Targeting Customers with Statistical and Data-Mining Techniques

James Howard Drew · D.R. Mani · Andrew L. Betz · Piew Datta

71 citations0/0 files storedJournal of Service Research

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Customer churn and segmentation Customer Service Quality and Loyalty Consumer Market Behavior and Pricing

Abstract

Operationalizing a relationship management program requires a retention strategy that is sensitive to an individual customer’s position in the service life cycle, while being financially sound for the provider. To this end, estimating a customer’s hazard function and remaining tenure with the company can lead to important insights into marketing tactics and constitute fundamental building blocks for methods of targeting important customers. The authors describe a way of estimating these quantities using a combination of statistical and data-mining techniques. The resulting customer hazard information leads to a generalization of lifetime value (GLTV) that explicitly accounts for company actions and their success in relationship management.

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References

1996The Behavioral Consequences of Service Quality 1999A Dynamic Model of Customers' Usage of Services: Usage as an Antecedent and Consequence of Satisfaction 1999Regaining Service Customers 1995Applications for the lifetime value model in modern newspaper publishing

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