IDEAS home Printed from https://ideas.repec.org/a/prg/jnlpep/v2019y2019i6id714p648-669.html
   My bibliography  Save this article

Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

Author

Listed:
  • Pavel Jasek
  • Lenka Vrana
  • Lucie Sperkova
  • Zdenek Smutny
  • Marek Kobulsky

Abstract

The article contributes to the knowledge of customer lifetime value (CLV) models, where extensive empirical analyses on large datasets from online stores are missing. Based on this knowledge, practitioners can decide about the deployment of a particular model in their business and academics can design or enhance CLV models. The article presents predictive performance of selected CLV models: the extended Pareto/NBD model, the Markov chain model, the vector autoregressive model and the status quo model. Six large datasets of medium and large‑sized online stores in the Czech Republic and Slovakia are used for a comparison of the predictive performance of the models. Online stores have annual revenues in the order of tens of millions of euros and more than one million customers. The comparison of CLV models is based on selected evaluation metrics. The results of some of the models which use additional non‑financial data on customer behaviour - the Markov chain model and the vector autoregressive model - do not justify the effort which is needed to collect such data. The advantages and disadvantages of the selected CLV models are discussed in the context of their deployment.

Suggested Citation

  • Pavel Jasek & Lenka Vrana & Lucie Sperkova & Zdenek Smutny & Marek Kobulsky, 2019. "Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(6), pages 648-669.
    • Handle: RePEc:prg:jnlpep:v:2019:y:2019:i:6:id:714:p:648-669
    DOI: 10.18267/j.pep.714
    as

    Download full text from publisher

    File URL: http://pep.vse.cz/doi/10.18267/j.pep.714.html
    Download Restriction: free of charge
    File URL: http://pep.vse.cz/doi/10.18267/j.pep.714.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.18267/j.pep.714?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. Herbert Castéran & Lars Meyer-Waarden & Werner Reinartz, 2022. "Modeling Customer Lifetime Value, Retention, and Churn," Springer Books, in: Christian Homburg & Martin Klarmann & Arnd Vomberg (ed.), Handbook of Market Research, pages 1001-1033, Springer.
    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. Chen, Yanyan & Mandler, Timo & Meyer-Waarden, Lars, 2021. "Three decades of research on loyalty programs: A literature review and future research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 179-197.
    2. Valentini, Tony & Roederer, Claire & Castéran, Herbert, 2024. "From redesign to revenue: Measuring the effects of servicescape remodeling on customer lifetime value," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    3. repec:prg:jnlpep:v:preprint:id:714:p:1-22 is not listed on IDEAS

    More about this item

    Keywords

    CLV models; forecasting; online marketing management; e-commerce; methodology; online shopping; online marketing;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    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:prg:jnlpep:v:2019:y:2019:i:6:id:714:p:648-669. 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: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    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.