IDEAS home Printed from https://ideas.repec.org/a/aml/intbrm/v2y2011i3p128-138.html
   My bibliography  Save this article

Predicting e-Customer behavior in B2C Relationships for CLV model

Author

Listed:
  • Kaveh Ahmadi

    (Islamic Azad University, Islamshahr Branch, Iran)

Abstract

E-Commerce sales have demonstrated an amazing growth in the last few years. And it is thus clear that the web is becoming an increasingly important channel and companies should strive for a successful web site. In this completion knowing e-customer and predicting his behavior is very important. In this paper we describe e-customer behavior in B2C relationships and then according to this behavior a new model for evaluating e-customer in B2C e-commerce relationships will be described. The most important thing in our e-CLV (Electronic Customer Lifetime Value) model is considering market\'s risks that are affecting customer cash flow in future. A lot of CLV models are based on simple NPV (simple net present value). However simple NPV can assess a good value for CLV, but simple NPV ignores two important aspects of B2C e-relationship which are market risks and big amount of customer data in e-commerce context. Therefore, simple NPV isn\'t enough for assessing e-CLV in high risk B2C markets. Instead of NPV, real option analyses could lead us to a better estimation for future cash flow of customers. With real option analyses, we predict all the future states with probability of each of them. And then calculate the more accurate of future customer cash flow. In this paper after a brief history of CLV, we explain customer behavior in B2C markets especially for e-retailers. Then with using real option analyses, we introduce our CLV model. Two extended examples explain our model and introduce the steps in finding CLV of customer in a B2C relationship.

Suggested Citation

  • Kaveh Ahmadi, 2011. "Predicting e-Customer behavior in B2C Relationships for CLV model," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 2(3), pages 128-138, October.
    • Handle: RePEc:aml:intbrm:v:2:y:2011:i:3:p:128-138
    as

    Download full text from publisher

    File URL: https://www.cscjournals.org/manuscript/Journals/IJBRM/Volume2/Issue3/IJBRM-51.pdf
    Download Restriction: no
    File URL: https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJBRM-51
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
    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. Matteo Cristofaro & Pier Luigi Giardino & Luna Leoni, 2021. "Back to the Future: A Review and Editorial Agenda of the International Journal of Business Research and Management," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 12(1), pages 16-33, February.

    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. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    9. 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.
    10. 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.
    11. Makoto Abe, 2009. "Customer Lifetime Value and RFM Data: Accounting Your Customers: One by One," CIRJE F-Series CIRJE-F-616, CIRJE, Faculty of Economics, University of Tokyo.
    12. 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.
    13. Gary Lilien & Rajdeep Grewal & Douglas Bowman & Min Ding & Abbie Griffin & V. Kumar & Das Narayandas & Renana Peres & Raji Srinivasan & Qiong Wang, 2010. "Calculating, creating, and claiming value in business markets: Status and research agenda," Marketing Letters, Springer, vol. 21(3), pages 287-299, September.
    14. Hea In Lee & Il Young Choi & Hyun Sil Moon & Jae Kyeong Kim, 2020. "A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks," Sustainability, MDPI, vol. 12(3), pages 1-14, January.
    15. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
    16. Neeraj Arora & Greg M. Allenby & James L. Ginter, 1998. "A Hierarchical Bayes Model of Primary and Secondary Demand," Marketing Science, INFORMS, vol. 17(1), pages 29-44.
    17. John Robst & Kimmarie McGOLDRICK, 1999. "The Measurement of Firm Information About Product Demand," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 15(2), pages 149-163, September.
    18. Sharad Borle & Siddharth S. Singh & Dipak C. Jain, 2008. "Customer Lifetime Value Measurement," Management Science, INFORMS, vol. 54(1), pages 100-112, January.
    19. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    20. 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.
    21. Stephan Curiskis & Xiaojing Dong & Fan Jiang & Mark Scarr, 2023. "A novel approach to predicting customer lifetime value in B2B SaaS companies," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 587-601, December.

    More about this item

    Keywords

    Customer Lifetime Value (CLV); e-Commerce Relationships; Net Present Value (NPV); customer's behavior; Customer Segmentation;
    All these keywords.

    JEL classification:

    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General

    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:aml:intbrm:v:2:y:2011:i:3:p:128-138. 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: Nabeel Tahir (email available below). General contact details of provider: .

    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.