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Big Data Analytics for Customer Lifetime Value Prediction

Telecom Business Review

Volume 12 Issue 1

Published: 2019
Author(s) Name: Aslekar Avinash, Piyali Sahu, Arunima Pahari | Author(s) Affiliation: Associate Professor, Symbiosis Institute of Telecom Management, Maharashtra, India.
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Abstract

Predicting all the values that a business can derive from its long-standing relationship with its customers is Customer Lifetime Value. As it helps to create a sustainable relationship with selected customers, the importance of CLV is growing at a brisk pace, generating higher revenue that in turn enhances business growth. With increasing competition, retaining existing customers is more profitable than acquiring new customers. To manage and allocate resources efficiently for each and every customer, big data analytics comes into play. Taking this into account, a large amount of data should be taken into consideration, such as clients attrition, objectives, diverse products and services that they use, clients characteristics viz demographic, psychographic, geographic etc. First step to this is data cleaning, pre-processing and data manipulation to achieve a meaningful outcome or information from the raw data, followed by data analysis and visualization. Techniques that can be recommended for the data analysis and visualization of CLV model can be Stepwise regression, Classification and regression trees (CART), Generalized linear models (GLM). To determine the dynamic view of customer behavior, future marketing strategies and to foster brand loyalty, prediction of a proper CLV model is much needed.

Keywords: CLV, Predictive Models, Pareto or NBD Model, Gamma-Gamma Model, Retail Industry CLV, Purchase Count, Lifetime Value, Monetary Value

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