Calculating Customer Lifetime Value and Churn Using Beta Geometric Negative Binomial and Gamma-Gamma Distribution in an Nft-Based Setting

Name: Calculating Customer Lifetime Value and Churn Using Beta Geometric Negative Binomial and Gamma-Gamma Distribution in an Nft-Based Setting
Creator: Sagarnil Das
Keywords: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Applications (stat.AP), Statistics - Applications

Sagarnil Das

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arXiv.org e-Print Ar...arrow_drop_down

arXiv.org e-Print Archive

Preprint . 2025

Data sources: arXiv.org e-Print Archive

https://doi.org/10.2139/ssrn.5...

Article . 2025 . Peer-reviewed

Data sources: Crossref

https://dx.doi.org/10.48550/ar...

Article . 2025

License: CC BY

Data sources: Datacite

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Article

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Calculating Customer Lifetime Value and Churn Using Beta Geometric Negative Binomial and Gamma-Gamma Distribution in an Nft-Based Setting

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2025Embargo end date: 01 Jan 2025Publisher:Elsevier BV

Authors: Sagarnil Das;

doi: 10.2139/ssrn.5087590 , 10.48550/arxiv.2501.04719

arXiv: 2501.04719

Calculating Customer Lifetime Value and Churn Using Beta Geometric Negative Binomial and Gamma-Gamma Distribution in an Nft-Based Setting

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Abstract

Customer Lifetime Value (CLV) is an important metric that measures the total value a customer will bring to a business over their lifetime. The Beta Geometric Negative Binomial Distribution (BGNBD) and Gamma Gamma Distribution are two models that can be used to calculate CLV, taking into account both the frequency and value of customer transactions. This article explains the BGNBD and Gamma Gamma Distribution models, and how they can be used to calculate CLV for NFT (Non-Fungible Token) transaction data in a blockchain setting. By estimating the parameters of these models using historical transaction data, businesses can gain insights into the lifetime value of their customers and make data-driven decisions about marketing and customer retention strategies.

10 pages, 8 figures

Related Organizations

University of Central Florida
United States

Keywords

FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Applications (stat.AP), Statistics - Applications

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