Customer Data Mining
An alternative to outsourcing data mining or performing these functions in house is to get the best of both worlds in a hybrid solution. One approach is to retain an outside consultant to mentor a one- or two-person data mining department. The consultant provides the real-world perspective and guidance on specific technical issues that only can be gained by years of in-the-trenches industry experience. An added benefit is that the consultant maintains the “company quantitative memory” during times of in-house staff turnover. The on-staff data miners, in turn, do all the heavy lifting, which keeps consulting fees at a reasonable level.
Another hybrid approach is to hire an outside firm for the most challenging assignments. That way, the ongoing heavy lifting is done by the in-house staff, which has a moderating effect on overall costs, while the outside firm focuses on “pushing the envelope.”
One example is a multibillion dollar company that sends hundreds of direct mail and e-mail promotions a year across its direct and store divisions. The firm has a keen interest in employing quantitative methods and test strategies to optimize this significant marketing investment. The company has a substantial budget for in-house database marketing, including many senior data miners and a state-of-the-art data repository.
Nevertheless, this firm always is on the lookout for new ways of thinking. It understands that its own people don’t have all the answers. Therefore, it engaged an outside data mining group to spearhead an approach to contact optimization. The project took about a year to complete, producing such strong results that the company hired the outside group to tackle other data challenges.
Regardless of whether you go with an in-house or outsourced solution, consider the experience and longevity of your data miners. Unfortunately, there are many service companies whose idea of a data mining department is to hire a handful of low- to mid-level staffers. Often, these individuals have solid academic credentials but little industry experience. What you learn in a statistics course has little bearing on most of the real-world decisions that data miners in the industry face. There are many non-statistics-based issues that must be navigated successfully to build an effective predictive model. Among them are: