Don't do Product Recommendations!

While it is attractive to talk about "product recommendations" as one single functionality, in reality it should be seen as a family of different functions for different situations and purposes.

The following example provides the motivation for the differentiation between the different types of product recommendations.

A couple enters a supermarket and their smartphone connects to the store's computer. You have the opportunity to give them an offer. You want to offer them a 10% discount of one product, which product should it be? You have the following information:

  • Models predict a likelihood of 10% to purchase Fat Free Milk
  • Models predict a likelihood of 2% to purchase Gruyère cheese

Which of these two products would you recommend?

What if in addition, you knew:

  • Average margin for milk is $0.50
  • Average margin for cheese is $1

Would you recommend the milk because it is more likely to be purchased? Or the cheese because it has a higher margin? Or the milk because it has a higher expected margin (likelihood times margin)?

An what if in addition you knew:

  • Average likelihood to purchase Fat Free Milk is 15% across all customers
  • Average likelihood to purchase Gruyère cheese is 0.1% across all customers

I believe that the best of the two products, for this specific situation is the cheese. The reason s are behind the numbers and the goal of the recommendation.

From the statement of the situation, it is reasonable to infer that the goal of the offer is mainly to increase the basket, with the additional benefits of having happy and loyal customers. While at first sight the 10% associated with the milk is a higher number, this number has two problems. First, it is high, and it is quite possible that the customer would have thought of buying it anyway. Secondly, it is lower than the average. Put in words, the customer is "more than 30% less likely to buy non fat milk than the average customer."

The cheese should be the winner because "the customer is 20 times more likely to buy Gruyère cheese than the average customer."


The variables that come into play for the selection of the best product are:

  1. Context of the recommendation. Stage in the process, purpose.
  2. Selection universe. The set of products from which the recommendation can be selected.
  3. Selection criteria

In future posts we will explore how each of these variables affects the way Product Recommendations should work.

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Issues related to Oracle Real-Time Decisions (RTD). Entries include implementation tips, technology descriptions and items of general interest to the RTD community.

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