An RFM and CLV analysis for customer retention and customer relationship management of a logistics firm
21 Nov 2019-International Journal of Applied Management Science (Inderscience Publishers)-Vol. 11, Iss: 4, pp 333-351
TL;DR: In this article, the authors performed a data analysis on the CRM data to identify the right segment of customers who are to be retained to maximise profit, which is known as RFM analysis.
Abstract: To retain a customer in a logistics industry is difficult as it highly depends on the customer relationship management (CRM) and the quality of the service delivered. The existing customers are to be retained for longer period of time for maximum profit contribution. Hence, it is important to perform a data analysis on the CRM data to identify the right segment of customers who are to be retained to maximise profit. This study segments the customers depending on the parameters recency, frequency, and the monetary value, known as RFM analysis. Customer lifetime value estimation is done on segments obtained using the RFM Analysis to identify the profit contribution of each of the segments. The analysis carried out aids the management to adopt decisions on marketing strategies based on the RFM scores and CLV of each segment. Company can also take decisions on budget spending in retaining the right customers.
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TL;DR: In this paper , the authors focus on customer value measurement and customer segmentation based on customer lifecycle value theory, and carry out customer value measuring and segmentation research from the perspective of customer value.
TL;DR: In this article , the authors proposed a model that calculates the customer lifetime value and simultaneously considers the network effects in an oligopoly market, where each company has a number of buyers and sellers and offers its services to the buyers free while receiving the sellers' membership fees in return.
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TL;DR: In this article, the authors proposed a data-mining approach to model non-contractual customer churn in B2B contexts, and compared several modeling techniques in terms of their ability to predict true churners.
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