Data Mining in the 21st Century

Kick old analytics habits to achieve better acquisition campaign performance

August 2006 By Peter Harvey

multipage

A data mining paradigm has occurred with an exponential increase in “atomic” (i.e., single element) or transaction-level data; the tools and experience already exist to leverage this new, powerful information to provide marketers with better performance across greater volumes of prospects.

The most immediate aspect of data mining for direct marketers is to begin making better use of the available transaction and behavioral data, turning compiled data into response lists. The evidence of quantitative boosts in campaign performance (increased prospect volumes and the dramatic reduction in archaic merge processes) is abundant. Let’s review several examples across a range of goods and services.

Auto Sales

Bar None, a national automotive credit service organization that uses broad market media, the Internet and direct mail, has been driving buyers into auto dealerships at an amazing rate. Deploying credit-based models, Bar None is achieving response rates greater than 1 percent. This is double the performance rate other marketers are realizing in marketing to prospects (excluding former dealer customers) for dealership clients. Where much of the automotive industry targets by distance to the dealership, Bar None created a more precise prospecting approach in targeting by driving distance, prior auto ownership history and a set of credit bureau-specific models that predict a buyer’s likelihood to respond to a direct channel offer as well as to convert.

According to Dan Staub, marketing executive for Bar None, “We are just beginning to tap the power of this high-level targeting ability. We expect that advancements in personal messaging, multichannel [e-mail and direct mail] campaigns and continuous model refinements have the capability to further boost performance across larger prospect bases for our dealer clients.”

Home Improvement

Scotts LawnService, an on-site lawn, tree and shrub care service—and an organization that already understands the marketing power of data mining—was intrigued by the ability to integrate demographics with property data (e.g., lot size, purchase price, square footage). Using a base of recent responders and non-responders, a model was created from hundreds of atomic demographic and property elements to identify prospects most likely to respond to a direct mail offer for lawncare service.

The challenge was to exceed current model performance by at least 25 percent. According to Scott Jablonski, marketing manager at Scotts, “We are testing new data sources and a new type of thinking. We’ve just begun to tabulate the results from our spring campaigns and are anxious to see whether results meet their model forecasts.”

Lending

Robert Groessner, CEO of direct mortgage lender HomeStar Direct, is a disciplined direct marketer looking to boost his firm’s campaign performance to the next level. A structured test of high-performance data—more than 300 individual-level credit elements modeled to identify interactions between variables, e.g., home equity, revolving debt, age—was launched, and the results were 30 percent to 40 percent higher than head-to-head testing against current control programs using creative and copy approaches developed based on more traditional models.

Groessner says, “Testing [new data approaches] not only convinced us to proceed further, but it helped us realize that the reason for this success is the combination of disciplined database marketing and ‘out-of-the-box thinking’ that we’re able to do with such an in-depth level of available data.”

Good Marketing Is Continual Innovation

This brings us to the next most important aspect of data mining that direct marketers face. It has to do with a question that one of my company’s most advanced clients asks me each time we meet: “What are you going to do for me next?”

The “next” will vary for each marketer, depending on its stage of marketing sophistication. But what is clear is that marketers must step away from the age-old practice of merging multiple response lists to develop prospecting files. The technology is here to do more, and those practicing disciplined analytics are seeing quantifiable, continuous performance improvements. So, what are you going to do next?



Peter Harvey is founder and CEO of Intellidyn Corp., an analytics and database marketing firm in Hingham, Mass. Harvey is a former Fortune 500 marketer with successful direct marketing campaigns for firms such as Bank One, Chase Retail, and NYNEX Corp. He can be reached at (781) 741-5503, ext. 100.

• MORE ON
• DATABASE & CRM