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What is Market Basket Analysis?

Market basket analysis studies the concept of affinity. Affinity is the natural liking or understanding of something. It can also mean the degree to which something tends to combine with another.

We measure and leverage this property of substances to naturally combine with others. All we need is to be able to identify an individual and have a detailed history of the products that they buy.

This type of analysis is now well known, especially in the on-line world. In e-commerce we are now all used to seeing the “Customers also bought…” suggestions. IP television providers show programming choice that changes by what we, and other customers, watch.

The Psychology

The reasons we buy are complex.  A great many purchases are made on impulse.  If we see an item or are made aware of it then a good percentage of people will buy.  In order to make more money a business just needs to trigger that impulse.  Businesses make suggestions that resonate with the customer.

The Big Issue

The theory and mathematics behind affinity are simple.  The issue is with the sheer volume of data that we need to analyze to tease out interesting affinities.  Firstly, we usually have a large volume of transactions to study. However, the issue is the number of combinations of products.  Here are some numerical examples:

  • If a company only has 10 products then the number of “baskets” of 2 items is 45.
  • If we increase our product range to 100 or 1,000 then the number of baskets jumps to 4,950 and 499,500.
  • Notice that a tenfold increase in products results in around 100 times more combinations.

Analyzing baskets of two items will tell us about affinities. However, with just two items, we usually already know the answer. If the output tells us that people who buy bread also buy butter then this is not useful.  To really gain the competitive edge we need to find more interesting behaviors and that means considering affinities between three or more items.  If we repeat our numerical example with a basket of three we see that the number of combinations explodes:

  • A company with 10 products will have 120 baskets of 3 items.
  • Increasing the number of products to 100 or 1,000 pushes the number of combinations to 162 thousand and 166 million.
  • A company with 10,000 products will have too many 3 item baskets to compute.

A Better Approach

Whilst it is possible to run large calculations, we can also employ differential analysis. This is benchmarking of sales in different stores. We could also benchmark different seasons, demographic groups or other attribute to see if we can see a significant improvement in a particular group. Having identified the improvement the business can then investigate the underlying reasons. Any factors that can be understood are then rolled-out through the business.

Another approach is to look for predefined baskets to offer a targeted promotion. Searching for customers buying charcoal, firelighters, matches, meats will tell you they are planning a barbeque. This then tells you the customers who, in all probability, have and enjoy a garden and who could have a propensity to buy other outdoor leisure goods.

The Secret Recipe For Success

  • The scope of an affinity analysis must be very carefully defined with upper management.  It is very easy to get “lost in the weeds” doing this kind of work.
  • Don’t scope something that is computationally hard.  Is three week’s compute time reasonable in the context of the rest of the project?
  • Bring the objective down to something practical and manageable.

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