The New fuzzy RFMD model. A method to identify new customer profiles due to an increase in online activity. Case study of a retail business

  1. Martinez, Rocio Gonzalez 1
  2. Carrasco, Ramón 2
  3. Sanchez Figueroa, Cristina 3
  4. Gavilán, Diana 2
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Department of Management and Marketing, Complutense University, Spain
  3. 3 Department of Statistic and Applied Economy, UNED University, Spain
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Revue:
Estudios de economía aplicada

ISSN: 1133-3197,  1697-5731

Année de publication: 2021

Titre de la publication: Sustainable Economic Development: Pattern and Perspective

Volumen: 39

Número: 8

Type: Article

DOI: 10.25115/EEA.V39I8.5524 DIALNET GOOGLE SCHOLAR

D'autres publications dans: Estudios de economía aplicada

Résumé

Gaining customer loyalty has become one of the main objectives of all companies. Retailers, especially the online ones, have the advantage of knowing their customers’ historical purchase data, which provides them with an understanding of the customers’ buying patterns. A widely-used tool in strategic marketing and customer loyalty is segmentation based on the traditional Recency, Frequency and Monetary (RFM) model. Subsequently, the fuzzy RFM model proved to be an improvement on the traditional RFM model. There has been a change in the retail customer profile, with the growth of a new cluster, the “One-Shot Customer”, new customers that buy from a retailer just once and never come back. In response to this change, the fuzzy RFM model has been modified to include a new dimension capturing Length or Duration. This study presents the new fuzzy RFMD model (Recency, Frequency, Monetary and Duration model), which can be used to better identify that new, large group of customers. The paper also provides a case study based on an e-commerce clothing retailer. Its customer database was segmented using the k-means algorithm and the Isolation Forest algorithm was applied to identify and correctly treat possible anomalies. The Customer Lifetime Value and the weights for the RFMD attributes were calculated by applying the Analytic Hierarchy Process (AHP) model. Results reveal the improvement that the weighted fuzzy RFMD model offers to retailers, enabling them to detect the One-Shot Customers and thus optimize their strategic marketing plans.

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