Tag: data

Predictive Selling in F&I

We have all seen how Amazon uses predictive selling, and now this approach is finding its way into our industry.  In this article I compare and contrast different implementations, and discuss how the technique may be better suited to online than to the F&I suite.

If you read Tom Clancy, you might like Lee Childs.  If you bought a circular saw, you might need safety goggles.  To draw these inferences, Amazon scans for products that frequently occur together in the order histories of its customers.  You can imagine that given their volume of business, Amazon can fine-tune the results by timeframe, department, price, and so on.

The effectiveness of predictive selling depends on two things: the strength of your algorithms, and the depth of your database.  Automotive Mastermind claims to use “thousands of data points,” mined from the DMS, social media, and credit bureaus.  An online auto retailer or platform site (see my taxonomy here) will also have data about which web pages the customer viewed.  Your typical F&I menu is lucky if it can read data from the DMS.

The face of predictive selling in F&I is the automated interview.  We all know the standard questions:

  • How long to you plan on keeping the car?
  • How far do you drive to work?
  • Do you park the car in a garage?
  • Do you drive on a gravel road?
  • Do you transport children or pets?

A system that emulates the behavior of an expert interviewer is called, appropriately, an “expert system.”  I alluded to expert systems for F&I here, in 2015, having proposed one for a client around the same time.  This is where we can begin to make some distinctions.

Rather than a set of canned questions, a proper expert system includes a “rules editor” wherein the administrator can add new questions, and an “inference engine” that collates the results.  Of course, the best questions are those you can answer from deal data, and not have to impose on the customer.

A data scientist may mine the data for buying patterns, an approach known as “analytics,” or she may have a system to mine the data automatically, an approach known as “machine learning.”  You know you have good analytics when the system turns up an original and unanticipated buying pattern.  Maybe, for example, customers are more or less likely to buy appearance protection based on the color of their vehicle.

At the most basic level, predictive selling is about statistical inference.  Let’s say your data mining tells you that, of customers planning to keep the car more than five years, 75% have bought a service contract.  You may infer that the next such customer is 75% likely to follow suit, which makes the service contract a better pitch than some other product with a 60% track record.  One statistic per product hardly rises to the level of “analytics,” but it’s better than nothing.

Another thing to look at is the size of the database.  If our 75% rule for service contract is based on hundreds of deals, it’s probably pretty accurate.  If it’s based on thousands of deals, that’s better.  Our humble data scientist won’t see many used, leased, beige minivans unless she has “big data.”  Here is where a dealer group that can pool data across many stores, or an online selling site, has an advantage.

If you are implementing such a system, you not only have a challenge getting enough data, you also have to worry about contaminating the data you’ve got.  You see, pace Werner Heisenberg, using the data also changes the data.  Customers don’t arrive in F&I already familiar with the products, according to research from IHS.

Consider our service contract example.  Your statistics tell you to present it only for customers keeping their vehicle more than five years.  That now becomes a self-fulfilling prophecy.  Going forward, your database will fill up with service contract customers who meet that criterion because you never show it to anyone else.

You can never know when a customer is going to buy some random product.  This is why F&I trainers tell you to “present every product to every customer, every time.”  There is a technical fix, which is to segregate your sample data (also known as “training data” for machine learning) from your result data.  The system must flag deals where prediction was used to restrict the presentation, and never use these deals for statistics.

Doesn’t that mean you’ll run out of raw data?  It might, if you don’t have a rich supply.  One way to maintain fresh training data is periodically to abandon prediction, show all products, let the F&I manager do his job, and then put that deal into the pool of training data.

Customers complete a thinly disguised “survey” while they’re waiting on F&I, which their software uses to discern which products to offer and which ones the customer is most likely to buy based upon their responses.

Regulatory compliance is another reason F&I trainers tell you to present every product every time.  Try telling the CFPB that “my statistics told me not to present GAP on this deal.”  There’s not a technical fix for that.

One motivation for the interview approach, versus a four-column menu, is that it’s better suited to form factors like mobile and chat.  This is a strong inducement for the online selling sites.  In the F&I suite, however, the arguments are not as strong.  Trainers are uniformly against the idea that you can simply hand over the iPad and let it do the job for you.

No, I have not gone over to the Luddites.  This article offers advice to people developing (or evaluating) predictive selling systems, and most of the advantages accrue to the online people.  The “home court advantage” in the F&I suite is that you can do a four-column menu, and there is a professional there to present it.

New Consolidation Stats from NADA

I chose consolidation for the first of my megatrends series, because it’s the least controversial.  Everyone seems to know it’s happening, and the records and rankings in Automotive News are dominated by big groups.

Ten years down the road, we don’t want to be the 13-point dealership group feeling that pain from the larger groups the way the smaller ones are now

This year, for the first time, NADA Data takes a look at consolidation.  Probably the best single number to look at is the ratio of rooftops to dealers, which represents the average number of stores in a dealer group.  This has grown from 1.8 to 2.2 over the last nine years – not exactly a revolution.  I was a little surprised to see such small numbers, but this is an artifact of how NADA presents the data.

NADA, logically enough, presents the number of dealers owning a group of a given size.  I would have preferred to see the number of stores, not owners, in each category.  This is a better reflection of the market coverage.  To show the distinction, I plotted the total count of both rooftops and owners.  You can see that, while the number of rooftops is recovering since 2010, the number of dealers is not.

Next, I recast the data in terms of rooftops.  The number of rooftops belonging to groups of ten or more has almost doubled over the period, from 12.2% to 21.3%.

Below, I have plotted the number of rooftops in three tiers, by size of the dealer group to which they belong.  The 2 to 10 tier has been remarkably stable, numbering roughly 8,200.  The single points have been in steady decline, losing 2,500 over the period.

Dealers know that single points are vulnerable to market shocks and competitive pressure, if for no other reason than being tied to a single make.  On present trends, we can expect them to vanish entirely within ten or fifteen years.