ChOracle: A Unified Statistical Framework for Churn Prediction

Khodadadi, Ali; Hosseini, Sayed Mostafa; Pajouheshgar, Ehsan; Mansouri, Farnam; Rabiee, Hamid R.

doi:10.1109/tkde.2020.3000456

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…T-tests and chi-square statistics were used to predict customer behavior and perceptions. Khodadadi et al [19] presented ChOracle, an oracle that forecasts user churn by modeling user return times to a service utilizing a blend of Temporal Point Processes and Recurrent Neural Networks. And they showcase ChOracle's outstanding performance across various real datasets.…”

Section: Literature Review Of Customers Churn Prediction Methodsmentioning

confidence: 99%

The causes of bank customer churn based on XGBoost and LightGBMmodels: the evidence from the Kaggle dataset

Cai

2024

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As the digital economy continues to grow, the expansion of Internet finance introduces new challenges for the conventional banking industry. Banks must deal with multiple pressures, such as digital transformation, declining customer loyalty, and fintech competition. Analyzing the potential factors of bank customer churn from multiple perspectives and constructing models for predicting churn can help bank managers understand the causes of churn, identify problems, detect potential churn customers promptly, and develop efficient retention strategies based on customer characteristics and preferences. In this paper, we used a combination of visualization, data mining, and machine learning methods to analyze the factors used to predict bank customer churn from multiple perspectives, such as feature selection (Random Forest Feature Importance Ranking), feature extraction (PCA), visualization, etc. We also constructed two churn prediction models based on the gradient boosting tree algorithms, XGBoost and LightGBM, compared the evaluation measures before and after feature selection and before and after tuning parameters, and interpreted the model through SHAP methods. After the paper, the following conclusions were drawn: (1) Total Trans Amt, Total Trans Ct, and Total Revolving Bal are pivotal in analyzing and predicting customer churn; (2) the SHAP Summary Plot can react to the visual analysis of predictors of customer churn to a certain extent; (3) the effect of feature selection on the assessment of the results is sometimes insignificant; (4) tuning parameter settings can enhance model performance to a certain extent, but the optimal parameters may vary based on the preprocessing method employed. These conclusions will assist banks in comprehending customer churn factors more deeply, constructing a higher performance churn prediction model, and conducting a comprehensive result synthesis analysis.

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Section: Literature Review Of Customers Churn Prediction Methodsmentioning

confidence: 99%

The causes of bank customer churn based on XGBoost and LightGBMmodels: the evidence from the Kaggle dataset

Cai

2024

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“…According to Yang et al [20], the aggregated vehicle fuel consumption data are protected against time-series-based differential attacks using a negative survey method. Khodadadi et al [21] used deep learning methods for the prediction of customer churn. ey used return time for the analysis, using recurrent neural networks.…”

Section: Literature Review Of Churn Prediction Methodsmentioning

confidence: 99%

“…e study of principal components is one of the outcomes of linear algebra mathematics because the nonparametric and straightforward method extracts relevant information from confusing sets. e transformation of the T can be obtained by minimizing the least-squares error, assuming that the CCPBI-TAMO, CPIO-FS Telecom Precision, recall, accuracy, F-Score, ROC [41] Xgboost, AdaBoost, catboost, decision trees, SVM, KNN Telecom Accuracy, AUC, precision, recall, F-Measures [37] Deep feed-forward networks Subscription companies Accuracy [38] Deep ANN, machine learning algorithms Telecom Accuracy, precision, recall, F1-score, and AUC [12] Neural network with bagging Telecom Accuracy, precision, recall, F-score, kappa, absolute error, relative error, and classi cation error [10] Transfer learning of ensemble Telecom Area under curve of ROC (AUC) and complexity [11] Ensemble algorithm Telecom Area under curve of ROC (AUC) [12] Begging and neural network Telecom Accuracy and precision of classi cation [42] Arti cial neural network (ANN) and self-organized map (SOM) Telecom Accuracy, recall, F-score, and precision [15] Pro t tree Telecom Accuracy, cost, and pro t [16] Minimax probability machines Telecom AUC and EMPC [17] similarity forests Telecom AUC, and tenlift AUPR [21] Temporal point processes (TPP) and recurrent neural networks (RNN) Telecom MAE and MRE [22] Cross-company just-in-time approach Telecom Accuracy, Kappa, and Recall [25] Multiobjective and colony optimization Telecom AUC [27] graph theory Telecom Top decile lift [31] Boosted…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions

Mirabdolbaghi

Amiri

2022

Discrete Dynamics in Nature and Society

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Currently, Customers are struggling to retain their business in today’s competitive markets. Thus, the issue of customer churn becomes a significant challenge for the industries. In order to achieve this, it is vital to have an efficient churn prediction system. In this paper, we discuss methods for reducing features using PCA, Autoencoders, LDA, T-SNE, and Xgboost. In this paper, a model for predicting light GBM churn is proposed. The model consists of five steps. The first step is to preprocess the data so that missing and corrupt values can be handled and the data can be scaled. Secondly, implementing a comprehensive feature reduction system based on popular algorithms reduces the features and selects the most suitable one. In the third step, light GBM’s hyperparameter is tuned using Bayesian hyperparameter optimization and genetic optimization algorithms. Lastly, interpreting the model and evaluating the impact of the features on model outputs by using the SHAP method, and finally ranking the churners by customer lifetime value. Aside from evaluating and choosing the best feature reduction methods, the proposed method is also evaluated using four famous datasets. It outperforms other ensemble and ML algorithms like AdaBoost, SVM, and decision tree on over seven evaluation metrics: accuracy, area under the curve (AUC), Kappa, Mathews correlation coefficient (MCC), Brier score, F1 score, and EMPC. In light of the evaluation metrics, our model shows a significant improvement in handling imbalanced datasets in churn prediction. Finally, in this paper, interpretability and how the features affect the model’s output are presented by the SHAP method. Then CLV ranking is suggested for better decision-making.

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“…Therefore, how to retain users is drawing increasing attention. Specifically, as the most critical part of a typical user retention procedure, predicting user churn has become a key concern for both academia and industry [14,33].…”

Section: Introductionmentioning

confidence: 99%

A Counterfactual Modeling Framework for Churn Prediction

Zhang

Zeng

Zhao

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Accurate churn prediction for retaining users is keenly important for online services because it determines their survival and prosperity. Recent research has specified social influence to be one of the most important reasons for user churn, and thereby many works start to model its effects on user churn to improve the prediction performance. However, existing works only use the data's correlational information while neglecting the problem's causal nature. Specifically, the fact that a user's churn is correlated with some social factors does not mean he/she is actually influenced by his/her friends, which results in inaccurate and unexplainable predictions of the existing methods. To bridge this gap, we develop a counterfactual modeling framework for churn prediction, which can effectively capture the causal information of social influence for accurate and explainable churn predictions. Specifically, we first propose a backbone framework that uses two separate embeddings to model users' endogenous churn intentions and the exogenous social influence. Then, we propose a counterfactual data augmentation module to introduce the causal information to the model by providing partially labeled counterfactual data. Finally, we design a three-headed counterfactual prediction framework to guide the model to learn causal information to facilitate churn prediction. Extensive experiments on two large-scale datasets with different types of social relations show our model's superior prediction performance compared with the state-of-the-art baselines. We further conduct an in-depth analysis of the prediction results demonstrating our proposed method's ability to capture causal information of social influence and give explainable churn predictions, which provide insights into designing better user retention strategies. CCS CONCEPTS• Information systems → Enterprise applications; Social networks; • Computing methodologies → Neural networks.

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ChOracle: A Unified Statistical Framework for Churn Prediction

Cited by 6 publications

References 37 publications

The causes of bank customer churn based on XGBoost and LightGBMmodels: the evidence from the Kaggle dataset

The causes of bank customer churn based on XGBoost and LightGBMmodels: the evidence from the Kaggle dataset

Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions

A Counterfactual Modeling Framework for Churn Prediction

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