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Chapter 8
The beer and diapers story
One of the stories that is often mentioned in the context of basket analysis
is the "diapers and beer" story. It states that when supermarkets first
started to look at their data, they found that diapers were often bought
together with beer. Supposedly, it was the father who would go out to
the supermarket to buy diapers and then would pick up some beer as
well. There has been much discussion of whether this is true or just an
urban myth. In this case, it seems that it is true. In the early 1990s, Osco
Drug did discover that in the early evening beer and diapers were bought
together, and it did surprise the managers who had, until then, never
considered these two products to be similar. What is not true is that this
led the store to move the beer display closer to the diaper section. Also,
we have no idea whether it was really that fathers were buying beer and
diapers together more than mothers (or grandparents).
Obtaining useful predictions
It is not just "customers who bought X also bought Y", even though that is how
many online retailers phrase it (see the Amazon.com screenshot given earlier); a real
system cannot work like this. Why not? Because such a system would get fooled by
very frequently bought items and would simply recommend that which is popular
without any personalization.
For example, at a supermarket, many customers buy bread (say 50 percent of
customers buy bread). So if you focus on any particular item, say dishwasher soap,
and look at what is frequently bought with dishwasher soap, you might find that
bread is frequently bought with soap. In fact, 50 percent of the times someone
buys dishwasher soap, they buy bread. However, bread is frequently bought with
anything else just because everybody buys bread very often.
What we are really looking for is customers who bought X are statistically more likely to
buy Y than the baseline. So if you buy dishwasher soap, you are likely to buy bread,
but not more so than the baseline. Similarly, a bookstore that simply recommended
bestsellers no matter which books you had already bought would not be doing a
good job of personalizing recommendations.
Analyzing supermarket shopping baskets
As an example, we will look at a dataset consisting of anonymous transactions at a
supermarket in Belgium. This dataset was made available by Tom Brijs at Hasselt
University. The data is anonymous, so we only have a number for each product and
a basket that is a set of numbers. The datafile is available from several online sources
(including the book's companion website) as retail.dat.
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