Use Your Data to Know Your Customers

Marketers are bombarded with solutions, plans and tips to increase campaign response rates, especially during trying economic times. But they can’t employ a campaign management solution without a solid foundation of clean, enterprise-level usable data.

Clean data — information that has been verified and is current — is also critical to modeling, forecasting and automating marketing decisions. Too many marketing executives fail to understand this and wonder why they can’t get their modeling and analysis to work.

Think about this: The U.S. Census Bureau estimates that 40 million people move each year, and 9 million of them don’t leave a forwarding address.

Meanwhile, one in 100 people gets married each year, and half of those people are likely to change their names. Another one in 200 gets divorced.

In short, depending on the target population, there is a good chance that a substantial percentage of the address and name information in an organization’s database will become obsolete within 12 months. Furthermore, those address and name changes are clues to customer lifecycle changes — changes that often find consumers breaking traditional purchasing patterns.

Sending files out for a periodic cleaning won’t solve the problem, especially since marketers gather new data about their customers on a daily basis. This includes what customers have most recently purchased or had serviced, demographic information from third-party sources, and Web site browsing patterns.

Integrating this data across the organization is also essential. For example, let’s say a Web application captures the information that [email protected] is now using a credit card with a different last name and address. Did that information make its way to the direct mail application? Can the marketer aggregate the data on Jenny’s husband, who is also a customer, to get a household view? Or will Jenny and her new husband be receiving multiple catalogs and fliers, thereby lowering response rates and driving up costs?

Meeting expectations

It’s not just about lowering the costs for marketing campaigns. Today’s customers expect marketers not only to know who they are, but also what they want. For example, it’s neat when the grocery receipt comes with a handful of coupons for products you are likely to buy. These customized coupon systems rely on a vast database of customer transactions. But glitches occur, and it can be a bit jarring to get some entirely irrelevant offer — like a diaper coupon when you’ve just sent your youngest child off to college.

A similar phenomenon occurs when customers interact with your company online, in person or by phone. Customers expect marketers to have a record of their last service call, what they purchased last month, or what flowers they order every Mother’s Day. And they expect recommendations that match their interests. It’s also important that, as some companies improve their customer-experience management capabilities, customers’ expectations rise.

Customer demands increase the complexity of storing, managing and analyzing data. In turn, this increases the potential for trouble. Marketers can’t afford to create an expensive marketing campaign based on faulty data, or offer instant recommendations that are based on an incomplete picture of the client. So what can they do?

If data is kept in silos, marketers need to integrate those silos to get a complete view of the customer. It’s quite possible that the Web team can’t merge lists with the direct mail team because the two maintain customer data sets in disparate systems that are not integrated with each other.

Build a better view

The value of integrating systems is obtaining a full view of the customer. Marketers can’t predict what a customer will want to buy and then push out that offer if they don’t have a clear picture of what they’ve already bought. For example, the leading provider of customized coupons processes 250 million transactions a week. This data helps its clients create incentives that are redeemed eight times more often than traditional incentives. Response rates sometimes reach 25%.

Fresh information can provide a boost to marketers’ efforts. One marketing support firm created a decision-support service that delivers data monthly, rather than quarterly. This helped its clients improve marketing return on investment by 10% to 15%. The data is delivered to a desktop with intuitive options for analyzing it.

Jeff Gilleland is the global customer intelligence strategist at SAS. Karen Heath is a managing practice principal in HP’s BI Solutions group.