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Abstract
The eld of direct marketing is constantly searching for new data mining techniques in
order to analyze the increasing available amount of data. Self-organizing maps (SOM)
have been widely applied and discussed in the literature, since they give the possibility
to reduce the complexity of a high dimensional attribute space while providing a
powerful visual exploration facility. Combined with clustering techniques and the
extraction of the so-called salient dimensions, it is possible for a direct marketer to
gain a high level insight about a dataset of prospects. In this presentation, a SOMbased pro le generator is proposed, consisting of a generic method leading to valueadding and business-oriented pro les for targeting individuals with prede ned
characteristics.
Moreover, the proposed method is applied in detail to a concrete case study from the
concert industry. The performance of the method is then illustrated and discussed. In
addition, an approach giving the possibility to incorporate business knowledge in order
to guide the clustering algorithm is presented and illustrated with two cases: one from
the ticketing sector and one from the banking sector.