Tag: online

Reverse Omnichannel

If digital retail is so great, then why does Apple have stores?  Shipping a six-ounce phone is nothing compared to delivering a car, and yet these iconic stores.  This, of course, is the true meaning of “omnichannel” retail – meeting your customer wherever they choose. 

Digital native retailers, in fact, are advised to add physical stores, even if they’re selling only sneakers or eyeglasses.  McKinsey advocates this as a way to gain cheaper traffic – ironic, considering the popular misconception that digital retail has lower costs. 

Brick and mortar car dealers have service departments which can keep them afloat in a recession.  They also have an easier time selling F&I products.  So, no surprise that online car dealer Driveway has lately opened a showroom, except … Driveway is the online brand of Lithia, a public dealer group with over 250 stores. 

If a Driveway customer in Oregon wants a vehicle that’s in stock in Texas, it’s shipped to the Portland store and delivered to the customer.

That’s right, the online brand of the leading dealer group now has its own store – with no cars.  This makes perfect sense to me.  I bought my last car online, in the dealership.  The salesperson ran the same configurator I would run at home, adding value with her knowledge of the product.

Omnichannel Auto Retail

Here is a brief history of how we got here, with links to contemporaneous coverage on the blog.  Schematically, the omnichannel evolution looked like this:

I started writing about digital retail way back in 2015, with a two-part post on Design Concepts for Online Car Buying.  I didn’t manage to land a gig building one, but I got the next best thing.  My job running e-commerce for Safe-Guard put me in touch with emerging leaders like Roadster and Accelerate.

These systems allow dealers to retrofit digital retail into their existing websites, while public groups Lithia, Asbury, and CarMax developed their own.  I covered the market for digital retail software from a few different angles.  See here, here, and here. 

Dealers invested in digital retail, but they didn’t always get the desired results.  Software vendors were the first to spot the disconnect between process and technology.  Roadster started writing about omnichannel in 2018.  Cox’s Mike Burgiss exhorted dealers to “sell the car, not the appointment.”  

The true revenue performance of a retailer’s online channel can be understated by up to 100 percent, or even more if not accounting for the influence online has on offline.

Around this time, I was writing about a Best Buy model for auto retail.  McKinsey linked the two concepts in this 2021 article.  For me, their most important observation is that our metrics don’t always give proper credit to the online channel. 

Digital Native Car Dealers

Digital natives Vroom and Carvana missed the memo about having a physical presence.  Hell, even Amazon has retail stores.  Lithia and CarMax are more like “digital immigrants.”  Driveway going back to its roots and opening a physical store reminds me of “reverse ETL” from Data Engineering.

Data Engineers spend a lot of effort extracting, transforming, and loading (ETL) data for use in analytics.  Then, we often find it useful to take the cleaned-up data and push it back into the transactional system whence it came. 

Another analogy might be how elephants evolved from seagoing mammals and back to land again, or how computing power was centralized in the mainframe era, and now recentralized in cloud services … but “reverse omnichannel” makes a better title. 

Lenders at Top of Funnel

Chase Auto recently rolled out a digital platform for car shopping … and financing.  I like it.  The link is here.  It seems that everyone today has a vehicle search page.  The original cast, Autotrader and Cars.com, with about a dozen TPC competitors, are now joined by OEM sites, public dealer groups, and marketplaces from Roadster and Carvana.

“More vehicle shoppers than ever have started to look for vehicle financing before ever setting foot in a dealership.”

Competition hinges on which information the customer will seek first.  In an era of reduced purchasing power, many customers will want to “secure financing before going to the dealer.”  That’s the prompt on the Chase website.  There’s a prequal button right there between the Lariat and the XLT.

Don’t take my word for it, though.  This J.D. Power study found that nearly half of all customers shop for financing before visiting a dealer – 62% among Gen Z – and they start more than 30 days out.

This is probably a negative development for captives, and indirect finance in general.  Banks have a lower cost of capital and better rates.  Chase, as you know, is also popular as an indirect lender.  They say there’s no conflict with their dealer channel, but what if they had to choose?

The reach hierarchy, by customer base, is:

  • Banks – eight digits (ongoing)
  • Car Makers – millions of cars per year
  • Dealer Groups – hundreds of thousands

Banks have more customers, by an order of magnitude, than even the largest car makers.  Ten years’ worth of loyal Toyota drivers doesn’t approach Bank of America’s 66 million customers.  The same ranking goes for website reach, with the banks getting 120 to 190 million visits per month, while Carvana, Ford, and Autotrader each get twenty something.

Capital One, by the way, also has a shopping platform.  Ally has a dealer locator.  Bank of America has a redirect to Dealertrack.  Capital One is pretty shrewd about encouraging buyers to bring the app with them into the dealership, so they can update the deal as needed.  Mobile-first responsive is good, but an app is better.  Bank customers will carry their bank’s app.

Captives have the home field advantage once the customer is in the dealership and, likewise, their position online is downstream from the OEM brand.  Captives are advised to be front and center on their manufacturer’s website.

Dealer groups, like AutoNation, must rely on their own brand to draw customer attention.  In terms of unit sales, even the largest dealer groups fall below tenth-ranked Subaru.  Note that Lithia chose to develop a new brand, Driveway, for their online business.

Of course, none of these is a direct measure of financing intent.  Only a fraction of online banking traffic is looking for an auto loan.  The point is that they’re looking for the loan first, and then the car.

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.

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.