Making recommendations different enough

One of the major uses of data analytics in practice is to make recommendations, either explicitly or implicitly. This is one area where the interests of marketers and the interests of consumers largely run together. If I want to buy something (a product or access to an experience such as listening to a song or seeing a movie) I’d just as soon buy something I actually want — so a  seller who can suggest something that fits has a better chance of getting my business than one that presents a set of generic choices. Thus businesses build models of me to try and predict what I am likely to buy.

Some of these businesses are middlemen, and what they are trying to predict is what kind of ads to show me on behalf of other businesses. Although this is a major source of revenue for web-based businesses, I suspect it to be an edge-case phenomenon — that is, the only people who actually see ads on the web are people who are new to the web (and there are still lots of them every day) while those who’ve been around for a while develop blindness to everything other than content they actually want. You see businesses continually developing new ways to make their ads more obtrusive but, having trained us to ignore them, this often seems to backfire.

Other businesses use permission marketing, which they can do because they already have a relationship. This gives them an inside track — they know things about their customers that are hard to get from more open sources, such as what they have actually spent money on. When they can analyse the data available to them effectively, they are able to create the win-win situation where what they want to sell me is also what I want to buy.

But there’s a huge hole in the technology that represents an opportunity at least as large as that on which Google was founded: how new and different should the suggestion be?

For example, if you buy a book from Amazon by a popular author, your recommendation list is populated by all of the other books by that same author, the TV programs based on that author’s books, the DVDs of the TV shows, and on and on. This is largely a waste of the precious resource of quality attention. Most humans are capable of figuring out that, if they like one book by an author, they may well like others. (In fact, sites like Amazon are surprisingly bad at figuring out that, when an author writes multiple series featuring different characters and settings, an individual might like one series but not necessarily the others.)

So what would be better? The goal, surely, is to suggest products that are similar to the ones the individual has already liked or purchased, but also sufficiently different that that individual would not necessarily have noticed them. In other words, current systems present products in a sphere around the existing product, but what they should do is present products in an annulus around the existing product. Different, but not too different.

This is surprisingly difficult to do. Deciding what similarity means is already a difficult problem; deciding what “just enough dissimilarity” means has been, so far, a too difficult problem. But what an opportunity!

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