K-Means Customers Clustering by their RFMT and Score Satisfaction Analysis

Abstract

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 kmeans 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.

Enhancing Customer Segmentation Insights by using RFM + Discount Proportion Model with Clustering Algorithms

“…Based on the elbow method, they determined the optimal cluster number to be 5. They did not explicitly focus on the clustering algorithm but discussed the results regarding customer statistics, which, if ignored, could have a negative impact on the company [15].…”

Customer Segmentation: Transformation from Data to Marketing Strategy

“…The dataset consists of 84,285 instances of customers with 37 attributes detailing the customer's habits and demographics, from January 2020 to December 2021. Missing values, incorrect types, inconsistent values, and outliers removal are handled using data preprocessing [15]. For the modified dataset, we apply RFM analysis and clustering algorithms to build customer segmentation or clusters.…”

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

“…In order to identify the probability of future purchases in the next few weeks, the value of the next one, as well as customer Applied Computer Systems _________________________________________________________________________________________________2022/27 satisfaction, five variables, which are recency (R), frequency (F), monetary value (M), interpurchase time (T), and satisfaction (S), should be first calculated. To do so, the RFMTS model [2] is used, Table II presents the dataset after generating the R, F, M, T, and S variables for each customer.…”

“…Customer satisfaction and trust are respectively the first and second important antecedents of customer loyalty [66]. After calculating the CLV for each customer, based on the RFMTS model [2], the CLV and satisfaction (S) values are normalized to a range between 0 and 1, using the well-known statistic normalization formula (6), as indicated in Table VIII. Predicting future customer behaviour and generating a dedicated global offer for each of them allows the company to consolidate its customer relationships, differentiate itself from its competitors, and increase its earnings.…”

“…These are classic questions that companies should answer to face the competition. Thus, the objective of the hybrid approach proposed in this study, which is an extension of a previous work [2], is to generate global offers by identifying, for each customer, a list of the most likely to purchase products, the probability of purchase in the next few weeks, and the average transaction value. Furthermore, to make the most complete offers on the market, the associated products that are generally bought with the initial ones are determined as illustrated in Fig.…”