Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques
In today’s data-driven hospitality sector, customer interactions increasingly occur through digital platforms, generating extensive behavioral and transactional information. This study analyse the prediction of Customer Lifetime Value (CLV) using machine learning models—Linear Regression, Random For...
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University of science and culture
2025-04-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_221737_7c7d968f0ba26030f74c4f707cabd7ad.pdf |
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| author | Leila Taherkhani Amir Daneshvar Hossein Amoozad Khalili MohammadReza Sanaei |
| author_facet | Leila Taherkhani Amir Daneshvar Hossein Amoozad Khalili MohammadReza Sanaei |
| author_sort | Leila Taherkhani |
| collection | DOAJ |
| description | In today’s data-driven hospitality sector, customer interactions increasingly occur through digital platforms, generating extensive behavioral and transactional information. This study analyse the prediction of Customer Lifetime Value (CLV) using machine learning models—Linear Regression, Random Forest, and LightGBM—trained on features derived from hotel website interactions and booking records. After comprehensive data preprocessing, the models were evaluated using MAE, RMSE, and R² metrics. LightGBM achieved the highest predictive performance (R² = 0.504), followed by Random Forest (R² = 0.497), while Linear Regression underperformed (R² = 0.386), highlighting the advantages of non-linear models in modeling intricate customer patterns. Residual analyses confirmed LightGBM's stability and low bias across diverse customer profiles. Apart from prediction, the study applies Recency-Frequency-Monetary (RFM) analysis to segment customers into distinct value-based groups. These segments form the basis for tailored marketing strategies, allowing hotels to allocate resources more efficiently, enhance customer retention, and develop targeted campaigns aligned with customer potential. By integrating web-derived behavioral data with advanced modeling and segmentation, this research offers hotel managers practical tools for strategic planning in customer relationship management. |
| format | Article |
| id | doaj-art-85f1d3d7c19a44e498853f56ff5c2929 |
| institution | DOAJ |
| issn | 2645-4343 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-85f1d3d7c19a44e498853f56ff5c29292025-08-20T03:12:22ZengUniversity of science and cultureInternational Journal of Web Research2645-43432025-04-0182799210.22133/ijwr.2025.508322.1272Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM TechniquesLeila Taherkhani0https://orcid.org/0000-0002-4643-9700Amir Daneshvar1https://orcid.org/0000-0001-7846-2107Hossein Amoozad Khalili2https://orcid.org/0000-0001-7222-2233MohammadReza Sanaei 3Department of Information Technology Management, Faculty of Management and Economies, Science and Research Branch, Islamic Azad University, Tehran, Iran.Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Industrial Engineering, Sari Branch, Islamic Azad University, Sari, IranDepartment of Information Technology Management, Qazvin Branch, Islamic Azad University, Qazvin, IranIn today’s data-driven hospitality sector, customer interactions increasingly occur through digital platforms, generating extensive behavioral and transactional information. This study analyse the prediction of Customer Lifetime Value (CLV) using machine learning models—Linear Regression, Random Forest, and LightGBM—trained on features derived from hotel website interactions and booking records. After comprehensive data preprocessing, the models were evaluated using MAE, RMSE, and R² metrics. LightGBM achieved the highest predictive performance (R² = 0.504), followed by Random Forest (R² = 0.497), while Linear Regression underperformed (R² = 0.386), highlighting the advantages of non-linear models in modeling intricate customer patterns. Residual analyses confirmed LightGBM's stability and low bias across diverse customer profiles. Apart from prediction, the study applies Recency-Frequency-Monetary (RFM) analysis to segment customers into distinct value-based groups. These segments form the basis for tailored marketing strategies, allowing hotels to allocate resources more efficiently, enhance customer retention, and develop targeted campaigns aligned with customer potential. By integrating web-derived behavioral data with advanced modeling and segmentation, this research offers hotel managers practical tools for strategic planning in customer relationship management.https://ijwr.usc.ac.ir/article_221737_7c7d968f0ba26030f74c4f707cabd7ad.pdfcustomer lifetime value (clv)machine learningrandom forestlightgbmrfm |
| spellingShingle | Leila Taherkhani Amir Daneshvar Hossein Amoozad Khalili MohammadReza Sanaei Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques International Journal of Web Research customer lifetime value (clv) machine learning random forest lightgbm rfm |
| title | Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques |
| title_full | Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques |
| title_fullStr | Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques |
| title_full_unstemmed | Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques |
| title_short | Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques |
| title_sort | analysis and optimization of customer lifetime value prediction using machine learning and deep learning models by rfm techniques |
| topic | customer lifetime value (clv) machine learning random forest lightgbm rfm |
| url | https://ijwr.usc.ac.ir/article_221737_7c7d968f0ba26030f74c4f707cabd7ad.pdf |
| work_keys_str_mv | AT leilataherkhani analysisandoptimizationofcustomerlifetimevaluepredictionusingmachinelearninganddeeplearningmodelsbyrfmtechniques AT amirdaneshvar analysisandoptimizationofcustomerlifetimevaluepredictionusingmachinelearninganddeeplearningmodelsbyrfmtechniques AT hosseinamoozadkhalili analysisandoptimizationofcustomerlifetimevaluepredictionusingmachinelearninganddeeplearningmodelsbyrfmtechniques AT mohammadrezasanaei analysisandoptimizationofcustomerlifetimevaluepredictionusingmachinelearninganddeeplearningmodelsbyrfmtechniques |