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Models Used for Measuring Customer Engagement

Author

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  • Mihai TICHINDELEAN

    (Lucian Blaga University of Sibiu)

Abstract

The purpose of the paper is to define and measure the customer engagement as a forming element of the relationship marketing theory. In the first part of the paper, the authors review the marketing literature regarding the concept of customer engagement and summarize the main models for measuring it. One probability model (Pareto/NBD model) and one parametric model (RFM model) specific for the customer acquisition phase are theoretically detailed. The second part of the paper is an application of the RFM model; the authors demonstrate that there is no statistical significant variation within the clusters formed on two different data sets (training and test set) if the cluster centroids of the training set are used as initial cluster centroids for the second test set.

Suggested Citation

  • Mihai TICHINDELEAN, 2013. "Models Used for Measuring Customer Engagement," Expert Journal of Marketing, Sprint Investify, vol. 1(1), pages 38-49.
  • Handle: RePEc:exp:mkting:v:1:y:2013:i:1:p:38-49
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    References listed on IDEAS

    as
    1. Makoto Abe, 2009. "“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 28(3), pages 541-553, 05-06.
    2. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    3. Eric T. Anderson & Duncan I. Simester, 2004. "Long-Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies," Marketing Science, INFORMS, vol. 23(1), pages 4-20, February.
    4. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    5. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
    6. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    7. Javier Sánchez-Garcia & Miguel A. Moliner-Tena & Luís Callarisa-Fiol & Rosa M. Rodríguez-Artola, 2007. "Relationship Quality of an Establishment and Perceived Value of a Purchase," The Service Industries Journal, Taylor & Francis Journals, vol. 27(2), pages 151-174, March.
    8. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
    9. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    probability model; parametric model; relationship marketing; Pareto/NBD model; RFM model;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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