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Customer attrition and churn modeling
- 1. Customer attrition and churn modeling Variables and model types
- 2. Some interesting facts ► Spikes in the churn rate at the end of the deposit period ► We have to adapt the active time line for every customer ► The last 3 months before the churn are the most informative ► Different account types have different patterns in churn ► Slow attriters pay down their outstanding balance until they become inactive ► Fast attriters quickly pay down their balance and either lapse it or close it ► Proper segmentation of the customers is the key for further precise modeling
- 3. Groups of variables ► Account transaction IN ► Account transaction OUT ► Service indicators ► Personal profile ► Customer level information
- 4. Basic attributes ► Age ► Sex ► Education ► Income ► Occupation ► Service starts ► Asset to liability ratio ► Date of account opening
- 5. Business variables ► Deposit accounts ► Deposit balance ► The number of deposits ► Monthly amount of consumption ► Contract ending date ► Decreasing account balance ► Decreasing number of credit card purchase ► Customer profitability for the bank base on duration and age ► Number of phone payments ► Number of ATM transactions ► Number of card payments ► Time of last transaction ► First contact date ► Average debit amount per month ► Average credit amount per month ► Status: active, not active, churn ► Account type (segment): saving, current, loan, deposit etc. ► Spending speed ► Annual charge date ► Maximum transaction amount ► Salary account
- 6. Models’ review Approach Best model Article CART + TreeNet + C5.0 CART Chandar, M Laha, A., and Krishna, P. Modeling churn behavior of bank customers using predictive data mining techniques. [J] National Conference on Soft Computing Techniques for Engineering Applications (SCT-2006). 2006, (3) 24-26 FUZZY C-Means clustering N/A Popovic,D. and Basic B.D. Churn prediction Model in retail banking Using Fuzzy C-means Algorithms .[J] Informatica 2009 (33) 243-247 Sampling techniques + gradient Boosting + weighted random forest Weighted random forest Buzes, J. and Poel D.V. Handling Class Imbalance in Customer Churn Prediction[J] Expert System with Applications 2009 (36) 5445-5449 SVM + random sampling + imbalance characteristic of data set N/A Benan He, Yong Shi, Qian Wan, Xi Zhao, Prediction of customer attrition of commercial banks based on SVM model. Procedia Computer Science 31 (2014) 423-430
- 7. Models’ review Approach Good performance Article Artificial –based classification (ABC) using Artificial Natural Networks (ANN) 3 Fan Li, Juan Lei, Ying Tian, Sakuna Punyapatthanakul, Yanbo J. Wang Model Selection Strategy for Customer Attrition. Risk Prediction in Retail Banking. Proceedings of the 9th Australian Data Mining Conference. Bayesian-based classification (BBC) or Naïve Bayes (NB) 6 Case-Based classification (CBC) k-NN 4 Rule-based classification (RUBC) 1 Regression based classification. (RBC) logistic regression (LR) 5 Tree-based classification (TBC) –greedy algorithm 2