Steve Schultz would like to thank Greg Martin, Quaero West Coast vice president, for contributing this month's column. Any company about to update their householding (or customerholding) process should become familiar with the opportunities as well as the pitfalls before undertaking this process. Depending on your business model and industry, your company may need a great customerholding process, but may not need a householding process or vice versa; however, you can certainly work with both. Because revising a complex matching algorithm can be applied generically to either process, I will use the term householding to refer to both in this article. To begin with you will need three things: first, the tool and platform for householding; second, a statistical tool for doing random selections; and third, you will need staff with the available time to run the matching, conduct sampling, build reports and inspect outcomes. If this process needs to be accomplished on a tight deadline and your company does not have significant staff availability, then you should consider outsourcing. The basic idea here is to apply the classic champion-challenger approach to the selection algorithm. This method will help you to avoid making a common mistake, which is to simply have someone select and inspect households by hand. If done manually, you will not know whether you are solving systematic problems (which is what you want to do), or a unique data issue, which may or may not create a better overall solution. Throughout this process, it is important to keep in mind how the results will be used within the company. Consider the sponsoring area, its current pain points and any legal ramifications. Engage the internal constituencies (direct marketing for data usage and front line sales for data input) as early as possible in the refinement process and ensure they know how to share future change requests to the matching process. Some additional considerations to keep in mind before undertaking this process include making sure the sampling tool can handle volume of data, having a tool to view the output datasets, ensuring your matching tool allows for exclusion files and determining whether you need a separate business matching routine.
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