment will be described in'old fashioned'terms such as sociodemographics and media behaviour. But if you do that, you further dilute the concept of segments: where it is itself fuzzy, you weaken the relation further, concentrating on the fact that segment x'more often than average'reads magazine Y, or has a slightly higher age than segment t. This creates problems, especially for qualitative researchers. When they have to find a consumer that matches the segmentation patterns, they will have difficulty finding the'real Motor-way man'. He may be there in the statistics, but he is certainly not there in the flesh.

HOW TO MOVE BEYOND THIS? Now that I have discussed the problems associated with segmentation, you could be tempted to ask the question'do we actually need to move beyond segmentation if it functions rather well, albeit not exactly the way we think it works?'I think we Downloaded from




definitively do need to go beyond this, because basically this is a'complexity'issue. A segmentation can be understood as a simplification that works for a while. As society grows more complex (and the pace at which the level of complexity increases only seems to increase) simplifications will be less likely to work. If there is obvious theoretical support for the statement that segmentation is a simplification, but a simplification that often'works', it is time to dive into the question: under what circumstances can it work? What are the boundaries? How do you prevent your segmentation model from working against you? In this part of my paper I would like to dive into this question, in order to answer how to move beyond this. As long as you hold the opinion that a segmentation is an'objective, quantitative'phenomenon, you need not ask yourself too many questions. You just apply the objective measurement and you are ready. But if you understand that this is not how segmentation works at the moment, that segmentation is in fact a way to distribute insights, you might want to ask yourself some important questions. Who is responsible for finding the insights anyway? How do they match with the brand strategic questions? Who decides on the insights being distributed? And on what grounds? For this reason, I would like to introduce a new concept, the concept of insight management.

INSIGHT MANAGEMENT The'insight'word is a sort of magical key, used for all kinds of different phenomena. But in fact you will find that there are many different stages of insight gathering and insight application that have to do with different stages in which an organization or brand finds itself. I therefore propose a model that can help to define different types of insights and insight gathering, matching different stages or challenges a brand or organization faces. In good tradition, this is a two-axis model. The first axis is about the'stage'you are at: the stage of creating new insights or the stage of sharing them, involving your organization in the insights. In the stage of creating, you could do with some uncertainty in the first stage. You could try out different insights. It could be a trip, trying to find the insights on which you really want to base policy. As soon as you have decided that you do want to base your policy on those insights, you are in the phase of distribution, or rather, you want to involve your company in this. Another measure, in my opinion, is the level of'thinking vs. doing'. Is your insight about vision (thinking, conceiving) or is it about action (doing)? In any organization you need the strategic insights, those that define your brand or company. They represent your vision of the world, the role you play in this world and the reason you are actually there. This is about making sense, about defining your identity. On the other hand, to keep food on the table, you will need to perform in the market. You will need to actually do something with your vision, bring it alive in the form of products, services, sales programs. Thus, you need tactical insights: how to implement you strategy. In order to get a better understanding of when and how to distribute insights, I would thus like to propose these two measures for creating a simple model that can foster understanding the different aspects of insights and that can enhance'insight management'. (See figure 1.) FIGURE 1 RESEARCH DESIGN OVERVIEW

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Create – vision In the quadrant'create – vision', it is important to ignore all your existing knowledge. You want to create new insights, fresh thinking as food to either radically change or to adjust your strategic plans and your vision. Obviously segmentation does not play an important role in this quadrant. It is more about creating the space to encourage new learning. Methods at hand would be: consumer journey, narrative approach. You would be especially interested in cross-category thinking. Here I would say it is not only about the method, but also about the creativity of those who are using them. The kind of insights you are looking for will be'fundamental', for instance, Philips with'Sense and simplicity'. Philips created this as a new insight regarding what consumers need (Sense and simplicity) and what Philips has to offer. This insight links the internal capacities with external needs. It was deemed a worthy foundation on which to base the entire brand and a measure on which to base all innovation. The creation of this insight was a creative act. The insight introduced a new abstraction level for the company, one that enabled Philips to actually'communicate'with their target group in a way that the technical staff, the marketing people and designers could all understand. Insights like this will never be about segmentation. The'Sense and simplicity'insight is beyond segments, it is the unifying principle that makes sense for all types of conceivable segments. Another example is Apple. Apple based its iPhone on a simple but powerful vision regarding IT and mobile telephones: adding'culture','beauty'and'creativity'to this sector. It created a new market by acting upon these powerful, market-changing insights based upon radically changing the category, defying the current category rules. So the insights were both strategic and creative. At the same time that Apple was changing the market, the other'ruling'telephone producers, were primarily pondering tactics and sales: how can we sell as many phones as possible? How can we entice consumers to buy our phone? Create – action In the left lower quadrant, we have the combination'create – action'. This is the field for converting insights into ideas. This is the innovation space. An insight is a beautiful thing, but you will have to actually implement it into your systems, you have to build routines around it. In this quadrant it is about'making new products/services'or changing routines within the company. It is about'applying insights'. In this quadrant, a segmentation as such (that is to say: segmenting of consumer), is not a good idea in my opinion. Here I would advocate using a model as a'need topology', so you would not use the model to cluster consumers, but to cluster types of needs. You can use this model to create products or services that are consistent in their need structure, without bothering too much about whether there is an actual segment of consumers behind that need cluster. That is something for a later stage. The powerful concepts based upon strong needs will become'self-segmenting'anyway. The

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weaker ones won't be very successful. At this stage the use of persona can be very useful. Here you use a persona not to represent a segment, but to represent a cluster of needs. In fact, a persona is nothing more than a'story'. You do not need quantitative research to create a persona; if you do, you will find that the quantitative side can only be supportive of a qualitative process. Again Apple as example. As opposed to thinking about'market share'of possible clients, the company has always been thinking about'extreme users'. Apple has always been committed to making products that are inherently coherent and extreme in what they offer – because, the more'extreme'the offer is, the more likely it will be able to actually change the market. The product attracted users and segments formed in due time, once the new market had become mature. This can be referred to as'selfsegmenting products'. The kinds of products that evolve from this kind of thinking will by definition be'blue ocean'as opposed to'red ocean'. Since it will have less'me-too'competition, the company will not have to bother about segments. Bringing something new to the market, the segments are not yet relevant. As opposed to'the product that targets a segment'it will been the other way round: the segments will coalesce around the product. Share – vision In the upper right quadrant, we are in the realms of'Share – vision'. Here we are not looking for rule-breaking insights. We want to refine those insights that we have chosen to work with. It is about expanding them, improving them, applying them in several fields. In this field, I would say most current types of research are quite valid, including segmentation research. The idea is that we want to apply our basic approach that we have chosen in a strategic way. So in this phase we are designing the research; this is still a phase of discovery. If you are dealing with segmentation research, it is the phase of further discovering your target groups. It is about understanding the factors that drive them. And internally, it is about discovering how your capabilities match your vision and strategy. In the example of Philips: you are looking at what it actually means to embrace'Sense and simplicity'. What does it mean in the different categories? What does it mean for your design? How is your company attuned to delivering on this insight? What do you need to change? Share – action In the last quadrant,'Share – action', we are beyond the discovering stage. This phase is primarily about communication and about implementation. If you have a segmentation study, you need to be certain that everyone within your company has the same view, that all procedures will be aligned to make it work. This is where the segmentation will have to be simplified, the'qualitative part'of the segmentation (the semantics, naming the segments, bringing them into life, telling how important they are) is now overruling the quantitative part. Here you could use Persona as representatives of a certain segment. A fundamental discussion about the segments can be very useful in the previous phase, but you don't want that here. What you do want is acceptance. However, you have to be aware that there is no such a thing as a final segmentation. It will always be a'work in progress'.

HOW TO MOVE BEYOND THIS One way to move'beyond'segmentation is to be conscious of what role it plays in the field of'insight management' – understanding when it is useful and what its use is in relation to other sorts of insights. The trick, in my view, is to be flexible regarding the kinds of insights you are looking for, and to understand when you are looking for what. The other way to'move Downloaded from




beyond'is to improve the quantitative side. This will require a better match between scientific reality and marketing needs. In this track, you do not use segmentation as an insight model, but as an actual instrument for improving sales – thus, as a purely tactical weapon. I would like to highlight both routes, and offer a few recommendations for each of them. Move beyond by insight management The way to move beyond current segmentation is to better align it to the insight management phase you are in. If you are in the phase of creating insights, segmentation is of little use. If you are in the phase of executing a strategy in the company into a questioning tactical statement, segmentation has to be communicated. If you are in the phase of stabilizing your insights, you might need to create a new segmentation model, or rethink the current ones. You would normally see cycles: just after the launch of a new segmentation model there will be little use in questioning the old one, all efforts will have to be put into communicating this within the company. But a healthy organization will not then simply take a nap; it should always be balancing on the brink of all these types of insights. A part of the organization should be constantly challenging the'absolute truths of the category and market'to prevent it from becoming a dinosaur. So, one of the answers to'how to move beyond this'is to understand that no segmentation whatever will be the end of your travel. This could mean that as a company you allow a certain part of your organization to move beyond the current thinking, to use methods that explicitly go beyond any conceivable segmentation to allow for experimental'self-segmenting'products. But there is another important trick that I feel is becoming more and more important. This has to do with the nature of insight. Insight is an emotional asset. It has'rational'aspects, but those are actually minor. Insight is the'feeling'that you get a grip on something. It is an experience. To understand this, you would only need to look into your own experience, the Aha moments you have had, the energizing feeling of actually understanding something better. This kind of insight is actionable by nature. Lehrer made this very clear in his book'Proust was a neuroscientist'and'How we decide'. If'insight'is an emotion, or has a strong emotional aspect, you cannot distribute it as a sort of'basic knowledge'. You need your employees to discover it for themselves, to make it their own: a sort of democratic sort of insight gathering. On the other hand, you don't want any sort of anarchy, each person acting upon its own insight, each insight standing alone as a personal experience. Only'giving'insights to the field will not do. An insight will have to be a'living thing'as opposed to a dead letter. You will have to allow for a'personal construction', an'individual interpretation'. This is true for all kinds of insights, and certainly for'segmentation', as well. This means that you will have to spend some energy'learning', creating procedures that allow your employees to have their own experiences. Insights management cannot be just top down; there will have to be a bottom up surge, as well. So, one other way to move beyond segmentation, is to make'insights'alive in your company, and to allow employee experiences to actually change the segmentation, to enrich it. Rather than being a'completely directive'approach, a segmentation can be a'platform for communication'. This leads me to conceive the following'future directions'of how segmentation can'move beyond'. For consultants, researchers and science: 1. I think that more effort should be given to theorizing and researching how qualitative models are made and how quantitative models can be translated into'meaning'. I feel that this is an underestimated part of the use of segmentation models. It is a field that should be looked upon more in detail. 2. I think that we need more thinking about'insight management'. The current thinking is primarily directed at'gaining insights'. Management consultancy should focus more on the role of insight creation and insight distribution. I feel that Downloaded from




there is a whole wasteland waiting for us. The main concern should be: how to create a balance between the need for'simplification'and the need for'matching complexity'. 3. I think that research methods should be built around the'insight model': we should find methods that help these different types of insights to be gathered and spread. Too often at the present, the client has to buy the kind of insight that matches our methods. It should be the other way round: we will have to build methods around the type of insights that our clients are looking for. 4. We should better link the'research'and'action'sides. I do not think that this means that researchers will have to become consultants. A better option would be: consultants and researchers working together on research that is not only actionable, but also acted upon. 5. Come up with methods that enable us to implement the bottom up method as well as the top down one. For brands and organizations: 1. Always utilize a little part of your energy as a brand to move beyond your current thinking. Use this to keep diversity in your thinking; it allows you to move when it is needed. You could install special teams to do that. You could also encourage your complete staff to do this and to embrace the model, and to keep on looking outside the model. 2. If you have chosen a segmentation model as an organizational asset, make it alive. Do not fall in the trap of making it a'dead model', narrowing it down to'four types of consumer'. Use it as a guide to more deeply explore consumer needs and as an inspiration for thinking out new communication, new products and new services. 3. Be careful with socio-economics such as'the typical vigilant saver is a single man between 35 and 55 years'. This is not useful within the'insights'part. It can be useful for other types of use (see below). 4. Build routines that encourage your employees to'live'and'explore' the segmentation models. These routines could be: workshops, consumer interaction, co-creation. 5. Listen to those in your organization that have experiences that'oppose'segmentation. These insights can be enriching, and can be a starting point for either a new cycle or for an interesting adjustment. Improve the quantitative side As I have stated in the'myths', generally the way segmentations currently work is'qualitative'. So, I have suggested improvements regarding this aspect. However, this does not mean that the quantitative part of the segmentation can now officially be done away with– far from it. I feel that there is still a giant leap to make towards actually applying segments on actual databases with consumer, because if you connect segmentation techniques with databases, you have the ultimate'action'tool for combining your insights with action. There is interesting work going on, with both scientists and researchers that put effort into using segmentation to segment consumers directly (Hattum, Hoijtink 2008). Some of the informants I have been talking with offered beautiful examples of how they managed to match modern science with practice, for instance, sending brochures to a segmented database, gaining impressive improvements in response. Here I think there are only a few recommendations to be made: 1. The marketing research community will have to embrace the new, fuzzy methods that are already accounted for in Downloaded from




science. 2. Ideally there is a more direct connection between science and marketing research. 3. The quantitative part of the model should be used for quantitative usage, such as'media choice'or'sales programs' 4. Segmentation can be used as a direct tactical tool if you apply it to databases. Fusion techniques are available for that. These methods can actually do without any interpretation or qualitative part. I hope that it is clear that these recommendations, and in fact, everything I have been writing in this paper is still in the stage represented by the upper right quadrant: it is about change and about the strategy of research. Therefore, it is all within the field of'trying'and not'proving'. But I do hope I have convinced the readers to stop looking for the Motor-Way Man, and to start digging for ways to help our clients better understand their consumers, and to act upon that understanding.

REFERENCES Guthrie, Michelle F. and Hye-Shin Kim (2009). “The relationship between consumer involvement and brand perceptions of female cosmetic consumers” In: Brand management Volume 17, 2, p. 114-133 Hattum, Pascal van and Herbert Hoijtink (2008). “The proof of the Pudding is in the Eating. Data fusion: An Application in Marketing” . Utrecht University, The Netherlands Lekakos, George (2009). “It's Personal. Extracting Lifestyle Indicators in Digital Television Advertising'In: Journal of Advertising Research December 2009 p404-419 Reason, P., and Goodwin, B. C. (1999).'Toward a science of qualities in organizations: lessons from Complexity theory and postmodern biology'In: Concepts and Transformations, 4(3), 281-317. Stienstra, Jochum (2010). Mythes van marketing Financieel Dagblad 20 maart 2010, P. 17 Straaten, Peter van (2010). Motor way man NRC Handelsblad zaterdag bijlage 17 april 2010 Wedel, Michel and Wagner Kamakura (1999).'Market Segmentation: Conceptual and Methodological Foundations'(Third edition, 2003, Kluwer, Dordrecht. Weick, Karl E. (1995). Sensemaking in organizations. Sage

THE AUTHOR Jochum Stienstra, Ferro Explore!, Netherlands.

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