RFM Analysis for Customer Lifetime Value with PARETO/NBD Model in Online Retail Dataset

Q2 Economics, Econometrics and Finance
Rama Aria Megantara, Farrikh Alzami, Ahmad Akrom, Ricardus Anggi Pramunendar, Dwi Puji Prabowo, Sasono Wibowo, Ritzkal Ritzkal
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 CLV analysis relies on various models and techniques, including the RFM analysis categorizes customers based on recency, frequency, and monetary value, helping to segment customers and predict future behavior. Then, The Pareto/NBD model combines probability distributions to estimate CLV and is commonly used for customer base analysis.
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Abstract

In recent years, there has been a growing interest in analyzing Customer Lifetime Value (CLV) due to its ability to provide valuable insights into customer profitability and worth. CLV analysis predicts the net profit attributed to the entire future relationship with a customer. This analysis involves calculating the present value of a customer's expected future spending with the company, facilitating an understanding of the economic value of long-term customer relationships. CLV analysis empowers businesses to identify their most profitable customers and develop strategies for retaining them, ultimately maximizing long-term profitability. CLV analysis relies on various models and techniques, including the RFM analysis categorizes customers based on recency, frequency, and monetary value, helping to segment customers and predict future behavior. Then, The Pareto/NBD model combines probability distributions to estimate CLV and is commonly used for customer base analysis. This research article explores the application of RFM analysis for estimating customer lifetime value using the Pareto/NBD model in an online retail dataset. This metric is crucial for businesses as it assists in identifying valuable customers and formulating retention strategies to maximize long-term profitability.
基于PARETO/NBD模型的在线零售数据集客户终身价值RFM分析
近年来,人们对分析客户生命周期价值(CLV)越来越感兴趣,因为它能够提供有关客户盈利能力和价值的有价值的见解。CLV分析预测的净利润归因于与客户的整个未来关系。这种分析包括计算客户在公司的预期未来支出的现值,促进对长期客户关系的经济价值的理解。CLV分析使企业能够识别最有利可图的客户,并制定留住他们的策略,最终使长期盈利能力最大化。 CLV分析依赖于各种模型和技术,包括RFM分析根据最近、频率和货币价值对客户进行分类,帮助细分客户并预测未来的行为。然后,Pareto/NBD模型结合概率分布来估计CLV,通常用于客户群分析。 这篇研究文章探讨了RFM分析在在线零售数据集中使用Pareto/NBD模型估计客户终身价值的应用。这一指标对企业至关重要,因为它有助于识别有价值的客户,并制定保留策略,以最大限度地提高长期盈利能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Buletin Ekonomi Moneter dan Perbankan
Buletin Ekonomi Moneter dan Perbankan Economics, Econometrics and Finance-Finance
CiteScore
2.20
自引率
0.00%
发文量
1
审稿时长
5 weeks
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