Market Basket Analysis

What is it?

Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don't buy a bar meal, you are more likely to buy crisps (US. chips) at the same time than somebody who didn't buy beer.

The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases.

Typically the relationship will be in the form of a rule:

IF {beer, no bar meal} THEN {crisps}.
The probability that a customer will buy beer without a bar meal (i.e. that the antecedent is true) is referred to as the support for the rule. The conditional probability that a customer will purchase crisps is referred to as the confidence.

The algorithms for performing market basket analysis are fairly straightforward (Berry and Linhoff is a reasonable introductory resource for this). The complexities mainly arise in exploiting taxonomies, avoiding combinatorial explosions (a supermarket may stock 10,000 or more line items), and dealing with the large amounts of transaction data that may be available.

A major difficulty is that a large number of the rules found may be trivial for anyone familiar with the business. Although the volume of data has been reduced, we are still asking the user to find a needle in a haystack. Requiring rules to have a high minimum support level and a high confidence level risks missing any exploitable result we might have found. One partial solution to this problem is differential market basket analysis, as described below.

How is it used?

In retailing, most purchases are bought on impulse. Market basket analysis gives clues as to what a customer might have bought if the idea had occurred to them . (For some real insights into consumer behavior, see Why We Buy: The Science of Shopping by Paco Underhill.)

As a first step, therefore, market basket analysis can be used in deciding the location and promotion of goods inside a store. If, as has been observed, purchasers of Barbie dolls have are more likely to buy candy, then high-margin candy can be placed near to the Barbie doll display. Customers who would have bought candy with their Barbie dolls had they thought of it will now be suitably tempted.

But this is only the first level of analysis. Differential market basket analysis can find interesting results and can also eliminate the problem of a potentially high volume of trivial results.

In differential analysis, we compare results between different stores, between customers in different demographic groups, between different days of the week, different seasons of the year, etc.

If we observe that a rule holds in one store, but not in any other (or does not hold in one store, but holds in all others), then we know that there is something interesting about that store. Perhaps its clientele are different, or perhaps it has organized its displays in a novel and more lucrative way. Investigating such differences may yield useful insights which will improve company sales.

Other Application Areas

Although Market Basket Analysis conjures up pictures of shopping carts and supermarket shoppers, it is important to realize that there are many other areas in which it can be applied. These include:

  • Analysis of credit card purchases.
  • Analysis of telephone calling patterns.
  • Identification of fraudulent medical insurance claims.
    (Consider cases where common rules are broken).
  • Analysis of telecom service purchases.

Note that despite the terminology, there is no requirement for all the items to be purchased at the same time. The algorithms can be adapted to look at a sequence of purchases (or events) spread out over time. A predictive market basket analysis can be used to identify sets of item purchases (or events) that generally occur in sequence — something of interest to direct marketers, criminologists and many others.

See Also: Suggested Books on Data Mining
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