At RRW, we spend more than 50% of our time helping our clients acquire new customers. The rest of the time is spent in working on customer marketing (retention, segmentation, etc) and other direct marketing activities for our clients. Of course we spend time running the business (yikes--accounting!) and writing this blog :)
My point--over the years, we've become pretty good at figuring out how to acquire profitable new customers. So, we thought we'd share one of our own success stories today.
Our client, a leading mortgage lender with aggressive new customer acquisition goals was faced with the following list targeting challenges:
- They needed to maintain their historic high response rates to their ongoing direct mail campaigns, but at the same time,
- Dramatically improve their approval rates. Over time, in a saturated refinance market, responsive applicants were increasingly failing to meet credit criteria, or they did not have the right equity requirements.
RRW Consulting recommended a data-centric, analytically based approach that leveraged available realty data as well as custom modeling services. We brought a new science to list selection that allowed this lender to achieve its response goals and yet dramatically improve conversion of loan applicants to customers.
We designed a three-prong data selection approach:
- We utilized industry-leading sources of homeowner and realty data to ensure that the appropriate equity was available for each prospective homeowner. Specifically, we relied on the “Available Equity” and “Loan to Value” list selections offered by those data compilers that specialize in the compilation of first and second trust deeds, integrated with county tax assessor data. This initial pre-select was the first “cut” in determining that the prospect would ultimately qualify for the loan.
- We built a Response Model that capitalized on prior prospect performance to predict future response performance. Essentially, we cloned prior responders to this lenders’ campaigns. We built a scorecard based on their demographic and psychographic characteristics that ranked the overall homeowner prospect universe from the most likely to respond to the least likely to respond.
- Finally, we developed an Approval Model that isolated traits of homeowners likely to be approved for the refinance loan. Since we were not extending a firm offer of credit, we could not utilize individual level credit data, making the task of predicting loan approval especially difficult. However, the model was able to successfully rank order prospects in terms of their likelihood to be approved for the loan. The models’ success can be attributed to the wealth of demographics included in the model, and in particular the inclusion of credit data aggregated to the ZIP+4 level.
As can be imagined, the list selection approach quickly became fairly complex, due to the number of realty selections employed combined with the set of response and approval models. We developed a data selection and testing matrix that allowed us to be very responsive to changes in the clients’ goals and fluctuations in the marketplace. For example, if the CEO needed more applications flowing to the call centers, we relied heavier on the response model and less on the approval model. Conversely, if the applicant quality was being questioned, we’d select and mail those applicants more likely to be approved. Finally, we also considered frequency of prospect mailings. We managed the campaigns to reach qualified prospects more frequently than those less likely to respond, or to be approved.
The scientific data selection approach resulted in success. Our client was able to retain their high response levels and also dramatically improved loan approval rates.