Effective ML Techniques to Predict Customer Churn

De, Soumi; Prabu, P.; Paulose, Joy

doi:10.1109/icirca51532.2021.9544785

Cited by 36 publications

(6 citation statements)

References 28 publications

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“…It has been proved that recent progress in deep learning yields productive ways for improving churn prediction models. Deep learning is powerful because it is able to learn hierarchical data representations and, consequently, it has been shown to noticeably outperform traditional machine learning models in identifying complex patterns in large datasets [7]. Various deep learning architectures, e.g., Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and their flavors like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) have been tentatively applied to churn prediction and these models, have displayed state-of-the-art performance on myriad sequential and time-series data -making them tailor-made for analyzing customer interaction sequences, and transaction histories [8].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Sedighimanesh,

Gheisari

2024

Preprint

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Background: In today’s competitive market, predicting customer churn with high accuracy is crucial for enterprises to maintain growth and profitability. Traditional predictive models often lack in accuracy due to the complexity of customer behavior.Objective: This research aims to improve the accuracy of predicting customer churn by utilizing the Particle Swarm Optimization (PSO) algorithm for optimizing the hyperparameters of a composite deep learning model. The performance of this enhanced model is evaluated against traditional models such as LSRM_GRU, LSTM, GRU, and CNN_LSTM to demonstrate the effectiveness of PSO in hyperparameter tuning.Methods: A composite deep learning approach was employed, integrating various neural network architectures to leverage their strengths in modeling complex customer interactions. The PSO algorithm was used to optimize the model’s hyperparameters. Customer transaction and interaction data from different business operations served as the dataset for testing and analyzing the model’s performance. Evaluation metrics including accuracy, precision, recall, F1 score, and ROC AUC were utilized for a detailed comparison with established models.Findings: The PSO-enhanced composite deep learning model showed superior performance across all metrics, significantly outperforming the LSRM_GRU, LSTM, GRU, and CNN_LSTM models. Notably, improvements in ROC AUC and F1 score highlight the robustness and balanced precision-recall trade-off of the proposed model, demonstrating its effectiveness in identifying potential churners.Conclusion: The integration of PSO for hyperparameter optimization in composite deep learning models for customer churn prediction has proven to significantly enhance predictive accuracy and performance metrics over conventional models. This underscores the potential of evolutionary algorithms in improving deep learning applications. Future research should explore the scalability of this approach across various sectors and further refine predictive accuracy with evolving customer data.

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Section: Related Workmentioning

confidence: 99%

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Sedighimanesh,

Gheisari

2024

Preprint

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“…The paper underscores the significance of customer retention for a company's growth. It reviews various machine learning techniques used in recent years for churn prediction, emphasizing the need for well-defined model evaluation measures and the potential of analyzing information-rich content in customer-company interactions [18]. The study underscores the integration of AI and ML in CRM tools, emphasizing the significance of churn prediction in the banking sector.…”

Section: Related Workmentioning

confidence: 99%

Customer Personality Analysis for Churn Prediction Using Hybrid Ensemble Models and Class Balancing Techniques

Ahmad,

Awan,

Nobanee

et al. 2024

IEEE Access

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Today's businesses rely heavily on focused marketing to improve their chances of growing and keeping their consumer base. Internet behemoths like Google and Facebook have expanded their business models around targeted advertisements that support business growth. Customer personality identification helps for churn prediction for companies. This problem arises in several companies where customer leaves companies for many reasons. This gap leads to conduct study for customer personality analysis. The collected dataset was highly imbalanced in nature. Two class balancing approaches CTGAN (Conditional tabular Generative adversarial networks) and SMOTE (Synthetic minority oversampling technique) has been utilized to equalize the both classes. There are three ensemble approaches such as bagging, boosting and stacking have been utilized for modeling purpose bagging approach uses Random Forest (RF) boosting utilizes XGBoost (XGB), Light Gradient Boosting Machine (LGBM) and ADA Boost (ADA B). The proposed Hybrid Model HSLR comprises of RF, XGB, ADA Boost, LGBM approaches as base classifiers and LR as a Meta classifier. Three testing independent set, k-fold with 5 and 10 folds have been utilized. To evaluate the performance of classifiers evaluation metrics such as Accuracy score, Precision, Recall, F1 score, MCC and ROC score have been utilized. The SMOTE generated data has shown results as compare with CTGAN generated data. The SMOTE approach has shown the highest results of 94.06, 94.23, 94.28, 94.05, 88.13 and 0.984 as accuracy score, Precision, recall, F1, MCC and Roc score respectively.

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“…The data set has been analyzed using the Automated Machine Learning pipeline PyCaret (Ali 2020) in the Google Colab notebook environment (Bisong 2019). Different algorithms, among those most commonly adopted in churn prediction applications, have been applied: Regression models such as Logistic Regression (LR), Linear Discriminant Analysis (LDA) and Ridge Regression (R; Bhatnagar and Srivastava 2019); Boosted Tree techniques (De et al 2021) such as Gradient Boosting (GB), Extreme Gradient Boosting (XGB), CatBoost (CAT), and Extra trees classifier The adopted strategy for estimating predictive performance is based on repeated stratified nested cross-validation (CV) that involves treating model hyper-parameter optimization as part of the model itself and evaluating it within the broader V-fold CV procedure for models evaluation and comparison. Namely, the CV procedure for model hyper-parameter optimization (i.e.…”

Section: Data Setmentioning

confidence: 99%

Fair evaluation of classifier predictive performance based on binary confusion matrix

2022

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Evaluating the ability of a classifier to make predictions on unseen data and increasing it by tweaking the learning algorithm are two of the main reasons motivating the evaluation of classifier predictive performance. In this study the behavior of Balanced $$AC_1$$ A C 1 — a novel classifier accuracy measure — is investigated under different class imbalance conditions via a Monte Carlo simulation. The behavior of Balanced $$AC_1$$ A C 1 is compared against that of several well-known performance measures based on binary confusion matrix. Study results reveal the suitability of Balanced $$AC_1$$ A C 1 with both balanced and imbalanced data sets. A real example of the effects of class imbalance on the behavior of the investigated classifier performance measures is provided by comparing the performance of several machine learning algorithms in a churn prediction problem.

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Effective ML Techniques to Predict Customer Churn

Cited by 36 publications

References 28 publications

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Customer Personality Analysis for Churn Prediction Using Hybrid Ensemble Models and Class Balancing Techniques

Fair evaluation of classifier predictive performance based on binary confusion matrix

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