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Marketing Models for the Customer-Centric Firm

In: Handbook of Marketing Decision Models

Author

Listed:
  • Eva Ascarza

    (Columbia Business School)

  • Peter S. Fader

    (University of Pennsylvania)

  • Bruce G. S. Hardie

    (London Business School)

Abstract

A customer-centric firm takes the view that there are three key drivers of (organic) growth and overall profitability: Customer acquisitionCustomer acquisition , customer retentionCustomer retention , and customer development (i.e., increasing the value of each existing customer (per unit of time) while they remain a customer). In this chapter we review the key data-based tools and methods that have been developed by marketing scientists (and researchers and practitioners in related fields such as operations research, statistics, and computer science) to assist firms in their understanding and implementing these activities more effectively.

Suggested Citation

  • Eva Ascarza & Peter S. Fader & Bruce G. S. Hardie, 2017. "Marketing Models for the Customer-Centric Firm," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 297-329, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-56941-3_10
    DOI: 10.1007/978-3-319-56941-3_10
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    Cited by:

    1. Anita Oppong & Livingstone Divine Caesar, 2023. "A contingency analysis of brand reputation and loyalty in the banking sector," SN Business & Economics, Springer, vol. 3(7), pages 1-29, July.
    2. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    3. Laura Grassi & Nicolas Figini & Lorenzo Fedeli, 2022. "How does a data strategy enable customer value? The case of FinTechs and traditional banks under the open finance framework," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-34, December.
    4. 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.
    5. Gubela, Robin & Bequé, Artem & Gebert, Fabian & Lessmann, Stefan, 2018. "Conversion uplift in e-commerce: A systematic benchmark of modeling strategies," IRTG 1792 Discussion Papers 2018-062, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. 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.
    7. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.

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