IDEAS home Printed from https://ideas.repec.org/a/exp/mkting/v1y2013i1p38-49.html
   My bibliography  Save this article

Models Used for Measuring Customer Engagement

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://marketing.expertjournals.com/wp-content/uploads/EJM_105tichindelean2013pp38-49.pdf
    Download Restriction: no
    ---><---

    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    3. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
    4. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    5. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    6. Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
    7. Glady, Nicolas & Lemmens, Aurélie & Croux, Christophe, 2015. "Unveiling the relationship between the transaction timing, spending and dropout behavior of customers," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 78-93.
    8. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
    9. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    10. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    11. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
    12. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    13. Füsun F. Gönül & Frenkel Ter Hofstede, 2006. "How to Compute Optimal Catalog Mailing Decisions," Marketing Science, INFORMS, vol. 25(1), pages 65-74, 01-02.
    14. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    15. Hoppe, Daniel & Wagner, Udo, 2014. "The role of lifetime activity cues in customer base analysis," Journal of Business Research, Elsevier, vol. 67(5), pages 983-989.
    16. Goić, Marcel & Jerath, Kinshuk & Kalyanam, Kirthi, 2022. "The roles of multiple channels in predicting website visits and purchases: Engagers versus closers," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 656-677.
    17. Tudoran, Ana Alina & Hjerrild Thomsen, Charlotte & Thomasen, Sophie, 2024. "Understanding consumer behavior during and after a Pandemic: Implications for customer lifetime value prediction models," Journal of Business Research, Elsevier, vol. 174(C).
    18. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    19. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    20. Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
    21. Jaiswal, Anand K. & Niraj, Rakesh & Park, Chang Hee & Agarwal, Manoj K., 2018. "The effect of relationship and transactional characteristics on customer retention in emerging online markets," Journal of Business Research, Elsevier, vol. 92(C), pages 25-35.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:exp:mkting:v:1:y:2013:i:1:p:38-49. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alin Opreana (email available below). General contact details of provider: https://marketing.expertjournals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.