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Use Multiple Databases To Build Best Universe

Marketers can easily fall into the trap of using data from one or two databases that only seem to fit the goals of their campaigns. They force-fit the marketing plan to the data instead of finding the data that best fit their plans.

There is an alternative.

Using multiple data sources, perhaps mixing and matching different data sources, provides many marketers with precise marketing universes and the best data possible.

Compilation techniques, demographic selections and coverage vary sharply among the thousands of managed databases and the dozen or so major national compilers. Each business-to-business and consumer database has a unique set of attributes, and by using multiple business or consumer data sources, your final list of data can garner the virtues of selections and records not available on a single file.

Compiled vs. managed lists. By and large, compiled lists are valued for their breadth of data, while managed or response lists are coveted because they represent consumers and businesses that have already proved themselves as buyers or responders.

Typically, for traffic building, brand awareness or lead generation, compiled databases are the way to go because they offer maximum coverage. If you use only response lists, you exclude much of your universe. If you are a cataloger or mail-order establishment and the goal is direct sales, response files are the better choice.

What combination is right for your campaign? It depends on what you are trying to do, what’s available and the market you need to reach.

Here are examples of database combinations that have worked for various marketers:

• A national weight-loss center’s strategy is to map all franchise and corporate locations to determine their individual location markets, then use multiple lists (managed and compiled) to get the maximum coverage with the various demographic selections in their market geographies.

• An energy company was looking to data-mine specific segments of a customer file made up of small businesses and needed to enhance the file with as much business information as possible. The client was able to get the most comprehensive coverage of business elements by using four different business databases to create one complete, unique enhancement file.

While building a predictive model, the client will see important elements that may have been missed had it used one data source exclusively.

• Several years ago, a major automotive company was in the early stages of test-marketing an electric car. The marketing group figured its product would catch on with “early adopters of hi-tech products.” This psychographic selection proved challenging because it truly did not exist on any database.

Instead, the data selection strategy was to create a unique database by identifying publications that appeal to people who like hi-tech toys, then finding the same names that were on these multiple files. A total of 23 separate databases, primarily managed (subscription) files and one survey-based compiled database, were used to create a new database of unique names across all of the lists, figuring the best prospects were the people who appeared on the most lists. Some names appeared on as many as nine of the 23 files, so the client mailed to those prospects first and captured a response rate five times higher than the campaign to prospects who appeared on only one list.

Use specialty merge/purge processes. Specialty merge/purge and other customized data-processing techniques enable you to mix and match databases and create unique files

For example, by using a customized merge/purge procedure referred to as “gathering,” you can identify the best of both lists and end up with a unique and very targeted file that is better than the sum of the parts. For example, one list may have a home phone number, one a business number, one may have a complete address, and one just a post office box. Under a normal suppression or merge/purge, one of those records, judged to be a duplicate, would be lost. Gathering captures all the information from both lists in one unique record, giving a fuller picture of that individual or business.

In the electric car campaign, a specialty merge/purge technique (referred to as “stacking”) revealed and grouped the duplicate – and more qualified – names across many files instead of discarding them, as is the case with many merge/purge procedures.

Even if you are not trying to create a unique list, duplicates should not necessarily be discarded when you merge/purge multiple files because names that show up on more than one of your selected files are often even better prospects.

If you had to pay for the duplicates anyway, consider doing a second mailing to these strong prospects or going after them first.

Test and analyze to determine which data segments are the best performers. Direct marketers should test and track response from their different data segments by attaching a key code to each record from each data segment to make the back-end analysis a breeze. You also can key-code any statistics that you want to capture, including age, length of residence, families with children and list source.

With key-coding, you can easily analyze which databases and which selections from those databases are your best performers.

Obviously, it costs more to use multiple databases than it would to use one – and specialty lists and merge/purge processes are often a significant investment.

However, depending on what they have been trying to accomplish, marketers have learned that the upfront investments in multiple databases and analysis services have more than paid off with the return on investment.

• Sandy Wilson is national director of strategic alliances at AccuData America, Cape Coral, FL.

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