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RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center

Author

Listed:
  • Gabriel Marín Díaz

    (Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain)

  • Ramón Alberto Carrasco

    (Department of Management and Marketing, Faculty of Commerce and Tourism Complutense, University of Madrid, 28223 Madrid, Spain)

  • Daniel Gómez

    (Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain
    Instituto de Evaluación Sanitaria, Complutense University, 28040 Madrid, Spain)

Abstract

In an increasingly globalized market, the relationship between the customer and the brand goes beyond the purchasing process. It is very important to understand the customer, to know their needs and to propose actions to increase the value of the brand for them. In the literature, there are several models capable of determining and segmenting customers according to variables dependent on the purchasing process. However, we have not found any study that applies to the business case of classifying customers according to their relationship with the contact centre. In this paper, we establish a working model that allows us to define the value of the customer in the process of interaction with the contact centre, so that we can propose actions both in the sales phase and during the post-sales service, so that the value and perception of the brand is increased. In this model, we propose using the value of recency, frequency, importance and duration of customer interactions with the post-sales service, thus obtaining a ranking, and grouping of customers to help establish personalized communication strategies. We have verified this model by presenting a business case applied to the telecom sector.

Suggested Citation

  • Gabriel Marín Díaz & Ramón Alberto Carrasco & Daniel Gómez, 2021. "RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center," Mathematics, MDPI, vol. 9(19), pages 1-27, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2362-:d:641620
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    References listed on IDEAS

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    Cited by:

    1. Honggang Wang & Ruixue Yu & Ruoyu Pan & Peidong Pei & Zhao Han & Nanfeng Zhang & Jingfeng Yang, 2022. "An Adaptive Control Algorithm Based on Q-Learning for UHF Passive RFID Robots in Dynamic Scenarios," Mathematics, MDPI, vol. 10(19), pages 1-17, September.
    2. Leticia Monje & Ramón A. Carrasco & Carlos Rosado & Manuel Sánchez-Montañés, 2022. "Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain," Mathematics, MDPI, vol. 10(9), pages 1-20, April.
    3. Gabriel Marín Díaz & José Javier Galán & Ramón Alberto Carrasco, 2022. "XAI for Churn Prediction in B2B Models: A Use Case in an Enterprise Software Company," Mathematics, MDPI, vol. 10(20), pages 1-29, October.

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