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Predict customer churn using combination deep learning networks model · Study

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ArticlePredict customer churn using combination deep learning networks model

Van-Hieu Vu2023

Article2023

Predict customer churn using combination deep learning networks model

24 citations·0/0 files stored·Neural Computing and Applications

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

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References

2021Customer churn prediction system: a machine learning approach 2022B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM 2020Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series 2022Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach 2020Leveraging unstructured call log data for customer churn prediction 2022Propension to customer churn in a financial institution: a machine learning approach 2023Deep Churn Prediction Method for Telecommunication Industry 2022Machine Learning to Develop Credit Card Customer Churn Prediction

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2025A comprehensive survey on customer churn analysis studies

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2023Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industriesarticle · 6 shared references 2025Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learningreview · 5 shared references 2024Customer churn prediction model based on hybrid neural networksarticle · 4 shared references 2024Improving Churn Detection in the Banking Sector: A Machine Learning Approach with Probability Calibration Techniquesarticle · 4 shared references 2022An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customersarticle · 4 shared references 2025A comprehensive survey on customer churn analysis studiesarticle · 4 shared references

Details

Year2023

Typearticle

Published2023-12-21

VenueNeural Computing and Applications

ISSN-L0941-0643

Languageen

Citations24

Files0 public / 0

ShelfCustomer churn and segmentation

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doi10.1007/s00521-023-09327-w

openalex_workW4390053155

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