Category: Software

Menu Selling on an iPhone

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.

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.

The Automotive eCommerce Ecosystem

Around the turn of the century, I was helping RouteOne to build their now-ubiquitous credit system.  Then, I moved on to aggregation models for the “I” side of F&I.  It was a lot of work.

We had to develop scores of unique interfaces for lenders and product providers.  We had to develop deal calculation engines, and then reverse engineer each DMS so our payments would match.  There were no automated sources for finance or product rates.  We had to walk ten miles in the snow …

Today’s eCommerce startups have it easy.  All of the key tasks are supported by readily available services, leaving the entrepreneur to focus on user experience and dealer support.

When I started writing about this space, the key challenges were price negotiation and trade valuation (and the test drive, but I’ll cover that in a later piece).  Today, you have reliable online trade valuation from Kelley, Trade Pending, and others.  Price negotiation can be handled through chat or one-price, generally on used vehicles.

You can have payment calculations, including incentives, from MarketScan, provider networks from PEN or F&I Express, and finance networks from RouteOne or Dealertrack.  Everything in this paragraph is an API, not to mention passing data from your eCommerce platform into the corresponding dealer system. Finally, even the old faithful DMS now exposes a variety of databases, like inventory.

A few months ago, I described the role of venture capital in driving process change.  I think this eCommerce ecosystem is equally important.  Entrepreneurs can enter the space at a very low cost, relative to ten years ago, and meet most of their requirements through interfaces.

Speculation on fractal based 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.

All about Surcharges

Now here is an article for specialists only.  Menu system developers must know how to correctly acquire and present service contract rates, and surcharges are the most difficult feature.  Integrators and product providers also struggle with this, and I am writing today in hopes of establishing some industry norms.

We start with surcharge policy, from the provider’s perspective, and then data transfer and presentation issues for the menu system.

A surcharge represents an ad hoc increase to the claims risk, and therefore the price, of a service contract.  It lies outside the conventional pricing model, which is:

  • Risk Class – it costs more to service a Camry than a Corolla
  • Coverage – which parts and services are covered
  • Term – contract duration in months and miles
  • Deductible – claims risk is mitigated by a higher deductible

A surcharge is an extra feature tacked on to the pricing model.  For instance, the provider might want an extra $200 to cover a vehicle having a modified suspension, a turbocharger, or four-wheel drive, or if the customer intends to use the vehicle commercially.

Adding a flat dollar amount to the price is straightforward, but not especially accurate from a claims perspective.  That turbocharger will grow more risky as time goes on, so it is smart to have the surcharge amount increase with the term.

Note that you do not need to stipulate a four-wheel drive surcharge for Subaru.  They are all four-wheel drive, and so you can account for this risk in the vehicle classification.  Fixed (irremovable) features of the vehicle may be treated either as surcharges or risk classes.  In this example, four-wheel drive is handled as a class code bump.

Likewise, deductibles can be treated as surcharges.  This is an efficient way to represent them in a printed rate guide, where a choice of deductibles would mean many additional pages.  In the example below, the rate guide is printed with a base deductible of $50 and four more as surcharges.  Note that the surcharge amounts vary with the term mileage.

Warranty Solutions uses a similar approach, except that the surcharge amounts vary with the cost of the base contract.  They reckon that the risk associated with the vehicle, coverage, and term is already reflected in the cost, and so the surcharge should be higher on a higher-cost contract.  In my time as a consultant, I have seen everybody’s rate guide, and every possible way to handle surcharges.

It is important to recognize that a printed rate guide is just one way to represent the provider’s evaluation of risk.  As with Sapir’s theory of language, the provider’s actuary can only evaluate risks that can be expressed by the pricing model.

Where rates are returned via web service, there is no need to treat deductibles as surcharges.  They should be an explicit part of the pricing model, as above.  Where the VIN is supplied as input, likewise, there is no need to specify vehicle surcharges.

Many rate guides distinguish between “mandatory” and “optional” surcharges, but all surcharges are required to be levied where applicable.  Therefore, the usage I prefer is:

  • Mandatory Surcharge – We know it from the VIN, like a turbocharger
  • Optional Surcharge – We have to ask, as with commercial use

The user experience for a mandatory surcharge is simply to notify that we have already applied it to all rates in the web service response.  For optional surcharges, the menu system must provide a checkbox or some other way to apply it.

In either case, it is best for the web service to apply the surcharge to all rates in the response.  This allows for a smaller payload, and no chance for error.  The only reason to send rates both with and without an optional surcharge is if the menu system lacks the ability to request it up front.

Menu systems today already have user controls for the well-known surcharges, like commercial use, lift kit, snowplow, van conversion, warranty preload, synthetic oil, and rental coverage.  As a developer, I don’t like the idea of hardcoding these controls.  I would rather the menu system generate the controls at deal time, using a separate web service to obtain the list.

There is one kind of surcharge that must be included separately in the rate response.  These are additions to coverage which the F&I Manager may upsell at deal time.  The mockup below shows the addition of optional electrical to one grade of coverage, which is included with the higher grade.

Because the F&I Manager may toggle this surcharge dynamically, there is no alternative but to include it in the rate response.  This means an extra branch at the coverage node, assuming a tree structure, or else sprinkle the surcharge among the leaf nodes and make the menu system do the math.

  • Upsell Surcharge – We may choose it dynamically at deal time

Either way, dynamic surcharges will bloat the rate response.  The workaround we used at MenuVantage was to treat them as optional surcharges, above, and ask the F&I Manager to choose prior to rating.  I frankly hate dynamic surcharges, a prejudice from my menu days, but people evidently find them useful.

That about does it for surcharges.  If anyone has anything to add, in the spirit of setting industry norms, please write in.

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.

Dealer Megatrends Part 2 – Fintech

Car dealers today face a growing array of new systems and capabilities.  These are primarily in F&I, thanks to disruptive new entrants in financial technology – fintech, for short.  Mark Rappaport has a nice roundup here, from a lender’s perspective, and I maintain a list on Twitter.

  • AutoFi – Auto finance plug-in for dealer web sites. See Ricart Ford for an example.
  • AutoGravity – Customer obtains financing (via smart phone) before visiting the dealership.
  • Drive – Online car selling, with delivery, from the Drive web site.
  • Honcker – Customer obtains financing (via smart phone) and they deliver the car.
  • Roadster – E-commerce platform for dealers, with full sales capability (as I anticipated here).
  • TrueCar – Customer sets transaction price (via smart phone) before visiting the dealership.

The new entrants blur familiar boundaries in the retail process.  They’re basically lead providers, but all aim to claim a piece of the F&I process.  AutoGravity, for instance, provides a lead already committed to a finance source.  TrueCar provides a lead already committed to a transaction price.  If you’re unfamiliar with the canonical process, see my schematics here and here.

In my previous Megatrends installment, Consolidation, I cited the influence of PE money.  It’s the same with fintech.  AutoGravity, to name one, is backed by $50 million.

The new F&I space is also home to “predictive analytics.”  Automotive Mastermind examines thousands of data points, to produce a single likely-to-buy score.  Similarly, Darwin Automotive can tell you which protection products to pitch.

The technology’s proprietary algorithm crunches thousands of data points, combining DMS information with … social media, financial, product and customer lifecycle information

My specialty is F&I, but it seems pretty clear that predictive analytics has a place in fixed ops as well.  In terms of the earlier article, you can see that consolidators have an edge in evaluating new technology.  Speaking of fixed ops, they’re also better positioned to obtain telematics data.

McKinsey says fintech can help incumbents, not just disrupt them.  That’s why I have focused on technologies a dealer could employ, versus apps like Blinker that are straight threats.  Of course, you have to adopt the technology.  Marguerite Watanabe draws a parallel with the development of credit aggregation systems.

Fintech will induce dealers to adopt an online, customer-driven process.  I see this as an opportunity. On the other hand, those that fail to adapt will be left behind.  This article is aimed at dealers, but the challenge applies equally to lenders, product providers, and software vendors.