article · International Journal of Advanced Computer Science and Applications
Customer relationship management strategies are essential for businesses seeking to boost profits, retain clients, and guide investment decisions. Traditional segmentation frequently relies on recency, frequency, monetary value, and interpurchase time. This research extends this framework into an RFMTS model by incorporating customer satisfaction as an explicit dimension. Using unsupervised k-means clustering on data generated from this model, the methodology groups online consumers to evaluate their satisfaction trajectories and behavioural shifts over time. The findings indicate that integrating satisfaction into customer segmentation significantly influences clustering outcomes. By distinguishing satisfied clients from dissatisfied ones, businesses can identify shortcomings that might otherwise lead to the departure of profitable, loyal customers. This approach enables organisations to craft targeted, personalised marketing strategies and address customer grievances before client attrition occurs.
Retaining existing customers is vital for commercial viability, but traditional metrics only track what people spend, not how they feel. By adding satisfaction scores to customer clustering, companies can identify at-risk clients before they leave. This approach provides clearer insight into operational strengths and weaknesses, enabling businesses to design targeted interventions that improve retention and protect ongoing revenue.
This methodology can be used by marketing teams and customer relationship management software developers looking to improve client retention systems. It helps businesses pinpoint high-value customers at risk of churn due to low satisfaction. Based on the abstract, the work demonstrates an analytical clustering approach on generated model data, representing an early-stage methodological development that requires further validation in production commercial systems.
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Businesses derive more revenue from building and maintaining long-term relationships with their customers. Therefore, it is essential to build refined strategies based on customer relationship management, with the purpose of increasing their turnover and profits while retaining their customers. In this context, customer segmentation, which is at the heart of marketing strategy, makes it possible to determine the answers to questions relating to the number of investments to be released, the marketing campaigns to be organized, and the development strategy to be implemented. This paper develops an extended RFMT (Recency, Frequency, Monetary, and Interpurchase Time) model, namely the RFMTS model, by introducing a new dimension as satisfaction ‘S’. The aim of this model is to analyze online consumer satisfaction over time and discern changes to implement customer segmentation. This article proposes an approach to a segmentation, by client clustering along the unsupervised machine learning method k-means based on data generated using the proposed RFMTS model, in order to improve the customer relationship and develop more effective personalized marketing strategies. The study shows that including satisfaction to the existing RFM model for customer clustering has a major impact and helps identify customers who are satisfied and those who are not, unlike previous attempts to develop new RFM models. By ignoring the “satisfaction” indicator, what went well and what didn't went well cannot be understood. Consequently, the business loses its unsatisfied, loyal, and profitable customers and either fails or relies only on the satisfied ones to continue making profits for an indefinite period of time.
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DOI: 10.14569/ijacsa.2022.0130658
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