Today, the maturity of these techniques, coupled with high-performance relational database engines and broad data integration efforts, make these technologies practical for current data warehouse environments.
The key to understanding the different facets of data mining is to distinguish between data mining applications, operations, techniques and algorithms.
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Applications |
Database marketing customer segmentation customer retention fraud detection credit checking web site analysis |
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Operations |
Classification and prediction clustering association analysis forecasting |
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Techniques |
Neural networks decision trees K-nearest neighbour algorithms naive Bayesian cluster analysis |
Data Mining Applications
A data mining application is an implementation of data mining technology that solves a specific business or research problem. Example application areas include:
· A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months. The data needs to include competitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a wide-area network that enables the representatives to review the recommendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the organization to be applied in specific sales situations.
· A credit card company can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be identified. Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing campaigns over conventional approaches.
A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those
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