IDEAS home Printed from https://ideas.repec.org/h/zbw/entr16/183702.html
   My bibliography  Save this book chapter

Comparison of Multivariate Statistical Analysis and Machine Learning Methods in Retailing: Research Framework Proposition

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016

Author

Listed:
  • Ćorić, Ivica

Abstract

The aim of this paper is comparison of multivariate statistical analysis and machine learning methods based on the model used for the measurement of current and forecasting of the future customer profitability. Modern customer profitability analysis shows that customer-company relationship is burdened, beside costs of product, with many other different costs generated by business activities. Such costs generated by logistics, post-sale support, customer administration, sale, marketing etc. are allocated in customer's base in non-linear way. Allocation can vary significantly from customer to customer, making the reason why each different customer's monetary unit of revenue does not participate in profit in the same way. The research model uses RFM model to define forecasting variables and neural network, multivariate regression analysis and binary logistic regression as forecasting methods. This paper shows the ways how proposed methods can be used in process of forecasting customer profitability giving comparison of their application in that field.

Suggested Citation

  • Ćorić, Ivica, 2016. "Comparison of Multivariate Statistical Analysis and Machine Learning Methods in Retailing: Research Framework Proposition," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2016), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016, pages 76-82, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    • Handle: RePEc:zbw:entr16:183702
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/183702/1/11-ENT63-Coric-76-82.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    2. Ramazan Esmeli & Mohamed Bader-El-Den & Hassana Abdullahi, 2021. "Towards early purchase intention prediction in online session based retailing systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 697-715, September.
    3. 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.
    4. 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.
    5. Lepthien, Anke & Papies, Dominik & Clement, Michel & Melnyk, Valentyna, 2017. "The ugly side of customer management – Consumer reactions to firm-initiated contract terminations," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 829-850.
    6. Butori, Raphaëlle & De Bruyn, Arnaud, 2013. "So you want to delight your customers: The perils of ignoring heterogeneity in customer evaluations of discretionary preferential treatments," International Journal of Research in Marketing, Elsevier, vol. 30(4), pages 358-367.
    7. Sunčica Rogić & Ljiljana Kašćelan & Vladimir Kašćelan & Vladimir Đurišić, 2022. "Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution," Information Technology and Management, Springer, vol. 23(4), pages 315-333, December.
    8. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
    9. Leigh McAlister & Shameek Sinha, 2021. "A customer portfolio management model that relates company’s marketing to its long-term survival," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 584-600, May.
    10. 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.
    11. Lhoest-Snoeck, Sietske & van Nierop, Erjen & Verhoef, Peter C., 2014. "For New Customers Only: A Study on the Effect of Acquisition Campaigns on a Service Company's Existing Customers' CLV," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 210-224.
    12. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
    13. 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.
    14. Ahmet Tuz & Begum Sertyesilisik, 2020. "Finding and Minding the Gaps in State-Of-The-Art Lean and Green Marketing in the Construction Industry," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 32(2), pages 187-203.
    15. 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.
    16. 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.
    17. Kati Stormi & Anni Lindholm & Teemu Laine & Tuomas Korhonen, 2020. "RFM customer analysis for product-oriented services and service business development: an interventionist case study of two machinery manufacturers," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 24(3), pages 623-653, September.
    18. Ho, Hillbun & Tien, Keng-Ming (Terence) & Wu, Anne & Singh, Sonika, 2021. "A sequence analysis approach to segmenting credit card customers," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    19. Libai, Barak & Bart, Yakov & Gensler, Sonja & Hofacker, Charles F. & Kaplan, Andreas & Kötterheinrich, Kim & Kroll, Eike Benjamin, 2020. "Brave New World? On AI and the Management of Customer Relationships," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 44-56.

    More about this item

    Keywords

    multivariate statistical analysis; RFM; machine learning; customer profitability; forecasting; knowledge;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:zbw:entr16:183702. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://www.entrenova.org/ .

    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.