Tag: top

Best Practices for Menu Selling

I was asked recently to opine on this topic, which I do today with some reservation, for I can see the venerable four-column menu approaching its sell-by date.  The image shown here is a MenuVantage prototype from 2003.  Don’t get me wrong.  As I wrote here, this is still the best tool for the traditional setting in the F&I office … for as long as that setting prevails.

Best practices for menu selling split into two broad categories: those that are good for selling, and those that are good for compliance.  I will present them in that order.

Every product appropriate to the transaction type and “car status” of the current deal (i.e. Used Lease) should appear in column one.  Some menu systems use deal templates, making it easy to select the proper layout every time.

The home court advantage in the F&I suite is that you can do a four-column menu, and there is a professional there to present it.

For most systems, column one automatically drives the layout of the accept/decline “waiver” form.  This is best practice for compliance, and it’s good selling too.  Why have a product that you only present on special occasions?

The practical limit for products in column one is six, maybe eight, so choose wisely when laying out the menu template.  Using bundles will allow you to squeeze in more products.  I generally don’t like bundled products, as I wrote here, but this is a reason to use them.

Every menu should include a second, longer term, with the correct APR for that term.  There is a charming story about this in Six Month Term Bump, plus a downloadable spreadsheet.  Twelve months is overkill, and likely to raise an objection.

The amount of product you can finance without changing the monthly payment is given by this formula.  Without doing the annuity math, a good approximation is: base payment times five.The monthly payment in column four should be roughly $30 more than the base payment without products.  That way, you draw the customer’s attention into the menu without a big price barrier.  Likewise, payments should increase in small increments from right to left across the bottom of the menu.

Obviously, the increments will be larger for more expensive deals, say 10% of the base payment.  This is easy to do, if you are manually setting up each menu.  It takes a little more planning to do this with templates.  You can either tweak the individual products at deal time, or you can set up a different template for highline vehicles.

For example, offer the platinum VSC coverage in column one and the gold in column two.  By the way, do not reuse the VSC coverage choices (like gold, silver, and platinum) as your column headings.  That’s an obvious source of confusion.  Finally, your menu system should feature sales tools and custom content for each product, like the famous depreciation chart for GAP.

I have a few more recommendations, related to compliance.  If you already have a good grasp of unfair and deceptive practices, you can skip this part.  Be warned, though, that consumer watchdogs and regulatory agencies are looking over your shoulder.

The chart below (and the pull quote) is from the National Consumer Law Center.  You can tell that the dealer in green is using a menu system with a fixed markup over dealer cost.  The dealer in red is certainly making more PVR but he is also courting a federal discrimination charge.

Menu trainers like to say, “present all the products to all the customers, all the time.”  They might add, “at the same price.”  The NCLC report goes on to show that minority car buyers are systemically charged more for the same products.  Some dealers simply don’t allow the F&I manager to vary from the calculated retail price.  In states like Florida, that’s the law.

Giving F&I managers the discretion to charge different consumers different prices for the same product … is a recipe for abuse.

The menu should display the price of each product, not just the package price.  Some turn this into a selling feature by also showing the price as a daily amount.  It makes a good layout to have the most expensive product at the top, with prices descending down the column.

All of these measures require some kind of audit trail.  I have seen some very strong systems that track exactly what was presented, by whom, when, for how much, and whether the price was changed.  At a minimum, you should collect the customer’s signature on the waiver form, with all the products, their prices, and your standard disclosure text.

Next week, I will resume writing about the brave new world of flow selling, self-closing, and predictive analytics.  We may find that many of these practices – especially regarding compliance – are still relevant.

Speculation on Fractal Programming Language

We flew east out of Panama City, and I looked down on the faceted green hills of the Cordillera de San Blas, perhaps for the last time.  In the sky were statistically similar puffs of white cumulus cloud.  Naturally, I was thinking of fractals.

I had spent the past few days coding technical analysis indicators in Python, which reminded me of coding same in C#.  This, in turn, reminded me that although the TA community talks a lot about geometric repetition, we have yet to invent a single fractal indicator, much less a trading strategy.

I write my trading strategies in C# on the MultiCharts platform.  Its procedures for time series data look a lot like the vector-oriented syntax of Python.  Here is how to do Bollinger bands in each:

  • StandardDeviationCustom(length, devs)
  • df[price].rolling(length).std() * devs

I have to admit not having much intuition about vector operations.  Matrices and summations just look like for loops to me – clearly an obstacle to the proper appreciation of Python.  I have worked with SAS and SYSTAT, though, so Python at the command prompt seems natural.

What I noticed with the Python exercise is that the classic TA indicators were designed with an iterative mindset, reflecting the programming languages of the day – Sapir’s theory, again – and so I imagine that the reason we have no fractal indicators is that our language can’t express them.

Here are some basic things I would expect from a fractal-oriented programming language:

  • Create a dataset from a generator function
  • Derive fractal metrics, like the Hausdorff dimension
  • Compare two datasets for statistical similarity
  • Compare a dataset to subsets of itself

Admittedly, I have only a cursory notion of how this would work.  That’s why this piece has “speculation” in the title.  Meanwhile, I will continue plugging away in C# and Python.

Predictive Selling in F&I

We have all seen how Amazon uses predictive selling, and now this approach is finding its way into our industry.  In this article I compare and contrast different implementations, and discuss how the technique may be better suited to online than to the F&I suite.

If you read Tom Clancy, you might like Lee Childs.  If you bought a circular saw, you might need safety goggles.  To draw these inferences, Amazon scans for products that frequently occur together in the order histories of its customers.  You can imagine that given their volume of business, Amazon can fine-tune the results by timeframe, department, price, and so on.

The effectiveness of predictive selling depends on two things: the strength of your algorithms, and the depth of your database.  Automotive Mastermind claims to use “thousands of data points,” mined from the DMS, social media, and credit bureaus.  An online auto retailer or platform site (see my taxonomy here) will also have data about which web pages the customer viewed.  Your typical F&I menu is lucky if it can read data from the DMS.

The face of predictive selling in F&I is the automated interview.  We all know the standard questions:

  • How long do you plan on keeping the car?
  • How far do you drive to work?
  • Do you park the car in a garage?
  • Do you drive on a gravel road?
  • Do you transport children or pets?

A system that emulates the behavior of an expert interviewer is called, appropriately, an “expert system.”  I alluded to expert systems for F&I here, in 2015, having proposed one for a client around the same time.  This is where we can begin to make some distinctions.

Rather than a set of canned questions, a proper expert system includes a “rules editor” wherein the administrator can add new questions, and an “inference engine” that collates the results.  Of course, the best questions are those you can answer from deal data, and not have to impose on the customer.

A data scientist may mine the data for buying patterns, an approach known as “analytics,” or she may have a system to mine the data automatically, an approach known as “machine learning.”  You know you have good analytics when the system turns up an original and unanticipated buying pattern.  Maybe, for example, customers are more or less likely to buy appearance protection based on the color of their vehicle.

At the most basic level, predictive selling is about statistical inference.  Let’s say your data mining tells you that, of customers planning to keep the car more than five years, 75% have bought a service contract.  You may infer that the next such customer is 75% likely to follow suit, which makes the service contract a better pitch than some other product with a 60% track record.  One statistic per product hardly rises to the level of “analytics,” but it’s better than nothing.

Another thing to look at is the size of the database.  If our 75% rule for service contract is based on hundreds of deals, it’s probably pretty accurate.  If it’s based on thousands of deals, that’s better.  Our humble data scientist won’t see many used, leased, beige minivans unless she has “big data.”  Here is where a dealer group that can pool data across many stores, or an online selling site, has an advantage.

If you are implementing such a system, you not only have a challenge getting enough data, you also have to worry about contaminating the data you’ve got.  You see, pace Werner Heisenberg, using the data also changes the data.  Customers don’t arrive in F&I already familiar with the products, according to research from IHS.

Consider our service contract example.  Your statistics tell you to present it only for customers keeping their vehicle more than five years.  That now becomes a self-fulfilling prophecy.  Going forward, your database will fill up with service contract customers who meet that criterion because you never show it to anyone else.

You can never know when a customer is going to buy some random product.  This is why F&I trainers tell you to “present every product to every customer, every time.”  There is a technical fix, which is to segregate your sample data (also known as “training data” for machine learning) from your result data.  The system must flag deals where prediction was used to restrict the presentation, and never use these deals for statistics.

Doesn’t that mean you’ll run out of raw data?  It might, if you don’t have a rich supply.  One way to maintain fresh training data is periodically to abandon prediction, show all products, let the F&I manager do his job, and then put that deal into the pool of training data.

Customers complete a thinly disguised “survey” while they’re waiting on F&I, which their software uses to discern which products to offer and which ones the customer is most likely to buy based upon their responses.

Regulatory compliance is another reason F&I trainers tell you to present every product every time.  Try telling the CFPB that “my statistics told me not to present GAP on this deal.”  There’s not a technical fix for that.

One motivation for the interview approach, versus a four-column menu, is that it’s better suited to form factors like mobile and chat.  This is a strong inducement for the online selling sites.  In the F&I suite, however, the arguments are not as strong.  Trainers are uniformly against the idea that you can simply hand over the iPad and let it do the job for you.

No, I have not gone over to the Luddites.  This article offers advice to people developing (or evaluating) predictive selling systems, and most of the advantages accrue to the online people.  The “home court advantage” in the F&I suite is that you can do a four-column menu, and there is a professional there to present it.

Car Dealer Megatrends – Conclusion

This is the conclusion of my series on car dealer megatrends.  The first three articles covered the long running trend toward consolidation, steadily improving process maturity, and disruption from new technology.  Like all good megatrends, these three flow together, reinforcing each other to produce a sea change in the industry.  Consolidation means bigger groups with more money to spend on technology, and the scale to exploit improved procedures.

Big dealer groups crave stability, and repeatable successes.  In my trade, software development, we have a formal process maturity model.  The bottom rung is where your success depends on “heroes and luck.”  When you own 20 stores, you are less interested in one superstar killing the pay plan, and much more interested in a hundred guys making base hits.  If you are not clear on this, I recommend the movie version of Moneyball, featuring Brad Pitt as Billy Beane.

We’re making less per transaction, but we’re doing more transactions.

I work mainly in F&I, but you can see the same general idea in the velocity method for new and used car sales.  That idea is margin compression.  The quote above is from Paragon Honda’s Brian Benstock and, last I checked, he was still hard at it.

The locus of high gross shifted from new cars to F&I, and then from finance to products.  Smart people tell me the 100% markup on products will soon be ended, either by competition or by the CFPB.  Today, when you read about the latest PVR record from Group 1 (or whomever) you will also read management downplaying expectations of further such records.

The executive, however, said the group’s F&I operations may have reached the peak in terms of PVR.

Dealership ROI is above 20% but, as you know, highly cyclical.  The stock market has been around 14% lately and, arguably, less volatile.  AutoNation has been chugging along at a steady 10%.  Investors will accept a lower return, in exchange for stability.

AutoNation was founded in the era of big box retail.  My colleague there, Scott Barrett, came from Blockbuster.  It was always our intention to remake auto retail in the image of Circuit City, which, by the way, was the parent of CarMax.

I spoke with an ex-AutoNation executive recently who told me that learning to live with margin compression is an explicit part of their strategy.  It is an iron law of economics that, in a free market, competition will drive margins toward zero.

Have a look at this NADA chart.  In five years, gross has been cut almost in half.  This is a breathtaking diminution, and then you go on the industry forums and find people bitching that vAuto has cut used car gross, and TrueCar has cut new car gross, and now some idiot proposes to cut F&I gross by putting VSC prices online.

Marv Eleazer has called this a race to the bottom, and he’s right, but this is not a race you can opt out of.  That’s not how competition works.  Think of it as a race run in Mexico City.  The smart dealers and big groups are already training to compete in the thin air of lower gross.