The more you know about a customer - age, gender, what he owns, what he spends and what his preferences are - the more likely it is that you can create and pitch a product or service that will hit a bull's-eye. Identifying and interacting with your customers in ever-smaller groups is a tantalizing prospect - and now easier and more cost effective than ever, thanks to the Web and other technological advances that gather, store and sort customer data.
"In the earlier days of market segmentation, some sociodemographic information was known, but it wasn't actionable. Now, it's radically refined into thousands of segments, and it tells you things about class culture, how they behave with their wallets and how to reach them," says Mike Riley, a vice president at Mercer Management Consulting Inc. in New York.
For example, among credit card issuers, Capital One Financial Corp. is a leader in applying information-based marketing that is based on hundreds of variables to identify thousands of customer segments and devise offers tailored to these segments in terms of interest rates, credit limits and interest-free periods, says Riley.
The Falls Church, Va.-based company then tests and evaluates the results, a strategy that has paid off in helping Capital One grab market share from bigger credit card issuers, says Mercer Vice President Nick Winter. While many firms gather data, most don't take time to study the results in detail and make cross-comparisons, he says.
By contrast, Citibank in New York uses traditional "push" segmentation (separating customers by demographic criteria, such as high income), while MBNA Corp. in Wilmington, Del., uses affinity segmentation (segmenting by common interest groups like professional associations and sports teams), a Mercer case study notes. NextCard Inc. in San Francisco uses self-segmentation, allowing customers to configure their own credit cards' terms.
One of the challenges organizations face in fine-tuning marketing campaigns is that the relationship between a company's IT staffers, who mine the raw data, and the marketing folks, who need it to make informed decisions, is often contentious, experts say.
In some instances where there's a particularly deep rift between these two groups, IT staffers are even regarded by their business counterparts as "data Nazis," says Riley.
"Business unit [staffers] say IT tries to control the process in a way that is unproductive," says Riley. "Instead of 'thou shalt do,' business units want IT to provide a set of standards and analytic tools as a foundation to rely on."
Tom Connellan, a partner at Performance Research Associates Inc., an Orlando-based marketing consultant and co-author of e-Service: 24 Ways to Keep Your Customers When the Competition Is a Click Away (Amacom Books, 2001), says, "The key role for IT staff is looking all the way down the value stream to see who the end customer is out there and how can I work with my internal partners to create value for that customer at the end of the value stream? For some, this is a real mind-shift, because IT is technology-centric, not customer-centric."
Most department stores, along with some mass merchants and specialty retailers, can join customer records across channels if a proprietary retailer credit card is used. For example, Saks Fifth Avenue in New York, a customer of Blue Martini Software Inc., analyzes sales transactions across sales channels on the Saks card.
"But most retailers can't reliably and cost effectively aggregate customers' other purchases if they paid cash or used a major nonstore credit card, unless they ask for the customer's name, address and phone number at checkout to link in-store purchases with online purchases," says Catharine Harding, director of retail solutions at Blue Martini and a former IBM retail consultant.
The Good Guys Inc., a Brisbane, Calif.-based consumer electronics chain with 79 stores on the West Coast, has been moving toward a microsegmentation strategy during the past year. Good Guys previously segmented its customers by how recently and frequently they bought products and how much money they spent - a traditional approach used by most catalog companies.
Now, the company rents lists from list brokers in order to cluster small segments of customers with distinct profiles and buying behaviors - such as luxury home theater buyers and digital camcorder buyers. That allows it to target past buyers with specific messages and prospective buyers who share similar demographics.
Good Guys' extensive cross-channel customer database - tied to names, addresses and phone numbers - is "enriched" with demographic data, which is pulled into pre-defined data formats and ready for immediate reporting and data mining through an integrated process in its Blue Martini data warehouse, says Harding.
Demographic data that fleshes out a clearer picture of the buyer with about 200 common variables - such as whether or not he owns or rents a home, has children or has moved recently - is generated by companies like Acxiom Corp. in Little Rock, Ark., and Experian Information Solutions Inc. in Orange, Calif.
"Once you know a buyer's life stage, you can do a lot of personalized marketing," says Harding. "A retailer may want to offer very attractive incentives to someone with a first baby. If a buyer moves, a consumer electronics retailer may want to pitch a big new TV, appliances and so on."
Good Guys' sales transaction data is sent to Direct Marketing Technology Inc., a subsidiary of Experian in Chicago that has maintained its relational customer database since 1991. "Our IT head would love to maintain it in-house, but he's acknowledged [that] the marketing people need to have their hands on the data and understands [that] the merge/purge process - deleting duplicates, filling in new or missing data - can't be done without a national firm who can run it through their change-of-address system every month for updates," says Kathy Castle, a marketing manager at Good Guys.
Among the major challenges in measuring the effectiveness of personalized marketing campaigns: It can take years for a customer's behavior patterns to become clear, if at all.
"The hardest thing is that some metrics used to evaluate behavior materialize over a long period of time," says Winter. "Let's say a person uses a credit card a lot and keeps high balances, which sounds like a profitable customer, but [he] eventually switches to another card, or is at risk of default. You don't have the whole picture right away."
McDonnell is a freelance writer in Brooklyn, N.Y. Contact her at Sharonfmc@compuserve.com.