## What is “Real” AI?

Clients ask me this all the time.  They want to know if a proposed new system has the real stuff, or if it’s snake oil.  It’s a tough question, because the answer is complicated.  Even if I dictate some challenge questions, their discussion with the sales rep is likely to be inconclusive.

The bottom line is that we want to use historical data to make predictions.  Here are some things we might want to predict:

• Is this customer going to buy a car today? (Yes/No)
• Which protection product is he going to buy? (Choice)
• What will be my loss ratio? (Number)

In Predictive Selling for F&I, I discussed some ways to predict product sales.  The classic example is to look at LTV and predict whether the customer will want GAP.  High LTV, more likely.  Low LTV, less likely.  With historical data and a little math, you can write a formula to determine the GAP-sale probability.

## What is predictive analytics?

If you’re using statistics and one variable, that’s not AI, but it is a handy predictive model just the same.  What if you’re using a bunch of variables, as with linear regression?  Regression is powerful, but it is still an analytical method.

The technical meaning of analytical is that you can solve the problem directly using math, instead of another approach like iteration or heuristics.  Back when I was designing “payment rollback” for MenuVantage, I proved it was possible to algebraically reverse our payment formulas – possible, but not practical.  It made more sense to run the calculations forward, and use iteration to solve the problem.

You can do simple linear regression on a calculator.  In fact, they made us do this in business school.  If you don’t believe me – HP prints the formulas on the back of their HP-12 calculator.  So, while you can make a damned good predictive model using linear regression, it’s still not AI.  It’s predictive analytics.

By the way, “analytics” is a singular noun, like “physics.”  No one ever says “physics are fun.”  Take that, spellcheck!

## What is machine learning?

The distinctive feature of AI is that the system generates a predictive model that is not reachable through analysis.  It will trundle through your historical data using iteration to determine, say, the factor weights in a neural network, or the split values in a decision tree.

“Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.”

The model improves with exposure to more data (and tuning) hence Machine Learning.  This is very powerful, and will serve for a working definition of “real” AI.

AI is an umbrella term that includes Machine Learning but also algorithms, like expert systems, that don’t learn from experience.  Analytics includes statistical methods that may make good predictions, but these also do not learn.  There is nothing wrong with these techniques.

Here are some challenge questions:

• What does your model predict?
• What variables does it use?
• What is the predictive model?
• How accurate is it?

A funny thing I learned reading forums like KD Nuggets is that kids today learn neural nets first, and then they learn about linear regression as the special case that can be solved analytically.

## What is a neural network?

Yes, the theory is based on how neurons behave in the brain.  Image recognition, in particular, owes a lot to the dorsal pathway of the visual cortex.  Researchers take this very seriously, and continue to draw inspiration from the brain.  So, this is great if your client happens to be a neuroscientist.

My client is more likely to be a technology leader, so I will explain neural nets by analogy with linear regression.  Linear regression takes a bunch of “X” variables and establishes a linear relationship among them, to predict the value of a single dependent “Y” variable.  Schematically, that looks like this:

Now suppose that instead of one linear equation, you use regression to predict eight intermediate “Z” variables, and then feed those into another linear model that predicts the original “Y.” Every link in the network has a factor weight, just as in linear regression.

Apart from some finer points (like nonlinear activation functions) you can think of a neural net as a stack of interlaced regression models.

You may recall that linear regression works by using partial derivatives to find the minimum of an error function parametrized by the regression coefficients.  Well, that’s exactly what the neural network training process does!

## What is deep learning?

This brings us to one final buzzword, Deep Learning.  The more layers in the stack, the smarter the neural net.  There’s no danger of overdoing it, because the model will learn to skip redundant layers.  The popular image recognition model, ResNet152 has – you guessed it – 152 layers.

So, it’s deep.  It also sounds cool, as if the model is learning “deeply” which, technically, I suppose it is.  This is not relevant for our purposes, so ignore it unless it affects accuracy.

## The Case for D2C

A while back, I wrote a survey of Direct to Consumer VSC Sales.  This was a “how to,” and today I am writing about the “why.”  The short version is that D2C is a large and unserved market.  Franchised dealers sell service contracts with 47% of new vehicles, which is great, but that leaves the other half unprotected.

Add 6% to reported F&I gross, plus 4 to 5 times that amount for the backfile

Depending on which “touchpoint” you wish to pursue (see here) this market includes roughly 67 million vehicles.  That’s how many are on the road, less than six years old, with no coverage.  Dealers are the group best positioned to serve this market.  Of course, a dealer can only address his local share of the market, not the whole 67 million.  See Profit Opportunity, below.

To succeed with D2C, you must have an existing relationship with the customer.  That’s because success requires digital marketing, and anti-spam laws limit what you can do without a relationship.  For example, an OEM can email their customer a solicitation for their factory-label protection products, but a TPA cannot.

So, the dealer has the inside track.  He has the relationship, the contact info, and a service department to verify eligibility.  Plus, every additional VSC aids in service retention.  Depending on the dealer group, it may also have the other ingredients.  Here’s the parts list:

### Direct to Consumer (D2C) Operation

• An advanced CRM with the ability to run a scheduled, multichannel contact program.  Salesforce calls this a “customer journey.”
• A source of premium finance, like SPP, Budco, or PayLink.  Dealers will already have one of these, for their instore F&I operation.
• A call center, which could be the BDC, to participate in the selling journey and also to deal with issues around premium finance.
• A branded website capable of presenting and selling the service contract, including Visa checkout and premium finance.
• A service facility.  If you’re not a dealer (trying to cover all bases here) there are still things you can do with Pep Boys and mobile facilities like Pivet.

Depending on the dealer group.  Obviously, if you’re Lithia, you already have a finance arm which could (with training) handle bounced Visa charges.  They’ll need to comply with the PCI security standards.  Maybe that’s best left to PayLink.

There is a host of such decisions, for which you will need expert assistance – but let’s get back to the “why.”  We are going to make a gross profit calculation in three steps:

### Direct to Consumer (D2C) Profit Opportunity

1. Compute the potential product gross that didn’t close with the vehicle sale.
2. Estimate the likely D2C conversion rate.
3. Add the backfile of customers from prior years.

Let’s look at AutoNation.  Sorry, NADA Average Dealer doesn’t provide enough detail.  Even the AutoNation data doesn’t provide much detail on product sales.  Still, we can draw some inferences using the 2019 annual report, industry norms, and these remarks from then-CEO Cheryl Miller.

AutoNation reported sales of 283,000 (new) and 246,000 (used) with F&I PVR of \$1,904.  That’s the headline figure, including finance reserve.  Owing to adjustments, the figure in the annual report is a little higher, and the calculation based on Miller’s summary is a little lower.

The \$1,350 (new) and \$1,050 (used) are averages across all units.  We can infer that product gross was roughly \$2,580 (blended) on 47% of units.  That leaves the other 280,000 vehicles unprotected, with a potential gross of \$720 million, equal to 71% of AutoNation’s reported F&I gross.

You can use 75% of F&I gross as a rule of thumb.  In general, product gross is two-thirds of F&I gross, and this is derived from fewer than half of the vehicles sold (omitting ancillaries).  The ratio of D2C opportunity to instore penetration is 53/47 of the two-thirds, which makes 75%.

This \$720 million is the potential gross AutoNation left on the table in 2019.  Okay, that’s not fair.  It’s only on the table assuming every one of the D2C prospects will buy the product, which they won’t.  Most won’t, in fact.  The conversion rate is the product of three factors:

• The number of contacts per customer, based on your touchpoints and your programmed journey.
• The take rate, which is the percentage of people who take action by clicking the PURL link, scanning the QR code, or whatever.  The industry norm here is 2-5%
• The close rate, which is the percentage of takers who are closed by the call center or self-close on the web site.  Expert closers can do 20-30%.  Remember, this is within the self-selected “takers.”

The conversion rate is what counts, but we break out the components for management purposes.  For instance, maybe the take rate is high but your closers are weak.  Success requires a lot of contacts, with compelling CTAs and good closers.

Let’s say we utilize all of our advantages as a dealer – an aggressive journey on all touchpoints – bringing our contacts to 10.  Multiply this by a conservative 20% close rate and 4% take rate.  This gives us a conversion rate of 8%.

In our AutoNation example, this would mean roughly \$58 million of additional gross.  All of this arithmetic generalizes, too.  Simply take reported F&I gross and multiply by 6% (8% of the 75%, above, makes 6%).  So, now I can crack the Lithia annual report with F&I gross of \$580 million, and reckon that D2C could mean \$35 million to them.

This incremental income recurs annually, since it’s based on one year’s volume – but we start the game with a backfile of unserved customers from prior years.  We might reasonably want to go back five years for new vehicle buyers and three years for used.

AutoNation is a convenient example because they sell new and used in roughly equal parts, so this works out to (blended) four years’ worth of volume.  If your mix skews more toward new vehicles, then your backfile opportunity will be richer.

In short, you can use the 6% rule to compute the annual recurring, and then add a one-time opportunity of 4 or 5 times that amount for the backfile.  This more than pays for setting up the operation.  So, is it worth the hassle to earn an extra 6% of F&I gross?  Ponder that next time you see the Car Shield ad on television.

## Workflow for Online Car Buying

A few years ago, I published a precedence diagram for the key operations of online car buying.  I was arguing against a linear process, and calling attention to some deadlocks.  Since then, I have been following the industry’s experiments with new process models, and coming to realize that these deadlocks are the great, unanalyzed, obstacle to process reform.

Practices that seem unfair, deceptive, or abusive may actually be crude attempts to solve the deadlock problem.

One example of a deadlock is that you can’t quote an accurate payment until you know the buy rate, and for that you need to submit a credit application.  This is usually solved by iteration.  You do a pre-approval or quote the floor rate, and then change it later.

Likewise, you can’t price protection products until you know the vehicle, but the customer wants to shop by payment.  Protection products are also priced by term, and you don’t know the desired term until you finish structuring the deal.

In fact, even the customer’s choice of vehicle depends on the monthly payment, which is downstream of everything else.  Virtually the only operation that’s not blocked by another operation is valuing the trade.

Like an interlocking puzzle, “we don’t know anything until we know everything.”  Choosing any one item to lock first, without iteration, will result in a suboptimal deal – buying too much car, for too long a term, or overlooking the protection products.

Practices that seem unfair, deceptive, or abusive may actually be crude attempts to solve the deadlock problem.  For instance, quoting a payment with some leg in it, or goal-seeking the full approval amount.

Can you see how this ties into current debates about the hybrid sales model?  F&I presents a menu with a six-month term bump, which might not be optimal, just to compensate for too tight a payment from the desk.

Fortunately, in the world of online car buying, the customer is free to resolve deadlocks through iteration.  This means:

1. Set up the deal one way
2. Change any feature, like the term
3. The change “cascades,” undoing other features
4. Revisit those other features
5. Repeat until all features look good together

The in-store process does not support iteration well, and probably never will, but an online process can.  All you need is the well-known concept of a “dirty” flag, to keep track of the cascading changes, along with navigation and a completeness gauge to guide the customer through steps #4 and 5.

You could analyze step #3 at the level of a dozen individual features.  I made that chart, too, but I believe it’s more useful to collect them into the canonical five pages shown here.

By the way, I have previously described the products page in some detail, along with the analytics to drive it.  Discussion of the “random survey question” is here.  Today’s diagram contemplates a mobile app, as do my recent posts, but the same approach will work for a web site.

## Deconstructing the Dealership

Remember when dealerships had body shops?  Two out of five still do, but they comprise less than 20% of this \$35 billion market.  Somewhere along the line, it became clear that collision repair was better done by specialist facilities, unconnected to the dealer.  Scale, capital investment, brand diversification, and (lack of) synergy were factors.

We may now wonder if parts and service belong in the dealership, thanks in some measure to the rise of automotive eCommerce.  Jim Ziegler warns that Valvoline Express is beating dealers in the shop and online.  Ward’s makes the same point, with emphasis on Google search optimization.  In the same vein, Amazon has come up with a way to sell tires online.

There can be much synergy between the two ends of the business, which can be leveraged to manage and sustain customer relationships – Vincent Romans

My approach is to “follow the money” and, sure enough, here is Carl Icahn buying up repair facilities.  Icahn Automotive Group is a classic consolidation play, with 2,000 locations including Precision Auto Care, Pep Boys, Just Brakes, AutoPlus, AAMCO, Cottman, and CAP.  Icahn is vertically integrated through Federal-Mogul Motorparts, which includes ANCO wipers and Champion spark plugs.

So, will eCommerce pick off the dealer’s profit centers one by one?  In this example, we see the convergence of powerful megatrends.  The traditional dealer model is challenged by two new ones, which I like to call the Best Buy model and Amazon model.

History tells us that the Amazon model will prevail in the end, but it doesn’t tell us what the transformation will look like, or how dealers should prepare.  To learn that, we employ an old tool from Business Process Reengineering, and we discover a surprising result.  Here is a breakdown of the traditional dealer operations:

## The Seven Profit Centers of a Car Dealer

1. New Sales
2. Used Sales
3. Finance
4. Insurance
5. Parts
6. Service
7. Collision Repair

We can consider each operation in terms of how it will respond to the new challenges – and whether it belongs with the others.  We have to start somewhere, so let us define new vehicle sales as the nucleus of the dealership.  The test drive is the process most resistant to eCommerce although, as I wrote last week, there are ways around it.

Used vehicle sales will certainly not stay in the dealership.  It is vulnerable to both consolidators and eCommerce.  This is a shame because taking vehicles in trade used to be a great synergy.  The new specialists are true “auto traders,” using high-volume analytics to trade both ways with the public and the auction.

Coming back to fixed operations, there is a clear synergy.  According to Cox research, customers who are properly introduced to the service department are two and a half times more likely to come back for service.  But there are other ways to exploit this synergy, like the “zero deductible at our dealership” service contract – and the Amazon tire store shows that parts can be separated from service.

Lithia Motors has 186 locations including, by my count, fourteen collision centers.  There is not much synergy between body shops and vehicle sales, or even service, but they run fine as standalone operations connected to the brand.  Likewise, given a branded service contract, I can see Lithia’s mass market franchises supporting shared service facilities.

F&I is the subject of fierce debate, too much to cover here.  Can it be merged into the sales function? Can protection products be sold successfully online?  What is the future of indirect finance?  Do “F” and “I” even belong together anymore?  For our purpose today, we need only observe that while F&I has a workflow linkage to sales, it does not need a physical one.  F&I could just as easily skype in from a call center.

As Carl Icahn would tell you, these are distinct businesses without much synergy, if synergy is defined as “positive return from shared personnel and facilities.”  Dealers organized along these lines will, indeed, be picked apart by eCommerce and consolidation.

On the other hand, if synergy means “positive return from shared customer contact and branding,” then these businesses will hang together.  Dealers organized along this principle will have diverse and independent operations, making them resilient to disruption.  They will have “optionality,” to use Nassim Taleb’s term.

You may be taken aback by this assault on the venerable “rooftop,” and I admitted earlier to being surprised.  However, decoupling and diversification (and divestiture) are textbook responses to an industry in flux.  Just look at how many departments are no longer in department stores.