Tag: analytics

Network Effects in Dealer Systems

Last month, I wrote that the recent acquisitions of several Digital Retail vendors were driven by the need to accumulate dealer data for predictive analytics.  Today, I’d like to discuss another of Professor Rogers’ five themes, “network effects,” and how it applies to F&I software.

We’ll consider a hypothetical company that supplies admin software for F&I products, and also sells one or more dealer systems.  Having two distinct, but related, customer groups will allow us to explore “cross-side” network effects.

If the value of being in the network increases with the size of the network, as it often does, then there is a positive network effect.  Social networks are the model case.  The more people who are on Facebook, the more valuable Facebook is to its users (and its advertisers).

This is the textbook definition of “network effects,” but it’s only one part of what Iansiti and Lakhani call Strategic Network Analysis.  Below is a handy outline.  This article will walk through the outline using our hypothetical company – and some real ones from my experience.

Network Strategy Checklist

  1. Network effects (good) – Value grows as the square of the node count … maybe.
  2. Learning effects (good) – There is valuable data to be gleaned from the network.
  3. Clustering (bad) – You can be picked apart, one cluster at a time.
  4. Synergies (good) – Your business includes another network that talks to this one.
  5. Multihoming (bad) – Easy for customers to use multiple networks.
  6. Disintermediation (bad) – Easy for customers to go around your network.
  7. Bridging (good) – Opportunity to connect your network to others.

By the end of this article, you will understand how networking relates to the data concept from the earlier article, and how to apply it to your own software.

Speaking of vocabulary, let’s agree that “network” simply means all of the customers connected to your software, even if they aren’t connected to each other.  It will be our job to invent positive network effects for the company.

The early thinking about networks dealt with actual communication networks, where adding the nth telephone made possible n-1 new connections.  This gave rise to Metcalfe’s Law, which says that the value of a network increases with the square of its size.

Working Your Network

If you are supporting a “peer-to-peer” activity among your dealers, like Bruce Thompson’s auction platform, Car Offer, then you have Metcalfe’s Law working for you.  By the way, Bruce’s company was among those in the aforementioned wave of acquisitions.

If you are supporting a dealer-to-dealer activity, like Bruce Thompson’s auction platform, then you have Metcalfe’s Law working for you. 

Research has shown that naturally occurring networks, like Facebook, do not exhibit Metcalfe-style connectivity.  They exhibit clustering, and have far fewer than O(n2) links.  Clustering is bad – point #3, above – because it makes your network vulnerable to poaching.

Even if you don’t have network effects, per se, you can still organize learning effects using your dealers’ data.  Let’s say you have a reporting system that shows how well each dealer did on PVR last month.  Add some analytics, and you can show that although he has improved by 10%, he is still in the bottom quintile among medium-sized Ford dealers.

That’s descriptive analytics.  To make it prescriptive, let’s say our hypothetical company also operates a menu system.  Now, we can use historical data to predict which F&I product is most likely to be sold on the next deal.  The same technique can be applied to Digital Retail, desking, choosing a vehicle, etc.

Note that we have data from our reporting system doing analytics for our menu system – and pooled across dealers.  Any data we can accumulate is fair game.  This is why I recently advised one of my clients to “start hoarding now” for a prospective AI project.

Cross-Side Network Effects

So far, we’ve covered points 1-3 for our hypothetical company’s dealer network.  I’ll leave their provider network as an exercise for the reader, and move on to point #4.  This is where your business serves two groups, and its value to group A increases with the size of group B.

I like to say “cross-side” because that clearly describes the structure.  Iansiti and Lakhani say “synergy.”  Another popular term is “marketplace,” as in Amazon Marketplace, which I don’t like as much because of its end-consumer connotation.

It’s hard to bootstrap a network, and it’s twice as hard to bootstrap a marketplace. 

Is there an opportunity for cross-side effects between dealers and F&I providers?  Obviously ­– this is the business model I devised for Provider Exchange Network ten years ago.  Back then, it was voodoo magic, but a challenger today would face serious problems.

It’s hard to bootstrap a network, and it’s twice as hard to bootstrap a marketplace.  In the early days at PEN, we had exactly one (1) dealer system, which did not attract a lot of providers.  This, in turn, did not attract a lot of dealer systems.  Kudos to Ron Greer for breaking the deadlock.

Worse, while PEN is a “pure play” marketplace, our hypothetical software company sells its own menu system.  This will deter competing menu systems from coming onboard.  I’ll take up another of Professor Rogers’ themes, “working with your competitors,” in a later post.

Finally, network effects are a “winner takes all” proposition.  Once everybody is on Facebook, it’s hard to enroll them into another network.  That’s not to say it can’t be done.  Brian Reed’s F&I Express successfully created a dealer-to-provider marketplace that parallels PEN.

This brings us to point #5, “multihoming.”  Most F&I product providers are willing to be on multiple networks.  When I was doing this job for Safe-Guard, we ran most of our traffic through PEN, but also F&I Express and Stone Eagle, plus a few standalone menu systems.

The cost of multihoming is felt more by the dealer systems, which are often small and struggle to develop multiple connections.  On the other hand, Maxim and Vision insisted on connecting to us directly.  This is point #6, “disintermediation.”

New Kinds of Traffic

Fortunately for our hypothetical company, Digital Retail is driving the need for new kinds of traffic between providers and dealer systems.  This means new transaction types or, technically, new JSON payloads.  Transmitting digital media is one I’ve encountered a few times.  Custom (AI-based) pricing is another.

Digital Retail is driving the need for new kinds of traffic between providers and dealer systems. 

Controlling software at both ends of the pipeline would allow them to lead the market with the new transaction types.  Key skills are the ability to manage a network and develop a compelling interface (yes, an API can be “compelling”).

As before, note that the same concepts apply for a dealer-to-lender network, like Route One.  There is even a provider-to-lender network right here in Dallas.  Two, if you count Express Recoveries.

So, now you have examples of Strategic Network Analysis from real-world F&I software.  This is one of the methods the Virag Consulting website means when it says “formal methods” to place your software in its strategic context.  

If you’ve read this far, you are probably a practitioner yourself, and I hope this contributes to your success.  It should also advance the ongoing discussion of data and analytics in dealer systems.

Looking for Work

I am ready for my next engagement.  This blog, together with my Linked-In profile, gives some indication of what I have accomplished and what I can do for your business.  There are also some case studies on my web site.

I am currently interested in digital retail, digital marketing, and artificial intelligence.  I generally do contract work, but will consider salaried.  If you have a job that requires my particular set of skills, please get in touch.

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.

DR and Public Dealer Groups

In today’s post, subtitled, “the good, the bad, and the ugly,” we look at where the Big Six public dealer groups stand on Digital Retail.  Some of them get it, some of them don’t, and others have missed the point.

“Once they start the process online, customers tend to buy a car at a much higher rate than … walking into our showroom” – Daryl Kenningham, Group 1

It’s not essential to spin up a distinct site, though many have taken this approach.  It’s a clever way to get in the same space as Carvana.  Thus, we have new brands Driveway, Clicklane, and Acceleride.  For example, you can enter Group 1’s DR process from either Acceleride or the Group 1 site. 

  • Penske – Penske started experimenting with DR way back in 2015 and something called Preferred Purchase.  Today, it’s still called Preferred Purchase, but it’s the DDC Accelerate system.
  • Group 1 – GP1 recently (2019) launched a Roadster implementation called Acceleride.  It is now selling more than 1,000 units per month, including new cars.  This is the top initiative in their investor deck, clearly showing management attention.
  • Asbury – Asbury was also an early adopter, starting with Drive (2016) and now their own Clicklane offering.  By my count, this is their third experiment – exactly what you want to see with digital transformation.
  • Lithia – Lithia has a branded DR site called Driveway which, unfortunately, requires users to create an account before entering the process.  As I wrote in Design Concepts for Online Car Buying, you don’t create an account until the customer is ready to save a deal.
  • AutoNation – AutoNation has made strategic investments in DR vendors like Vroom, and launched its own AutoNation Express in 2014.  As with Driveway, step one is a lead form.
  • Sonic – Sonic announced a plan to use Darwin but, alas, there is still no sign of DR on either the Sonic or EchoPark site.  Maybe the new eCommerce team will fix that. 

I can understand why new-car dealers might want to start with a lead form.  New cars are commodities, and vulnerable to price shopping.  This is where used-car dealers CarMax and Carvana have an advantage.  Otherwise, DR requires a strong commitment to price transparency.

Digital Retail is synergistic with modern sales practices, like one-touch and hybrid teams.  Sonic is the leader here, and has the highest used-car ratio, so you would expect them to have an edge.

Finally, it’s hard to sell protection products online.  Groups with growing DR penetration are likely to see reduced PVR.  This has long been a knock against Carvana.  Experts agree that the solution here is an AI-based “recommender.” 

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.

Analytics for Menu Presentation

Last week, I presented a single-column format for menu selling on an iPhone, with the glib recommendation to let analytics determine the sort order.  Today, I will expand on that.  Our task is to sort the list of products in descending order of their relevance to the current deal, which includes vehicle data, consumer preferences, and financing terms.

This sorting task is the same whether we are flipping through web pages or scrolling down the mobile display.  The framework I present here is generalized and abstract, making the task better suited to automation, but ignoring the specific F&I knowledge we all take for granted.  I’ll come back to that later.

For now, let’s assume we have six products to present, called “Product One,” and so on, and four questions that will drive the sorting.  Assume these are the usual questions, like, “how long do you plan on keeping the car?”

That answer will be in months or years, and the next one might be in miles, but we are going to place them all on a common scale from zero to one (I warned you this would be abstract).  Think of using a slider control for each input, where the labels can be anything but the range is always 0.0 to 1.0.

Next, assign four weights to each product, representing how relevant each question is for that product.  The weights do not have to be zero to one, but I recommend keeping them all around the same starting magnitude, say 1 to 5.  Weights can also be negative.

For example, if there’s a question about loan-to-value, that’s important for GAP.  High LTV will correlate positively with GAP sales.  If you word that question the other way, the correlation will still be strong, but negative.  So, now you have a decision matrix that looks something like this:

Yes, we are doing weighted factor analysis.  Let’s say that, for a given deal, the answers to our four questions are, in order:

[0.3, 0.7, 0.1, 1.0]

To rank the products for this deal we simply multiply the decision matrix by the deal vector.  I have previously confessed my weak vector math skills, but Python has an elegant way to do this.

Product Two ranks first, because of its affinity for high-scoring Question Four.  Product Four takes second place, thanks to the customer’s response to Question Two – whatever that may be.  By now, you may have noticed that this is the setup for machine learning.

If you are blessed with “big data,” you can use it to train this system.  In a machine learning context, you may have hundreds of data points.  In addition to deal data and interview questions, you can use clickstream data, DMS data, contact history, driving patterns (?) and social media.

If not, you will have to use your F&I savvy to set the weights, and then adjust them every thousand deals by manually running the numbers.

For example, we ask “how long will you keep the car?” because we know when the OEM warranty expires.  Given make, model, and ten thousand training deals, an AI will dope out this relationship on its own.  We can  do it manually by setting one year past the warranty as 0.1, two as 0.2, etc.  We can also set a variable indicating how complete the manufacturer’s coverage is.

Same story with GAP.  Give the machine a loan amount and a selling price, and it will “discover” the correlation with GAP sales.  If setting the weights manually, set one for LTV and then calculate the ratio for each deal.

Lease-end protection, obviously, we only want to present on a lease deal.  But we don’t want it to crowd out, say, wearables.  So, weight it appropriately on the other factors, but give it big negative weights for cash and finance deals.

I hope this gives some clarity to the analytics approach.  In a consumer context, there is no F&I manager to carefully craft a presentation, so some kind of automation is required.

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 do 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.