L.L. Bean Augments Database for the Multichannel World

L.L. Bean Inc. is in the final stages of a database upgrade that the direct marketer expects will let it handle customer data from its Web, call center and retail channels more efficiently and improve the targeting of its marketing campaigns.

Before implementing the system, L.L. Bean, Freeport, ME, had used a homegrown selection system to retrieve the names of customers from its more than 35 million-count database that it wanted to use for a particular mailing.

“We had been using the selection system since the dawn of time,” said Steve Fuller, vice president of marketing for L.L. Bean. “But as the Web came along, and as we started to put more retail stores in place and become more sophisticated in our marketing efforts, it was really clear that our homegrown system wasn't going to be able to keep up unless we invested a lot of money in it.”

The old system, for example, did not let L.L. Bean gather data in a timely fashion.

“If we wanted to do a mailing around the Tysons Corner, VA, store, for example, and select everybody who had been to this store and met specific criteria, in the old environment it was a 24-hour request,” Fuller said. “When they invented this homegrown tool, that was a gigantic leap forward, but now it is comical.”

About a year ago, the company decided it wanted to update the system and started looking at CRM software vendors. L.L. Bean selected Affinium Suite from Unica Corp. last winter and began implementing it in the spring. The company is 75 percent through with installation and plans to complete it by the fall.

Fuller also said “the snapshot counts we were doing with our own selection tool that took us 24 hours are now taking us about 45 seconds.”

Affinium's open architecture will let L.L. Bean integrate and streamline as many as 15 marketing processes. L.L. Bean plans to integrate the solution into its multichannel CRM strategy and integrate Affinium with its e-mail service provider.

L.L. Bean will use campaign management technology to create predictive segmentation variables across all channels for improved analysis of expected customer behavior. L.L. Bean conducts more than 300 marketing campaigns yearly.

“We will use Unica to overnight drive a piece of marketing to customers based on their channel preference and their behavior in a recent day,” Fuller said. “Rather than having Unica do big, sophisticated modeling, which we can do now, it will allow us to make a lot of decisions and turn around a lot of customer contacts much faster than we currently can.”

Affinium also will help L.L. Bean trim the number of catalogs it sends.

“We cut back on catalogs to customers by over 30 percent last year, but it was like bailing wire and twine [to get our system] to work [to do this],” he said. “This new system helps us facilitate this new strategy we've taken.”

By next year, the system also may let L.L. Bean do things like quickly drive the name of a customer who has a high return rate in a particular product category into a call center that specializes in high return rates. Or it could help L.L. Bean determine whether a customer is high value, mid-value or low value, then decide what each type of customer's next treatment will be.

“The high-value customers could be sent to our call centers, for example, and we could do outbound phone calls to them, thanking them for the purchase and asking them if there is anything we can do to help them,” Fuller said.

In general, he said, “this tool will allow us to recognize customer value, either high or low, much more efficiently than we currently can do it, and much more readily.”

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