Remember when marketing was simple? Your division operated in a manageable geographic region. You defined your consumer targets by age, say, and by income. If you were in a business-to-business market, you divided up companies by size.

But the wild proliferation of brands and channels in rapidly globalizing markets now flusters even the most sophisticated marketers. In this environment, how should your sales force tailor its strategies to its accounts? Different customers have different attitudes, needs, and preferences, but the old distinctions no longer take you very far. What should you be looking at today? The current purchasing behavior of your customers? The benefits they seek to obtain? Demographics or its business-to-business equivalent: "firmographics"?

Ford’s Model T strategy—any color you wanted, so long as it was black—worked until customers had an alternative. Soon-to-be-deregulated utilities, among other companies, are now miserably aware of this reality. How will the utilities build loyalty among the most profitable customers before competition takes them away? Utilities had so little need for marketing in the past that some know very little about them and have no idea what products and services might keep them loyal after the coming of choice. Companies often address this problem by developing segmentation schemes breaking down markets into sets of customers or potential customers who share attributes that might be based on demography (income, say, or age) or on values or needs.¹ Consider some obvious examples. Video camera manufacturers capitalize on the fact that families expecting their first children are likely customers. Telephone companies try to sell call waiting to families with teenage children. The USAA insurance agency targets military personnel because it has come to believe, correctly, that this group is likely to be significantly more loyal, and therefore more profitable, than others.

How do you find customers who care mainly about service without interrogating them all?

Unfortunately, easy cases permitting marketers to establish meaningful differences among groups of customers and then to identify them—a phenomenon we call "actionable segmentation"—are rare. More often, despite decades of research and many refinements in the basic model, the segmentation process creates very real difficulties for marketers. No doubt such methodologies as conjoint or latent-class analyses permit them to use values, needs, and attitudes to devise groups (for instance, price-, service-, and quality-oriented segments) that include almost all customers. Yet it usually turns out to be very difficult to identify the flesh-and-blood people actually inhabiting the segments. How do you find customers who care mainly about service or quality without interrogating them all? A leading insurance company based in the United States spent a lot of time, trouble, and money dividing its world into segments, only to run into exactly this problem. In the end, the company abandoned segmentation entirely.

The basic difficulty is that value-based segments generally don’t fit neatly into demographic ones. Many companies therefore start with the simpler task of identifying differences based on demography or on the different attributes of different companies. Companies in consumer markets, for example, typically divide their customers into baby boomers, generation Xers, and so forth. Likewise, many companies that sell to other businesses segment customers on the basis of such characteristics as their size, the volumes of their accounts, and the industries in which they compete. Unfortunately, though advertising agencies and sales forces find this approach easy to understand and to implement, it really is no more effective than value-based segmentation schemes: by no means do all baby boomers have the same preferences and purchasing behavior, and businesses of the same size, account volume, and industry may very well have rather different values and needs.

Segmentation schemes based on demographics or company characteristics are not extremely actionable

Segmentation based on demographics or company characteristics thus is not very actionable; these approaches don’t help you get to your customer with the right offer. In what follows, we describe four ways of solving the segmentation dilemma. Targeting and self-selection, the simplest of them, step around the problem; scoring models and dual-objective segmentation deal with it head on.

Targeting

Sometimes a segmentation strategy works even if you can’t identify who is in which segment.

In the early 1990s, price wars at the pump threatened the profitability of oil companies. To turn the situation around, Mobil Oil queried 2,000 customers in a segmentation study revealing that only 20 percent of gasoline buyers were price shoppers, who spent an average of $700 annually, while customers in other segments spent as much as $1,200. Although Mobil could not distinguish price-sensitive shoppers from price-insensitive ones, the news that 80 percent of its customers were price-insensitive, heavier users shifted the company’s focus away from pricing. As a result, Mobil reaped an extra $118 million a year in earnings from an additional two cents a gallon on its gas—a major accomplishment.²

In general, selecting targets is the first task of any segmentation strategy. Before worrying about how to identify and reach individual customers in any given segment, it is worth attempting to determine whether the collective traits of a market segment might themselves suggest profitable strategies.

Self-selection

The basic idea of self-selection is to reverse the roles of a company and its customers: instead of trying to find, say, price-sensitive people, the company figures out what segments it wants to reach and gives the consumers in them ways of finding it.

Companies most commonly try to get customers to select themselves by multiplying stock-keeping units (SKUs); different sizes of cereal packets or washing powders are the most obvious example. The segments walk up to the supermarket shelf and buy the most appropriate offering. Coupons, which allow consumers to select themselves on the basis of price sensitivity, are another classic mechanism. Although everyone receiving coupons has the option of getting a discount, only customers who are both price-sensitive and relatively indifferent to the trouble of clipping and saving coupons tend to redeem them. As a result, supermarkets earn smaller margins—mostly on the fraction of their transactions precipitated by the coupons—and get the full price from consumers happy to pay it.

Similarly, airlines often have lower airfares for people willing to include Saturday night stays in their trips. These companies realize that consumers in the price-sensitive segment will sacrifice flexibility for low cost but may not know who these customers are. Sniffing a discount, however, these people come looking for the airline. Packaging the same product in different ways can also do the trick. Quidel, a company based in San Diego, California, specializes in developing rapid diagnostic tests. One of its products can detect pregnancies in their earliest stages. Until recently, Quidel undertook almost no consumer marketing, focusing instead on doctors. In 1993-94, its pregnancy and ovulation products had almost an 80 percent share of the medical market but just 18 percent of its consumer counterpart.

Quidel conducted a market segmentation study whose findings prompted it to target two kinds of women separately: those who want to get pregnant (the "hopefuls") and those afraid that they might be pregnant (the "fearfuls"). Not surprisingly, demographics and related characteristics didn’t help the company distinguish between the two segments, so it created different packages for them. Exhibit 1 shows the correspondence between the concerns of both segments and the brand names, prices, box designs, and shelf placements of the products created for them.³

Companies can thus promote self-selection through a variety of mechanisms, the most popular being coupons, pricing structures based on times of the day or days of the week (telephone and airline pricing, for example), and different versions of products. These approaches are most suitable when the base of customers is large but the dollar volume for each of them is too small to make other approaches to segmentation or mass customization economical.

Scoring models

A telecommunications company that wanted to segment its market undertook a survey of telecommunications managers to identify the groups into which its market naturally divided. It identified several groups defined by market need, such as the price-, convenience-, and quality-oriented segments. As usual, the needs of these managers could not be predicted from the nature of the companies employing them. When the telecommunications company’s salespeople used their own judgment to place customers in segments, the results were better but still too weak to make a real difference.

To make it possible to act on the segmentation scheme, the company developed a scoring model—a tool, based on a statistical approach called discriminant function analysis (DFA), that lets marketers use the answers of customers to a few key questions to place them in appropriate segments. Credit card companies employ scoring models to classify customers as good or bad risks.

The telecommunications company analyzed the answers of half of the respondents to the original market research survey to establish as precisely as possible the mathematical relationships between their answers and their segments. The answers of the other half of the respondents were then used to test the model’s predictive accuracy; in other words, these responses were input to the scoring model and its segment predictions tested accordingly.

When the intuitive segmentation of the salespeople was compared with the outcome of the model, the most significant finding was that they were much too ready to put accounts into the price-oriented segment and were therefore giving value away. Exhibit 2 compares the predictive accuracy of simple firmographic information, the unaided judgment of salespeople, and the scoring model.

A similar approach helped a US company sell office machines to business customers in Japan. Because the company had to compete with a deeply entrenched competitor, the company’s market share was much lower there than in the United States. Market research suggested that the Japanese market was divided among three segments whose preferences were the competitor’s product, convenience, and price, respectively. Because the apparent differences among the business customers who made up this company’s potential market were not significant, it was almost impossible to predict the segment to which any particular customer belonged.

The company first created a scoring model to identify customers likely to favor the competitor’s products. Another scoring model placed the remaining customers in the convenience or the price segment. These scoring models were based on publicly available information (such as the revenue of a company and the number of its full-time employees) and answers to key questions salespeople put to prospects over the telephone before meeting them (Exhibit 3). Having assigned those companies that did not prefer buying office equipment from the competitor to the convenience or the price segment, the sales force knew which to target with a full-featured version of the product and which with a no-frills version. Although the company did not expand its sales force, it increased sales by 40 percent.

Dual-objective segmentation

Dual-objective segmentation trades a little precision in delineating segments for a much greater ability to identify the customers in them

Recent advances in market research and modeling techniques have begun to make it possible to convert unactionable segmentation schemes into actionable ones.⁴ In essence, these models of dual-objective segmentation (DOS) trade a small amount of precision in delineating segments for a significantly increased ability to identify the customers who belong in each of them by optimizing a function that is a weighted sum of value-based segmentation and demographic segmentation (Exhibit 4).

Consider an example. Recognizing that many companies in the highly fragmented European market were eager to outsource their information technology activities—both to reduce costs and to focus on their core businesses—a well-known technology company became interested in offering them its network systems management services. Before starting out, the company invited senior technology managers to identify the factors that would dispose them to outsource. Their answers permitted them to be grouped in six distinct segments, a number that struck the optimal balance between precision and breadth.

Analysis quickly established that the alignment between the size of an account and the receptivity of the customer was weak. This was not unexpected. More problematic was the fact that four of the six segments had an outsourcing probability of 40 to 50 percent—too many segments with too low a probability of outsourcing. Having distilled six segments and discovered promise in four of them (performance seekers, people who valued the ability to manage an entire network, those focused on operations, and technology seekers) the company was at a loss to know which one or two it should pursue.

Now, the segmentation scheme originally required the technology company to assign all customers to the segments that best described them. As with just about any classification system, however, each segment contained customers that clearly belonged in it and customers that were more peripheral. In the DOS process, a computer works through an algorithm that makes tiny changes in the segmentation system by experimentally reclassifying customers on the periphery of one segment into another segment. Then the computer runs the analysis that correlates membership in a segment with the likelihood of outsourcing.

Of course, pushing customers from one segment into another that seems less obviously appropriate for them has a price: blurring the clarity of the segments. The upside, however, is that the process typically generates a much higher correlation between membership in a segment and the likelihood of outsourcing. Eventually, 34 percent of the respondents were reclassified. The post-DOS numbers clearly suggested that the company should target the performance seekers and the technology seekers—an actionable outcome (Exhibit 5). As a result, the salespeople, who now realized that only members of these two segments should be pursued aggressively, were trained to ask all new clients a few questions that would make it possible to place them in the appropriate segment.

DOS offers the greatest gains when firmographics alone has very little ability to predict behavior. We applied the DOS approach to three other case studies (Exhibit 6). The first estimated the size of the market for dietary supplements by analyzing consumers’ views about which factors contribute to a healthy lifestyle. The second aimed to size the potential market for chlorinated solvents by examining chemists’ prospective need for the product. The third was an effort to decide which benefits managers were most likely to extend the insurance coverage of their companies to include long-term disabilities.

As in the example of the network systems company, the pre-DOS correlation between the traits and attitudes of potential customers for solvents was very low; after DOS, it more than doubled. For dietary supplements and long-term disabilities, the pre-DOS correlation was high, so the improvements achieved through DOS were less compelling—an increase of 34 percent and of only 3 percent, respectively. Most important, in every case the improvement entailed a sacrifice of only 5 percent in the fit between company and segment.

Getting the science right is a big part of the battle, but only a part. Many segmentation schemes are carried out and then ignored because key decision makers were not involved in the process of segmentation and did not understand how it was carried out. It is critical to discuss the impact that the segmentation exercise of any company is meant to have on the way its key people do business. One way to draw these people into the research and to give them a stake in it is to get them to speculate about what the segments of a given market might be and what can be done to attract each.

Remember also to talk to decision makers in your target segment after the segmentation exercise and before you begin to market to them. They will reveal—in their own language, not yours—what they want and how to reach them. Their views also act as a reality check on the segmentation scheme, which of course will never be totally effective. Given the inevitable limitations of such a scheme, be sure to undertake a pilot project before rolling out a national or international marketing campaign. This gradual approach also helps bring the organization on board before the stakes become perilously high.

Finally, be prepared to think beyond the current model. Typically, segmentation exercises reveal several new ways to serve customers—new channels, new ways of doing business, new ways to train and support the sales force. You may find that your goals expand along with your customer base.