Abstract
In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details.
Highlights
We live in a fast-changing digital world
In 2021, we have models, such as the PRFM of Hajmohamad et al [79], which works on the profit margin, and that of Hwan and Lee [80], which applies the TexRank algorithm to improve the RFM by including website-specific weights and is able to work with clients without their purchase history
We developed our proposal to be able to improve strategic marketing decisions based on the fuzzy linguistic 2-tuple RFM model per product hierarchy
Summary
We live in a fast-changing digital world. In today’s age, customers expect sellers to talk directly with them and offer the perfect product, with the right message and at the correct time. Big Data analytics have an immense potential to empower customer experience management, as they can help organizations to achieve a better and faster understanding of the customer journey and make decisions to improve the customer experience (Wedel and Kannan [1]). Even if they have the data, organizations still face difficulties in understanding and managing those data and generating relevant insights (Said et al [3]). Information about customers is achieved through the use of analytics (Wedel and Kannan [1]), and despite its use becoming more common, many companies still use basic and poor analytics to extract information from their customers (Moorman [4]; Ramsbotham et al [5])
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