Original Research · Open access
Attrition Rate Prediction Using A Frequency-Recency-Monetization-Based Smoteen-Boosted Approach
Abstract
Businesses today seek to generate more revenue for their products and services. However, attrition or churn for products and services over the digital market space has continued to plague businesses to proffer consumers with various alternates and choices to services and products for competitive money’s worth. Today’s businesses must thus, seek to yield better value while meeting consumer needs cum demands through a retention strategy. Thus, our study utilizes the recency-frequency-monetization as retention model that explores SMOTEEN-based AdaBoost ensemble to study the impact of imbalanced dataset to identify consumer churn. With chi-square feature selection, our proposed model yields F1 0.9819, Accuracy 0.9882, Recall 0.9848, Precision 0.9886, and Specificity 0.9824 respectively with 9632-correctly identified and 15-incorrectly classified cases. Conversely, other scheme yields ranges of F1 [0.6305–0.8875], Accuracy [0.6432-0.8879], Precision [0.6320–0.8805], Recall [0.6128-0.8876] and Specificity [06370-0.8894] respectively. This shows that model yields enhanced performance, and that the explored RFM-retention strategy was found to curb churn and improve monetization.
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Onoma, P., Agboi, J., Ugbotu, E. V., Aghaunor, T. C., Odiakaose, C. C., Ojugo, A. A., Eboka, A. O., Binitie, A. P., Ezzeh, P. O., Ejeh, P. O., Onochie, C. C., Geteloma, V. O., Emordi, F. U., Orobor, A. I., & Obruche, C. O. (2025). Attrition Rate Prediction Using A Frequency-Recency-Monetization-Based Smoteen-Boosted Approach. International Journal of Advanced Computing and Intelligent System, 3(1), online first. https://doi.org/10.66917/ijacis.a000020
Onoma, Paul, et al. “Attrition Rate Prediction Using A Frequency-Recency-Monetization-Based Smoteen-Boosted Approach.” International Journal of Advanced Computing and Intelligent System, vol. 3, no. 1, 2025, pp. online first. doi:10.66917/ijacis.a000020
Onoma, Paul, Joy Agboi, Eferhire Valentine Ugbotu, Tabitha Chukwudi Aghaunor, Chris Chukwufunaya Odiakaose, Arnold Adimabua Ojugo, Andrew Okonji Eboka, Amaka Pat Binitie, Peace Oguguo Ezzeh, Patrick Ogholuwarami Ejeh, Chris Chukwudi Onochie, Victor Ochuko Geteloma, Frances Uche Emordi, Anderson Ise Orobor, and Chris Obaro Obruche. “Attrition Rate Prediction Using A Frequency-Recency-Monetization-Based Smoteen-Boosted Approach.” International Journal of Advanced Computing and Intelligent System 3(1) (2025): online first. https://doi.org/10.66917/ijacis.a000020
Onoma, P.; Agboi, J.; Ugbotu, E. V.; Aghaunor, T. C.; Odiakaose, C. C.; Ojugo, A. A.; Eboka, A. O.; Binitie, A. P.; Ezzeh, P. O.; Ejeh, P. O.; Onochie, C. C.; Geteloma, V. O.; Emordi, F. U.; Orobor, A. I.; Obruche, C. O. (2025) ‘Attrition Rate Prediction Using A Frequency-Recency-Monetization-Based Smoteen-Boosted Approach’, International Journal of Advanced Computing and Intelligent System, 3(1), pp. online first. doi:10.66917/ijacis.a000020
BibTeX
@article{msisijacis-a000020,
title = {Attrition Rate Prediction Using A Frequency-Recency-Monetization-Based Smoteen-Boosted Approach},
author = {Paul Onoma and Joy Agboi and Eferhire Valentine Ugbotu and Tabitha Chukwudi Aghaunor and Chris Chukwufunaya Odiakaose and Arnold Adimabua Ojugo and Andrew Okonji Eboka and Amaka Pat Binitie and Peace Oguguo Ezzeh and Patrick Ogholuwarami Ejeh and Chris Chukwudi Onochie and Victor Ochuko Geteloma and Frances Uche Emordi and Anderson Ise Orobor and Chris Obaro Obruche},
journal = {International Journal of Advanced Computing and Intelligent System},
year = {2025},
volume = {3},
number = {1},
doi = {10.66917/ijacis.a000020}
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