Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry
Open accessAbstract
A top priority in any business is a constant need to increase revenue and profitability.One of the causes for a decrease in profits is when current customers stop transacting.When a customer leaves or churns from a business, the opportunity for potential sales or cross selling is lost.If a customer leaves the business without any form of advice, the company may find it hard to respond and take corrective action.Ideally companies should adopt a proactive and identify potential churners prior to them leaving.Customer retention strategies have been noted to be less costly than attracting new customers.Through data available within the Point of Sales (POS) systems, customer transactions may be extracted and their buying patterns may be analysed.This paper demonstrates how through transactional data features are created and may be identified as significant to predict churn within the retail industry.The data provided within this paper pertains to a local supermarket.Therefore, the churners identified and results attained are based on real scenarios.The novelty of this paper is the concept of implementing deep learning algorithms.Convolution Neural Networks and Restricted Boltzmann Machine are the selected deep learning techniques.The Restricted Boltzmann Machine attained the best results that of 83% in predicting customer churn. Index Terms-Customer churn
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Zhu et al. improved the performance of the prediction model by compressing sparse data through a long-short-term memory autoencoder and then concatenating the data with fully connected neural networks (FCNNs).
“Zhu, Lingxue, and Nikolay Laptev.(2017) improved the performance of the prediction model by compressing sparse data through a longshort-term memory autoencoder and then concatenating the data with fully connected neural networks (FCNNs) [143].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [143]
The Implementation section describes two deep-learning algorithms, and the convolutional-network section describes fully connected layers, but neither passage addresses Zhu and Laptev's claimed method. Searches for their names, an LSTM autoencoder, sparse-data compression, and concatenation found no passage supporting the attributed claim. Page numbers were not available in our copy. (LLM, conf=0.76)
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Research works printed but not linked: 12
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A Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108 · see citing works
- Authors
- Dingli, A., Marmara, V., & Fournier, N. S.
- Affiliations
- University of Malta (MT)
- ORCID
- Dingli, Alexiei
- Venue
- International Journal of Machine Learning and Computing
- Publisher
- EJournal Publishing
- Published
- 2017-10-01
- Volume
- 7
- Issue
- 5
- Pages
- 128
- ISSN
- 2010-3700 (ISSN-L)
- OpenAlex
- W2780576732
- Cited by
- 51
- References
- 0
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 5 · 2023: 14 · 2022: 7 · 2021: 3 · 2020: 13 · 2019: 2
- FWCI
- 7.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Machine Learning and Computing (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- customer churn prediction, customer retention, retail trade, supermarkets, convolutional neural networks, restricted Boltzmann machine, deep learning model comparison
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