用BGNBD与伽马伽马模型分析NFT用户生命周期价值与流失率。
Calculating Customer Lifetime Value and Churn using Beta Geometric Negative Binomial and Gamma-Gamma Distribution in a NFT based setting
- 结合频次与价值建模，用历史交易数据估计用户行为参数。
- 可量化NFT用户终身价值及流失概率，支持精准营销决策。
- 适合区块链生态中需评估用户长期价值的项目方参考。
客户终身价值（CLV）是衡量客户在其生命周期内为业务带来的总价值的重要指标。贝塔几何负二项分布（BGNBD）和伽马伽马分布是两种可用于计算CLV的模型，能够同时考虑客户交易的频率和价值。本文阐述了BGNBD与伽马伽马分布模型的原理，并说明如何在区块链环境下的NFT交易数据中应用这些模型来计算CLV。通过利用历史交易数据估计模型参数，企业可以获得对客户生命周期价值的深入洞察，从而制定基于数据的市场营销和客户留存策略。
原文摘要 · Abstract (English)
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.
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