What's in Your Database?
At this point, you also want to clean and update your postal addresses, says Eric Niebergall, director of data processing products, Donnelley Marketing. With this process you're not just making sure your addresses are deliverable, but keeping valuable customers from dropping off your radar due to recent moves.
Once you've performed these standard data reviews, you can determine which gaps in your data are important to fill so you can prioritize any enhancement investments.
For example, says Bernice Grossman, president of DMRS Group Inc., a New York City database development consulting firm, it's not uncommon for marketers to have telephone number fields that are incomplete or filled with nonsensical data such as a string of No. 9s. A telephone append easily can fix this data need.
Other types of data that might be appended include age, gender, income and presence of children on consumer housefiles. For B-to-B databases, a marketer typically would append SIC codes, employee size, sales volume, etc.
Finally, one of the key processes in prepping your database for mining is eliminating duplicates. Multichannel marketing has made tracking customer activity much harder these days, explains Kasmarski. If multiple departments capture customer information, they need to agree on a standard coding system so customers can be identified across channels.
Miglautsch cautions marketers to be careful in merging customer data from disparate sources. "Once you merge the wrong records together … you just got rid of the lifetime value for one good customer and gave it to [what could be] a first-time customer."
Grossman points out that not all data problems can be fixed—at least not without considerable expense. She advises marketers to flag records that aren't complete or correct, and to be careful when using them in future data mining projects.
The time frame for all this cleansing, de-duping and general prepping? Niebergall estimates that the first effort takes between five and 10 days for basic processing on files that run a few million names. Future maintenance that is conducted, say, once a quarter, can be automated and knocked out in anywhere from less than 24 hours to three days.