Relationship between Product Based Loyalty and Clustering based on Supermarket Visit and Spending Patterns

Loyalty of customers to a supermarket can be measured in a variety of ways. If a customer tends to buy from certain categories of products, it is likely that the customer is loyal to the supermarket. Another indication of loyalty is based on the tendency of customers to visit the supermarket over a number of weeks. Regular visitors and spenders are more likely to be loyal to the supermarket. Neither one of these two criteria can provide a complete picture of customers’ loyalty. The decision regarding the loyalty of a customer will have to take into account the visiting pattern as well as the categories of products purchased. This paper describes results of experiments that attempted to identify customer loyalty using thes e two sets of criteria separately. The experiments were based on transactional data obtained from a supermarket data collection program. Comparisons of results from these parallel sets of experiments were useful in fine tuning both the schemes of estimating the degree of loyalty of a customer. The project also provides useful insights for the development of more sophisticated measures for studying customer loyalty. It is hoped that the understanding of loyal customers will be helpful in identifying better marketing strategies.

[1]  Gediminas Adomavicius,et al.  Using Data Mining Methods to Build Customer Profiles , 2001, Computer.

[2]  Anthony K. H. Tung,et al.  Breaking the barrier of transactions: mining inter-transaction association rules , 1999, KDD '99.

[3]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[4]  Mu-Chun Su,et al.  Application of neural networks in cluster analysis , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[5]  A. Souchon,et al.  Relationship Marketing and Customer Loyalty in a Retail Setting: A Dyadic Exploration , 2001 .

[6]  A. Ehrenberg.  The Pattern of Consumer Purchases , 1959 .

[7]  A. Rangaswamy,et al.  Customer satisfaction and loyalty in online and offline environments , 2003 .

[8]  G. J. Goodhardt,et al.  The Beta‐Binomial Model for Consumer Purchasing Behaviour , 1970 .

[9]  Paul S. Bradley,et al.  Scaling Clustering Algorithms to Large Databases , 1998, KDD.

[10]  Andrew W. Moore,et al.  Accelerating exact k-means algorithms with geometric reasoning , 1999, KDD '99.

[11]  P. Kersten.  The fuzzy median and the fuzzy MAD , 1995, Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society.

[12]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[13]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[14]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[15]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[16]  Robert Groth,et al.  Data mining - a hands-on approach for business professionals , 1997 .

[17]  W. Lodwick,et al.  Fuzzy clustering in data mining for telco database marketing campaigns , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[18]  Vijay V. Raghavan,et al.  Data Mining: Research Trends, Challenges, and Applications , 1997 .

[19]  R. Clarke.  A Hidden Challenge to the Regulation of Data Surveillance , 2003 .

[20]  Tsau Young Lin,et al.  Rough Sets and Data Mining: Analysis of Imprecise Data , 1996 .

[21]  Pawan Lingras,et al.  Statistical, Evolutionary, and Neurocomputing Clustering Techniques: Cluster-Based vs Object-Based Approaches , 2005, Artificial Intelligence Review.

[22]  Piew Datta,et al.  Statistics and data mining techniques for lifetime value modeling , 1999, KDD '99.

[23]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[24]  William J. E. Potts,et al.  Generalized additive neural networks , 1999, KDD '99.