# 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.