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Certain classification techniques, such as decision trees, can produce a set of profiles, or rules, highlighting the characteristics of profitable customers. This information can then be used to select profitable customers. Now that we know which customers are likely to purchase, which of those are likely to be loyal, and which will be the most profitable, we can perform response modeling to determine who is likely to respond to a campaign. Then, we may even go one step further to determine which channel is best for contacting such customers.

Consider a dataset consisting of various predictor attributes, such as “age,” “income,” “wire transfer within last 10 days,” and a target attribute indicating if the case was fraudulent or not. A classification algorithm like decision tree or support vector machine can then predict the likelihood of fraud on new data. Cases with a high probability of fraud are then good candidates for investigation. However, we can also predict the likelihood of fraud on the original data. This allows for a comparison between actual target values and the predicted values.

We finished with a discussion on the value of data mining, exploring reliability as well as a specific example in monetary terms. 00 expected profit would be multipled by the probability of response assigned to each customer. This gives a more precise expected outcome. 24 Chapter 1 Overview of Data Mining The next chapter takes a more in-depth look at solving problems in industry. htm, June 26, 2004. [Davenport 2006] Thomas Davenport, “Competing on Analytics,” Harvard Business Review, January 2006.

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A fuzzy-algorithmic approach to the definition of complex or imprecise concepts by Lotfi Asker Zadeh

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