IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v37y2010i10p1761-1777.html
   My bibliography  Save this article

Monitoring heterogeneous serially correlated usage behavior in subscription-based services

Author

Listed:
  • Y. Samimi
  • A. Aghaie

Abstract

Effective monitoring of usage behavior necessitates applying accurate stochastic models to represent customer heterogeneous time-dependent behavior. In this research, it is assumed that the sequence of customer visits over a subscription period occurs based on the Poisson process, while usage at each purchase occasion follows an autoregressive Bernoulli model of first order. The autocorrelated observations are derived from a two-state Markov chain model. Generalized linear models are employed to describe heterogeneous behavior across customers. In order to monitor the number of visits as well as the fraction of visits eventuated in a purchase, control statistics are defined on the basis of generalized likelihood ratio (GLR) test. Furthermore, in the case of the marginal logistic model for dependent observations, a chi-square test statistic based on the asymptotic multivariate normal distribution of quasi-likelihood estimates is employed. Performances of the monitoring schemes are compared with an illustrative case provided by simulation. Results indicate that the adjusted Shewhart c chart resembles the deviance residual control chart for monitoring the frequency of customer visit. On the other hand, the GLR statistic based on the conditional logistic regression is more powerful in detecting unnatural usage behavior when compared with the chi-square control statistic based on the marginal logistic model.

Suggested Citation

  • Y. Samimi & A. Aghaie, 2010. "Monitoring heterogeneous serially correlated usage behavior in subscription-based services," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1761-1777.
  • Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1761-1777
    DOI: 10.1080/02664760903159103
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903159103
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/02664760903159103?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. O. Grigg & V. Farewell, 2004. "An overview of risk‐adjusted charts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 523-539, August.
    2. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    3. C. D. Lai & K. Govindaraju & M. Xie, 1998. "Effects of correlation on fraction non-conforming statistical process control procedures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 535-543.
    4. W. Buckinx & G. Verstraeten & D. Van Den Poel, 2005. "Predicting Customer Loyalty Using The Internal Transactional Database," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/324, Ghent University, Faculty of Economics and Business Administration.
    5. 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. 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.
    2. LEE, Janghyuk & KERBACHE, Laoucine, 2004. "Internet media planning : an optimization model," HEC Research Papers Series 806, HEC Paris.
    3. 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.
    4. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
    5. 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.
    6. 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.
    7. Makoto Abe, 2008. ""Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," CIRJE F-Series CIRJE-F-537, CIRJE, Faculty of Economics, University of Tokyo.
    8. Makoto Abe, 2006. ""Counting Your Customers" One by One: An Individual Level RF Analysis Based on Consumer Behavior Theory," CIRJE F-Series CIRJE-F-408, CIRJE, Faculty of Economics, University of Tokyo.
    9. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    10. Teck-Hua Ho & Young-Hoon Park & Yong-Pin Zhou, 2006. "Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value," Marketing Science, INFORMS, vol. 25(3), pages 260-277, 05-06.
    11. 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.
    12. 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.
    13. Angelovska, Nina, 2021. "Analysis Of Customer Activity, The Importance Of Timing For Effective Marketing Actions: Case Of Group Buying Site, Grouper," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 12(2), pages 156-170.
    14. Hassan Assareh & Kerrie Mengersen, 2012. "Change Point Estimation in Monitoring Survival Time," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    15. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    16. Barry L. Bayus, 2013. "Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm Community," Management Science, INFORMS, vol. 59(1), pages 226-244, June.
    17. 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.
    18. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
    19. Chao Wang & Ilaria Dalla Pozza, 2014. "The antecedents of customer lifetime duration and discounted expected transactions: Discrete-time based transaction data analysis," Working Papers 2014-203, Department of Research, Ipag Business School.
    20. Kim, Chul & Jun, Duk Bin & Park, Sungho, 2018. "Capturing flexible correlations in multiple-discrete choice outcomes using copulas," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 34-59.
    21. Eymann, Torsten (Ed.), 2009. "Tagungsband zum Doctoral Consortium der WI 2009 [WI2009 Doctoral Consortium Proceedings]," Bayreuth Reports on Information Systems Management 40, University of Bayreuth, Chair of Information Systems Management.

    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:taf:japsta:v:37:y:2010:i:10:p:1761-1777. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.