Customer Lifetime Value Prediction and Segmentation Using Machine Learning


There is a fierce competition in the telecom sector that is prompting the companies to invest heavily on marketing including acquiring new customers. But to be truly profitable, it is crucial not only to attract new customers, but to make sure old customers are retained with the company for as long time as possible. This turns the focus on customer lifetime value (CLTV). Knowing what drives CLTV gives ideas of what is best to invest in, and this information can be very valuable for the telecom company in designing their marketing strategy [1]. Many companies are now considering changing their marketing approach from Product centric to Customer-centric. For this approach to work, it is essential to understand each customer’s worth or value, which then helps focus the resources on targeted marketing. The purpose of this project is to Analyze the

Customer sales data of the company and predict the Customer lifetime value. Based on the predicted CLTV, customer segmentation is done to determine focus groups. The goal of this project is to provide a guide for marketing decision making and planning marketing strategies and plans for future, using a machine learning models to predict customer lifetime values and segmentation.

Published in:

International Journal of Research in Engineering and Science (IJRES- August 2021 Volume 9-Issue 8).


Ramamani Venkatakrishna

Pradeepta Mishra

Sneha P. Tiwari

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