This article discusses how to work out which segmentation variables are appropriate from a list of variables. If you do not yet have a list, please first read How to identify relevant variables for market segmentation.

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The choice of segmentation variables is one of the key strategic decisions when segmenting a market.  As befits such an important decision, an enormous amount of work has gone into exploring all manner of different segmentation variables.  Demographics have been used, such as age, gender, height, weight, race and social class.  Firmographics – which are characteristics of companies, such as number of employees, turnover and industry – are regularly used in business segmentations.  Many other variables have been proposed, including decision-making processes, situational factors, personality, profitability, benefits sought, and even star sign.  Just about every consumer variable seems to have been used when segmenting a market.

The number of variables that have been developed creates an enormous challenge.  Too many have been proposed to make it practical for us to empirically compare them all when trying to segment a market.  Consequently, we need to instead employ some theory.   The key bit of theory is that we need to distinguish between a segmentation variable, which is a difference between analysis units (e.g., people) that is strategically meaningful, and the measurement of a segmentation variable, which is an empirical question.  This distinction can be amplified by examining what is perhaps the world’s first segmentation case study.  The world’s first historian, Herodotus, writing two and a half millennia ago, tells of how Egyptian priests would sell the offcuts of their sacrificial bulls in towns containing Greeks but would throw them away if the towns contained no Greeks.   The segmentation variable in this case is willingness-to-pay for bull offcuts.  The ethnicity of the towns’ people – that is, whether they are Greek or not – is a measurement of this underlying variable.  Whether there were Greeks in the town was not, in itself, interesting to the priests when working out whether to market their product; rather, they were interested in whether there was sufficient demand, and they worked this out by checking to see if there were any Greeks in the town.

This distinction between the true variable of interest and the observable variable, which is a measurement of the variable of interest, is a standard distinction that occurs throughout the science.   When this distinction is employed it becomes clear that many of the popular segmentation variables should be viewed as being measures of segmentation variables, rather than true segmentation variables in themselves.  Consider the widespread use of firm size as a segmentation variable.  Whether or not a firm has five or 500 employees is interesting only because of the types of differences that would be expected to be correlated with the number of employees.  A bigger firm would, in general, be expected to need more phone lines and thus provide more profitability.  It would also be expected to need a switchboard and more stable IT backbone.  Thus, the number of employees is the observed variable; and it is being used to account for lots of different segmentation variables, such as profitability, preferences for switchboards, internet speed, and many other variables that are correlated with firm size.

While there are an infinite number of segmentation variables, they can be grouped into five broad groups in terms of the underlying types of differences between people that they are trying to explain:

  1. Preferences for product benefits, where consumers differ in terms of the product benefits that they seek to obtain from in a transaction. In the market for coffee, some consumers are caffeine intolerant and consequently choose decaffeinated coffee, while others may seek a caffeine “hit”.
  2. Consumer interaction effects, where the preferences or behaviour of one or more people influence the preferences or behaviour of another group. For example, the clothes worn by Pink may be emulated by some consumers, and avoided by others.
  3. Choice barriers, which are factors that prevent consumers from choosing the products that are most consistent with their underlying needs and wants. For example, somebody who has no interested in apps may buy an iPhone because they are unaware that their needs could be met just as well by a much cheaper Android phone.

Although understanding demand is often central to market segmentation, sometimes it is useful to focus on the firm’s own needs and wants.  While preferences for product benefits, consumer interaction effects and choice barriers may all have supply-side implications, there are two additional types of differences between customers that are relevant to the segmentation strategy:

  1. Bargaining power, where customers differ in terms of their ability to negotiate reduced prices. In the construction market, larger companies have a much greater degree of bargaining power, which enables them to get “better deals” than smaller companies.
  2. Profitability, where buyers provide different levels of profit to the firm. In financial services, for example, a small proportion of consumers of financial services provide much of the industry’s profit.

Each of these is discussed in more detail in the next sections.

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Preferences for product benefits

The classical marketing view of segmentation is that it is all about customer needs and how these shape preferences for different benefits provided by products.  This reflects a widespread recognition that if consumers differ in terms of the relative importance they attach to different benefits provided by products and services, these differences provide a justification for segmentation. In financial services, consumers differ in terms of their preferences for electronic versus over-the-counter transactions.  Consumer preferences for ice cream vary according to flavor and fat content.  Consumer needs for decay-preventing oral care products differ according to the strength of their tooth enamel.

The more precisely a product meets a consumer’s needs, the more likely the consumer will be to buy the product.  Tools like cluster analysis and latent class analysis allow us to create segments of customers that are relatively similar in terms of their product benefit preferences.

Consumer interactions

A quick tour of world cuisine serves to remind us that we are not born with many of our tastes, whether we’re talking about Australians and Vegemite, Americans and root beer, Chinese and the fallopian tubes of frogs (or “fat of the snow toad” as they are called to make them sound more appealing) or Danes and ammonia-flavored licorice.  For reason of pragmatism, food tastes are usually assumed to be fixed preferences that can be measured at a point in time.  However, there are many circumstances in which our preferences for products and product attributes can be seen as being a direct consequence of the views and behaviour of others; and this provides us with opportunities for segmentation.

In many markets, lots of people will not buy a product until others have already bought it.  There are a few reasons for this.  Some people only hear about new products by word of mouth, so they cannot buy until others have bought.  For some people, waiting to see others buy is a way of reducing risk.  In some situations, the actual economic benefit of a product changes depending upon who else is using it.  For example, social media products like Facebook and LinkedIn only become useful when lots of people that you know are already using them. For these reasons, technology companies like HP court potential early adopters, seeking their input into design and sometimes even giving them free products, in the hope that they become adopters and advocates.

Pester power, whereby children persuade their parents to buy products is another form of consumer interaction which has a long and sordid history in marketing.  The basic idea, which is generally denied by most companies that practice it, is that you develop marketing communications specifically for small children (which are a segment), so that they irritate their parents to the point of purchase.  The same idea occurs in business markets, where within an organization there are lots of people that are not decision makers, but who are either gatekeepers which control the flow of information to the decision-makers, or who are influential and can persuade the decision-makers.

In grouping the various types of consumer interaction effects together it is not being suggested that they are the same, but that they all share a common basic implication.  When consumer interaction effects exist in a market, firms cannot view market segmentation as being about measuring needs and finding groups of similar people; rather, the existence of consumer interaction effects requires marketers to recognize the interrelationships between consumers.  In the U.S. some banks not only assess the profitability of individual consumers, they also assess the profitability of households in recognition that the satisfaction of a low profitability consumer could impact upon the banking behaviour of another more profitable member of the same household.  Similarly, in many countries the consumption of gelato can be understood in terms of social group, with Italian communities, Italophiles and “foodies” being heavy consumers.

In markets where consumers differ only in their preferences for different product benefits, economies of scale and manufacturing technologies become key determinants of the number of segments that a firm should create. By contrast, rather than providing an opportunity for better meeting consumers’ needs, consumer interaction effects can present a constraint on marketing strategy, dictating the number of segments and how differences between the segments need to be taken into account.  Consider the market for a game of soccer in the UK.  Regardless of what similarities and differences consumers may have in terms of price sensitivity, comfort, food and so on, the consumer interaction effects – that is, brawling between supporters of different Premier League teams – makes it mandatory that stadia separate the supporters of the different teams.

The methodological challenges presented by consumer interaction effects are also distinct from those faced when focusing solely on preferences for product benefits.  At the most fundamental level, how consumers are likely to interact needs to be known before we start to form segments, otherwise we will fail to collect relevant data for forming segments.  For example, if most people will buy software only if it looks better than what they currently use and they know a few people who have used it and liked it, we need to find a segment of people that will buy without knowing anybody who has adopted -- and focus on understanding these consumers’ needs and wants.

Choice barriers

Grouping consumers solely according to the product benefits they seek and how they interact in making purchase decisions implicitly assumes that consumers have what has been described in the economics literature as an “irrational passion for dispassionate rationality.” That is, they have perfect knowledge of the available products and other consumers, and appropriately consider these factors when purchasing products.  However, few would maintain that this is an accurate description of consumer behavior.  Factors that constrain “homo-economicus” from maximizing his utility can be described as choice barriers.  Researchers have identified a variety of choice barriers, including consumers’ awareness, knowledge and perceptions, switching costs, and decision-making and information-processing style.

From a segmentation perspective, the role of choice barriers is quite distinct.  Product benefit preferences and consumer interaction effects are generally viewed as intrinsic characteristics of consumers that may not be easily changed by the marketer; however, choice barriers can be created, reinforced, weakened, and destroyed.  When choice barriers are used as segmentation variables they generally lead to two different types of strategies.  First, they lead to strategies of prioritizing customers according to the likely impact of the choice barriers on purchasing.  Less effort gets expended on customers whose choice barriers make them unlikely to leave.  For example, banks tend to end up charging their customers worse interest rates  than they offer to competitors, because they know that it’s a hassle for their customers to defect.  Second, choice barriers often lead to obvious marketing initiatives designed to reduce the barriers.  Advertising helps with awareness.  Ease of opening accounts reduces switching costs.

Where choice barriers are key segmentation variables, it is generally better to segment using judgment rather than statistical methods.  A common example of this is loyalty segmentation.

Bargaining power

The fourth generic type of segmentation variable is bargaining power. Where one consumer can obtain the same product as another consumer but at a lower price, bargaining power is a factor.    Differences in bargaining power enable firms to implement price discrimination (i.e., charge higher prices to consumers with lower bargaining power); an obvious example of this is the price differences charged to locals versus tourists in many countries.

In some situations, bargaining power is caused by another type of segmentation variable.  For example, a consumer who is unaware that some bank managers have the ability to negotiate lower interest rates than those advertised will generally have a lower degree of bargaining power than a consumer who is aware.  While the most common cause of bargaining power is the level of competition, other causes include social advantage, being a shareholder of the organization selling the product, network membership, and government legislation.

Continuing the banking example, the number of banks with branches in an area differs greatly by geography.  The number of bank branches in rural areas is generally lower than in urban areas.  The result of this is that consumers differ in their bargaining power, with rural consumers generally having less of an ability to force banks to compete for their patronage.  Of course, ethical and legal concerns may prevent banks from taking this segmentation opportunity.  Another example of segmentation by bargaining power is in the ice cream market, where the prices are often higher in locations where there is no competition, such as the theater and at sporting venues.

The three previously discussed generic types of segmentation variables are, in the main, characteristics of consumers.  By contrast, bargaining power is a direct function of supply rather than a characteristic of demand.  Even if two consumers have identical needs, wants, resources, and decision-making processes, differing levels of competition for their business may dictate that they should be treated differently.

At a methodological level, the key distinction between bargaining power and the other types of segmentation variables is that bargaining power requires an understanding of the environment that the buyer is in – such as the number of competitors bidding for their trade – rather than an understanding of the characteristics of the buyers.

Profitability

The fifth type of generic segmentation variable is profitability.  Where one consumer provides a greater amount of profit to a firm than another (or have the potential to), profit may be a useful segmentation variable.  Profitability segmentation -- also known as the “80:20 rule”, the Pareto Rule, and “volumetric segmentation”, and customer lifetime value -- has undergone a renaissance in recent years with modern database technology greatly improving our ability to measure and access the segments. The most visible example is the proliferation of loyalty programs, which reward consumers according to their volume and value of purchasing.

Although a retail bank could attempt to satisfy the needs of all of its customers, it would be at a serious competitive disadvantage by doing so, potentially acquiring a large number of highly satisfied but unprofitable customers.  The economics of banking – where around five to ten percent of customers can account for 90 percent or more of industry profitability – makes it essential for banks’ segmentation strategies to focus primarily on customer profitability rather than on customer needs.

The obvious implications of differences in the amount of profitability that different customers provide have led some to conclude that profitability is generally more appropriate for segmentation than demographics or psychographics.  Furthermore, segmentations based on “hard numbers” -- rather than “soft” marketing concepts such as brand attitude -- can be easily communicated throughout an organization. Nevertheless, profitability segmentation is not always applicable; it can be very difficult to calculate profitability, particularly over a customer’s lifetime, and in many industries there are no customer databases that identify members of each segment, which makes accessibility poor.

While customer profitability is a single segmentation variable, often it must be calculated using multiple pieces of data.  Banks construct individual measures of profitability for use when segmenting markets by combining information on balances, interest rates and transacting behavior, while many other firms use recency, frequency and monetary value as proxies for profitability.

Most service companies use profitability segmentation.  Banks try to be nice to customers with home loans and large portfolios of products, offering them various free services, removing bank fees and giving them personal relationship managers.  By contrast, customers who are unprofitable find they wait in long lines, pay high fees and often feel, quite rightly, that the bank wishes they would defect.

A particularly appealing aspect of profitability segmentation is its simplicity.  If you can allocate customers into segments, it is straightforward to work out how they need to be treated.  The Australian carrier Qantas, with its frequent flyer program, treats its most frequent flyers, Platinums, with great respect.  They are regularly referred to by name.  They get priority access to good seats.  A nicer lounge.  Priority check-in.  And fancy bottles of wine if they participate in market research.

Many marketers are uncomfortable with profitability segmentation, which is essentially exploitative in its focus; it does not fit well with the marketing philosophy of creating shareholder value by creating value for customers.  Ultimately this is a question of philosophy; it is undeniable that firms can profit by segmenting based on profitability.  A more compelling criticism of segmenting using profitability is that it can confuse cause and effect.  That some customers are profitable is often a consequence of marketing activities; the customers that are unprofitable are perhaps the ones whose needs are not being met, rather than customers that should be de-prioritized (the normal consequence of using profitability as a segmentation variable).   Similarly, the most profitable customers may already be completely happy, so any additional attention could either be unwanted or may simply increase costs and reduce profitability.  There is evidence that “loyalty programs” focused on high-value customers may not work.

Ultimately these criticisms of profitability segmentation have some validity but they are insufficient grounds to reject its use.  Of the five generic types of segmentation variables, profitability is by far the easiest to incorporate into an operational segmentation.  This is because often good measures of profitability exist in customer databases (e.g., the purchasing record of a customer can be used to estimate profitability), whereas the other variables are often too weakly correlated with the type of data on a customer database to permit a segmentation being operationalized.

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