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Learning about the customer for improving customer retention proposal of an analytical framework

Author

Listed:
  • Dora Simões

    (University of Aveiro)

  • Joana Nogueira

    (University of Aveiro)

Abstract

The market faces new challenges in retaining customers, since they have very high expectations, which translate into the demand for a swift response and intransigence to empty promises on the part of brands. These requirements result from the ability to disseminate and infuse information, which in turn makes customers more informed, more participative, and more uncompromising. This change in behavior implies redesigning the strategic management of the brands, in terms of the relationship with the customer. In view of this challenge, the relevance of developing an adequate differentiation model for customer retention prevails. Based on this premise, this paper presents a proposal based on RFM and ABC analytical methods applied to customer relationship management and contextualized in a particular case of the printing industry. The proposed model defines a set of metrics aimed at customer segmentation, which improves the customers knowledge. The outcomes will allow to define more assertive marketing strategies for customer loyalty and to increase the volume of a brand's revenue.

Suggested Citation

  • Dora Simões & Joana Nogueira, 2022. "Learning about the customer for improving customer retention proposal of an analytical framework," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(1), pages 50-63, March.
    • Handle: RePEc:pal:jmarka:v:10:y:2022:i:1:d:10.1057_s41270-021-00126-7
    DOI: 10.1057/s41270-021-00126-7
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    References listed on IDEAS

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    1. Wen-Yu Chiang, 2017. "Discovering customer value for marketing systems: an empirical case study," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5157-5167, September.
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    Cited by:

    1. Maria Petrescu & Anjala S. Krishen, 2023. "Mapping 2022 in Journal of Marketing Analytics: what lies ahead?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(1), pages 1-4, March.
    2. Rebecca Dingus & Hulda G. Black & Nicole A. Flink, 2024. "Analytics for all marketing majors: sparking interest in the uninterested," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 126-141, June.
    3. Joni Salminen & Mekhail Mustak & Muhammad Sufyan & Bernard J. Jansen, 2023. "How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 677-692, December.

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