Part 1B: How Do I Better Market to my Existing Customer Base?

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Part 1B: How Do I Better Market to my Existing Customer Base?


 Pricing Gap and Expected Interest Rate

 Now that we’ve seen where leakage may be occurring within our institution, let’s take our targeted marketing even deeper to specifically identify who should be offered what product and at what rate. The method Visible Equity uses to solve this problem is called Pricing Gap analysis. Very simply, this approach just allows us to identify customers who are paying more than they should with other institutions.

 In order to do this we start by collecting data on each customer’s highest rate loan. For each loan type category, we gather data from one of the credit bureaus on what our customers are paying for auto loans, first and second mortgages, HELOCs, etc. With this data in hand, Visible Equity then estimates a model to determine what your institution could offer them, based on your historical underwriting criteria.






One effective method to model loan pricing is a statistical technique called linear regression. If you recall any statistics classes from your college days, you may remember that a linear regression model makes predictions based on associated independent variables and their relative effects. In pricing gap analysis, this model produces a pricing suggestion based on the loan’s observed attributes (such as borrower FICO and loan origination year), the institution’s historical underwriting standards, and even market conditions at the time of the loan. We can then take these pricing suggestions, or expected interest rates, and compare them with the customers’ actual interest rates. We simply find the difference between rates, isolate where the differences are large, and then target those specific customers for refinancing opportunities.

 An example will make this method more tangible. Say we are looking at a borrower who has an auto loan outside our institution where he is currently paying 12% interest. Given what we know about this borrower, our pricing gap model suggests we could offer him a much lower rate. For 60 months, our model suggests an interest rate of 7.2%. For a 72 month loan term, the suggested rate is 8.1%. Looking at the differences, we calculate a pricing gap of either 4.8% or 3.9% depending on the term. Our sample institution could then contact this borrower and offer a refinancing opportunity.

 Approaching this analysis from a higher level, we could view a list of all customers with auto loans from a demo data set along with actual interest rates and estimates of expected interest rates:

 We note at first glance a borrower with a 25% actual interest rate, 13.1% expected interest rate, and a pricing gap of 12. While we obviously don’t have all of the information about the origination of this loan, it does present us with a marketing opportunity worth investigating. We can see from this table what the resulting pricing gaps would be for each individual and can therefore target the top of this list.

 Identifying situations like the ones presented gives rise to opportunities of significant improvement in terms of customer satisfaction and institution performance.


Keaton Baughan

Product Manager