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Transforming the Customer Experience: Automotive Lending

Seventy percent of digital transformation projects fail to reach their stated goals, at least according to research from McKinsey Consulting. This statistic seems shocking because it’s easy to forget just how much complexity there is in creating transformative new experiences for customers. We take it for granted that, with a few taps on our smartphones, we can summon a driver to pick us up or track the real-time location of our pizza as it’s being delivered or browse through a shockingly good playlist of recommended songs for us to listen to; because we quickly move on from bad experiences.

When you pull back the covers, you see just how much sophistication, creativity, and detail went into enabling those experiences. It’s staggering. And it’s extremely difficult to achieve.

At FICO, we are tremendously proud of the work we do with leading banks, automotive lenders, and telecommunication providers to transform their customers’ experiences. In this series of blog posts, I want to share a few examples of these transformative experiences and the underlying technologies, analytics, and know-how that go into creating them.

First up, buying a new car.

According to our research, 58 percent of U.S. consumers reported waiting more than 30 minutes to secure financing for the purchase of an automobile. While many consumers grudgingly accept long waits in the dealership as the cost of doing business, 13 percent of U.S. consumers obtained an auto loan online in 2019 (up from five percent in 2018) and listed convenience and speed as two of the top three factors driving that decision. While you may argue that some of this ‘slowness’ is intentional as the dealer works out the deal to their best end, some of the delay is also caused by the rehash procedure that the dealer has to conduct with their lenders to arrive at an acceptable offer. 

Clearly the automotive lending experience needs to continue to improve.

And FICO has been working with leading lenders to do just that, as illustrated in the video below.

The difference between the ‘before experience’ and the ‘after experience’ was the speed with which the finance and insurance guy in the back office was able to get a differentiated, likely to be accepted, and approved counteroffer back into the hands of the salesman. Not just a long list of options to sift through, but a small set of meaningful options that the borrower will want to consider that also work for the lender and dealer.

Automating that counteroffer process — in order to improve the car buyer’s experience — is much more complex than you would think as you can’t simply iterate a bit up on rate or a bit up and down on down payment. Instead, it needs to be comprehensive where nearly every possible counteroffer is evaluated for acceptance, and then the bundle is rapidly reduced to those that will interest the buyer. It should take into account that some buyers care most about the rate, some care most about the down payment, while others care most about the monthly payment. It has to be fast (we saw what happens when the salesman is forced to stall). And any approved offer also needs to conform to the lender’s credit risk criteria (which can be arduous to determine when the offer terms are constantly changing).

What we’ve found at FICO is that this problem cannot be comprehensively solved within a lender’s existing decision engine. Success is made possible by bringing the credit and pricing logic plus behavioral analytics into an optimization solver where only then does it become possible to analytically search for the best possible counteroffers within the lender’s and dealer’s constraints and return those offers to the dealer…in <five seconds!

FICO’s real-time automated counteroffer solution — Alternative Deal Structure Optimizer – does exactly that. If you’re interested in changing your dealer’s experience and winning more business, we’d love to talk with you.

In the next installment of this blog series, I’ll explore the challenging world of acquisition and retention in telecommunications. 

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