Match ‘Em Up
KATHY’S DESIGNING HER NEW restaurant’s kitchen and is looking to buy several new high-end stoves, refrigerators and the like. As the salesman prepares to organize the bill of sale, he suggests she may want to consider a warranty on the new equipment. For a mere 7.5% of the purchase price, Kathy can have peace of mind for a full four years. She accepts the offer.
Flash forward: The contractor has finished the installation. Now that the kitchen is complete, Kathy wants to take pictures of the room for posterity, so she buys a new digital camera. As she’s paying for it she thinks it’s a good idea to add an extra memory card for increased storage. Kathy tosses one into her shopping cart.
Clearly, marketers have to realize that cross selling as many different products as possible in both business-to-business and consumer settings can increase a customer’s value to an organization. While there are many ways to approach cross selling, a particularly simple one is to become aware of the products or services customers are inclined to purchase together, or even later as add-ons.
Market basket (or association) analysis is a technique that tackles this data-mining problem. In a nutshell, it means looking at what customers put in their real or virtual shopping cart during a visit to your store.
A primary objective of market-basket analysis is to identify appealing relationships among the purchase behavior characteristics in a customer file. Here’s an example.
Frank is the manager of Frank Training, an e-commerce site that sells corporate training DVDs. Each transaction is captured in a database consisting of the DVD titles bought by each customer. Additional items such as workbooks, premiums to inspire employee education like mugs or pins, or training manuals also are available. As a result, each record on Frank’s customer file corresponds to one complete customer transaction. While this transaction may consist of only one DVD, some could include several DVDs from a single purchase, as well as other merchandise.
Let’s examine the eight customers’ transactions shown in the table on page 60. (Actually, Frank has millions of transactions recorded in his database
