Data mining applications automate the process of searching through huge quantities of data to discover patterns which are predictors of purchasing behaviors.

Our data mining process will model virtually any customer activity. The key is to find patterns relevant to current business problems.

Two Styles of Data Mining

1. Directed Data Mining:
   • Top-down approach
   • Used when we know approximately what we are looking for or what we want to predict, for example: Which customers are likely to buy a specific type of car?
   • Predictive model uses experience to rank possible outcomes in the future by calculating a score for each outcome;
   • Model is seen as a black box because we care only about the predictions and not how it actually works;
   • The goal of building a predictive model is to apply knowledge gained in the past to the future.
2. Undirected Data Mining:
   • Bottom-up approach
   • Finds patterns in the data and leaving it up to the user to determine whether or not the patterns are important;
   • We do want to know how the model works;
   • Human interaction is necessary because only people are able to determine the significance, if any, of the patterns;
   • Often used during data exploration stages;
   • Example: Examining a decision tree and noting an interesting pattern.

Response Marketing Data mining services include:

• RFM modeling;
• Demographic & psychographic profiling;
• Cross-sell & up-sell modeling;
• Churn & defection modeling;
• Lifetime value modeling;
• Segmentation classification modeling;
• Predictive modeling (multiple regression, chain).

Data mining helps marketing users to target marketing campaigns more accurately, and to align campaigns more closely with the needs, wants, and attitudes of customers and prospects.

Data Mining makes marketing and selling two to three times more effective.