research-article

A flexible framework for customer behavior prediction based on ensemble learning

Authors:

Thi Minh Nguyen,

Thi Hau NguyenAuthors Info & Claims

SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology

Pages 126 - 134

https://doi.org/10.1145/3628797.3628973

Published: 07 December 2023 Publication History

Abstract

Predicting customer behavior is crucial for businesses, including churn and purchasing behavior. We propose a tailored model for this purpose, applied to two types of problems: customer churn and purchasing behavior prediction. Our study incorporates ensemble learning strategies, such as stacking and voting, to improve predictive performance. By combining Random Forest, CNN, and Boosting algorithms using hard voting and optimizing classifier weights with an evolutionary algorithm, our model achieves significantly higher performance in terms of AUC and F1 score. We evaluated the performance of our model on four different datasets. For the Campaign dataset, the AUC was 94.82%, and the F1 score was 68.29%. For the Cell2Cell dataset, the AUC was 86.82%, and the F1 score was 79.99%. In the case of the Bank dataset, we achieved an AUC of 87.81% and an F1 score of 89.11%. Lastly, on the Online Shoppers dataset, we obtained an AUC of 94.55% and an F1 score of 70.79%.

References

[1]

Sulaiman Olaniyi Abdulsalam, Micheal Olaolu Arowolo, Yakub Kayode Saheed, and Jesutofunmi Onaope Afolayan. 2022-06-12. Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms. Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 10, 2 (2022-06-12), 431–440. https://doi.org/10.52549/ijeei.v10i2.2985

[2]

Ibrahim Al-Shourbaji, Na Helian, Yi Sun, Samah Alshathri, and Mohamed Abd Elaziz. 2022. Boosting ant colony optimization with reptile search algorithm for churn prediction. Mathematics 10, 7 (2022), 1031. Publisher: MDPI.

[3]

Hoss Belyadi and Alireza Haghighat. 2021. Supervised learning. In Machine Learning Guide for Oil and Gas Using Python. Elsevier, 169–295.

[4]

Matthias Bogaert and Lex Delaere. 2023. Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art. Mathematics 11, 5 (2023), 1137. Publisher: MDPI.

[5]

Anouar Dalli. 2022. Impact of hyperparameters on deep learning model for customer churn prediction in telecommunication sector. Mathematical Problems in Engineering 2022 (2022). Publisher: Hindawi.

[6]

Soumi De and P. Prabu. 2023. A Representation-Based Query Strategy to Derive Qualitative Features for Improved Churn Prediction. IEEE Access 11 (2023), 1213–1223. https://doi.org/10.1109/ACCESS.2022.3233768 Conference Name: IEEE Access.

[7]

Esam Mohamed Elgohary, Mohamed Galal, Ahmed Mosa, and Ghada Atef Elshabrawy. 2023. Smart evaluation for deep learning model: churn prediction as a product case study. Bulletin of Electrical Engineering and Informatics 12, 2 (2023), 1219–1225.

[8]

Yasyn ELYUSUFI and M’hamed AIT KBIR. 2022. Churn Prediction Analysis by Combining Machine Learning Algorithms and Best Features Exploration. International Journal of Advanced Computer Science and Applications 13, 7 (2022). https://doi.org/10.14569/ijacsa.2022.0130773

[9]

Samah Wael Fujo, Suresh Subramanian, and Moaiad Ahmad Khder. 2022-01-01. Customer Churn Prediction in Telecommunication Industry Using Deep Learning. Information Sciences Letters 11, 1 (2022-01-01), 185–198. https://doi.org/10.18576/isl/110120

[10]

C. García-Martínez, O. Cordón, and F. Herrera. 2007-07. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. European Journal of Operational Research 180, 1 (2007-07), 116–148. https://doi.org/10.1016/j.ejor.2006.03.041

[11]

Louis Geiler, Séverine Affeldt, and Mohamed Nadif. 2022-09. A survey on machine learning methods for churn prediction. International Journal of Data Science and Analytics 14, 3 (2022-09), 217–242. https://doi.org/10.1007/s41060-022-00312-5

[12]

Vani Haridasan, Kavitha Muthukumaran, and K. Hariharanath. 2023. Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries.Intelligent Automation & Soft Computing 35, 3 (2023).

[13]

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning. Springer. https://doi.org/10.1007/978-0-387-84858-7

[14]

Pankaj Hooda and Pooja Mittal. 2023. Implementation and performance enhancements of optimised kernel MSVM model for early churn prediction in telecom sector. Semiconductor Optoelectronics 42, 1 (2023), 280–298.

[15]

Nagaraju Jajam and Nagendra Panini Challa. 2023. Customer Churn Detection for insurance data using Blended Logistic Regression Decision Tree Algorithm (BLRDT). International Journal of Intelligent Systems and Applications in Engineering 11, 1 (2023), 72–83.

[16]

Praveen Lalwani, Manas Mishra, Jasroop Chadha, and Pratyush Sethi. 2022-02-01. Customer churn prediction system: a machine learning approach. Computing 104 (2022-02-01), 1–24. https://doi.org/10.1007/s00607-021-00908-y

Digital Library

[17]

Venkata Pullareddy Malikireddy and Madhavi Kasa. 2021. Customer churns prediction model based on machine learning techniques: a systematic review. In 3rd international conference on integrated intelligent computing communication & security (ICIIC 2021). Atlantis Press, 167–174.

[18]

Ruba Obiedat. 2020. Developing Decision Tree based Models in Combination with Filter Feature Selection Methods for Direct Marketing. International Journal of Advanced Computer Science and Applications 11, 1 (2020). https://doi.org/10.14569/ijacsa.2020.0110180

[19]

Lewlisa Saha, Hrudaya Kumar Tripathy, Tarek Gaber, Hatem El-Gohary, and El-Sayed M. El-kenawy. 2023. Deep churn prediction method for telecommunication industry. Sustainability 15, 5 (2023), 4543. Publisher: MDPI.

[20]

Joydeb Kumar Sana, Mohammad Zoynul Abedin, M. Sohel Rahman, and M. Saifur Rahman. 2022. A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection. Plos one 17, 12 (2022), e0278095. Publisher: Public Library of Science San Francisco, CA USA.

[21]

Guankai Sang and Siyuan Wu. 2022. Predicting the Intention of Online Shoppers’ Purchasing. In 2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE. https://doi.org/10.1109/aemcse55572.2022.00074

[22]

Mohammad Javad Shabankareh, Mohammad Ali Shabankareh, Alireza Nazarian, Alireza Ranjbaran, and Nader Seyyedamiri. 2022. A stacking-based data mining solution to customer churn prediction. Journal of Relationship Marketing 21, 2 (2022), 124–147. Publisher: Taylor & Francis.

[23]

Shivam Sharma and Hemant Kumar Soni. 2020. Discernment of Potential Buyers Based on Purchasing Behaviour Via Machine Learning Techniques. In 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE). IEEE. https://doi.org/10.1109/icadee51157.2020.9368935

[24]

R. Sudharsan and E. N. Ganesh. 2022-12-31. A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy. Connection Science 34, 1 (2022-12-31), 1855–1876. https://doi.org/10.1080/09540091.2022.2083584

[25]

Muhammad Usman Tariq, Muhammad Babar, Marc Poulin, and Akmal Khattak. 2021-06-01. Distributed model for customer churn prediction using convolutional neural network. Journal of Modelling in Management ahead-of-print (2021-06-01). https://doi.org/10.1108/JM2-01-2021-0032

[26]

Stéphane CK Tékouabou, Ștefan Cristian Gherghina, Hamza Toulni, Pedro Neves Mata, and José Moleiro Martins. 2022. Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods. Mathematics 10, 14 (2022), 2379. Publisher: MDPI.

[27]

Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Luiz Fernando Capretz, Hammed Adeleye Mojeed, Saipunidzam Mahamad, Shakirat Aderonke Salihu, Abimbola Ganiyat Akintola, Shuib Basri, Ramoni Tirimisiyu Amosa, and Nasiru Kehinde Salahdeen. 2022. Intelligent Decision Forest Models for Customer Churn Prediction. Applied Sciences 12, 16 (2022), 8270. Publisher: MDPI.

[28]

Jing Xu, Jie Wang, Ye Tian, Jiangpeng Yan, Xiu Li, and Xin Gao. 2020. SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning. PLOS ONE 15, 11 (2020), e0242629. https://doi.org/10.1371/journal.pone.0242629 Publisher: Public Library of Science.

[29]

Tianpei Xu, Ying Ma, and Kangchul Kim. 2021. Telecom churn prediction system based on ensemble learning using feature grouping. Applied Sciences 11, 11 (2021), 4742. Publisher: MDPI.

[30]

Zhi-Hua Zhou. 2012. Ensemble methods: foundations and algorithms. CRC press.

Digital Library

Cited By

Zhang YLi YZhang GDing ZWu YPeng Y(2025)Application of Ensemble Learning Based on High-Dimensional Features in Financial Big DataArtificial Intelligence Security and Privacy10.1007/978-981-96-1148-5_10(117-130)Online publication date: 18-Jan-2025
https://doi.org/10.1007/978-981-96-1148-5_10
Thotakura YManikanta Yarramsetty DDoppalapudi KShasank Alaparthi SSaleti S(2024)Enhancing Customer Churn Prediction: Advanced Models and Resampling Techniques in Dynamic Business Environments2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)10.1109/ICEC59683.2024.10837309(1-6)Online publication date: 23-Nov-2024
https://doi.org/10.1109/ICEC59683.2024.10837309

Index Terms

A flexible framework for customer behavior prediction based on ensemble learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology

December 2023

1058 pages

ISBN:9798400708916

DOI:10.1145/3628797

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 07 December 2023

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SOICT 2023

SOICT 2023: The 12th International Symposium on Information and Communication Technology

December 7 - 8, 2023

Ho Chi Minh, Vietnam

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Cited By

Zhang YLi YZhang GDing ZWu YPeng Y(2025)Application of Ensemble Learning Based on High-Dimensional Features in Financial Big DataArtificial Intelligence Security and Privacy10.1007/978-981-96-1148-5_10(117-130)Online publication date: 18-Jan-2025
https://doi.org/10.1007/978-981-96-1148-5_10
Thotakura YManikanta Yarramsetty DDoppalapudi KShasank Alaparthi SSaleti S(2024)Enhancing Customer Churn Prediction: Advanced Models and Resampling Techniques in Dynamic Business Environments2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)10.1109/ICEC59683.2024.10837309(1-6)Online publication date: 23-Nov-2024
https://doi.org/10.1109/ICEC59683.2024.10837309

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