Compiled Lists - Take a Second Look (968 words)
It's Time to Take a Second Look
Compiled files have long been the stepchild of the list universe—often passed over in favor of transaction-based response lists.
While these massive databases are not desirable for every marketer, the breadth and depth of information they contain are vital to many high-volume mailers, and may be worth a second look by other mailers faced with declining list universes.
Compiled files have evolved from their "phone book" predecessors. Today, there are few pure compiled lists, says David Schneider, vice president of sales, Knowledgebase Marketing. The sources still are records compiled into one file, but most now are "overlaid with response data that allow mailers to look at behavior in ways never before possible."
For example, infoUSA, one of the largest data compilers, adds self-reported data gleaned via Internet surveys to its database. These data are incorporated with other sources to determine hobbies, interests, lifestyle, ailments, investments, Internet and mail-order purchasing behavior, etc.
For whom do compiled files make sense? "Mailers that want to reach such an expansive universe that the cost and time it would take to build enough volume from response lists isn't feasible or economical," observes Schneider. He points out some of the biggest users of compiled files are financial services, insurance and telecommunications providers that market to a broad audience and need the saturation and volume of names these files offer.
Robert Dunhill, president of list compiler Dunhill International, suggests mature or well-established mail-order businesses also may benefit from compiled files by using them as a supplement to response lists to get more comprehensive coverage of their target markets. What's more, compiled data are much more affordable than response data because they aren't as specific.
There are, however, mailers that exclusively use compiled files to generate leads rather than sales. Schneider explains that retailers often will use compiled lists to mail traffic-building promotions because they can get high penetration inside a tight geographic area. An example would be a plus-size women's clothing store mailing a store coupon to women who live within a two-mile radius of the store and wear a size 16 or larger.
Make It Work
Before you can sift through vast amounts of data, you must understand who your customers are. This requires a complete analysis of your housefile.
The degree to which you need to analyze your housefile, says Schneider, depends on the product or service sold.
For instance, the analytics involved in cloning newspaper subscribers is not as complex as those required of a marketer selling long-term care to senior citizens at a monthly premium of $700. In this example, says Schneider, "You're selling a niche product, and then analytics are key."
If this is the case, Schneider advises marketers to start by creating a customer clone model used to select records on a compiled file that shares the same characteristics as your customers.
Next, conduct a test mailing utilizing names from the compiled file. The response from this test mailing then can be used to build a logistic-regression model that looks at the percentage difference between the same variable on one file against another. The percentage of difference is used to detect and establish a segment that's likely to respond.
Schneider gives an example: If you mail 100,000 names from your housefile, and analysis of the responses finds that 90 percent of responders are homeowners, you'd use this model to select and mail an additional.100,000 names from a compiled file. Response analysis reveals that 99 percent of these responders also are homeowners.
As the percentage of responders to this mailing is equal or greater than that of the housefile mailing, the marketer then knows this is a segment likely to respond. Had the percentage of homeowners who responded to the mailing selected from the compiled data been, say, 50 percent, we'd have known that homeownership is not a response indicator.
But, as Dunhill is quick to point out, modeling isn't always practical for all mailers, particularly smaller volume mailers, which account for 90 percent of all businesses. For them, an alternative is to analyze their circulation lists and select the names on a compiled file that match those demographics and psychographics, suggests Dunhill.
Once names have been selected, the marketer should test an appropriate cross-section of the file selected on an nth name basis.
"There are a hundred ways to slice and dice a database," says Kathy Sullivan, vice president of marketing, infoUSA. Indeed, marketers can select names based on numerous criteria and in different combinations that allow them to refine and whittle down a vast amount of data.
For instance, business-to-business mailers can select on title, employee size, sales volume, SIC and NAICS codes, number of PCs and square footage. Indeed, there are 50 or more selects from which to choose.
On the consumer side, marketers can choose from more than 70 selects, including lifestyle, gender, income and ethnicity by area code, county or state level. Names also can be selected by behavior cluster, such as baby boomers who live in Ohio.
A marketer, however, may be working from his or her own prospect file compiled from business cards and other sources. In this example, says Sullivan: "We can take their files and append data and give them more intelligence than they could get on their own. We can take a client's in-house file and update it for them with the most current information, and run it through NCOA or postal processing."
When using a compiled list, says Sullivan, be sure the data come from multiple sources. The more sources a compiler uses, the more meaningful the data will be. A marketer will have more selection criteria to drill down to the exact target, and the sources can be cross-referenced to ensure consistency.