Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Customer Churn Prevention For E-commerce Platforms using Machine Learning-based Business Intelligence

Author(s): Pundru Chandra Shaker Reddy, Yadala Sucharitha* and Aelgani Vivekanand

Volume 17, Issue 5, 2024

Published on: 20 September, 2023

Page: [456 - 465] Pages: 10

DOI: 10.2174/2352096516666230717102625

Price: $65

Abstract

Aims & Background: Businesses in the E-commerce sector, especially those in the business- to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations.

Objective: The main objective of this research is to detect probable churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies.

Methodology: Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services.

Results: The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model.

Conclusion: To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle.

Keywords: Index terms e-commerce customer churn, hybrid algorithm, personalized retention, support vector machine, machine learning, artificial intelligence.

« Previous Next »
Graphical Abstract

[1]
J. Shobana, C. Gangadhar, R.K. Arora, P.N. Renjith, and J. Bamini, "“E-commerce customer churn prevention using machine learning-based business intelligence strategy”, Measurement", Sensors, vol. 27, p. 100728, 2023.
[2]
M. Pondel, M. Wuczyński, W. Gryncewicz, Ł. Łysik, M. Hernes, A. Rot, and A. Kozina, "Deep Learning for Customer Churn Prediction in E-Commerce Decision Support", In 24th International Conference on Business Information Systems (BIS 2021), 2021.
[http://dx.doi.org/10.52825/bis.v1i.42]
[3]
P.C.S. Reddy, S. Yadala, and S.N. Goddumarri, "Development of rainfall forecasting model using machine learning with singular spectrum analysis", IIUM Engineering Journal, vol. 23, no. 1, pp. 172-186, 2022.
[http://dx.doi.org/10.31436/iiumej.v23i1.1822]
[4]
K. Kumar, S.V. Pande, T.C.A. Kumar, P. Saini, A. Chaturvedi, P.C.S. Reddy, and K.B. Shah, "Intelligent controller design and fault prediction using machine learning model", Int. Trans. Electr. Energy Syst., vol. 2023, pp. 1-9, 2023.
[http://dx.doi.org/10.1155/2023/1056387]
[5]
P.C. Shaker Reddy, and Y. Sucharitha, "IoT-enabled energy-efficient multipath power control for underwater sensor networks", Int. J. Sensors Wirel. Commun. Control, vol. 12, no. 6, pp. 478-494, 2022.
[http://dx.doi.org/10.2174/2210327912666220615103257]
[6]
S. Sang, C. Xu, Z. Wang, C. Side, B. Fowler, J. Fan, and D. Miao, "Accurate prediction of topology of composite plates via machine learning and propagation of elastic waves", Compos. Commun., vol. 37, p. 101465, 2023.
[http://dx.doi.org/10.1016/j.coco.2022.101465]
[7]
R. Dhanalakshmi, N.P.G. Bhavani, S.S. Raju, P.C. Shaker Reddy, D. Mavaluru, D.P. Singh, and A. Batu, "Onboard pointing error detection and estimation of observation satellite data using extended kalman filter", Comput. Intell. Neurosci., vol. 2022, pp. 1-8, 2022.
[http://dx.doi.org/10.1155/2022/4340897] [PMID: 36248921]
[8]
K. Ashok, R. Boddu, S.A. Syed, V.R. Sonawane, R.G. Dabhade, and P.C.S. Reddy, "GAN Base feedback analysis system for industrial IOT networks", Automatika, pp. 1-9, 2022.
[9]
P.C.S. Reddy, G. Suryanarayana, and S. Yadala, "Data Analytics in Farming: Rice price prediction in Andhra Pradesh", In 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), vol. 2022, 2022, p. 1p. 1
[10]
Y. Sucharitha, and P.C. Shaker Reddy, "An autonomous adaptive enhancement method based on learning to optimize heterogeneous network selection", Int. J. Sensors Wirel. Commun. Control, vol. 12, no. 7, pp. 495-509, 2022.
[http://dx.doi.org/10.2174/2210327912666221012154428]
[11]
H. Jain, G. Yadav, and R. Manoov, "Churn prediction and retention in banking, telecom and IT sectors using machine learning techniques", In Advances in Machine Learning and Computational Intelligence Proceedings of ICMLCI, vol. 2019, 2020, pp. 137-156
[12]
P.C. Shaker Reddy, and A. Sureshbabu, "An enhanced multiple linear regression model for seasonal rainfall prediction", Int. J. Sensors Wirel. Commun. Control, vol. 10, no. 4, pp. 473-483, 2020.
[http://dx.doi.org/10.2174/2210327910666191218124350]
[13]
K.A. Muthappa, A.S.A. Nisha, R. Shastri, V. Avasthi, and P.C.S. Reddy, "Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs", Appl. Nanosci., pp. 1-10, 2023.
[http://dx.doi.org/10.1007/s13204-023-02814-5]
[14]
P. Chillakuru, M. Madiajagan, K.V. Prashanth, S. Ambala, P.C. Shaker Reddy, and J. Pavan, "Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm", Soft Comput., pp. 1-11, 2023.
[http://dx.doi.org/10.1007/s00500-023-08358-8]
[15]
Y. Sucharitha, Y. Vijayalata, and V.K. Prasad, "Predicting election results from twitter using machine learning algorithms", Recent Adv. Comput. Sci. Commun., vol. 14, no. 1, pp. 246-256, 2021.
[16]
P. Lalwani, M.K. Mishra, J.S. Chadha, and P. Sethi, "Customer churn prediction system: A machine learning approach", Computing, vol. 104, no. 2, pp. 271-294, 2022.
[http://dx.doi.org/10.1007/s00607-021-00908-y]
[17]
R. Sudharsan, and E.N. Ganesh, "A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy", Connect. Sci., vol. 34, no. 1, pp. 1855-1876, 2022.
[http://dx.doi.org/10.1080/09540091.2022.2083584]
[18]
D. AL-Najjar, N. Al-Rousan, and H. AL-Najjar, "Machine learning to develop credit card customer churn prediction", J. Theor. Appl. Electron. Commer. Res., vol. 17, no. 4, pp. 1529-1542, 2022.
[http://dx.doi.org/10.3390/jtaer17040077]
[19]
S. Kim, and H. Lee, "Customer churn prediction in influencer commerce: an application of decision trees", Procedia Comput. Sci., vol. 199, pp. 1332-1339, 2022.
[http://dx.doi.org/10.1016/j.procs.2022.01.169]
[20]
J. Faritha Banu, S. Neelakandan, B.T. Geetha, V. Selvalakshmi, A. Umadevi, and E.O. Martinson, "Artificial intelligence based customer churn prediction model for business markets", Comput. Intell. Neurosci., vol. 2022, pp. 1-14, 2022.
[http://dx.doi.org/10.1155/2022/1703696] [PMID: 36238670]
[21]
H.K. Thakkar, A. Desai, S. Ghosh, P. Singh, and G. Sharma, "Clairvoyant: AdaBoost with cost-enabled cost-sensitive classifier for customer churn prediction", Comput. Intell. Neurosci., vol. 2022, pp. 1-11, 2022.
[http://dx.doi.org/10.1155/2022/9028580] [PMID: 35103057]
[22]
M. Mirkovic, T. Lolic, D. Stefanovic, A. Anderla, and D. Gracanin, "Customer churn prediction in B2B non-contractual business settings using invoice data", Appl. Sci., vol. 12, no. 10, p. 5001, 2022.
[http://dx.doi.org/10.3390/app12105001]
[23]
A. Muneer, R. Faizan Ali, A. Alghamdi, S. Mohd Taib, A. Almaghthawi, and E.A.A. Ghaleb, "Predicting customers churning in banking industry: A machine learning approach", Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 1, pp. 539-549, 2022.
[http://dx.doi.org/10.11591/ijeecs.v26.i1.pp539-549]

Rights & Permissions Print Cite
Article Metrics
13
2
© 2024 Bentham Science Publishers | Privacy Policy