Abstract
When the marketing service has to contact customers to propose them a product, the probability that these customers will buy this product is calculated beforehand. This probability is calculated using a predictive model. The marketing service contacts the clients having the highest probability of buying the product. In parallel and before the commercial contact it may be interesting to realize a typology of the customers who will be contacted. The idea is to propose differentiated campaigns by group of customers. This article shows how it is possible to build such a typology so that it respects the nearness of the customers with respect to their appetency score.
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Lemaire, V., Clérot, F., Creff, N. (2015). K-means Clustering on a Classifier-Induced Representation Space: Application to Customer Contact Personalization. In: Abou-Nasr, M., Lessmann, S., Stahlbock, R., Weiss, G. (eds) Real World Data Mining Applications. Annals of Information Systems, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07812-0_8
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