Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model

Sun, Yuechi; Liu, Haiyan; Gao, Yu

doi:10.1016/j.heliyon.2023.e13384

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…Customers who make repeat purchases of products over time have a higher value than customers who make one-time purchases [14]. In (1) and ( 2), the basic formula for calculating CLV is given [15].…”

Section: Customers Lifetime Valuementioning

confidence: 99%

An boosting business intelligent to customer lifetime value with robust M-estimation

Elveny,

Y. Syah,

M. Nasution

2024

IJ-AI

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When a business concentrates too much on acquiring new clients rather than retaining old ones, mistakes are sometimes made. Each customer has a different value. Customer lifetime value (CLV) is a metric used to assess longterm customer value. Customer value is a key concern in any commercial endeavor. When there are variations in customer behavior, CLV forecasts the value of total customer income when the data distribution is not normal, and outliers are present. Robust M-estimation, a maximum likelihood type estimator, is used in this study to enhance CLV data. Through the minimization of the regression parameter from the residual value, robust Mestimation eliminates data outliers in customer metric data. With an accuracy of 94.15%, R-square is used to gauge model performance. This research shows that CLV optimization can be used as a marketing and sales strategy by companies.

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Section: Customers Lifetime Valuementioning

confidence: 99%

An boosting business intelligent to customer lifetime value with robust M-estimation

Elveny,

Y. Syah,

M. Nasution

2024

IJ-AI

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“…Em relac ¸ão à utilizac ¸ão de abordagens de aprendizado de máquina na previsão de CLV, há uma carência de trabalhos na área. Porém, tem-se trabalhos como o de [Sun et al 2023], cujo objetivo é fazer a segmentac ¸ão de clientes com base em algoritmos de aprendizado de máquina e modelos de análise de gerenciamento de relacionamento com o cliente e a criac ¸ão de um modelo de identificac ¸ão de segmentac ¸ão de valor do cliente em uma relac ¸ão não-contratual.…”

Section: Abordagens De Aprendizado De Máquinaunclassified

“…Tem surgido nos últimos anos a utilizac ¸ão de técnicas de aprendizado de máquina para este cálculo, como no trabalho de [Desirena et al 2019], que utiliza redes neurais para aumentar o CLV na indústria de seguros por meio de recomendac ¸ões mais assertivas. Já o trabalho de [Sun et al 2023] faz uso de aprendizado de máquina para medir o CLV e a segmentac ¸ão com base no valor do ciclo de vida do cliente.…”

Section: Introduc ¸ãOunclassified

Customer Lifetime Value Prediction: A Machine Learning Approach

Ramos,

Silva

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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A previsão do Customer Lifetime Value (CLV) é de suma importância em diversos modelos de negócios. No entanto, esse cálculo varia de acordo com o contexto e o escopo do negócio. Este trabalho tem como objetivo realizar a previsão do CLV utilizando algoritmos de aprendizado de máquina e compará-lo com os principais modelos utilizados na literatura em 3 bases de dados diferentes. Durante o trabalho são descritas as escolhas e decisões tomadas para a construção dos modelos, sendo mostrado que os modelos de aprendizado de máquina apresentaram melhores resultados no cálculo do número esperado de transações, e obtiveram resultados muito semelhantes no modelo de cálculo do valor médio por transação.

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“…Machine learning enables marketers to analyze customer shopping history data in real-time, follow patterns and dynamics of changes in customer preferences (Aryuni, Madyatmadja and Miranda, 2018;Tabianan, Velu and Ravi, 2022;Sun, Liu and Gao, 2023). Riza, et al used the k-means and apriori methods to segment potential customers based on customer characteristics and transaction patterns with certain types of merchants (Riza, Seminar and Maulana, 2018).…”

Section: Introductionmentioning

confidence: 99%

Customer Segmentation For Digital Marketing Based on Shopping Patterns

Alie,

Gustriansyah

2024

jabm

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Customer segmentation is the customer grouping based on similar shopping behavior or patterns. Inappropriate customer segmentation can have negative impacts, such as lost marketing opportunities, resource inefficiencies, loss of potential customers, and decreased performance, and business profits, especially in customer satisfaction. Therefore, this study aims to develop a customer segmentation model for digital marketing. This model is based on customer shopping patterns using the Recency-Frequency-Monetary (RFM) model and the Partitioning Around Medoids (PAM) method. The research data is historical customer purchase data consisting of 18,535 transactions and 541,909 transaction details from 4,339 customers for 3,665 product items over two years. The research variables focus on the model used: recency, frequency, and monetary. The five customer segments generating from this study are main, potential, general, minimum, and prospective customer. The internal validation results show that the minimum C-Index value is 0.1429 (close to zero), and the maximum Calinski-Harabasz Index value is 512.9553. It shows that the quality of customer segmentation results is good. In other words, the model can identify correlations between customer segments and shopping patterns and preferences. In this way, marketers can optimize services, adjust strategies, and offer the right products for each customer segment. Further research can be directed at product segmentation.

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Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model

Cited by 9 publications

References 43 publications

An boosting business intelligent to customer lifetime value with robust M-estimation

An boosting business intelligent to customer lifetime value with robust M-estimation

Customer Lifetime Value Prediction: A Machine Learning Approach

Customer Segmentation For Digital Marketing Based on Shopping Patterns

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