There are many ways to segment a market. The most common method is demographic, because demographic information is easy to acquire. Product purchase behaviour is another objective type of segmentation based on information that also is readily available. Other types of segmentation require more sophisticated data collection and analysis. Marketers usually begin with objective segmentation methods and develop these more sophisticated methods over time, as shown in figure below
Objective Methods of Segmentation Demographic segmentation. Bank of America and other financial institutions pursuing the Hispanic market are practicing demographic segmentation—the most basic kind of segmentation. Other recent examples of demographic segmentation include a new effort by Wells Fargo to make its online services more accessible to the blind and visually impaired, a home mortgage–like product developed by HSBC that addresses religious law forbidding Muslims to pay or receive interest, and a sponsorship by several mutual funds (Calvert, Domini, TIAA-CREF) of a gay and lesbian conference.
The great advantage of demography as a segmentation variable is that it is based on observable, measurable characteristics. Demographic variables in the consumer market can include age, sex, race, religion, personal income, household income (HHI), marital status, number and ages of children, home ownership, education, professional status (type of job), language, ethnic group, physical disability, and sexual preference. In the business market, demography can include size of business (by number of employees, revenues, or other measures), type of industry, length of time in business, ownership characteristics (public corporation, privately held), management structure (hierarchical or flat), and so forth.
Geographic segmentation. Geography is also a basic, measurable segmentation variable. Clearly, a company that does business only in certain geographic regions (such as a local bank) would limit its target market to potential customers in that region. Geographic segmentation also applies to creation of sales territories and efforts to expand nationally or internationally, as well as to pinpointing potential markets by type of neighbourhood, urban versus rural locales, single or multiple locations for businesses, and the like.
Life-cycle segmentation. Another common method of segmenting a market is based on the fact that customers’ needs change as they enter different phases of the life cycle: for example, young marrieds are likely to buy a home, growing businesses are likely to need a line of credit. Although identifying potential clients by life cycle phase is strongly correlated with marketing success, the data points are often difficult to find. If you’re looking for people who have just bought a home (say, to target for a home equity product or mortgage insurance), it’s easy to find this information. If you’re looking for people who are thinking about buying a home, the task is much more difficult.
Product segmentation. When General Motors’ Alfred Sloan called in 1924 for “a car for every purse and purpose,” he was segmenting his customers to match his cars—Chevrolets for the young and less affluent, Cadillacs for an older, wealthier crowd, other brands in between. Banks have also traditionally segmented their markets by product—credit card customers are in one silo, home equity loan customers in another, and certificate of deposit owners in a third. From this rudimentary information, whole customer relationship management (CRM) systems can be built, yielding valuable data on account size, profitability by product, purchasing patterns, usage (debit versus credit), number of products, churn and retention rates by product, transaction method (phone, online, in branch) and other variables.
Segmentation by Psycho-graphic Clusters
Demographic, geographic, and purchase variables are relatively easy to find and use, but they have limitations. Take, as an example, two 30-year-old men with similar incomes and education. Both are married and childless, but one has $100,000 in investable assets, and the other has no savings. What variable explains this difference in behaviour?
Attitudes and behaviours are much more difficult to observe and measure than demographics, but they can offer more insight into what customers actually buy and why. And by analysing why customers have bought in the past, it is possible to project who would be most likely to buy in the future.
Benefits-based segmentation. One way to segment a market is by which benefits appeal to customers. For example, British researchers sent a seven-page questionnaire to some two thousand randomly selected households in order to determine why different demographic segments chose their bank. The results, shown in table below , demonstrate their conclusion that “segmentation criteria must be relevant to the purchase criteria of customers.” Based on their research, for example, a bank that offers limited access to ATMs probably shouldn’t bother aiming for the youth segment, whereas a bank that has ATMs but pays lower-than-average interest on deposits would find the 18- to 26-year-old market a suitable target niche.
Knowing why customers buy is very helpful for creating targeted campaigns.
For example, SouthTrust Bank’s equity line of credit direct mail solicitation was segmented by the purpose of the loan: home improvement, debt consolidation, and children’s education were the three leading categories. Knowing this made it easier to customize the messages for a direct mail campaign targeted to prospects in each of these segments.
Lifestyle segmentation. Lifestyle segmentation operates on the principle that “birds of a feather flock together.” Similarities of interests, attitudes, and activities are common among people who live in the same neighbourhood—for example, suburban soccer moms often read the same magazines, shop in the same stores, and share political and social viewpoints with their neighbours. The tools that are used to group customers and prospects into attitude and behavioural segments include cross-tabulation analysis, data mining, predictive modeling, cluster analysis, and other statistical techniques. The resulting variables have many names, including psycho-graphics, behavioural models, values-based analysis, and lifestyle analysis.
One common way of determining the lifestyle characteristics of one’s customers is to overlay one’s own database with a commercial “cluster analysis.” Cluster systems, such as PRIZM, ACORN, and MOSAIC use census and other quantitative and qualitative data to divide the United States and other countries into clusters, based on demographic and lifestyle similarities.
Business customers can also be segmented by psycho-graphic criteria and buying behaviour. For example, businesses can have different types of personalities: entrepreneurial, buttoned-down, consensus-driven. Decision-making styles can vary—in some businesses, decisions are made by one individual while in others they are made by committee. Purchasing decisions may be based on different personality factors: some businesses seek name brands or added-value services, whereas others look for the cheapest solution. Some businesses are “innovators” or “early adopters,” that like to be on the cutting edge. Others are followers or “laggards” in their adoption of new technology. The sales force needs information about important behavioural factors such as length of sales cycle, relationship (preferred provider versus competitive bidder), and expectations for delivery, maintenance, training, and other services.
When lifestyle characteristics are combined with profitability data, organizations can develop deep knowledge of their customers. As customer relationship management systems have become capable of predicting and projecting, segmentation schemes have developed that can increase lifetime customer value—that is, both the length of a customer’s tenure and the long-term profitability of that customer.
An early user of customer-value segmentation methodology was Chemical Bank (now part of J. P. Morgan Chase), which took all the various data points it had collected about its customer base and then used a lifestyle approach to append behavioural and attitudinal data. This combination of data enabled the bank to view who their most profitable customers were in terms of lifestyle characteristics.
Table below shows two of the identified groups. The insights provided by these segmentation methods are useful in several ways: They can help a company target new prospects who resemble their current best customers. They can help cross-sell and up-sell current customers who are thought to have additional assets outside the institution. They can help
Lifestyle and Profitability Segments
retain current customers by predicting life-cycle or service issues that require intervention.
Behavioural segmentation can also help determine levels of service for current customers in order to maximize profitability. This must be done carefully, however. There have been a few public relations disasters when companies have too publicly announced that they were shifting unprofitable customers to cheaper methods of service, like ATMs or online access. Further, by making this information available to branch staff, there is the risk of inadvertently revealing potentially embarrassing information to customers. At the same time, if better service is to be offered to better customers, the customer-facing staff must be aware of which profitability “bucket” the customer falls into.
Segmentation by lifetime value is still relatively rare. A 2003 survey of 97 financial firms conducted by GartnerG2 reported the percentages of firms employing segmentation, based on the criteria shown in figure below. Respondents could list more than one type of segmentation, so totals exceed 100 percent.
Types of Segmentation Practices
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