IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v89y2013i3p231-245.html
   My bibliography  Save this article

Capturing the Evolution of Customer–Firm Relationships: How Customers Become More (or Less) Valuable Over Time

Author

Listed:
  • Mark, Tanya
  • Lemon, Katherine N.
  • Vandenbosch, Mark
  • Bulla, Jan
  • Maruotti, Antonello

Abstract

Few studies have examined the influence of marketing activities while accounting for customer dynamics over time. The authors contribute to this growing literature by extending the hurdle model to capture customer dynamics using a hidden Markov chain. We find our dynamic model performs better than static and latent class models. Our results suggest the customer base can be segmented into four segments: Deal-prone, Dependable, Active, and Event-driven. Each segment reacts differentially to marketing activities. Although catalogs influence both purchase incidence and the number of orders, this marketing activity has the largest impact on purchase incidence across all four segments. In contrast, retail promotions are more likely to influence the number of orders a customer will make for all of the segments except for the Deal-prone segment. For this segment, retail promotions have the strongest impact on purchase incidence.

Suggested Citation

  • Mark, Tanya & Lemon, Katherine N. & Vandenbosch, Mark & Bulla, Jan & Maruotti, Antonello, 2013. "Capturing the Evolution of Customer–Firm Relationships: How Customers Become More (or Less) Valuable Over Time," Journal of Retailing, Elsevier, vol. 89(3), pages 231-245.
  • Handle: RePEc:eee:jouret:v:89:y:2013:i:3:p:231-245
    DOI: 10.1016/j.jretai.2013.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435913000389
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jretai.2013.04.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    2. Rust, Roland T. & Kumar, V. & Venkatesan, Rajkumar, 2011. "Will the frog change into a prince? Predicting future customer profitability," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 281-294.
    3. Rishin Roy & Pradeep K. Chintagunta & Sudeep Haldar, 1996. "A Framework for Investigating Habits, “The Hand of the Past,” and Heterogeneity in Dynamic Brand Choice," Marketing Science, INFORMS, vol. 15(3), pages 280-299.
    4. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    5. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    6. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    7. 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.
    8. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
    9. Antonello Maruotti, 2011. "Mixed Hidden Markov Models for Longitudinal Data: An Overview," International Statistical Review, International Statistical Institute, vol. 79(3), pages 427-454, December.
    10. Baksh, M. Fazil & Böhning, Dankmar & Lerdsuwansri, Rattana, 2011. "An extension of an over-dispersion test for count data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 466-474, January.
    11. Michael Lewis, 2005. "Research Note: A Dynamic Programming Approach to Customer Relationship Pricing," Management Science, INFORMS, vol. 51(6), pages 986-994, June.
    12. Romana Khan & Michael Lewis & Vishal Singh, 2009. "Dynamic Customer Management and the Value of One-to-One Marketing," Marketing Science, INFORMS, vol. 28(6), pages 1063-1079, 11-12.
    13. Fournier, Susan, 1998. "Consumers and Their Brands: Developing Relationship Theory in Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(4), pages 343-373, March.
    14. Jan Bulla & Andreas Berzel, 2008. "Computational issues in parameter estimation for stationary hidden Markov models," Computational Statistics, Springer, vol. 23(1), pages 1-18, January.
    15. Peter S. Fader & Bruce G. S. Hardie, 2010. "Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity," Marketing Science, INFORMS, vol. 29(1), pages 85-93, 01-02.
    16. Roland T. Rust & Peter C. Verhoef, 2005. "Optimizing the Marketing Interventions Mix in Intermediate-Term CRM," Marketing Science, INFORMS, vol. 24(3), pages 477-489, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peschel, Anne O., 2021. "Scarcity signaling in sales promotion: An evolutionary perspective of food choice and weight status," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    2. Paul Valentin Ngobo, 2017. "The trajectory of customer loyalty: an empirical test of Dick and Basu’s loyalty framework," Journal of the Academy of Marketing Science, Springer, vol. 45(2), pages 229-250, March.
    3. Song, Tae Ho & Kim, Sang Yong & Kim, Ji Yoon, 2016. "The dynamic effect of customer equity across firm growth: The case of small and medium-sized online retailers," Journal of Business Research, Elsevier, vol. 69(9), pages 3755-3764.
    4. Chen, Jialie & Rao, Vithala R., 2023. "Evaluating strategies for promoting retail mobile channel using a hidden Markov model," Journal of Retailing, Elsevier, vol. 99(1), pages 66-84.
    5. 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.
    6. Yang, Zhao & Algesheimer, René & Dholakia, Utpal, 2017. "When Ethical Transgressions of Customers Have Beneficial Long-Term Effects in Retailing: An Empirical Investigation," Journal of Retailing, Elsevier, vol. 93(4), pages 420-439.
    7. Zhang, Hongchao & Yu, Yu & Qin, Yinggao, 2023. "The effects of constrained mobile coupons in the mobile channel," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    8. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    9. Mark, Tanya & Bulla, Jan & Niraj, Rakesh & Bulla, Ingo & Schwarzwäller, Wolfgang, 2019. "Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 528-541.

    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. 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.
    2. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    3. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    4. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    5. Mark, Tanya & Bulla, Jan & Niraj, Rakesh & Bulla, Ingo & Schwarzwäller, Wolfgang, 2019. "Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 528-541.
    6. Lewis, Michael & Whitler, Kimberly A. & Hoegg, JoAndrea, 2013. "Customer Relationship Stage and the Use of Picture-Dominant versus Text-Dominant Advertising: A Field Study," Journal of Retailing, Elsevier, vol. 89(3), pages 263-280.
    7. Chang, Chun-Wei & Zhang, Jonathan Z., 2016. "The Effects of Channel Experiences and Direct Marketing on Customer Retention in Multichannel Settings," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 77-90.
    8. Reimer, Kerstin & Albers, Sönke, 2011. "Modeling Repeat Purchases in the Internet when RFM Captures Past Influence of Marketing," EconStor Preprints 50730, ZBW - Leibniz Information Centre for Economics.
    9. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    10. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    11. 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.
    12. 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.
    13. Andrés Musalem & Yogesh V. Joshi, 2009. "—How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach," Marketing Science, INFORMS, vol. 28(3), pages 555-565, 05-06.
    14. Kohsuke Matsuoka, 2020. "Exploring the interface between management accounting and marketing: a literature review of customer accounting," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(3), pages 157-208, September.
    15. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    16. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.
    17. George, Morris & Kumar, V. & Grewal, Dhruv, 2013. "Maximizing Profits for a Multi-Category Catalog Retailer," Journal of Retailing, Elsevier, vol. 89(4), pages 374-396.
    18. Youngsoo Kim & Ramayya Krishnan, 2019. "The Dynamics of Online Consumers’ Response to Price Promotion," Service Science, INFORMS, vol. 30(1), pages 175-190, March.
    19. Rajkumar Venkatesan & Alexander Bleier & Werner Reinartz & Nalini Ravishanker, 2019. "Improving customer profit predictions with customer mindset metrics through multiple overimputation," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 771-794, September.
    20. Bügel, Marnix S. & Verhoef, Peter C. & Buunk, Abraham P., 2011. "Customer intimacy and commitment to relationships with firms in five different sectors: Preliminary evidence," Journal of Retailing and Consumer Services, Elsevier, vol. 18(4), pages 247-258.

    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:eee:jouret:v:89:y:2013:i:3:p:231-245. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing .

    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.