Enhance What You've Got, to Get What You Want
By Hallie Mummert
As a direct marketer, you probably often ask yourself, "What do I know about my customers? What do I know about my prospects?" But you really should ask yourself: What do I need to know about my customers and prospects to help me market smarter?
A great place to start is your own transactional data. But this isn't always enough to tell you which offer is the best one for the second sales attempt— or, more precisely, which customers might not respond to a cross-sell offer, say for car insurance, because they don't own cars.
To enhance your relationship with customers and maximize your chances for successful prospecting, you need to consider enhancing your database.
Benefits of Enhancement
Before we discuss the nitty-gritty of how enhancement works, let's look at the benefits of enhancing your lists.
"There is one major benefit—no matter what your ultimate use of the data—and that is enhancement aids profiling," says Dennis Kooker, COO at KnowledgeBase Marketing, in Richardson, TX.
Behavior tells you some of your customers' preferences, he explains, but it doesn't tell you how to market to them. Lifestyle data, demographics, and geographics fill in the unknowns to help you paint a more complete picture of these people.
For example, says Gary Van Roekel, vice president of marketing, Melissa Data Corp., geographic data can help you find zones such as retirement communities or places of seasonal residence. With this insight, you can target a consumer segment that's likely to have grandchildren, or avoid sending offers to a vacation home during the off-season.
Enhancement is most effective for gaining insight into your current customers, but it can improve response to prospecting lists, too.
Mary Ann Kleinfelter, president and owner of Marketing Solutions Today in Milford, NH, notes that when it's hard to get the budget to prospect, you need to prospect smarter. "With prospects, you have no transaction-level data; so you need to enhance these files to know enough to prospect effectively," Kleinfelter says.
What data variables will yield the most insight? To find out, Kleinfelter recommends marketers start with their best customers and append data variables that are relevant to their products and objectives. For example, if you market childrens' book clubs geared toward different age ranges, it would be helpful to know if customers who signed up for the baby book club have older children, too.
Once the data are appended to your file, Kleinfelter advises marketers to segment by data element, and mail the same test effort to each segment. Your response analysis will reveal which types of people respond best, and how much the data required to find them is worth to you.
Van Roekel adds, "There's no reason to mail broadly when you can narrow your audience to a specific, reachable characteristic. In this way you find a more receptive prospect, plus you save on postage and production costs."
A side benefit from enhancing your database is that it makes your housefile more attractive on the list rental market.
How Data Are Collected
Just as you would never rent a list without assessing its quality, you should cast a critical eye on the databases that will be used for enhancement projects.
The two main factors of data collection are source and hygiene, says Sterling Hunt, vice president of data processing at infoUSA and its data marketing services arm, Donnelley Marketing.
Companies that collect data do an analysis of the source of the information and see how well it validates. If they're satisfied with the quality of the data, Hunt says, they purchase it from the seller. In some instances, they may plan to use only a portion of the data they purchased.
Sources of information can be broken into two kinds, says KnowledgeBase's Kooker. Public data are information that can be collected at courthouses and through other published records. Non-public data are from permission-based sources, such as surveys and warranty cards, and non-permission-based sources, such as data collected from customers by the primary business and shared with third parties.
The availability of non-permission, non-public data has been restricted by legislation. Two examples, Kooker offers: the Shelby Act that took driver's license data completely off the market, and the Graham Leach Bliley Act that restricts financial institutions from sharing even basic name/address data when customers opt out. He notes that many customers have not opted out of having their data rented, keeping intact a valuable source of data.
Kooker points out that some important data variables simply don't exist in data form, and must be predicted by models. For example, no one collects income on an individual basis, so list compilers build models that predict income ranges based on home values in specific geographic areas.
"These models tend to be extremely accurate," says Kooker.
Once a list compiler has selected information to populate its database(s), it proceeds to put the data through a hygiene phase to ensure that they are clean and parsed correctly. The last process is a matching phase, in which the master database is appended.
These two phases are "where the art of compiled data files is at work," Hunt asserts.
How Often to Enhance
The no-fail guideline for how much data you collect or append is the amount you're willing to maintain, says Kleinfelter. "Don't append data, unless you have a plan and the resources to keep it clean," she advises.
To keep data clean and current, you need to source each data element you've appended to your files, says Hunt. Each data element should be flagged in your database according to its entry date.
Then, find out from the list compiler how fast that piece of data decays, Kleinfelter advises, and schedule your updates accordingly.
Hunt agrees, and suggests marketers update phone numbers monthly to meet appending needs and comply with Do-Not-Call requirements.
Your update schedule also depends on the volume of new records you add to your file through prospecting, Hunt adds. If you do a fair amount of prospecting, you may need these new customer records enhanced before the next update comes around.
In some rare instances, you can do updating yourself, Kleinfelter notes. Simple data questions, such as whether the customer owns a PC or Macintosh, can be integrated into the end of customer service and order processing calls, as well as on order forms and Web site log-ins.
Are More Sources Better?
When it comes to data collection, multiple sources help validate the accuracy of the data, says Kooker.
For enhancement, however, using multiple vendors may be overkill. According to Kooker, "It depends on how much of your file needs coverage, and the cost of making sense of all the data. The value of the fields and the kinds of fields differ from compiler to compiler … the marketer would have to do the work to assess each vendor's data."
And that means added cost.
Hunt says you'll likely see a point of diminishing return with the fourth or fifth data vendor, and no return on investment for the work being done. He reminds marketers that you pay a charge per thousand just to look for matches, so processing costs need to be less than the ROI.
And don't assume that because your entire file didn't get appended, you went to the wrong list compiler.
"The major sources of compiled data are fairly consistent in terms of their match logic and match results. A data service bureau may be helpful in guiding you to the best possible data source depending upon your needs," says Van Roekel.
What's more important, he adds, "is the append rates will be based on the quality of the core data that you submit. Keeping your housefile clean should be an ongoing process because data are always deteriorating. We always recommend that you clean your own list using both National Change of Address and Delivery Sequence File before the data is submitted for enhancement services."
Kleinfelter finds that you can prevent poor enhancement results by: 1) watching the data hand-offs to ensure that the list compiler can read your file correctly; 2) eyeballing a data dump of non-matches to see why they didn't hit. If you haven't cleaned your file well enough before having it enhanced, it's not the list compiler's fault.
If you know your data is clean, then it's time to go back to the list compiler and ask tougher questions about its matching procedure and the completeness of its data.
Regardless of how many matches came back, Kleinfelter says it's always wise to eyeball the results to make sure data appended to the correct fields in the record structure. Before rolling out any mail or phone campaign, do a test run. And if you're using enhanced data for personalization on direct mail or Web sites, do an internal test with any new data to prevent mistakes that damage customer trust.
According to Hunt, list compilers might use one of two different methods to price data appending services:
1. A flat per thousand charge for processing the marketer's entire file, regardless of how many matches result.
2. A match charge only for records appended, which may include a passing charge to cover the vendor's processing costs—especially if it's the third or fourth data pass for the file.
Basic appending costs from $100/M for a low volume of data to $2/M or $3/M for a high volume, Kooker estimates.
Also, the more specific the data, and the harder it is to get, the more expensive it is. An example of this comes from Van Roekel: "On the high side, you can expect to pay $20/M for records appended to get mortgage information, but only $3/M appended to find homeowners." He adds that many vendors offer discounts on bundled services.
But the append is not the end. Kleinfelter recommends marketers factor in the cost to clean the data and analyze it for accuracy—not to mention the cost associated with any mishaps, such as having to correct a file that was appended improperly.