An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 51

In the e-bookstore example, by browsing through past purchases, association
models can discover other popular topics among the buyers of the particular topic
viewed. They can then generate individualized recommendations that match the
indicated preference.
Association modeling techniques generate rules of the following general
format:

IF (ANTECEDENTS) THEN CONSEQUENT

For example:

→

IF (product A and product C and product E and ...) product B

More specifically, a rule referring to supermarket purchases might be:

→

IF EGGS & MILK & FRESH FRUIT VEGETABLES

This simple rule, derived by analyzing past shopping carts, identifies associated
products that tend to be purchased together: when eggs, milk, and fresh fruit are
bought, then there is an increased probability of also buying vegetables. This
probability, referred to as the rule's confidence, denotes the rule's strength and
will be further explained in what follows.
The left or the IF part of the rule consists of the antecedent or condition: a
situation where, when true, the rule applies and the consequent shows increased
occurrence rates. In other words, the antecedent part contains the product combi-
nations that usually lead to some other product. The right part of the rule is the con-
sequent or conclusion: what tends to be true when the antecedents hold true. The
rule's complexity depends on the number of antecedents linked to the consequent.
These models aim at:

• Providing insight on product affinities: Understand which products are
commonly purchased together. This, for instance, can provide valuable infor-
mation for advertising, for effectively reorganizing shelves or catalogues, and for
developing special offers for bundles of products or services.
• Providing product suggestions: Association rules can act as a recommen-
dation engine. They can analyze shopping carts and help in direct marketing
activities by producing personalized product suggestions, according to the
customer's recorded behavior.

This type of analysis is also referred to as market basket analysis since it
originated from point-of-sale data and the need to understand consumer shopping