Kelly and I were sipping coffee at Digital Dealer, greeting participants, and speculating on how the ultimate online buying experience would come to pass. Presenters had talked about Amazon, obviously, and the recent opening of a Hyundai digital showroom on Amazon Autos.
A while back, I organized the various offerings into categories like: online platforms where multiple dealers may list their inventory (basically lead providers) versus eCommerce plug-ins to be placed on individual dealer web sites.
One key variable was whether the site actually holds inventory, i.e., is a dealer, not just a technology play. Carvana, for example, or Shift. Increasingly, what I notice is that the good technology either evolved from a dealership, or – I found this intriguing – they will buy a dealership to serve as a test bed.
Your rapper name is a top twenty dealer group plus a digital retail system.
Roadster came from a concierge buying service which, as far as I know, they still operate. A2Z Sync came out of Denver-based Schomp group. The Gogocar people operate a Kia dealership. This brings me to the next level of dealer technology tie-ups, those where big dealer groups choose an online retail solution and commit to it.
We philosophically do not believe that software development is our expertise. Instead, we’d prefer to partner with third parties – Craig Monaghan
That prediction is … continuing the consolidation megatrend, we will see dominant groups taking the lead in online retail, but unable to master the technology on their own. This is what I call the “Kodak syndrome.” Incumbent leaders are not agile enough to ride a paradigm shift. This means not only the dealer groups, but the traditional software vendors.
I expect to see the Sonics and Asburys of the world buying up the digital retail people, absorbing their talent, and denying access to their competitors. I characterized this as a “land rush” in the earlier piece. Direct to consumer is the final frontier.
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:
Set up the deal one way
Change any feature, like the term
The change “cascades,” undoing other features
Revisit those other features
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.
In today’s post, I add to the copious literature on technical debt with a discussion geared to my audience of F&I entrepreneurs, and extend the metaphor into organizational design. What I noticed, writing Maturity Model, is that software development is rich in models and metaphors that apply outside the trade.
Technical debt is incurred when software developers take shortcuts, usually because they are under time pressure. This debt is accrued in program code, but it must eventually be paid off with real money, just like the debt on your balance sheet. Here is a brief discussion of how that works.
Someone once insisted that my team “just code IF State = TX, and get on with it!” Not on my watch. We will categorize Texas, and then we will add other states to the category as we discover them. For example, the category might be “waiver GAP states,” or “spousal consent states.”
Operating or back-office issues, often related to IT, are recurring concerns for strategic buyers. Problems with IT underinvestment have proved to be ordeals during many integration efforts.
If you go down the road of IF State = TX, then in short order you will have code with IF <list of ten states> do this logic, and for <five of them> also do this other logic, but for <three of the ten> do this instead and for <the other two> do both of those things.
Congratulations, you have saved forty hours of programmer time versus stubborn Mark Virag and some academic exercise involving categories. Now you are married to this gnarly decision tree, and you will be debugging it forever. The technical term is Big Ball of Mud.
One warning sign of technical debt is the “cut and paste” approach. If your developers implemented the latest dealer, provider, lender, product, or state by copying code from the one before, then I guarantee you have technical debt.
Any developer worth his salt will, instead, make the copied code into a reusable method. Developers are trained to do things the right way and, in my experience, only take such shortcuts because the boss told them to.
Why not cut and paste, if it gets the job done? Because, if there are any bugs in the copied code, now they’re multiplied and scattered throughout the code base. You will have to spend programmer time to fix each one separately, as they are encountered over time.
I could go on with examples all day. The point I want to make is that technical debt is real money. You may go “quick and dirty” this week, and save $5,000 of developer time, but you will be paying those same developers later when they have to fix the bugs.
You may reasonably decide that you are a little short this month, and take a loan from the invisible bank of technical debt, but you should do so consciously. Don’t fool yourself that technical debt is free. I have provided an example here in the form of TILA box, something my F&I readers will understand.
Now that I have that off my chest, let’s discuss investment decisions. For example, if you’re a startup and strapped for cash, you may choose to pile up technical debt because it’s off balance sheet and may be the only kind of financing you can get.
Of course, no one actually thinks about it this way. What they tell their developers is, “just keep patching it until we’re profitable and you can overhaul it later.” You may even sell the company, rickety software and all, if the acquirer fails to do proper diligence.
When I was doing international software search for BMW, our due diligence guy in Munich was Dr. Dettweiler. We would find some software that looked pretty on the outside, and then the doctor would fly in and discover it was all a façade, like a movie set held up with sticks.
McKinsey specifically warns against acquiring a company with a big ball of mud in the back office. Like process maturity, this is a concept that goes beyond software development.
In my time as a consultant, I have designed an organization or two, and it’s a lot like programming. You have to have the right boxes on the org chart, with the right procedures and job descriptions, kind of like designing objects that will respond to business events (except they’re people).
Organizational debt is caused by the same kinds of things that cause technical debt. For example:
The structure worked fine ten years ago when we had one-tenth the number of people.
It was never actually “designed” to begin with, but we reorganize ad hoc every other year.
The structure is based on specific people instead of job functions.
There are processes for which no one is actually responsible, so things “slip through the cracks.”
Fortunately, people are remarkably resourceful. They will create their own procedures and informal networks. Good people can prop up a bad organization, like those sticks holding up the movie façade, but they can only hold out so long. Sooner or later, they will start to slip – and customers will start to notice.
Now I feel like I really am writing a pitch for consulting services. Call now! Free reorg with every digital transformation. Seriously, though, my point is that organizations can harbor technical debt just as software can. This is why I am a fan of formal methods like ISO certification and, yes, professional organizational design.
More broadly, I am starting to notice that software development concepts – like process maturity, technical debt, iteration, and agile teams – are applicable throughout the enterprise. We’ll explore this further in an upcoming post.
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.
Remember when dealerships had body shops? Two out of five still do, but they comprise less than 20% of this $35 billion market. Somewhere along the line, it became clear that collision repair was better done by specialist facilities, unconnected to the dealer. Scale, capital investment, brand diversification, and (lack of) synergy were factors.
We may now wonder if parts and service belong in the dealership, thanks in some measure to the rise of automotive eCommerce. Jim Ziegler warns that Valvoline Express is beating dealers in the shop and online. Ward’s makes the same point, with emphasis on Google search optimization. In the same vein, Amazon has come up with a way to sell tires online.
There can be much synergy between the two ends of the business, which can be leveraged to manage and sustain customer relationships – Vincent Romans
My approach is to “follow the money” and, sure enough, here is Carl Icahn buying up repair facilities. Icahn Automotive Group is a classic consolidation play, with 2,000 locations including Precision Auto Care, Pep Boys, Just Brakes, AutoPlus, AAMCO, Cottman, and CAP. Icahn is vertically integrated through Federal-Mogul Motorparts, which includes ANCO wipers and Champion spark plugs.
So, will eCommerce pick off the dealer’s profit centers one by one? In this example, we see the convergence of powerful megatrends. The traditional dealer model is challenged by two new ones, which I like to call the Best Buy model and Amazon model.
History tells us that the Amazon model will prevail in the end, but it doesn’t tell us what the transformation will look like, or how dealers should prepare. To learn that, we employ an old tool from Business Process Reengineering, and we discover a surprising result. Here is a breakdown of the traditional dealer operations:
We can consider each operation in terms of how it will respond to the new challenges – and whether it belongs with the others. We have to start somewhere, so let us define new vehicle sales as the nucleus of the dealership. The test drive is the process most resistant to eCommerce although, as I wrote last week, there are ways around it.
Used vehicle sales will certainly not stay in the dealership. It is vulnerable to both consolidators and eCommerce. This is a shame because taking vehicles in trade used to be a great synergy. The new specialists are true “auto traders,” using high-volume analytics to trade both ways with the public and the auction.
Coming back to fixed operations, there is a clear synergy. According to Cox research, customers who are properly introduced to the service department are two and a half times more likely to come back for service. But there are other ways to exploit this synergy, like the “zero deductible at our dealership” service contract – and the Amazon tire store shows that parts can be separated from service.
Lithia Motors has 186 locations including, by my count, fourteen collision centers. There is not much synergy between body shops and vehicle sales, or even service, but they run fine as standalone operations connected to the brand. Likewise, given a branded service contract, I can see Lithia’s mass market franchises supporting shared service facilities.
F&I is the subject of fierce debate, too much to cover here. Can it be merged into the sales function? Can protection products be sold successfully online? What is the future of indirect finance? Do “F” and “I” even belong together anymore? For our purpose today, we need only observe that while F&I has a workflow linkage to sales, it does not need a physical one. F&I could just as easily skype in from a call center.
As Carl Icahn would tell you, these are distinct businesses without much synergy, if synergy is defined as “positive return from shared personnel and facilities.” Dealers organized along these lines will, indeed, be picked apart by eCommerce and consolidation.
On the other hand, if synergy means “positive return from shared customer contact and branding,” then these businesses will hang together. Dealers organized along this principle will have diverse and independent operations, making them resilient to disruption. They will have “optionality,” to use Nassim Taleb’s term.
You may be taken aback by this assault on the venerable “rooftop,” and I admitted earlier to being surprised. However, decoupling and diversification (and divestiture) are textbook responses to an industry in flux. Just look at how many departments are no longer in department stores.
Followers of my Twitter feed know that I have lately been looking at mobile apps, to see if anyone can present protection products on an iPhone. I wrote about this three years ago and, according to my informal survey, the field is still open.
I don’t think anybody has a good way to present a menu on a consumer web site, much less an iPhone.
Not only is the iPhone a restrictive form factor but we must assume that the customer, not an F&I person, is operating it. We would like to apply our Best Practices for Menu Selling, but the app must be able to apply them on its own.
For example, if we want to retain the package concept with the carefully chosen payment intervals, we can use an accordion control. I proposed this for a client once, in an F&I context, but it doesn’t make sense for consumer use.
No, the best way to “present all the products, all the time,” is simply to make one long column with everything in it. The iPhone presents challenges, but there are offsetting advantages. We can show fifteen products in one column, and the customer has his leisure to scroll through them.
I prefer scrolling to swiping for a few reasons. In the prototype shown here, we have the obligatory vehicle photo. After the first scroll, that’s gone and the screen space is devoted to products.
The prototype shows monthly prices for the vehicle and the products. This assumes the finance process is settled, and the app can choose products matching the finance term. Touching any of the products will open up a full page with details, coverage choices, and a “sales tool” as in the earlier article.
I recommend using analytics to determine the sequence of products in the column, and even to A/B test the format of the product blurbs. I have in mind a few different formats:
Text with graphic and price, as shown here.
No price ‘til you open it.
Lead with the sales tool.
I discuss analytics here, but I am not a fan of the full “ownership survey.” Of the eight standard questions, maybe you can sneak in one or two elsewhere in the process. Apart from that, we’re counting on data points found in the deal itself.
I also think “less is more” when confronting the customer with choices. As you can see in the mockup above, there must be no complicated grades of coverage (or deductible). If you’re configuring the app for a specific dealer, you may want to filter some options out of the dealer’s product table.
Depending on who’s managing the app, the products themselves may be rethought. If you want to offer chemical, dent, key, and windshield as a combo product, then that’s a single choice. Alternatively – since we have unlimited column space – you can offer each one individually. What you do not want is a product having fifteen different combinations.
Coming back to my informal survey of mobile apps, and the workflow given here, I believe there are already good examples of vehicle selection, credit application, trade valuation, and payment calculator. Menu selling has been the only missing link, until now.
Feeling quantitative again today, so … suppose you have an F&I Director, or a menu trainer, or somebody, and their goal is to move product index from 1.0 to 1.2 over some time period. To keep the numbers simple, let’s say the variable comp component is $10K.
One way to do this is to say that 1.2 pays $10K and the current performance, 1.0, pays zero. This makes sense, right? Why pay for no improvement? This only works, however, if you place a cap on it. As the salesman, I could come back and say, fine, if the two points of product index are worth $10K to you, what happens if I hit 1.4? Are you willing to pay me $20K for that?
Most people resist the idea of capped pay plans. Mathematically, you are making a linear relationship between compensation and performance, and you should be willing to honor that relationship up and down the line. The problem here is that the line is too steep.
So, let’s try a shallower slope. Once again, 1.2 pays $10K, but this time the zero point is 0.0. That means the current performance, 1.0, still pays $8,333 and the salesperson doesn’t go hungry unless the index actually falls all the way to 0.0. Obviously, this plan is too weak.
The weak plan may be desirable if your sales force is really counting on some of that money, and the “variable” is not as variable as advertised. It also protects the company on the up side. In this example, I can achieve a 1.5 index and it only earns me an extra $2,500 (on top of the $10K).
Now we have examples of two pay plans, one too strong and one too weak, as in the story of The Three Bears. The key to making the pay plan just right is to observe that the zero point is arbitrary. In the papa bear case, too strong, we set the zero point at 1.0, the current performance. In the mama bear case, we set it at an index of 0.0.
Recall that a line on a graph is determined by two points. One point is fixed by the target and the bonus (1.2, $10K) the other point is set by where you place the zero (z, $0) and between them they determine the slope:
If you’re making this chart right now remember to place the independent variable, product index, on the x-axis. All that remains is to compute the y-intercept of your line. The point where the bonus is zero, z, is the x-intercept. A little algebra gives the y-intercept as:
Now we are ready to start plotting. This time, let’s split the difference and set the zero point at 0.5. This seems to be just about right. Comp for standing still, 1.0, is $7,143; for 1.5, only $14,286, and our guy doesn’t starve until 0.5.
If you have this set up in a spreadsheet, you can tweak the zero point until you have the desired amount of exposure for both parties. Below is the chart for my “goldilocks” line, with the mama bear case for comparison.
We always give careful thought to the target, but sometimes neglect the slope of the payoff line. Next week, we will talk about two-dimensional pay plans, combining product index and PVR.