Beyond the Shopping Cart

A case study of using offline data to find your best online customers

By Jesus Mena

Customer retention and repeated sales is really the only salvation for dot-com profitability. But as Wall Street has shown us over the past year, business-to-consumer (B2C) e-commerce profitability is a difficult nut to crack. Studies have shown that e-retailers spend an average of $100 to $250 acquiring a new customer who then only spends about $24, with most never returning. On the average, only 35 percent of buyers make a second purchase at a site they buy from initially. Given these dismal figures, it is quite apparent that if you want to survive and be profitable you need to go beyond cookies and meta tags. You need to learn what your online customers are like offline.

Unfortunately, the traditional ways of gathering information on your Web site visitors and customers are limited. To foster long-term customer relationships and repeat online sales, you need to do more than track clickstream behavior, which is what most of today's ad networks (DoubleClick), collaborative filtering tools (NetPerception), Web data warehouses (PrimaryKnowledge), and customer relationship managers (E.piphany) do. Imagine walking into a brick-and-mortar store and - on the basis of what aisle you wandered into and what you picked up or examined - that retailer is going to know what you will eventually purchase. This flimsy premise is why a predominant number of ad networks and e-commerce systems are tracking cookies and meta tags to try and cross- and up-sell online.

Customer Retention Is the Key

The truth is that this kind of tracking may help e-retailers sell some low-hanging fruit like CDs, but it won't suffice in establishing profitable and long-term customer relationships or selling high-ticket items like sports utility vehicles. Fostering repeated sales requires knowledge about customers' preferences, consumption rates, behavior, and lifestyles. This generally requires knowing things such as a customer's age, income, values, lifestyle, and life stage, which can let, for example, a financial site know whether to offer an auto loan, a credit card, or a savings account. A visitor from a ZIP Code with the demographics of a "college campus" is a prime candidate for a credit card but not a home loan. This approach is more than cross- or up-selling; this is anticipating the needs of your customers and providing solutions that make sense to them. For the credit card issuer, capturing a college student can lead to a long and profitable relationship. The point is that the content provider or e-commerce site that develops these types of demographic profiles will get a bigger share of their online customers' business over a longer period of time.

Who Buys Air Conditioners Online?

This case study analysis involves an e-commerce site that sells air conditioners online, primarily the kind that fit into windows. We wanted to go beyond log analysis, which is all that this e-retailer had been doing, to determine the best way to reach its customers and increase sales. We wanted to identify the lifestyles and preferences of the online customers; we also wanted to find out which neighborhoods they came from; what their households were like; and the types of structural dwellings in which they resided.

In order to discover the characteristics of these online customers it was important to not only know who the site's "buyers" were, but also who their "browsers," or nonbuyers, were. To obtain such insight we used two data sets created from purchase and contest forms. Having registration and order forms on your site is critical to the creation of visitor and customer databases and, of course, consumer profiles. Forms represent a feed-forward system by which customers can tell a retailer what they want and who they are.

It is best to use commercially provided offline demographics matched by a ZIP Code or a physical address rather than asking online visitors to provide them. Two things can happen when you ask for age or income data from your visitors: They will either not provide it or even worse, provide incorrect information. Commercial demographics include information on consumers' personal, household, financial, recreational, home, and auto ownership characteristics and have been used by database marketers for years in segmenting their customers and potential prospects.

There are several sources of demographics at various levels; for this case study analysis we used data from CACI, Acxiom, and Data Quick. CACI provided us with neighborhood demographics; Acxiom gave us household-level psychographics; and Data Quick appended real estate-related information. These external offline demographics can tell you who your online visitors and customers are, where they live, and subsequently how they think, behave, and are likely to react to your online offers and incentives. The demographics and socioeconomic profiles are aggregated from several sources including credit cards issuers, county recorder offices, census records, and other cross-referenced statistics.