Quantify the value of market basket analysis

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Market Basket Analysis

Market Basket Analysis applies association rule learning to purchase data with the goal of identifying cross-selling opportunities. Given a data set, the algorithm trains and identifies product baskets and product association rules. Product baskets (referred to as item sets) are groups of products purchased together at checkout. Product association rules predict the purchase of one or more other products (the consequent) given the known presence of one or more other products (the consequent) given the known presence of some products in a basket (the antecedent).

What is Differential market basket analysis?

Differential market basket analysis can find interesting results and can also eliminate the problem of a potentially high volume of trivial results.

Differential analysis compares results between stores, between customers in different seasons of the year, etc. If the results show 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 can infer 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, via data mining or other methods, may yield useful insights which will improve company sales.

How to quantify the value of market basket analysis ?

The market basket analysis is certainly not the exclusive domain of the supermarkets. If a company can identify a customer as having purchased a product, then market basket analysis can deliver cross-sale opportunities. Consider the following issues if you are into market basket analysis:

Identifying high volume combinations of products

For each customer, you need to compare all items in a basket with all other items in the same basket and then count the number of times each combination exists for the period under consideration e.g. 18 months.

Exclude:

Multiple purchase of the same product

Non-products e.g. postage, delivery charges, VAT, etc.

Establishing the potential

The results from above are then sorted to reveal the highest number of purchases or a specific combination.

Now identify for each high volume combination, the number of customers that bought only one of the products.

Compare these two result sets to establish where there is a strong propensity to purchase a specific product combination and there is a demonstrable market for its promotion to other customers i.e. customers who purchased one product but not the other (see below).

Types of customers

Type of basket - each order or perhaps customer lifetime purchase

Timing of purchases, etc.

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.

Analysis of telecom service purchases.

Despite the terminology, there is no requirement for all the items to be purchased at the same time. Algorithms can be adapted to look at a sequence of purchases (or events) spread out over time. Predictive market basket analysis can be used to identify sets of item purchases (or events) that generally occur in sequence, which is something of interest to direct marketers, criminologists, and others. Significant increases in cross-sell revenues can be achieved by using knowledge of customer purchasing habits such as:

Which product combinations are bought

When they are purchased; and in

What sequence

Developing this understanding enables businesses to promote their most profitable products and encourage customers to buy items that might have otherwise been overlooked or missed. Obvious examples of this are supermarkets who store huge volumes of consumer sales, each basket being tracked by individual loyalty cards.

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