Using segmentation in the real world

(note: This post might require a little bit of analytic expertise to understand. Let's see how it goes. I may need to write up a post on explaining clustering...)

I am always looking for creative uses of analytics in business and I saw something interesting in Border's about a week ago,; I noticed they were making product recommendations. Not on their web site, but in their stores. In effect, they were taking a page out of Amazon's playbook and moved product recommendations into the real world.

So what did I see? If you go into a Border's you'll notice a couple of new aspects of how they are merchandising their books. First, they are running a 3 books for the price of 2 promotion, with about 20 books to chose from. Second, they have a section that has a series (may 6) pairs of books, side by side. The book on the right is labeled "If you liked this book" and the book on the left is labeled "You might like this book." I would bet that both of these merchandising efforts are being driven by someone who is mining Border's purchase data. For each case, let me tell you what I think they are doing.

3/2

In the first example, the 3 for 2 promotion: They have figured out what set of books would be interesting to some targeted (though fairly large) set of customers and are both selling and cross-selling them at the same time. The tricky part is getting a large enough set of books so that people can find three books that they would want to buy as a package. Amazon gets around this by showing you something like 20 items for cross-sell on any given page (I went to the latest Harry Potter book and counted 27 cross-selling opportunities). They probably ran a cluster analysis on customers' book purchases. A cluster analysis is a way of grouping like things together, in this case, they were probably grouping the types of books purchased by homogeneous groups customers. The goal in this clustering exercise is to find a set of books purchased by groups of customer and figure out how many people are in each customer group (to give you market sizing.) They don't even need to know anything about the customers except that they like a certain set of books. They may be doing other kinds of optimization to ensure that they promotion is profitable, but let's table that for now. So the output would be a series of list of 10-20 books, all of interest to a specific customer type.

<a quick aside>The number of books (10-20) is just illustrative. Could be more, could be less. In this case, the number of books is driven by a business need. Obviously, you can't have a 3 for 2 promotion with only 2 books. Alternatively, you could not have reasonably display 100 books. This kind of segmentation must be closely linked to business needs. For those who have heard me on my segmentation soapbox...well, lets just say some folks are tired of hearing me talk about the limits of segmentation. </a quick aside>

For a pilot, they probably picked the largest segments (for example, foodies and history buffs), pull the books of interest to the segments, and get the books out on the floor. My guess is that they actually have thew books for a couple of different segments on display. So, say there are 20 books eligible for the promotion. I found 3 books I wanted to read, but would have had a hard time coming up with another set of three. It will be interesting to see if they refresh the merchandising with new books and if they are creating different segments for each store.

Pairwise

For the pairwise book recommendations, this is a pretty straight forward analytic exercise. I would have done the analysis by category (Sci-Fi, Business, Romance, etc.) to see which most popular books had been purchased by people who have also purchased books with relatively few sales (and maybe high profit margins). My first thought is that a bi-variate correlational analysis would be a good first stab at this. In effect, the most popular book is recommending the weaker selling book. I have seen local books stores do something similar by having staff recommendations in each section. I like the Borders approach better. It is data driven.

While I have suggested some possible ways that they have developed their promotions, there are other options. Also, I assume they are testing the heck out of this new merchandising approach. All in all, very clever.