Machine Learning
Marketing Science
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
A recommender system to avoid customer churn: A case study
Expert Systems with Applications: An International Journal
A proximate dynamics model for data mining
Expert Systems with Applications: An International Journal
Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Investigating the post-complaint period by means of survival analysis
Expert Systems with Applications: An International Journal
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Expert Systems with Applications: An International Journal
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Data mining for credit card fraud: A comparative study
Decision Support Systems
Ensembles of probability estimation trees for customer churn prediction
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Tuning metaheuristics: A data mining based approach for particle swarm optimization
Expert Systems with Applications: An International Journal
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
An integrated classification method: combination of LP and LDA
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Selecting prospects for cross-selling financial products using multivariate credibility
Expert Systems with Applications: An International Journal
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Improving customer retention in financial services using kinship network information
Expert Systems with Applications: An International Journal
A novel approach to estimate proximity in a random forest: An exploratory study
Expert Systems with Applications: An International Journal
Customer event history for churn prediction: How long is long enough?
Expert Systems with Applications: An International Journal
Optimal customer selection for cross-selling of financial services products
Expert Systems with Applications: An International Journal
Kernel Factory: An ensemble of kernel machines
Expert Systems with Applications: An International Journal
Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines
Expert Systems with Applications: An International Journal
A Joint Model of Usage and Churn in Contractual Settings
Marketing Science
Automated trading with performance weighted random forests and seasonality
Expert Systems with Applications: An International Journal
Hi-index | 12.09 |
In an era of strong customer relationship management (CRM) emphasis, firms strive to build valuable relationships with their existing customer base. In this study, we attempt to better understand three important measures of customer outcome: next buy, partial-defection and customers' profitability evolution. By means of random forests techniques we investigate a broad set of explanatory variables, including past customer behavior, observed customer heterogeneity and some typical variables related to intermediaries. We analyze a real-life sample of 100,000 customers taken from the data warehouse of a large European financial services company. Two types of random forests techniques are employed to analyze the data: random forests are used for binary classification, whereas regression forests are applied for the models with linear dependent variables. Our research findings demonstrate that both random forests techniques provide better fit for the estimation and validation sample compared to ordinary linear regression and logistic regression models. Furthermore, we find evidence that the same set of variables have a different impact on buying versus defection versus profitability behavior. Our findings suggest that past customer behavior is more important to generate repeat purchasing and favorable profitability evolutions, while the intermediary's role has a greater impact on the customers' defection proneness. Finally, our results demonstrate the benefits of analyzing different customer outcome variables simultaneously, since an extended investigation of the next buy-partial-defection-customer profitability triad indicates that one cannot fully understand a particular outcome without understanding the other related behavioral outcome variables.