Predicting customer churn: A systematic literature review

De, Soumi; Prabu, P.

doi:10.1080/09720529.2022.2133238

Cited by 23 publications

(6 citation statements)

References 33 publications

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“…However, the generalization ability of many models has yet to be verified. While the choice of machine learning models is contingent on dataset characteristics, a limited number of studies have conducted experiments across diverse churn datasets from various domains to establish the models' validity and assess their performance [15]. Therefore, in the future, there is still a need to focus on this aspect of generalization ability, to construct more advanced hybrid models with better generalization ability and novel feature engineering methods, among others.…”

Section: Literature Review Of Customers Churn Prediction Methodsmentioning

confidence: 99%

“…Meanwhile, customer retention helps improve financial performance and supports the bank's sustainability [14]. It is widely acknowledged that the expense associated with acquiring new customers surpasses the expense of retaining existing ones [15]. This is because of the necessity for banks to allocate more resources to attract new customers to substitute for the missing ones and spend more time establishing stable relationships with new customers.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in the information-intensive financial services sector, it is not enough to focus on customer acquisition; banks also need to analyze indepth the factors that contribute to customer churn and identify trends in churn in a timely manner. Especially in a situation where customers can independently terminate their use of banking services at any time and freely choose services from different financial institutions, understanding customer churn tendencies in advance and conducting an analysis of customer churn will help banks allocate resources more efficiently to retain their customers, ensure sustainable profit [15], and achieve long-term sustainability. The financial industry, as a highly relationship-related industry, is in dire need of improving customer brand experience (BE) as well as facilitating customer engagement (CE) [16] to increase customer inertia and loyalty and thus achieve long-term customer retention.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cai

2024

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“…Moreover, there is a lack of research on leveraging the information-rich content from interactions between customers and companies (e.g., email, chat logs). Remarkably, this area remains largely unexplored in literature [8]. On the other hand, leveraging knowledge bases, including the domain knowledge and the experience of experts in the field, could be an excellent source for effective feature engineering processes [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Generative AI-enabled Knowledge Base Fine-tuning: Enhancing Feature Engineering for Customer Churn

Shahabikargar,

Beheshti,

Mansoor

et al. 2024

Preprint

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Customers are the most critical component in a business’s success regardless of the industry or product. Companies make significant efforts to acquire and, more importantly, retain their existing customers. Customer churn is a significant challenge for businesses, leading to financial losses. To address this challenge, understanding customer’s cognitive status, behaviors, and early signs of churn is crucial. However, predictive and ML-based analysis, being fed with proper features that are indicative of a customer’s cognitive status or behavior, is extremely helpful in addressing this challenge. Having practical ML-based analysis relies on a well-developed feature engineering process. Previous churn analytical studies mainly applied feature engineering approaches that leveraged demographic, product usage, and revenue features alone, and there is a lack of research on leveraging the information-rich content from interactions between customers and companies. Considering the effectiveness of applying domain knowledge and human expertise in feature engineering, and motivated by our previous work, we propose a Customer Churn-related Knowledge Base (ChurnKB) to enhance the feature engineering process. In the ChurnKB, we leverage textual data mining techniques for extracting churn-related features from texts created by customers, e.g., emails or chat logs with company agents, reviews on the company’s website, and feedback on social media. We use Generative AI (GAI) to enhance and enrich the structure of the ChurnKB regarding features related to customer churn-related cognitive status, feelings, and behaviors. We also leveraged feedback loops and crowdsourcing to enhance and approve the validity of the proposed ChurnKB and apply it to develop a classifier for customer churn problems.

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“…High churn rates can adversely affect a company's revenue and growth, prompting the need for effective strategies to predict and mitigate churn [2,3]. By understanding the factors that lead to customer churn, businesses can develop targeted interventions to retain valuable customers and enhance their overall service experience [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry

Noviandy,

Idroes,

Hardi

et al. 2024

Infolitika J. Data Sci.

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Customer churn is critical for businesses across various industries, especially in the telecommunications sector, where high churn rates can significantly impact revenue and growth. Understanding the factors leading to customer churn is essential for developing effective retention strategies. Despite the predictive power of machine learning models, there is a growing demand for model interpretability to ensure trust and transparency in decision-making processes. This study addresses this gap by applying advanced machine learning models, specifically Naïve Bayes, Random Forest, AdaBoost, XGBoost, and LightGBM, to predict customer churn in a telecommunications dataset. We enhanced model interpretability using SHapley Additive exPlanations (SHAP), which provides insights into feature contributions to predictions. Here, we show that LightGBM achieved the highest performance among the models, with an accuracy of 80.70%, precision of 84.35%, recall of 90.54%, and an F1-score of 87.34%. SHAP analysis revealed that features such as tenure, contract type, and monthly charges are significant predictors of customer churn. These results indicate that combining predictive analytics with interpretability methods can provide telecom companies with actionable insights to tailor retention strategies effectively. The study highlights the importance of understanding customer behavior through transparent and accurate models, paving the way for improved customer satisfaction and loyalty. Future research should focus on validating these findings with real-world data, exploring more sophisticated models, and incorporating temporal dynamics to enhance churn prediction models' predictive power and applicability.

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Predicting customer churn: A systematic literature review

Cited by 23 publications

References 33 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

Generative AI-enabled Knowledge Base Fine-tuning: Enhancing Feature Engineering for Customer Churn

A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry

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