Jan Davis, Advisor, Market Vue Partners
How do you identify which customers are most likely to respond to your next marketing message? If you’re like most marketers, you target based on their past behavior.
- Who responds to email and who to direct mail?
- Which customer groups watched American Idol last night?
- Who made a purchase in the last 30, 60 or 90 days, and who hasn’t responded to the last seven offers you sent?
- Who carries a credit card balance, and who pays off the card every month?
You may layer in some demographics – age, income, ethnicity, presence of children in the home – to refine your best customer segments, creative and offer. But it’s past behavior that drives your targeting most effectively. So what do you do when you’re prospecting or dipping down into lapsed or unresponsive customer segments?
There are a number of options for “behavioral data” for prospecting. Here are three that are effective targeting tools:
1) Credit data. The Big Daddy of behavioral data is credit data. But you can only access it if you are extending a “firm offer” of credit or insurance. The credit grantors – banks, mortgage lenders, collection agencies – began pooling their customer data on paper files within individual cities and towns decades ago. Now they contribute balance and payment data electronically to the credit reporting agencies (CRAs) – TransUnion, Experian, Equifax. The CRAs merge the data and add key public records, like liens and bankruptcies.
They maintain credit files on more than 200 million individuals in the US and then sell individual credit reports back to the lenders who are granting credit and to insurance companies who are underwriting policies. When lenders and insurance companies want to extend offers to new prospects, they work with the CRAs to identify appropriately responsive and credit-worthy consumers. Make no mistake, credit data is an enormously powerful predictor. It’s a holistic picture of an individual’s financial life, particularly how he or she manages debt.
2) Past purchase data. In the late 1980s, the first cooperative marketing databases made their appearance. Taking a page from the credit reporting agencies, the “co-op” companies convinced first cataloguers, then retailers and others to contribute the data about their customers’ past purchases to an independent third-party. Just like the CRAs, the co-ops merged the data from members to create detailed records for each consumer. These co-op companies subsequently added demographics to their databases to provide an even more comprehensive picture of their members’ customers and prospects. They then sell targeted prospect lists and “best customer” lists back to their members.
Market Vue works closely with a leading national retail co-op and has found their data provides the raw material for highly descriptive and predictive analyses of customers and prospects. The co-op database has almost 2000 members and millions of consumer records, including data down to the SKU-level. One particularly valuable metric for Market Vue’s clients is “wallet share,” the percentage of dollars in a category which your customers spend with you. Low wallet share means you are missing opportunities to sell to your own customers!
3) Shipping data. One of the newest behavioral data sources is shipping data, for example, the files from QA Data Info. Due to the explosive growth in online shopping, shipping records have become an immediate and valuable data asset to understand and incorporate into your targeting mix. Like the cooperative databases, the origin of this database is transactions generated by member companies – 72 million in the past 12 months, averaging more than 1.5 million a week. Each transaction includes the physical delivery address as well as the product description, ranging from aircraft parts to apparel to office supplies to business services. Since the data is available weekly, it’s an excellent resource for “hot-lines,” names of people and businesses that have purchased recently. And although past behavior may not always be the best predictor of future behavior, it continues to trump the alternatives.
All three of these types of data resources bring a different set of “observed behaviors” to the marketer. Credit data is off limits to marketers who are NOT extending firm offers of credit or insurance to consumers. But marketers of many types may find uses for both past purchase co-op databases and shipping databases. Each of them makes a unique contribution to the full picture of a consumer and to the prediction of future behavior.