Customer Life Time Value Model Framework Using Gradient Boost Trees with RANSAC Response Regularization

Lavneet SINGH, Nancy Kaur, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

For many years, Customer lifetime value has been a mainstay concept in direct response marketing and has been increasingly considered. This paper presents a mathematical model framework for determination of customer lifetime value. The proposed multi-layer framework is based on a systematic theoretical taxonomy and on assumptions grounded in customer behavior. CLV, a recent marketing paradigm, pursues long-term relationship with profitable customers. It can be a starting point to understand relationship management and measure the true value of customers to be deployed toward the targeted customers and profitable customers, to foster customers’ full profit potential. Corporate success depends on an organization ’ ability to build and maintain loyal and valued customer relationships. In this paper, we propose a framework for analyzing customer value and segmenting customers based on their value. We also conducted an in-depth data analysis to find each member’s behavior and important attributes which plays a significant role in calculating CLV in multiple revenue bands. From the experimental validation, we concluded that our proposed framework works much better in predicting customer’s life time value in terms of revenue compared to other methods in past literature.
Original languageEnglish
Title of host publicationProceedings 2018 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationRio de Janeiro, Brazil, Brazil
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1527-1534
Number of pages8
ISBN (Electronic)9781509060146
ISBN (Print)9781509060153
DOIs
Publication statusPublished - 8 Jul 2018
EventIEEE International Conference on Neural Networks -
Duration: 1 Jan 2011 → …

Conference

ConferenceIEEE International Conference on Neural Networks
Abbreviated titleICNN
Period1/01/11 → …

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  • Cite this

    SINGH, L., Kaur, N., & CHETTY, G. (2018). Customer Life Time Value Model Framework Using Gradient Boost Trees with RANSAC Response Regularization. In Proceedings 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1527-1534). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2018.8489710