Birds of a Feather

Geodemographics: Birds of a Feather (2)

  • by Susan Mitchell
    American Demographics February 1995

Marketers use geodemographic “cluster systems” to reach new customers, choose new business locations, target direct mail, and do other tasks. Now the major providers have recently revised their cluster systems to include 1990 census data. Here is an overview of the latest in clustering and some advice for customers who are buying a cluster system.

Consumers want the best value for their money. So do marketers. That is why products that make marketing more efficient is in great demand. Helping companies identify and reach their best prospects is a booming business. One of the most sophisticated tools for capturing customers is the geodemographic segmentation system.

Products like Claritas’s PRIZM, Strategic Mapping’s ClusterPLUS 2000, NDS/Equifax’s MicroVision, and CACI’s ACORN use data from the decennial census and other sources to separate the nation’s neighborhoods into similar groups known as clusters.

“Cluster systems” are based on the premise that birds of a feather tend to flock together. Look at your own neighborhood. The homes and cars are probably of similar size and value. If you could look inside the mailboxes and cupboards, you would probably find many of the same magazines and cereals.

Some cluster systems use catchy names that try to capture the essence of each segment, such as PRIZM’s “Blue Blood Estates.” Others are more plainly descriptive, such as the “Urban New Families, New Homes” segment of ClusterPLUS 2000. However, the names are not important; it is the information inside that counts.

The idea behind all geodemographic cluster systems is the same. Each system divides neighborhoods into groups based on similarities in income, education, and household type, as well as attitudes and product preferences. But each of the four major cluster systems is dynamic and changeable, and the 1990 census gave them an enormous infusion of new data. Two of the players used census data to completely overhaul their systems last year, creating new sets of clusters organized in new ways.

To make matters even more complicated, two direct-marketing companies have recently introduced cluster systems that don’t start with the census–Metromail’s DNA and Trans Union’s SOLO. This business can get confusing, but there are ways to decide which system to buy and how to put it to good use.

Completely new clusters sprang up after vendors incorporated 1990 census data. The changes reflect major shifts in U.S. society. Claritas increased the number of PRIZM segments from 40 to 62, and Strategic Mapping changed the number of ClusterPLUS 2000 segments from 47 to 60.

Racial and ethnic diversity is a key point of difference between cluster systems of the 1980s and 1990s. Five of the clusters in the new PRIZM system are Hispanic. “The emergence of larger numbers of Latinos across a variety of neighborhood types shows that this group is not monolithic in its settlement patterns, lifestyles, and product preferences,” says Michael Reinemer, former director of communications for Claritas. In addition, while there are no specifically Asian clusters, the company found above-average concentrations of Asian households in the most upscale clusters.

Degree of urbanity is one of the factors researchers use when constructing clusters. Both Claritas and Strategic Mapping found that in the 1990s, settlement patterns in the U.S. have a greater degree of variation than simply urban and rural. This is reflected in the inclusion of “edge cities” or “second cities” in urbanity scales. “The second city is a distinctive pattern,” says Dave Miller of Claritas. “It represents a different lifestyle of city living that is not urban living.” The inhabitants of Nashville, Chicago, and the “edge city” of Walnut Creek, California, are all city dwellers–but life in the three cities differs substantially.

The disadvantage of changes in the systems is that the old and new ones are not comparable. Even if segment names don’t change, differences in how they are defined may be great enough to prevent direct comparison. This is one of the reasons Equifax National Decision Systems chose not to overhaul its MicroVision system. “We infused the new [census] data into the existing structure so our clients could have continuity with the old system,” says senior product manager Diane Zablit.

Merging new data into an existing or updated cluster system is no mean feat. Geodemographic segmentation systems start with millions of raw statistics. They divide the nation’s households into groups based on similarities, much as biologists divide living things into orders, families, and so on. In fact, geodemographic systems trace their ancestry to two statistical parents. The first is a classification system used in the biological sciences, and the second is the geographic divisions (zip codes) created by the U.S. Postal Service.

It takes fancy statistical methods to tease out the patterns that link millions of households. Fortunately for marketers, biologists had already worked on this problem while trying to categorize species of plants and animals. The statistical technique known as multivariate regression analysis was first applied to census and marketing survey data in 1970 by Jonathan Robbin, now a consultant in Washington, D.C. When matched to census geography and zip codes, the resulting product became the first geodemographic segmentation system, Claritas’s PRIZM. Just as a species is “a class of individuals having common attributes and designated by a common name,” a cluster is a class of households with common demographic and lifestyle characteristics, designated by a label.

Many decisions go into building geodemographic segmentation system. The number of clusters must be large enough to provide substantial distinctions between groups, but small enough to be manageable. A system that differentiates on the basis of every characteristic of Americans would produce more than 260 million U.S. clusters, each containing a single individual. On the other hand, a system based solely on the characteristic “resides in the U.S.” would yield a single cluster with more than 260 million people. “That’s the challenge of clustering,” says Joan Hammel, spokeswoman for Strategic Mapping. “It’s got to be homogeneous, yet diverse enough so you don’t have one-size-fits-all clusters.”

Segmentation schemes are only as good as the data that go into them. The power of cluster systems lies in their ability to predict consumer behavior, and this power depends on incorporating data on lifestyle choices, media use, and purchase behavior into the basic demographic mix.

The ACORN system from CACI Marketing Systems divides over 220,000 census block groups into 40 clusters based on 61 characteristics. The characteristics range from general (income, age, and household type) to highly specific (type of cars owned, home value, preferred radio formats). Claritas’s PRIZM system incorporates data from “automobile registrations, magazine subscription lists, consumer product-usage surveys,” and other sources, says Michael Reinemer.

Most systems begin with census block groups that contain, on average, about 340 households. Block groups are the basic unit of geography because “they delineate actual neighborhoods, using natural boundaries like major streets,” says Reinemer. Regardless of the level of geography used, however, researchers can append cluster codes to virtually any list or adapt them to any type of geography, from zip codes to television markets and sales territories.

In the 1980s, the major cluster systems began selling custom segmentation schemes for specific industries. A segmentation system for the auto industry might divide the nation into neighborhoods dominated by owners of imports, sport-utility vehicles, and luxury cars. ClusterPLUS 2000 uses over 450 “atomic clusters, or minute but homogeneous segments of the 60 larger clusters that can be used to develop industry-specific or vertical marketing clustering,” says Hammel. This additional flexibility appeals to many users. “Before, you had to buy the canned software,” says Mark Darling, director of strategic planning at American Isuzu Motors. “If you can customize the information, it’s more meaningful.”

Clusters can also be grouped into larger divisions of clusters, just as species can be grouped into classes or orders. Claritas divides 62 PRIZM clusters into 15 broader groups based on degree of urbanization and socioeconomic status. The “Urban Cores” group, for example, comprises “Single City Blues,” “Hispanic Mix,” and “Inner Cities.” These three clusters are all “multiracial, multilingual communities of dense, rented row homes and high-rise apartments” with high shares of singles, solo parents with preschool children, and perennial unemployment. Similarly, Strategic Mapping divides the 60 ClusterPLUS 2000 segments into 6 “urbanity” groups (such as “suburban”), 11 “multifactor” groups (such as “average income, seniors, few children”), and 10 socioeconomic groups.

Marketers use cluster systems to find new customers, locate sites for stores, buy advertising, target direct mail, and develop new products. If a direct-mail campaign gets a strong response from one zip code, for example, a cluster system can locate other zip codes with similar characteristics.

One of the most powerful features of geodemographic segmentation systems is their ability to find customers. “There are two primary ways people use segmentation systems,” says Strategic Mapping’s Hammel. “Some people know who their customers are and want to know where to find them. Others need to figure out who their customers are, and then go find them.'”

Cluster systems can reveal niches of potential customers in unlikely places. Equally important, they can show that some favorite groups are in reality poor prospects. “There are some surprises that can let the air out of your notion of who your customers are,” says Jim Keryan, GTE’s staff administrator of market assessment. Darling of Isuzu used ClusterPLUS 2000 to find the target market for the launch of the 1989 Amigo convertible. What he found went against his expectations. “Chicago wasn’t the first place we imagined a soft-top car would sell,” says Darling. “But the system predicted it. Sure enough, the top two dealers in sales were in Chicago.”

Clusters tell you a lot about customers. Do they travel a lot? Do they read health and fitness magazines? The profile that emerges enables companies to design marketing campaigns that address customers’ lifestyles.

Since cluster systems include information about media use, they can also help place media dollars. “You can pick a particular newspaper zone, a certain TV daypart, or a given cable program or system that will give you the best penetration into your target neighborhoods,” says Claritas’s Reinemer. Buick used the PRIZM system to decide where to buy billboard advertising, and Isuzu found that lifestyle magazines were a better buy than news weeklies.

Cluster systems are also useful for site selection. If you want to locate a store in Washington, D.C., you could physically drive through its neighborhoods to try to get a feel for who lives in them. Or you could take a “virtual” drive through a cluster map. Coming in from the eastern suburbs on a PRIZM-coded map, you pass through “Second City Elite,” “Winner’s Circle,” and then “Kids & Cul-de-Sacs.” Once inside the Beltway, you pass through “Money & Brains,” “Inner Cities,” and “Bohemian Mix,” all without ever leaving the office.

The lifestyle data in cluster systems help businesses plan product designs or modifications. “One of the things we know, for example, is that sport-utility- vehicle owners are likely to ski. We want the vehicle design to accommodate the consumer,” says Darling of Isuzu.

Clusters sell advertising, too. Cable Networks, Inc. sells time on cable television systems. It uses cluster system maps to “show our customers where the big purchasers of certain items are located within cable system geographies,” says director of marketing research Laura James. Advertisers are far more impressed by a cluster-coded map than they would be with a page of basic statistics, she says.

Clusters tell us a lot about where different kinds of Americans live. Some clusters are scattered across the country, while others are concentrated in just a few areas. But even a seemingly homogeneous area will sometimes show surprising variety when delineated by clusters. On a PRIZM-coded map of downtown Jackson, Mississippi, for example, some areas are predominantly populated by “Southside City” residents. This cluster is dominated by young and old African Americans who are employed primarily in low-paying blue-collar service jobs. They have little education, rent apartments, read sports and fashion magazines, and eat instant grits. But in the middle of this low-income area are a couple of “Towns & Gowns” neighborhoods. People in this cluster also rent apartments, but they are college graduates with better-paying white-collar service jobs. They like to ski, read beauty and fitness magazines, and use ATM cards.

Some clusters have similar socioeconomic profiles but are distinctly different in their habits. The PRIZM clusters “Executive Suites” and “Pools & Patios,” for example, are both composed of affluent, well-educated professionals. But “Executive Suites” households listen to jazz radio and read business magazines. “Pools & Patios” people listen to news radio and read epicurean and leisure magazines. Reaching these two affluent groups obviously requires different strategies.

Not every business needs the full resources of a major geodemographic segmentation system. To effectively use geodemographic segmentation systems, businesses should have a minimum of 2,000 customers. The systems work best with a minimum of 5,000 to 10,000 customers, says Hammel of Strategic Mapping.

Before buying a cluster system, make sure you understand your own company’s resources. “Look at what you already have available internally,” says GTE’s Jim Keryan. “For example, you need to understand how your information and billing systems work together. Then dig through your own database. This is the information you will eventually merge with a segmentation system.” You also need to clearly understand how you will use a segmentation system.

Next, study the different systems to make sure they can do what you want them to. The systems differ in the data they use, the number of segments they offer, the base level of geography on which the system is built, and the way they describe or name segments–among other things. One system isn’t necessarily better than another simply because it has more clusters or catchier names.

In the end, your decision may depend on which system you best understand, or which system makes you feel most comfortable. But whatever your choice, be prepared to put enough time and resources into your system to get the most out of it. As Patrick Harrison, marketing line manager at Buick, puts it: “Geodemographic segmentation systems provide the science of marketing. You provide the art.”

Taking It Further

The four major general-purpose neighborhood-based cluster systems are: ACORN, from CACI Marketing Systems, 1100 North Glebe Road, Arlington, VA 22201; telephone (800) 292-2224; ClusterPLUS 2000, from Strategic Mapping, Inc., 70 Seaview Avenue, Stamford, CT 06192-0058; telephone (203) 353-7500; MicroVision, from Equifax National Decision Systems, 5375 Mira Sorrento Place, Suite 400, San Diego, CA 92121; telephone (800) 866-6510; and PRIZM: Next Generation, from Claritas, 201 North Union Street, Alexandria, VA 22314; telephone (800) 284-4868. See also “Rx for Cluster Headaches” in the premier issue (March/April 1994) of Marketing Tools magazine; for subscription information, call (800) 828-1133.