Estimating potential customer value using customer data

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Dans ce livre en anglais issu d'un travail de recherche fait avec la CCSU Thierry Vallaud propose une approche simple de la
modélisation des potentiels clients avec uniquement des données contenues dans les bases de données clients. Après une phase de
revue des méthodes il expose le modèle qu'il explicite par un exemple concret.
Ce livre devrait vous permettre de calculer
votre potentiel client et de proposer alors peut être de nouvelles approches.



This study outlines a method of determining individual customer potential, based solely on data present in the customer
database: descriptive information and transaction records.

We define potential as the incremental turnover that any particular company could do with their present customers.

In order to successfully calculate this potential in a large database with multiple variables, we propose grouping together
customers who look like each other (known as clones), by means of an appropriate clustering technique: Kohonen Networks.

This method is applied to actual data sets, and various techniques are employed to check the stability of the clusters obtained.
Real potential is then determined by means of an empirical approach: practical application to a major French retailer's database of
5 million customers.


Thierry Vallaud is in charge of the data mining and model building at SOCIO Logiciels. He has published many articles and books
on statistics, data mining and efficiency of the marketing mix.


Publié le : jeudi 1 janvier 2009
Lecture(s) : 135
Licence : Tous droits réservés
EAN13 : 9789999996004
Nombre de pages : non-communiqué
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Abstract: This study outlines a method of determining individual customer potential, based solely on data present in the customer database: descriptive information and transaction records. We definepotentialthe incremental turnover that any particular company could do with as their present customers. In order to successfully calculate this potential in a large database with multiple variables, we propose grouping together customers who “look like each other” (known as clones), by means of an appropriate clustering technique: Kohonen Networks. This method is applied to actual data sets, and various techniques are employed to check the stability of the clusters obtained. Real potential is then determined by means of an empirical approach: practical application to a major French retailer’s database of 5 million customers.
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