Month: October 2017

Wanted: eCommerce Product Manager

Gartner Group says “the API is the product.”  I am looking for an experienced product manager who knows what Gartner Group is and why they say that.  The API in question is Safe-Guard’s collection of dealer-facing web services.  This is a topic I have worked on and written about extensively, as here, and now I plan to try the product manager approach.

The successful candidate will have solid product management experience, preferably with an API, and maybe some pragmatic marketing or agile development.  Software development experience a plus.  Self-starter.  Relocation.   Salary commensurate with experience.

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.

How to Worry about Mobility

I was impressed by this article, How to Worry about Climate Change.  It was neither activist nor skeptical, but rather placed the threat in an appropriate policy context.  So, I was inspired to update my earlier post on the “mobility revolution.”

McKinsey has some new research out which, I feel, overstates the case.  The case, as you may recall, is that four trends will come together in some kind of perfect storm:

  • Electrification
  • Connectivity
  • Autonomous driving
  • Shared mobility

The best research on mobility is still this series of papers from the BCG.  Like me, BCG is reserved about the U.S. market.  I strawman McKinsey a little by focusing on U.S. car dealers.  Their focus is on manufacturers, with a European orientation.

The right way to worry about mobility is to ignore the interaction effects, and look at each trend individually.  This is where I differ from McKinsey.  They model three different outcomes – small, medium, and large – for each of the four trends.  This gives them eighty-one different scenarios to evaluate (consultants love this stuff).

Electric Cars

My local BMW dealer has a lot full of i3s and i8s.  Electric cars won’t change auto retail at all – service, obviously, but not sales.  This “revolution” only affects dealers if Tesla succeeds in doing it without a dealer network.  From my perspective, not having a dealer network is a weakness, and a sign that the company lacks confidence in its product.


It turns out Jacques Nasser was right.  Kids today will ride in a hamster box as long as it has satellite, wireless, navigation, and a sound system.  Gone is my generation’s enthusiasm for hemi heads and dual overhead cams.  No one drives a stick anymore, and the steering wheel will be next (see below).

Connectivity will change auto retail the same way electric cars will – new features to sell and service.  I have the BMW connectivity app on my iPhone.  Connectivity in terms of telematics will open up new opportunities for service retention, as I described here.  There are new opportunities in F&I, and even lot management, as people invent more things to plug into the OBD port.

Autonomous Cars

I am deeply skeptical about self-driving cars.  People who promote them tend to focus on SAE level four, and overlook the greater challenge of full autonomy.  I see self-driving in limited contexts, like self-parking and advanced cruise control.  Check out BMW’s lane-departure technology.  This is cool stuff, and what it means to car dealers is … more expensive cars!

Remember that the nightmare scenario for self-driving cars only occurs when the cars are smart enough to be widely shared, i.e., robot Uber drivers.  A car that can autonomously drive the kids to school is years and years away.

A close examination of the technologies required to achieve advanced levels of autonomous driving suggests a significantly longer timeline; such vehicles are perhaps five to ten years away.

Like “catastrophic anthropogenic global warming,” that date keeps moving out as we approach it.  In 2012, Sergey Brin said self-driving cars would be widely available by 2018.  In 2016, Mark Fields said no steering wheel by 2021.  McKinsey, in any year, always says, “five to ten years from now.”  For a clear-eyed look at the challenges, see here.  For more about luxury driver assistance see here.

That about does it for my deconstruction of three mobility trends that should not worry car dealers.  Next week, I’ll report on that fourth one.  Now that I am living in a big, modern metropolis, I can see shared mobility first hand.  I may not even need a second car.