![]() ![]() ![]() Using the affinity rule in the preceding example, the merchant might work with the dairy category on a milk promotion to increase sales of milk. Suppose that a merchant is tasked with bringing in more margin dollars to the cereal category. Rules with a very high support value occur frequently in your transaction history, while rules with a high confidence value represent a strong affinity between products.Īfter users have identified selling patterns, they can begin to take action based on those patterns, as well as the needs and goals of their product category. The probability that a customer will buy milk, juice, and cereal is known as the support percentage, while the conditional probability that they will buy cereal when they buy milk and juice is known as confidence. After a rule is defined, a user can use the AA interface to understand how strong the affinity is, using rule confidence and support. In other words, if a customer purchases an item from the subclasses milk and juice, the customer will also purchase an item from subclass cereal. The output of that analysis provides a rule that defines the association found between products at the subclass or class level of the merchandise hierarchy.Ī rule consists of one to three antecedents (IF attributes) and a single consequent (THEN attribute). Market basket analysis involves the use of data mining techniques to search for sales patterns between products within a given group of transactions. ![]()
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