Earticle

초록

영어

Customer Churn Prediction is the process of identifying customers who are likely to stop using a company's products or services in the near future that is critical for the long-term financial stability of a business. Retaining existing customers is often more cost-effective than acquiring new ones, making churn prediction a key focus for customer relationship management (CRM). This study aimed to identify customer churn in data source containing 8,047 sale transactions with various features such as sales, profit, and product category. The four techniques of churn labels generation were introduced base-on features of Time, Value, and Feedback. Additionally, a combination of Time and Value also used to test. The data was split into 80% training and 20% testing subsets, focusing on seven selected features and the Multiple Criteria churn label. Four machine learning models—Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), and Support Vector Machines (SVM) were used to create model. The results showed that LR (73.60%) and SVM (71.70%) performed a good performance in terms of accuracy. However, to compare with dataset that included churn label as E-commerce Customer Behavior and Purchase Dataset [23] the results showed that the proposed techniques can be used to impute a churn label attribute which effecting to a classification model.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Customer Churn in Retail Business
B. Customer Lifetime Value (CLV)
C. Machine Learning Algorithms
D. Feature engineering for create new target label with Multiple Criteria Techniques
III. RESEARCH METHODOLOGY
A. Churn prediction process
B. Data Collection and Data Preparation Procession
C. Feature Engineering
D. Data Splitting
E. Model Selection
IV. PERFORMANCE EVALUATION
V. Experiment Results
VI. CONCLUSION
REFERENCES

저자

• Ekasak Chitcharoen [ Department of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]
• Khanyaluck Subkrajang [ Department of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]
• Maleerat Maliyaem [ Department of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]

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