Five Low-tech Data Mining Tips
3. Define the right objective.
A proper sampling, however, is of little use if the manager establishes incorrect marketing objectives. For example, using data mining, a cataloger found that a third of all its responders were captured in the top performing segment—the top 10 percent of the file. Ostensibly, these were superior results. However, additional investigation demonstrated that more than 30 percent of the merchandise purchased by this high-responding segment was returned to the retailer! This program attained its objective, but it had the wrong goal. The retailer should have defined the objective as maximizing net response, not gross.
4. Incorporate data that add insight.
It is the data that make or break a data mining effort. More data are beneficial, but only if the information contributes additional insight into the problem being tackled. By incorporating several metrics that point to the same underlying dimension—for example, files with six different measures of an individual’s age—some data mining algorithms may be “fooled” into producing less than optimal results. More data aren’t always better. However, this does not imply that considering additional data for analysis is incorrect. Always look for new sources of incremental data. Adding data that simply mimic what already are available is not the best use of scarce resources.
5. Build a data mining team.
Finally, there is the issue of wholly relying on data mining to arrive at marketing solutions. While these analytic approaches provide numeric results, marketers need to be able to draw insight and formulate conclusions. Don’t entirely depend on so-called fully automatic data mining solutions. No modeling algorithm or technology tool can substitute for human understanding and domain knowledge. Data mining must be a collaborative effort between industry professionals and experienced analysts.
No serious marketer can discount the value of data mining. Data are being captured. Methods are readily available to analyze them. Benefits are clear. By applying some additional effort to the non-technical aspects of analysis, mediocre results quickly can translate into superior ones.