Introducing Microsoft Association Rules - Data Mining

Put yourself in the role of a supermarket manager. One of your many responsibilities is to ensure that you sell the highest volume of product. Your goal is to sell more and be more profitable than your peers managing other stores in the chain are. Understanding the purchasing patterns of your customers is the first step toward reaching this goal.

By using the Association Rules algorithm to perform market basket analysis on your customers’ transactions, you can learn which products are commonly purchased together and how likely a particular product is to be purchased along with another. For example, you might find that 5% of your customers have bought ketchup, pickles, and hot dogs together, and that 75% of those customers that bought ketchup and hot dogs also bought pickles. Now thatyou have this information you can take action. You could change the product layout to increase sales. You can use the insight to manage stock levels. You can determine whether baskets containing pickles, hot dogs, and ketchup are more or less profitable than those without. If more profitable, you could run a special to encourage this kind of shopping.

Additionally, you may want to learn more about the customers who shop at your store. With your courtesy cards and video club cards, you have collected several bits of information. You may learn that while 15% of your female customers have video cards overall, 75% of those customers rent their homes and live close to the store. While it is possible to derive such patterns from standard SQL queries, you would have to write hundreds or thousands of queries to explore all the possible combinations. This type of data exploration is made easy with the association algorithm.

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