Affinity Analysis: What’s up with the drag-along effect?
Part 2 of a 4-part series
In my last blog, I introduced the idea of affinity analysis. Now I’m going to talk about the drag-along effect.
To refresh – we’re using a simple definition of affinity analysis based on the retail experience where companies try to gain insight into what products customers buy together.
If you really want to dig into the weeds of affinity analysis as more general topic, Wikipedia is a good place to start. But for this space, let’s start by diving into one of the more concrete areas of affinity analysis – namely the drag along effect. (Again, thanks to Shantanu Goswami at SAP for the input here. I’m sharing what he explained to me.)
It’s often beneficial to see purchasing behavior in terms of primary products and associated products. When a man buys a suit (primary product) he may also buy a tie (associated product). In a nutshell, the tie here describes the drag along effect. Sometimes it’s hard to see what’s dragging what – as when customers buy socks and underwear. But you get the point.
OK, so let’s say that you detect a drag along affect for batteries associated with high-end cameras you may sell. This correlation is obvious enough to detect – with or without sophisticated business intelligence tools. But do you know which brand of batteries people are buying? Is there a correlation with factors such as time of day or the age or gender of the customer? Do some stores sell more batteries than others? If so, why?
Insights like these take analysis and in-memory technology to crunch massive amounts of data. My expert, Shantanu, will explain in a bit more detail.
What other examples of the drag-along effect are out there? Are you getting this insight? How are you getting there?
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