Focus On: Database Analysis: A Closer Look at Models

Unfortunately, the combination of both techniques yielded less than half the desired mail volume. To select the remaining 50 percent, the organization tested a response model against RFA. The model-build process considered more than 100 behavioral characteristics to differentiate responders from non-responders, but the final equation only contained the 10 most significant variables, including upgrade/downgrade, direct mail responsiveness, tenure on the file prior to lapsing and consecutive years of giving. The utility of the model was measured relative to an audience of names selected using RFA. Not only did the model deliver superior results relative to the RFA control group, but it also outperformed the primary RFA and secondary hit/no hit audiences across all three performance metrics - response rate (+168 percent), gross revenue (+81 percent) and CPDR (-11 percent). These highly responsive and profitable names were overlooked because the reach of RFA did not extend deep enough into the file to effectively differentiate opportunities.