A DATA MINING METHODOLOGY FOR CROSS-SALES

In this paper we discuss the use of Data Mining to provide a solution to the problem of cross-sales. We define and analyse the cross-sales p roblem and develop a hybrid methodology to solve it, using characteris tic rule discovery and deviation detection. Deviation detection is use d as a measure of interest to filter out the less interesting characte ristic rules and only retain the best characteristic rules discovered. The effect of domain knowledge on the interestingness value of the di scovered rules is discussed and techniques for refining the knowledge to increase this interestingness measure are studied. We also investig ate the use of externally procured lifestyle and other survey data for data enrichment and discuss its use as additional domain knowledge. T he developed methodology has been applied to a real world cross-sales problem within the financial sector, and the results are also presente d in this paper. Although the application described is in the financia l sector, the methodology is generic in nature and can be applied to o ther sectors. (C) 1998 Elsevier Science B.V. All rights reserved.