The Art of Data Diving

The Bowling Proprietors Association of America, a sort of trade association for bowling alleys, had long believed that its members’ best customers are league bowlers.

That made sense. The average league player bowls 35 times a year, contributing $262.50 in revenue to the bowling alley (based on an average of three games per visit at a cost of $7.50). The lifetime value over four years is $1,050.

Given that, why should the group or its members bother with social bowlers, who play only 12 times a year?

Here’s why. These casual players spend more for each visit than the league players—$30—and that this adds up to $360 per year.

And it’s simple once you add up the numbers. Unlike league bowlers, social players tend not to own their own shoes, so they must rent them for an average cost of $3. And they spend $10 for food and $2 for video games.

To determine this, of course, the league had to “capture behavioral clues that are hidden within transactional details,” according to Sandra Gudat, CEO of Customer Communications Group Inc. And this is part of what Gudat refers as data diving. The purpose is to “get the right offer to the right customer at the right time using the right method.”

Here’s another case. BB&T, a regional bank in southeast, wanted to know why less than 50% of its Check Card customers actually used their cards. This was probably due to age or other demographic factors, right?

Wrong. BB&T did performance and data analysis, and appended third-party lifestyle data. It also conducted focus groups at which it asked customers to pick pictures and images to describe who they are.

The bank found that age was not a prime factor in who used the cards. Income was. And knowing that, the bank could identify the most promising customer segments and create the appropriate incentives.

For example, it offered airline miles to affluent customers and no fee to families.

It entails developing subsets of customers who can be identified and marketed to as a group, and are of sufficient size to make it worthwhile to do so. And it happens in two stages.

The first is identifying the right customer segments. To do this, you use traditional tools like RFM, RFM + profitability, demographics, transactional purchase history and statistical modeling.

The second step is putting a face on your customer segments. And for this need tools like primary research, data analysis and data enhancement. (She defines primary research as focus groups, telephone and mail surveys, Internet polls and one-on-one interviews.)

And just what do you need to know?

As Gudat sees it, you have to answer what news writers call the five W’s and an H: Who, what, where, when, why and how? She breaks them down as follows:

Who: Life stage, interests, demographics and psychographics.

What: What do they buy now? What will they want to buy next? What factors are influencing them to buy?

Where: Preferred channels.

When: Seasonality. Purchase cycle patterns.

Why: Why do they buy my service/product, how do they use it, how does it fit into their life?

How: How do they use my product? How do they perceive my company, product or service?

Of course, data diving isn’t only about driving sales—it can also be done to recognize special customer relationships.

For example, Pier 1 Imports found that 2,000 of its high-value customers spent $20,000 a year apiece. “These women dressed their houses by season just like you would change your wardrobe,” sys Gudat.

The firm wanted to make sure that its 700 store managers knew these customers. So it sent them letters from the president, including a certificate for a complementary gift for the customer.

And when the high spender came in, the manager was trained to greet her personally and present her with the certificate. Thus, Pier 1 used data to create a “targeted and customized handling procedure,” Gudat says.

Takeaways

Gudat offers these pointers and caveats on the art of data diving:

*There are no silver bullets. Determine the “customer relevance factors” for your customers and work backward.

*Set the tone. Speak the target customer’s language, not just the language of promotions and sales

*Don’t develop more segments than you have the ability to support.

*There’s no personalization panacea. You may experience diminishing returns with personalization, “where the cost exceeds what you’re going to bring back,” Gudat says.

*Define the generational fingerprint. What are the experiences that shape who your customers are today?

*Determine their preferred communications channels.

This article is based on a presentation at the National Center for Database Marketing conference in Chicago.