An Olap tool can be used to solve the lost customer problem. Because it would show clearly the spending habits of the customers and if there are any significant decreases in sales from that customer. This would raise a red flag for Mary.

The Olap would list the customers in order of spending habits, showing what they bought each year and how much they spent.

Olap may not be the best way to show a lost customer because it only shows how much was spent, and not the frequency of the shopper.

The decision tree analysis may not be the best application to avoid lost customers. There is still no physical interaction between Mary and her customers, only data of all the shoppers which can easily have the more important customers lost in the shuffle.

R:(recently) F:(frequently) M:(money) Using this application Mary can take a

more direct approach to finding out just how many times a customer has been in the store, also when was the last time that a customer has shopped there, and finally how much money the customer has spent.

Depending on the rates for each customer Mary would be able to easily pick out the customers that she wants to target.

MBA – is a data mining technique for determining sales patterns. (What else is in your basket).

Would not be used to solve the lost customer problem because all it shows is what products compliment each other. In other words what products customers buy together. Does not show individual customer buying habits, only all buying habits as a whole.

If we owned Carbon Creek Gardens the application that we would choose would be the RFM application.

This program seems to satisfy the needs of being able to target lost customers and the top buyers.