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.

Digital Retail Consolidation

There has been a wave of buyouts and tie-ups lately, and so it is time to reexamine the competitive landscape.  We start by fleshing out the model I described in DR and Dealer Websites.  This is a commerce-oriented model, placing software products along the customer journey.

Looking at the three big DMS vendors, we see Roadster and Gubagoo filling important gaps for CDK and Reynolds.  Cox has long been in this space, now with Accelerate, and MMD before that.  Cox is the only one of this group to own a listing platform, Autotrader.

Last year, CDK sold its dealer marketing operation to Ansira, including the dealer site business formerly known as Cobalt.  The new entity, Sincro, now has a tie-up with Tekion.  As far as I know, this is indeed the first real-time interface from website to DMS.  I have worked with clients on other DMS interfaces, but none that cross the Buy Now boundary.

In the dealership, I list only the DMS, although the model could be extended to break out other point-of-sale systems.  Note that CDK and Dealertrack no longer have their own menu systems.  Both are now offering Darwin under license.  To round out the DR theme, I include TrueCar’s tie up with AutoFi, and Fox Dealer’s acquisition of TagRail.

So far, so typical.  Everybody wants a DR partner, and the big vendors have always acquired the innovative upstarts.  But now, we discover a new theme. CDK paid a lot of money for Roadster, $360 million, to plug a gap in its product line.  Why did J.D. Power, not a software vendor, pay even more for Darwin?

Digital Retail Acquisitions are Big Data Plays

J.D. Power is primarily a data business.  They own ALG and Autodata.  According to the press release, they are “focused on maximizing the value of our extensive data and analytics assets.”  Darwin, through its powerful DMS interface, has been reading and analyzing sales data for thousands of dealers.

MotoInsight, the Canadian DR company (my profile here) was recently acquired by a unit of Thoma Bravo, which in turn owns J.D. Power.  Seeing a pattern yet?  The Autodata merger is pretty recent, and also mentions analytics.

“In working with Modal, we are leveraging aggregated purchase data and AI to improve conversion.”

Another DR player, Modal, recently teamed up with a data science company called Inmar.  I couldn’t find the commercial terms, but founder Aaron Krane has stepped back.  There’s a new CEO, and a plan to “catapult analytics to the forefront.”

The press release for the recent acquisition of Dealer Socket by Solera, “the preeminent global data intelligence and technology leader” specifically mentions machine learning.  While we’re at it, let’s note that Automotive Mastermind is a unit of IHS Markit, as are Carfax and R.L Polk.

You’ve probably heard that “data is the new oil.” Opinions vary, but I think the metaphor holds up here.  If you study analytics the way I do, it’s easy to see the data resources underlying these transactions.  You can also check out this book, or the usual sources like HBR and Sloan.

Digital Retail is “the engine,” giving customers a self-sufficient buying experience.  This engine is amenable to endless AI-based use cases, from recommenders to NLP chatbots … and AI runs on data.

Car Search Aggregation

I was rereading Professor Rogers’ book and I had this brainstorm that somebody should develop an aggregator site to sit on top of digitally-enabled car dealers, the way Kayak does for airlines.  It could scrape all the listings into one vehicle-search page, aided perhaps by a standardized listing API. 

It turns out I am not the first one to have this idea, but the research was interesting.

First, we have to make a distinction between providing a lead and selling the car.  This is not always easy, because few customers require full digital retail, and most lead providers have some limited DR capability.  Still, this distinction is important to an aggregator:

To make said distinction, imagine the dealer in this diagram has a DR system and also uses a third-party classified site.  If you are a DR skeptic, imagine this is Carvana (or CarMax) with their own integrated car-selling site. 

I am using a thin line for leads and a thick line for deals.  This notation helps to show that the Kayak site should only connect to DR-capable dealers.  Otherwise, it’s lead provider on top of lead provider, with no added value.

Once a platform is widely established in its category, it is extremely hard to launch a direct challenger with a similar service – David Rogers

Here, I am just doing what any good futurist does – working backward from the goal state.  What the market wants is a single place to shop, like Amazon.  Rogers would call this a “platform,” and network effects says it’s a winner-take-all business.  There can be only one. 

Once you recognize this three-layer model, you can infer all sorts of fun things.  Like, suppose Carvana (or CarMax) decided to open up their DR capability to other dealers.  These would be certified and operationally compatible dealers, whose inventory Carvana could sell for a commission.  I’ll leave it to you to negotiate who earns the F&I gross:

I have been writing about digital retail for a few years now, speculating on how the goal state would be achieved.  Note that “DR aggregation” on the left side of the diagram, and “platform aggregation” on the right, correspond to the two vectors I described here.  

I have long advocated platform sites adding DR capability, as some are doing now.  This brings us to an interesting piece of history.  Airline booking sites Orbitz and Kayak were founded by the same guy, Steve Hafner, in that order:

Initially, Hafner undertook what we would call the DR piece, while Kayak opted to be simply an aggregated lead provider.  I still think it’s a good idea for listing sites to develop DR features, but history suggests the TrueCar approach – linking up with Roadster – is the correct one.

The Robotaxi Myth

I started following the Waymo situation a few weeks ago, when Ars Technica asked “why hasn’t Waymo expanded its driverless taxi service?” My glib reply on Twitter was that ride hailing is not a good use case.  Since then, we have learned that the recently-departed executive team had not been moving fast enough to satisfy their investors.  First to go was the Chief Safety Officer – not a good sign.

Indeed, the robotaxi is the absolute worst use case, according to this very thorough analysis by Tim Lee.  Lee’s recommendation is to put autonomous cars on the road, now, doing something they can actually do, and proceed from there.

Lyft has just bailed out, as Uber did last year.  The New Republic calls self-driving cars “a series of very expensive and glitzy pilot projects” which, while unkind, is pretty accurate.  Level 5 automation will be in the pilot stage for a long time.  We (and Alphabet) should temper our expectations.

Good use cases for self-driving exist in SAE level 4, constrained conditions – like airport shuttles, food delivery, and taxi services in closed communities.  Shuttle services like this are popping up all around the country. 

A zero-to 25-mph self-driving car—we believe that problem is very, very solvable.

This quote from Voyage founder Oliver Cameron sums it up – and vindicates Waymo’s decision to hunker down in Chandler, Arizona.  I agree with Lee that the winners will be those companies that are able to commercialize level 4.

Who asked for self-driving cars, anyway?  Certainly not consumers.  This study from MIT, Consumers Don’t Really Want Self-Driving Cars, and this more recent one from AAA found the same result.  Nearly half of respondents said they would never purchase a car that completely drives itself. 

They’re looking for driver assistance systems that work to help them stay in active and safe control of the vehicle.

What do they want?  You guessed it: automatic emergency braking, lane keeping, and blind spot warning.  These features come under the heading of SAE level 2, also known as Advanced Driver Assistance Systems (ADAS). 

Several people have come to grief from thinking that their level 2 vehicles were “full self-driving.”  Ironically, the better the system, the greater the false sense of security, says AAA’s Greg Brannon.  This is a shame because the robotaxi myth prevents us from properly appreciating level 2. 

List of Advanced Driver Assistance Systems (ADAS)

  • Adaptive Cruise Control
  • Anti-lock Braking System
  • Automatic Emergency Braking
  • Blind Spot Detection
  • Dynamic Brake Support        
  • Electronic Stability Control
  • Forward Collision Warning
  • Lane Departure Warning      
  • Lane Keeping Support
  • Parking Assist           
  • Rear Cross Traffic Alert
  • Rear Visibility System

Proponents say that replacing human drivers will save lives, but ADAS is already doing that. It’s also, as I wrote here, adding value to new vehicles.  I suspect that, just as the technology must work its way up from level 2, so must we drivers.  As we become more accustomed to ADAS features, we will be better prepared to supervise semi-autonomous (level 3) vehicles.

My Shift in the Call Centre

A User Story

I enter the PCI compliant cleanroom at eleven o’clock with only a quinoa bowl from Freshie’s, and log in to Salesforce on my locked down computer.  No cell phone, no scratch paper – and there are cameras.  I wave to Peter on Camera #1 and start to dial.  I do not have high hopes of reaching anyone in the middle of the workday.  Amid all the DNRs, I may catch an inbound call off of our direct mail campaign, or someone out on the floor may catch it while I am dialing.

I log in to the dialer and it presents my first call.  To save time, I hit “dial” and the phone rings while I paste the number into Salesforce and search for the Opportunity.  Our Cisco dialer has a predictive mode, but I am not using it.  For low volumes, preview dialing is supposed to be a better experience.  The Ministry of Commerce prefers it, too.  This number is guaranteed to be in Salesforce, with a prospect status, because the dialer file is generated nightly from the Opportunity table in Salesforce. 

Bonjour et félicitations pour votre achat d’un véhicule Nissan

My first call goes to voicemail, which is par for the course.  I recite the voicemail script, which I know by heart, and log the status in Salesforce.  I really wish the dialer could leave that damned message on its own.  I must recite it a hundred times a day.  I will dial this number three times before dropping it from the file, spread over a two-week period, in case my prospect is away on vacation.  Salesforce applies this business logic when it generates the dialer file. 

For the next few hours, I get voicemail, no answer, not interested, and never call me again, which I duly note in Salesforce.  This last category will be added to the phone number filtering logic, along with the Do Not Call list we purchased from the Ministry.  I recognize the next number.  Merde!  It’s Dave Duncan.  I try to cancel the call, but too late.

Dave proceeds to grill me about my affiliation with Nissan.  No, I do not work for your local dealer.  If I did, we would have an “existing business relationship” and we wouldn’t have to honor the DNC list.  No, I do not work for the factory, its captive, nor the captive’s department of protection products – but we are the one and only factory authorized direct marketer of said products.  That’s why it’s their number on your Caller ID. 

By six o’clock, I have a live prospect.  I alt-tab to my SPP system, which allows me to quote rates as well as set up a payment plan.  I also have a custom Product object in Salesforce which connects to the rating API, but I find it easier to work in SPP because most customers will want a payment plan anyway.  SPP calls the same API. 

Things are going well until my prospect insists upon seeing the contract.  I recite the talk track about cancellation and full refund within thirty days, but to no avail.  I can also email a specimen contract and we can review it right now while he’s on the line (better odds of closing).  I end up emailing a custom link, or PURL, from SPP that will open right to the rates and contract we discussed. 

I flag this one for callback in a few days.  It’s possible he will self-close on the SPP site, and then Salesforce will close the Opportunity automatically when it receives the file from SPP.  In any case, I now have an email address we can use for the next digital marketing campaign.  Speaking of digital marketing, whenever a voicemail greeting begins, “the Rogers mobile customer you’ve dialed,” I flag those as numbers to which the digital team can send text. 

My next prospect, I actually close on the call.  I am sitting in this fakakta cleanroom just in case I have to handle credit card information which, at last, I do.  My guy buys a 72-month plan, which I set up for 24 monthly payments on SPP.  Then, I download both contracts – the protection plan and the payment plan – and attach them to the Opportunity. 

Salesforce won’t close the Opportunity, though, for another day until it receives confirmation from SPP that all is well with the credit card.  If not, it will indicate that status, send an email, and I will have to call him back.  Once the Opportunity does close, as a win, Salesforce Connector will pick it up and Marketing Cloud will include both contracts in a direct mail welcome package, ending the Customer Journey. 

So, to summarize my workflow, I am manually pasting phone numbers into Salesforce and VINs into SPP.  Salesforce and SPP are each capable of rating and contracting via API, and the customer can check out with or without my assistance.  These tasks could be improved with some Computer Telephony Integration and an SPP interface.  Instead of sending data directly to SPP, all I really need is the logic to generate that PURL and then Salesforce could either launch it for me or send it to the customer as needed. 

At eight o’clock, the end of my shift, I doff my headset and run the job to generate tomorrow’s dialer file.  This is basically a query against the Opportunity table, applying the “next date to call” rules.  Without CTI, the best time to call is not supported.  Jeanette will have to pick those out of the comments manually.  Tomorrow is my day off. 

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.

Caution: Learning Curve Ahead

In last week’s episode, I warned that dealer groups proceeding aggressively into Digital Retail may suffer for it.  This has gotten some pushback.  Regular readers know that I have been a staunch proponent of Online F&I for many years.  Indeed, my work at PEN and F&I Express has done much to advance the cause. 

I gave this warning in the spirit of full disclosure, and to manage expectations.  Now I am in the awkward position of having to press my charge against a technology which I actually support.  If that sounds complicated, consider this:

Luddites – Veteran F&I Director Justin Gasman, quoted recently in Wards, says that F&I will never be totally digital.  “People who say that are from tech companies,” he quipped.  I call this the Luddite position but, in fairness, I am one of the tech guys he’s referring to.

Boosters – Cox Automotive regularly produces surveys with findings like: 63% of customers would be “more likely” to buy F&I products if they could learn about them online.  Coming from an opinion poll, this is mere boosterism. 

Realists – My position is somewhere between these extremes, hence the warning.  I was addressing the Big Six dealer groups, who are regularly ranked on F&I performance.  I do not want to be the consultant telling Mike Jackson to go all in, and then have to explain why he has slipped out of first place.

If you go to a dealer and say, “Hey, look, we’ve got this great solution, but the profitability is only half of what you had before,” that’s really going to slow down adoption.

Automotive News interviewed some realists last year, and they all share my cautious optimism.  The quote above is from Safe-Guard’s David Pryor.  The consensus goes something like this:

  1. Present F&I products online, early in the process, and include pricing.
  2. Use an API to select the right coverage, and AI to make recommendations.
  3. Experiment with (A/B test) various digital media.
  4. Integrate DR with your instore process, training, and metrics.

Roadster’s COVID-19 Dealer Impact Study found that dealers who already had Digital Retail saw improved gross, while the COVID adopters did not.  “Not a magic bullet,” it says, instead emphasizing the improved efficiency.  Other realists, as here, had the same experience.

Digital Retail is like any other new process.  There is risk, reward, and a learning curve.  That’s not too complicated.

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.”