Tag: online

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 I am certain that 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 will do it 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.

In the Amazon Wilderness

I concluded Car Dealer Megatrends with the clear and present dominance of consolidated groups, which I like to call the Best Buy phase.  Today, I will indulge in a little futurism, and explore the Amazon phase.  In the Amazon phase, it will be possible to buy a new car enitrely online and have it delivered.

By 2025, experts estimate 30-40% of car sales will be online.  The high end of that range is from Mark O’Neil.  Used cars are easier to sell online, witness Carvana, Vroom, and Shift, but new cars will be there too.  An estimated 25%, and that’s only seven years away.

The industry is rapidly solving problems like pricing and trade valuation.  The only challenge people still talk about is the test drive.  Carvana solves this with its seven day return policy, and Shift will bring the car to you for a test drive.

 “The current dealer model is not a dying breed,” Benstock said. “It’s dead. It’s absolutely dead.”

I will order a new BMW sight unseen, because I know the product and I trust the manufacturer.  Their online configurator is awesome, and I really would press the “build and ship as shown” button, although the process isn’t quite there yet.  We’ll come back to BMW later, but for now let’s assume a test drive is required.

The tension between Best Buy and Amazon centers on a practice known as “showrooming.”  This is where you sample the product at Best Buy, interrogate the Best Buy sales associate, and then turn around and order the product from Amazon.  Amazon even makes a clever app you can use to scan product codes while you’re in Best Buy.

As auto retail moves into its Amazon phase, I can imagine the same challenge for dealers.  You have invested in a monument to your manufacturer’s brand image, where customers can sample the product and then go order it online.

I had been pondering the showrooming challenge for a while when I ran across this piece in the Wall Street Journal.  Nordstrom is opening stores with no stock, where shoppers can try on clothes and accessories, and then have them delivered.

It will contain eight dressing rooms, where shoppers can try on clothes and accessories, though the store won’t stock them.

The Nordstrom story reminded me of the old “catalog showrooms” operated by mail order retailers like E.L. Rice and Service Merchandise.  Ironically, this was the last gasp of mail order, put out of business by brick and mortar retailers – including, ultimately, Best Buy.

All of this goes to show that, in the Amazon phase, showrooming and fulfillment can be disconnected.  Where the customer goes, to test drive and learn about the vehicle, does not have to be the dealership or even affiliated with the dealership.  This opens up a world of new possibilities.

I can think of several applications for standalone test drive centers.  For instance, suppose a manufacturer wanted to enforce its ideas about how to present its vehicles, and also – since this is the Amazon phase – protect its own position online.

Were it not for U.S. franchise laws, manufacturers would run their own retail outlets.  In Europe, they have company stores, where ideas about brand image, sales training, and product positioning do not depend on a network of autonomous dealers.

An OEM test drive center would bypass the dealer network (or complement it, if you prefer).  It would be staffed by salaried, factory-trained product experts with no other objective than to educate customers in the finer points of their company’s vehicles.

There would be minimal inventory, attractive video displays, simulators, and samples of paint and fabric.  No transactions would take place, but there would be plenty of Wi-Fi bandwidth and gourmet coffee for the online shoppers.

As I said, this is just one scenario.  The new techniques of digital retail will create untold opportunity for dealers willing to adapt.  Our exploration of the Amazon phase has just begun.

Three Requirements for Online Car Buying

In this post, I am going to elaborate on Dealer Systems in the Consumer Space.  Every system in F&I must have a counterpart in the consumer space.  The diagram below shows the traditional dealer process in orange, and consumer systems in blue.

online-retail

Each of the six tasks is now, or will be, available to customers online.  Obviously, these are web based systems and, for best results, they are also mobile.  Each consumer system must:

  • Share data with its dealer-system counterpart
  • Share data with other consumer systems
  • Save deal data for later use

Each consumer system must share data with its dealer-system counterpart.  If it quotes a VSC rate, the customer will expect to see that rate on the menu in the dealership.  If it obtains a credit decision, the customer will expect the dealer to know about it.  There are various ways to accomplish this.  In the VSC example, both systems should be reading rates from the same API.

Consumer systems must also share data among themselves.  Vehicle data is input to VSC rating, price is input to deal structuring, and the “line five” subtotal is input to credit processing.  It’s a good idea to keep a data-flow diagram handy.

Finally, the consumer systems must cooperate to store in-process deal data.  Customers should be able to choose which tasks they wish to do online, and then save the deal to be completed at the dealership.

I am mainly addressing new entrants from outside the industry, who may have a good system for one of the tasks, but fail to connect with the others.  This may also include dealer groups moving into online car buying, and system vendors like Cox.  My chart of platform capabilities is here.

Taxonomy of Online Car Shopping Sites

I have been writing an ad hoc series about online car shopping. It started with a technical point about how software vendors should migrate into this space, and then along the way I started characterizing the sites themselves. In this article, I present a classification scheme which may be of interest to technology strategists.

One way to look at car shopping sites is in terms of functionality. In my last article, I presented six key functions:

  • Specify vehicle and trim
  • Price vehicle
  • Price protection products
  • Value trade
  • Structure deal
  • Obtain financing

For each function, there are grades of support. Does the site sell protection products, for example, and are they customized for the chosen vehicle? This would be a way to rank the sites, like Consumer Reports. I have strong opinions here, but they’ll have to wait for a later article. For strategy purposes, the sites are better characterized by three business decisions:

Control of inventory – Traditional car shopping sites are platforms for common inventory search across multiple dealerships. Because they do not control the inventory, there are limits on the functions these sites can provide. Looking at inventory (and delivery) is a way to characterize the site’s relationship with the dealer.

Disclosing the price – Most of the downstream functions are blocked until the price is settled. This is what separates shopping sites from buying sites. Unfortunately, price transparency is a problem for many dealers. In addition to no haggle pricing, we now have innovative solutions from TrueCar and Make My Deal.

Different makes – A site that specializes in a single make can also specialize in financing and protection products. In addition, some information systems may be standardized. On the other hand, most car buyers begin by comparing similar models of different makes, like the Touareg versus the 4Runner.

My approach groups the sites into eight categories, and gives us a way to describe the differences. For example, when the customer moves from a platform site to an individual dealer, the make (and the lot) is specified. Now the dealer can offer customized financing and products.

The difference between a platform site and, say, Carvana, is that Carvana owns the inventory and can quote a price. In fairness to new car dealers, price transparency is less of a problem with used cars. Carvana, Vroom, and CarMax have the inside track. Otherwise, AutoNation Express would be in this category.

You could slice it thinner, but I think eight categories is enough. I leave it to the reader to evaluate all twelve single-feature comparisons.

 

Taxonomy2

One thing I learned studying syntax as an undergrad, is that you construct a paradigm to fit the data you have, and then you prove the paradigm by using it to find new data. Here, I drew a blank for case #2, and then I realized it would be the web site for an OEM company store, as in Europe. Sure enough, Tesla fills this slot.

There is one more feature I would have liked to include, but I felt it was too much, and that is systems integration. AutoNation Express has the distinct advantage that whatever payment calculator, menu presentation, or other gadget they may add to the site, it is guaranteed to work seamlessly with an AutoNation dealer.

Another way to prove out my taxonomy is to explain and predict trends in the industry. Speaking of AutoNation, one of these trends is toward increasing consolidation by the big, public dealer groups. They are represented online by cases #1 and 3.

To defend themselves online, private dealers will migrate into the most capable of the platform sites, and there will be a shakeout. The winning platforms will not be mere lead providers. They will have to offer advanced shopping features, as I have described previously, and they will have to solve the problem of systems integration with a diverse dealer base.

Eventually, as both camps wrestle with the pricing issue, there will be a breakthrough. Like the first prehistoric fish to draw breath on shore, the platform sites will struggle into case #5.

The one strategic curveball would be if a consolidator decided to open up their site to outside dealers, blurring the line between cases #1 and 5, or a platform site started acquiring new car franchises. We’ll leave this chimera for another story.

Ancient Relic Found

Get PricingSince moving to Toronto, I have been renovating the house and making a study of Canadian F&I.  Some things here are more advanced, like the variety of financing plans, and some are backward.  Pictured here is something I haven’t seen in a long time – the dreaded “secret price” button.  This is a far cry from dealer sites in the U.S. and contrary to everything I believe about e-commerce.  For my new friends in Canada, here is the right approach.  As a consultant, this just means more work for me.