Live from Retail Systems 2006: Levenger’s Data Warehouse Epiphanies

Chicago—Like many other companies that evolved from cataloger to multichannel merchant, Levenger had too many personal reporting systems whose results conflicted with one another. “We were basically operating with the headlights off,” chief information officer Marnie Barrett told attendees of her Tuesday session at Retail Systems here.

To create a single view of customers in one place and consolidated reporting for multiple divisions, the marketer of “tools for serious readers” worked with Junction Solutions to create a data warehouse to complement its existing Ecometry transactional system. Once implemented, the data warehouse enabled Levenger to make improvements across the organization, from merchandising to marketing to fulfillment.

Even before the data warehouse, Levenger had noticed that different products sold better in different channels. But the Delray Beach, FL-based company hadn’t been aware of specifics. “We’d been looking at sales and what’s selling through on a consolidated basis,” Barrett said. With the data warehouse in place, “we found that what we thought were dogs weren’t dogs in all channels.”

Levenger now looks at its monthly and weekly best and worst sellers and product categories by channel. For instance, big-ticket furniture items such as reading chairs and task seating sell better in the company’s four stores than via the catalog, the Website, or the business-to-business division, in part because store customers can sit on it and feel it. Within the b-to-b division, on the other hand, smaller products such as business card holders, portfolios, and pens that can be personalized far outperformed other items, leading Levenger to refine its merchandise selection for that channel. What’s more, while brighter colors sold well in other channels, for b-to-b sales black, blue, and brown were overwhelmingly the colors of choice.

The company’s improved data mining capabilities also enabled it to determine the most popular price points for acquisitions vs. for purchases by long-time customers, Barrett said. As a result, Levenger was able to better merchandise its prospecting offers. Similarly, the company restructured the density and product positioning of its print catalogs based on analysis of product performance.

Levenger also pulled back somewhat on its new product offerings, especially within the array of leather bags it had introduced during the previous two years, to reemphasize its tried-and-true sellers. “Data mining allows use to see the behaviors that change based on changes in the product mix,” Barrett explained. “We were known for innovation, and I think that became a little diluted when we got too much into totes.” Once the merchandise mix was adjusted, she added, a significant number of 24-month buyers became reactivated.

Another byproduct of the data warehouse has been what Barrett called “intelligent remarketing.” Levenger can now refer to customer history to determine which products to offer individuals in subsequent contacts: “If someone bought a pen, we can now offer them a refill,” she said. “We can be more granular and intelligent in our marketing.”

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