Demographic Segmentation

To gain an edge in nabbing profitable clients, savvy companies often use segmentation techniques to slice and dice customer data in an effort to match the best sales prospects to specific products and services.

All customers leave data "footprints" about themselves in each transaction they make, which companies can in turn analyze to help determine future buying preferences. These bits of customer relationship management (CRM) information - such as the date of purchase, the buyer's location and the types of products purchased - can be used by a retailer to create a sales and marketing campaign for, say, women between the ages of 35 and 55 in the Toronto area whose household income is in the top 10 percentile and who show a predisposition for buying upscale shoes.

To mine such data electronically, companies use online analytical processing (OLAP) or data-mining applications from vendors such as SAS Institute Inc. in Cary, N.C., and Information Builders Inc. in New York.

But there are big challenges in gathering and making use of this data. The data itself must be solid and reliable, the categorization and definitions in the data must be consistent, and the folks who launch these queries must be flexible in the types of methodologies they use, says John Thompson, vice president of marketing at WhiteCross Systems Inc., a maker of analytical software in San Francisco.

Roots of Segmentation

The concept of using automation to conduct segment analysis goes back to the turn of the past century, when the government employed punch cards to tabulate the U.S. census, says Aaron Zornes, an analyst at Meta Group Inc. in Stamford Conn.

When the first commercial computers were introduced in the 1950s, customer segmentation was one of the first applications for which they were used. Pioneers in this area include catalog companies such as Minnetonka, Minn.-based Fingerhut Cos. and others.

As recently as the mid-1990s, companies would typically ship customer data to a third party, such as San Antonio-based Harte-Hanks Inc., for analysis and segmentation. The third party would send the results back to the originating company for use in direct-mail campaigns. But the advent of relatively affordable OLAP and data warehousing tools has made it possible for retailers and other companies to run these analyses themselves.

Now, enterprise managers prefer to keep that data in-house, merge it with accounting and sales figures and run queries from their own desktops, says Zornes. Among the companies providing the applications are Unica Corp. in Lincoln, Mass.; Hyperion Solutions Corp. in Sunnyvale, Calif.; and Chordiant Software Inc. in Cupertino, Calif. This type of analytic software generally starts at US$250,000 for an enterprise license.

"The trend has been quicker and easier access to the information, creating a dashboard for a senior executive to look at on the fly," says Kaenan Hertz, director of CRM digital intelligence at the Student Loan Marketing Association, or Sallie Mae, in Reston, Va.

Making Data Accessible

Historically, data at Sallie Mae was stored in a single mainframe, which made it difficult for employees to access the information and run their own segmentation queries.

To remedy that, Sallie Mae recently formed a special team to make the CRM data accessible to end users through a client/server network. Extracting the data and making sure it was valid required significant customization work, says Hertz.

The key challenges that Sallie Mae has faced since then include limiting access to specific data for certain employees and ensuring that the segmentation programs run fast enough across millions of cross-referenced records, according to Hertz. The company factors in not only the age and sex of the borrower when running a query, but also what school he went to and for how many years.

Sallie Mae recently installed software from E.piphany Inc. in San Mateo, Calif., to help segment customer data and run more efficient e-mail marketing campaigns.

The Web has provided companies with yet another sales channel from which to extract data in addition to phone, fax, catalog and direct mail.

To assist its sales efforts, online retailer eBags Inc. gives customers the option of filling out profile forms when they visit the eBags.com Web site, says Mike Frazzini, vice president of IT at the Greenwood Village, Colo.-based firm.

Using software from Broadbase Software Inc. (which has merged with Redwood City, Calif.-based Kana Software Inc.), eBags cross-references this data, learning how customers came to its Web site and what they're interested in.

The US$500,000 system, which went live recently, has already started to pay for itself via improved response rates to e-mail campaigns. Frazzini says he would like to see the system run analyses on customers while they're shopping at the site and pitch product offers on the fly.

While the Web has helped end users access segmentation applications more easily, as a sales channel it hasn't been a magic bullet for segmentation.

"I view the Internet as a sort of new touch point, but it hasn't been as strong a touch point as everyone would have thought," says Malcolm Fowler, vice president of business development at Vancouver-based Ernex Marketing Technologies Inc., a subsidiary of the Royal Bank of Canada.

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More about ADVENTBroadbase SoftwareChordiant SoftwareE.piphanyHyperion solutionsInformation BuildersKana SoftwareMeta GroupSASSAS Institute AustraliaUnicaWhiteCross Systems

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