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What happened to strategic segmentation?


Segmentation that combines insight with descriptive, predictive and operational capability represents the most complete and powerful capability for marketers, when achievable, and therefore a gold standard. However, the author notes a trend towards simpler, limited modes. These tend to isolate capabilities, such as descriptive market segmentation, or predictive analytics for targeting tactical campaigns, or real-time ‘black-box’ behavioural algorithms, such as collaborative filtering and next best action. The author suggests this is brought about by failures in technique, process or imagination, giving typical causes, and posits a simple ‘test of effectiveness’ self-audit for organisation decision-making.


This discussion paper suggests that parallel to the rise of advanced statistical predictive techniques (as used for example on online social networking sites), consultancies and clients have grown wary of attempting to implement full-scale segmentation, and have settled for lesser (although useful) models. The sections that follow therefore discuss:

  1. 1

    the nature of the problem;

  2. 2

    a model that elaborates six limited forms of segmentation (‘segmentation lite’);

  3. 3

    the nature and potential of a full-scale strategic segmentation approach (‘gold standard’);

  4. 4

    reasons for failure;

  5. 5

    a ‘test of current effectiveness’ audit checklist to guide executive action.

The problem

Over 20 years ago, database marketers started formulating new segmentation objectives to reflect a meaningful and actionable blend of behavioural data and imputed, fused or collated attitudinal information.1, 2, 3 The goal was to organise knowledge and marketing strategy, as well as communication and service behaviours, by applying a combination of descriptive, predictive and operational targeting and execution capabilities. Marketers aimed to understand the brand's customers in terms of segments that were:

  1. 1

    collectively related to the brand according to universal properties of the brand (in the absence of which it would make sense to create a new brand);

  2. 2

    heterogeneous with respect to other segments with the differences rooted in brand choice, not merely in broadly held values, geodemographic or lifestyle characteristics;4

  3. 3

    relatively stable over time;

  4. 4

    relatively homogeneous within themselves, so that they might also be targeted as a meaningful community (or tribe) with predictable results;

  5. 5

    although liable to member individuation, typically because of instability over and in time, for example with respect to timing of an offer or selection (eg changing lifestage status or from prospect to customer — since the goal is to find prospects like your best customers), enabling targeting and execution of appropriate actions at the right time;

  6. 6

    insightful, descriptive and predictive, so that marketers and the organisation can understand the different segments (or communities) and develop appropriate strategies, innovations and communications messages;

  7. 7

    employing systems and organisational capability that enable strategic, tactical and operational interventions at market, segment and individual customer level;

  8. 8

    as well as having tracking and evaluation research capability.

The model relationships can be expressed graphically in several forms, including the two versions in Figure 1.

Figure 1
figure 1

Two modes of expressing the brand/segment/individual relationship

This three-dimensional model differs from both the competing models: one-solution-for-all, which communicates to everyone in the same way, and mass customisation, which assumes a mass of individuals receiving individualised communication from the brand, sometimes described as ‘segment-of-one’. Its purpose was to ensure understanding, insight and focus across the organisation and drive business and marketing strategy through differentiated value propositions and individualised tactical/operational execution. This became a de facto gold standard among experienced database marketers, and was implemented using a variety of techniques including clustering, factor analysis, regression and data mining, collating and fusing data from across the organisation, third parties, and research. This was successfully implemented on many occasions. For example, working with Ogilvy from 1992 to 2001 involved dozens of projects and a documented global methodology for Ogilvy practitioners in over 50 countries. It also remains a stated goal among many leading marketing thinkers.5

In 2007, I suddenly realised that the gold standard was progressively being abandoned as a result of a combination of project failures and spectacular advances in statistical, data mining and rule-based customer management techniques, particularly in the online and call centre spaces. Woodcock, Stone and Foss, the CMAT development team, were in fact already reporting the trend by 2002 in their CMAT global survey: for example, they showed that only 19 per cent of companies had carried out basic decile analysis, 54 per cent did not recognise customer behaviours in planning, and there was a broadly negative trend in analytical and planning capability since their previous study 3 years earlier (the only exception being customer retention).6 The occasion for my discovery was a meeting of the senior consultants and associates of a consulting division of one of the big consumer profiling houses in the UK. There was a 2-day meeting to discuss case studies, leading-edge practices, tools and methods. Everyone was experienced, smart and involved in significant projects in major client organisations. There was considerable discussion on customer journeys, uplift models, call centre performance improvement, next best action (or best next-action), collaborative filtering, campaign management tool kits, and analytical and process software and its application.

However, not one project involved gold standard segmentation. Yet, if rule-based action works so well in the absence of a strategic understanding of customer types, think how much better it would work in the presence of one.

A conversation with the chief consulting officer yielded this admission: ‘Isn’t that all debunked now? We can’t get it to work and we haven’t found any clients who want it. Do you have any evidence of success?’

Recent personal experiences support this analysis, while market research consultancy firm Incite also report in their Spring 2009 newsletter7 that segmentations form a key part of company or brand strategy in fewer than 40 per cent of companies, a fall of 16 per cent since their similar survey in 2004. They also report a 31 per cent increase in the perception that segmentation studies are complex and require experience to do well. It is true that ultimately segmentation requires expertise to do well. However, the rapid increase in perception of difficulty suggests a rising concern among marketers, consistent with the fall in strategic commitment.

What is segmentation?

Segmentation means different things to different people. They use a variety of different approaches, dimensions and evidence types, as indicated in Figure 2.

Figure 2
figure 2

Segmentation approaches and dimensions

Woodcock et al. orient their discussion around a blend of three approaches: customer profitability, customer buying values (needs and desires that form the basis for purchase decisions) and behavioural variation, perhaps including lifestyle and attitudes.6 Marketing textbooks8, 9 typically describe ‘market segmentation’ and ‘customer segmentation’. Market segmentation provides a straightforward descriptive account of the customer base and typically supports business and channel strategy and customer portfolio management. Examples include business and consumer segments, and first-class and economy customers. Customer segmentation is typically a predictive and targeting technique for campaign planning using data mining and modelling techniques. Both of these approaches are reflected in the model described below (see also Figure 3).

Figure 3
figure 3

Descriptive and predictive dimensions

A behavioural/market model of segmentation

These two approaches provide a two-dimensional classification model that reveals interactions among six widespread and useful approaches, each of which nevertheless falls short of the power of ‘gold standard’ segmentation. The model is structured on two axes: behavioural analytics, which are typically designed for predictive capability, and market description (see Figure 3).

  1. 1

    Campaign analytics: typically means selecting the best people for a particular direct marketing or sales initiative based on past behaviours. Analysts use data mining techniques10 that build on traditional recency-frequency-monetary techniques. Although useful, listing groups by value provides limited communication insight. Targeting customers who have bought mortgages but not bank accounts makes sense, but it works better when informed by deeper insight. The nature of campaign analytics means that an analytical or direct marketing team can report financial gains and even win marketing awards without the organisation achieving significant customer insight.

  2. 2

    Community collaborative filtering and behavioural affinities: an advanced approach in the online space practised by brands such as Amazon. By finding statistical similarities in customer behaviour, automatic algorithms make recommendations (eg ‘people like you do things like this’) or brand behaviour (eg ‘we would like to promote you’). Some practitioners argue that this is superior to traditional segmentation because it responds to differences in customer behaviour at different times. However, this is somewhat analogous to front-line service staff developing expertise about customers that does not filter up to top management.

  3. 3

    Next best action (NBA): like affinity modelling, it aims to choose the next appropriate action for each customer based on past behaviour in the current situation. For example, customers who responded to one of the last three catalogues might be sent a new one, or customers whose savings policy approaches redemption might be recipients of a promotion generated by financial governance and marketing initiative (‘your money is now available to you/would you like to reinvest it?’). Heuristics and/or rules may derive from executive ideas, statistical modelling or collaborative filtering. Again, this is a useful technique that works even better in the context of a segmentation framework. These methods may be combined. Amazon use analytics and promotional planning to send e-mails with the content modified by product promotions and collaborative filtering. Executed well, they make a significant difference and organisations rightly commit significant resources to this. Some marketers, particularly online, promote automated algorithms as the primary strategy. However, all such techniques can work even better when guided by a master layer of segmentation. The next three classes11 are also important tools for business success. Each is widely practised, indeed probably more than the previous three, not least because they seem easier. All may use research; indeed satisfaction and service research is extensive, and brand research may help.

  4. 4

    Customer-led market differentiation: consumers and businesses, prospects, loyal customers and defectors, trade/channel customers and end-users, enterprises and small businesses, children and pensioners, men and women, and companies in different industries are candidates for different treatment. Unfortunately, they are usually obvious and do not provide competitive advantage. To achieve traction against the competition requires deeper insight. Often this means segmentation of these generic markets. In other cases, a more powerful variation can be found that cuts across markets, which can be particularly useful for international marketing.12 In a research project for a software company targeting industry-specific software plug-ins, it was found that industry variation was much less important than end-user attitude and behaviour. Differences between bankers, lawyers and other professionals were less important than differentiation cutting across all professional groups.

  5. 5

    Product-led customer differentiation: this often dovetails with customer-led segments (for example, men's razors typically produce male customers and males want men's razors). There may be significant non-financial differences between customers buying low-end three-series BMWs and those buying top-end seven-series models, but given the similarity in build quality and design ethos across the range, it is not surprising to find that there are also segment profiles that cut across the range. Another problem with product line segmentation is that when organisations are structured to enhance product expertise (retail banking, insurance, credit card, mortgage), this often reduces overall customer insight. The problem with most market segmentation is that it is conventional, probably used by competitors, and therefore lacking competitive insight and differentiation. The final approach, needs-based segmentation, can provide this edge, but often fails to be operationalised.

  6. 6

    Needs (or attitudes)-based communication insights: the same Incite study found that eight out of 10 respondents claimed to segment customers using consumer-based research (a figure that has remained stable for 5 years). However, this is typically used for advertising planning. Agency planners and brand managers use insights to position the brand and advertise more effectively in different media or channels. Consider boiled sweets: they mask hunger, replace smoking, cleanse or refresh mouths, distract or are playful, relieve stress, and provide comfort, fun, friendship or innocent romance. Although most confectionery consumers do not justify or want customer relationship marketing (CRM) investment, this approach can be and is used successfully in direct programmes: van Hattum and Hoijtink recently reported how a European mail-order garden products company used data fusion to create four different creative treatments for the cover and introduction to their catalogue (the rest of the catalogue remaining the same) to make them more appealing.13 Yet, most advertisers and advertising agencies like a single positioning and brand proposition with a single creative treatment, because it is simple and achieves economies of scale.14

Examples of segmentation and ‘gold standard’ segmentation opportunity

All these techniques have their place in contributing to the full potential of segmentation, as illustrated by the following three examples.

Learner drivers

Learner drivers choosing a driving school and progressing through their training programme are of different types and behaviours. Some tend to be more nervous, others more cocky. Some are still at school and being assisted by parents, who are financially and emotionally involved. Others are at work, or are perhaps university students; they are older and paying for themselves. They are male and female, rural or urban, practice with their parents or without, persistent and dedicated or dilettante, worried or relaxed about the cost (a full course can cost over £1,000), interested or concerned or ‘not bothered’ about their social relationship with the instructor, and their value can vary from the price of a single lesson, to the cost of an initial discounted package of four lessons, to 15 or more times that amount. Indeed, they are a very good example of the non-Gaussian (ie not a standard bell-curve population) customer base. Their needs, attitudes and behaviours are different. As a company, you can leave all this to the instructors to sort out, in which case all of your expertise is embedded in your instructor base (who are probably planning to leave and set up their own independent service to these very customers), or you can try to navigate a more intelligent strategic approach that integrates behaviours across the organisation. It is in fact relatively easy to identify a typology and also to embed it into a database based on a few key indicators. Appropriate actions can be implemented online, where the learner has their own space, in local centres, through the choice of instructors and their actions, by applying automated service and recognition programmes using text, e-mail and physical mail channels and where appropriate communications to parents.

The National Trust

The National Trust, with millions of members, is one of the largest membership organisations in the UK. Some people are members just because they value the idea of heritage but others because they see it as an opportunity to support quality family time together and to contribute to the development of their children. For some, it is important that it is a pleasurable social and entertainment experience, and the quality of tea, scones and clotted cream is vital to that experience. Others are amateur historians or primarily love the wild spaces. The National Trust provides relaxation, relief or renewal after the stresses of a week's work, or a place to engage and to turn on the brain for the first time in the week. Some members never visit properties and thousands are active volunteers of their time. Their needs, attitudes and behaviours are different, and this provides a relatively straightforward basis for segmentation, which can be implemented through direct communications, events, specific promotions and activities at National Trust properties. Segmentation needs to take account of needs orientation, behavioural history and level of financial contribution.


Another elegant and comprehensive description of this practice was published more than 10 years ago and describes Harley-Davidson's segmentation approach,15 which I recently validated.16 Swinyard describes a strategic ethnographic research project for Harley-Davidson that identified characteristic societal and philosophical beliefs, lifestyle and behaviours (including media consumption), attitudes to bikes and biking, and product consumption patterns. Examples of questions are shown in Table 1.

Table 1 Biking and lifestyle questions

Swinyard demonstrates how Harley-Davidson's product and marketing strategy maps onto the insights provided by these precisely differentiated, and extremely interesting, real customer groups. Consider the difference between say Dream Riders, the largest and most valuable group, who are ‘unremarkable motorcyclists that seem to like the idea of motorcycling better than motorcycling itself’ and are the most likely to have bought a new bike, and those belonging to Harley Owners Group (HOG) Heaven, who feel a sense of ‘at-one-ness’ from just owning a Harley-Davidson, with the act of ownership representing self-actualisation. Harley-Davidson use their HOG relationship marketing programme in their database to recognise individual customers and apply the insights to the design of product development, communications and event programmes.

General principles and implementation

The goal of segmentation is to identify ethnographically differentiated groups of people (the segment typology) who each have a practical level of shared meaning, attitudes, emotional resonance, behaviours, and actual or potential (for prospects) relationship with the brand or organisation. Following my own 1994 example,17 I shall describe the segments in the typology as customer communities,18 since each is relatively homogenous in their relationship with the brand/organisation. A community is generally considered to be a group of people who share something significant in common. Whereas it is hard to develop organisational strategy for a customer segment-of-one, it is realistic to plan and operationalise differentiated value propositions for communities. Understanding these communities typically requires the intersection of needs/attitudinal differences and behavioural/financial differences. The typology is then translated into business strategies and marketing behaviours. Operationalisation can include the use of automated rule-based and statistical algorithms at execution time.

Figure 4 shows a conceptual and systems-oriented architecture for developing and using such segmentation with layers for strategy management, typology management, action planning and operational/execution capability management. The development of strategy and customer segmentation typology must be an iterative process. These two then provide parameters that control or influence the more personal and/or automated statistical and rule-based algorithms that support marketing and service activity at the ‘right action’ layer. It is easy to operationalise particular campaigns or service transactions based on the financial worth of customers while using higher-order insights for strategic organisation. For example, you could organise three different call centre operations teams that use a variety of techniques to prioritise how fast or extensively individual callers to each of these teams are serviced. The operational layer provides data and systems infrastructure.

Figure 4
figure 4

Conceptual and systems architecture

Tesco and Amazon

Community segmentation is possible with strategic commitment, as demonstrated by Tesco. Senior executives have said19 that board members can describe in detail ‘during an elevator ride’ each of their main customer segments and how store design, merchandise assortment, shelf stocking, marketing communications and so on reflect their interlacing financial value, lives, needs and shopping habits. This strategic knowledge derives from commitment to practical action and innovation. The very richness and abundance of potential data represents an inhibitor for most retailers. Tesco turned it to their advantage. Using a research technique known as Charles Osgood's Semantic Differential Procedures, they can impute information about customers from the products that they buy: ‘This was the new insight that drove the next stage in Clubcard segmentation: that each product we buy says something about us and, by scoring in this way, you can build a picture of what attitudes and beliefs drive our behaviour; at least, what drives it while we are in the supermarket’.20

Tesco approached the challenge of applying 45,000 Osgood profiles (each item in the product range) with 20 different Likert scales for each, generating a total of 1.2 million potential rating decisions, using a ‘rolling ball’ technique based on affinity modelling, analysing in this case not customers but products, and looking at what customers were buying together and applying these profiles to the products, in a project that took a team 3 months of hard work. Once all the products are profiled, the result is Tesco's possibly unique grocery segmentation capability. As customers buy products, their profiles are automatically developed by the profiles of the products they buy. If they succeed in the tough US market it may be down to the segmentation capability they have developed.

Amazon's analytics are pioneering and brilliantly successful. Their policy of stocking every book, and similarly wide ranges in other categories, makes it much easier for them to operate on autopilot. Even if Amazon runs on black box algorithms, it would still make many reasonable recommendations to customers; it would just have little idea what kinds of customers it had. However, if Amazon did implement the layered segmentation architecture with its community insights sitting over affinity-based and NBA algorithms (doing so would be a closely guarded secret), it would gain extraordinarily detailed knowledge of its customers. Which would you rather run: a customer black box or a customer base you understand?

Differences in approach

Community-based segmentation is therefore amenable to all of the analytical and behavioural algorithms, but adds two benefits:

  1. 1

    More knowledge about customers: The business does not rely on either the abstraction of a data point or the simplicity of an obvious segment. Instead, more sophisticated stakeholder research (most customers and frontline staff) is used to understand and categorise the needs, attitudes, requirements and emotional value that underpin different customer communities, that is, getting to know customers as people with a relationship to us.

  2. 2

    A more customer-focused organisation: broad practical insights inform value solutions and treatments for communities, which are executed for individual customers with the help of real-time modelling techniques. As a result, the business can be managed as an integrated set of capabilities sharing a common platform but geared towards differentiated customer value.

My experience suggests that this leads to a number of advantages, apart from improved financial results:

  1. 1

    There is a better feel for the business, which means that you can manage it more intelligently. This leads to better performance management and more productivity in marketing, and less conflict, argument and time wasting.

  2. 2

    There is potential for more relevant value propositions and creative. This makes for more interesting work and better learning and makes the company more customer-focused.

  3. 3

    An enriched understanding of the brand itself: the brand's universal principles mean different things to different customers and so segmentation develops a richer more layered understanding of the meaning and essential value of the brand.

  4. 4

    Marketing costs reduce as a result of better targeting and more relevance; this applies to both paid-for media advertising and direct communications.

  5. 5

    Employee satisfaction increases as they deal better with real people with more granular solutions.

  6. 6

    Customer experience is enhanced and therefore brand and customer equity is increased: solutions become more relevant and appropriate.

So why doesn’t everyone do this? 20 barriers

Companies across financial services, FMCG and packaged goods, consumer services, business services, the auto industry, technology and telephony complain that although they may be successful with the lesser techniques they have not been able to implement an enriched segmentation model successfully. A key problem is that they cannot map attitude-based segmentation based on research back onto a database. My experience is that this is usually because the methodology and approach are wrong or misguided. Reasons for this derive from lack of experience, lack of expertise and inappropriate ideas:

  1. 1

    Being too busy;

  2. 2

    Trying it, spending a lot of money, getting it wrong, vowing never to do it again;

  3. 3

    Managing the brand using a single message;

  4. 4

    Managing advertising and CRM independently in different spaces, that is, without having an integrated planning process; this typically leads to a breakdown in consumer insight and certainly action;

  5. 5

    Organising the business around products;

  6. 6

    Making segmentation too complicated, or too simplistic;

  7. 7

    Being satisfied with impressive but partial gains from data mining, next best action, market segmentation, etc;

  8. 8

    Decision chain gaps between strategy/board level (who may have much more exciting things like mergers, acquisitions and share prices to think about) and operational senior managers (who learn the political art of avoiding obvious failure); a not untypical customer-focused project might begin with the following brief: ‘we can only spend $X, it has to be delivered by month Y, we are not allowed to do Z, and the mission is to achieve a 10 per cent productivity improvement in customer targeting’; a more useful brief takes the following form: ‘Provide an actionable resource that provides competitive advantage at low risk with a return on investment of more than X per cent to meet our strategic goals of A, B, C’;

  9. 9

    Divisional segmentation: for example, when the printer division and the computer division each have their own marketing database;

  10. 10

    Not designing the project as an integrated multi-disciplinary process. Success requires integration of knowledge and insights from a variety of sources and use of different methodologies, including internal interviews, qualitative and quantitative research and data analytics. However, proper planning reduces the complexity of this — for Green Homes Concierge (an LDA/Mayor of London project to help Londoners make their homes green), meeting with services, marketing and operations management, focus group work and interviews with frontline staff, in-depth interviews of sufficient customers, acquisition of publicly available studies of green consumers, fusion with Mosaic and the database, management and team briefing cost less than 20 per cent of commonly quoted segmentation studies;

  11. 11

    Attachment to market, demographic or financial factors, such as age, lifestyle or value (escaping boredom with the playful, tasty pleasure of sweets is not age-related). It will also sometimes be more useful to orient on a needs/attitudes insight and stratify financial value when implementing campaigns and service;

  12. 12

    Alternatively, aiming for ‘pure statistical, data-led segmentation’ without any assumptions or hypotheses to guide data collection and analytics typically ensures a hodgepodge of models; prior knowledge needs to be shaped, for example by workshops and qualitative research;

  13. 13

    Excessive reliance on complex (and potentially arcane) statistical method;

  14. 14

    Assuming Gaussian (bell-shaped curve) populations when this is not the case (thus eliminating assumed ‘outliers’ that are actually significant population members);

  15. 15

    Gaps in skills and experience: significant projects usually require the participation of analysts, researchers, marketers and agency planners, as well as front-line people, together providing expertise in statistics and modelling, qualitative and quantitative research, customers, and marketing and service applications;

  16. 16

    Having executive project leadership that does not understand the realities of modelling and is not interested in getting involved;

  17. 17

    Insufficient involvement of top management from across the business functions;

  18. 18

    Not finding simple and effective ways of tagging customers to segments; this typically leads to insights without the capability to act on them; Moskowitz et al. indicate one of many approaches to overcome this problem,12 while Tesco and Harley-Davidson indicate other approaches.

  19. 19

    Not finding ways to alert individual staff of the type of person they are dealing with; an example might be an icon or a colour code;

  20. 20

    Not communicating the model to staff and senior management so that they ‘get it’ and commit; this often stems from using PowerPoint to present knowledge-rich descriptive profiles rather than converting the community segments into real living people, for example by using day-in-the-life archetype descriptions (such as CustomerPrints, which were developed for OgilvyOne in the late 1990s,21 Personas, developed for online user profiling, and Nuanced Portraits22) and perhaps videos or theatre.

Assessing your experience and practice: A segmentation audit

This paper invites discussion about the art of the possible and the effect on business success of segmentation methods. The following self-audit checklist of capability is intended to summarise segmentation objectives and guide consideration of next steps. The points can be scored (eg on a scale of 0–7) or used to promote discussion.

  1. 1

    Does your segmentation model provide competitive advantage through insight (ie capability not shared by other key players in your category)? Points to consider:

    • 1.1 Does it provide management with revealing insights about the value of customers?

    • 1.2 Does it provide management with revealing insights about attitudes and non-financial behaviours of your customers?

    • 1.3 Is it understood and used by board members for strategic decision-making?

    • 1.4 Has your understanding of customer segments enriched your understanding of the brand?

    • 1.5 Does it show why your best customers belong to you and not other brands?

    • 1.6 Are the insights real in action?

  2. 2

    Does your segmentation model provide competitive advantage through operational and structural capability (ie capability not shared by other key players in your category)? Points you may wish to consider:

    • 2.1 Is the business or organisation organised according to these segments?

    • 2.2 Does every person in your company who has anything to do with customers or customer value development understand enough to adjust their actions where appropriate?

    • 2.3 Are business systems and processes operationally geared to it?

    • 2.4 Are business systems and processes operationally effective at applying it at all appropriate points?

    • 2.5 Can you apply rules and/or statistical algorithms that work for the customer and business wherever useful?

    • 2.6 Is it used strategically to refine segment-level differentiated value propositions?

    • 2.7 Is it used tactically to refine timing and content of customer-specific value offers?

    • 2.8 Has it reduced marketing costs (paid-for media advertising and direct communications) as a result of better targeting and more relevance?

    • 2.9 Has it reduced service costs (on a like-for-like service basis)?

  3. 3

    Does your segmentation enhance all stakeholder experience? Points you may wish to consider:

    • 3.1 Has customer experience measurably improved as solutions become more relevant, appropriate and better delivered?

    • 3.2 Has it increased employee satisfaction as they deal better with people?

    • 3.3 Has it improved brand/corporate reputation through being seen to deal with people better?

    • 3.4 Has customer segmentation increased the equity value of the organisation?

    • 3.5 Is management easier and more personally rewarding?

    • 3.6 Have stress levels reduced?

  4. 4

    Overall, does your segmentation provide a cushion and response mechanism against the activities of your competitors?

References and Notes

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Jenkinson, A. What happened to strategic segmentation?. J Direct Data Digit Mark Pract 11, 124–139 (2009).

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  • segmentation
  • collaborative filtering
  • analytics
  • strategy
  • audit
  • data mining