Consumer Profile Identification and Allocation

Patrick Letrémy (1, 2) , Marie Cottrell (1, 2) , Eric Esposito (3) , Valérie Laffite (3) , Sally Showk (3)

1 SAMOS - Statistique Appliquée et MOdélisation Stochastique
2 CES - Centre d'économie de la Sorbonne
3 RDD GDF - Research and Development Division Gaz de France

We propose an easy-to-use methodology to allocate one of the groups which have been previously built from a complete learning data base, to new individuals. The learning data base contains continuous and categorical variables for each individual. The groups (clusters) are built by using only the continuous variables and described with the help of the categorical ones. For the new individuals, only the categorical variables are available, and it is necessary to define a model which computes the probabilities to belong to each of the clusters, by using only the categorical variables. Then this model provides a decision rule to assign the new individuals and gives an efficient tool to decision-makers. This tool is shown to be very efficient for customers allocation in consumer clusters for marketing purposes, for example.

Mots clés: Kohonen Maps; Profiles; Logistic regression; non-ordered Polychotomous Logit Model

Domaines: Statistiques [math.ST]; Théorie [stat.TH]; Réseau de neurones [cs.NE]

Patrick Letrémy, Marie Cottrell, Eric Esposito, Valérie Laffite, Sally Showk. Consumer Profile Identification and Allocation. Francisco Sandoval, Alberto Prieto, Joan Cabestany, Manuel Grana. Computational and Ambient Intelligence - 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, Springer, pp.530-538, 2007, Lecture Notes in Computer Science - 4507, ⟨10.1007/978-3-540-73007-1⟩. ⟨hal-00133833v2⟩