Customer Churn Prediction Research Articles

Customer Churn Analysis in Telecom Industry using Logistics Regression in Machine Learning with Kaplan–Meier and Cox Proportional Hazards Model

Customer churn poses a significant challenge for companies in the telecom industry, impacting revenue and profitability. In this paper, we present a comprehensive survey of customer churn analysis methodologies, focusing on the application of logistic regression, Kaplan-Meier survival analysis, and the Cox Proportional Hazards Model. Logistic regression serves as a fundamental technique for binary classification, enabling the prediction of churn based on customer attributes and behaviors. Kaplan-Meier survival analysis offers insights into customer survival probabilities over time, facilitating the identification of high-risk cohorts. Additionally, the Cox Proportional Hazards Model provides a flexible framework for analyzing the impact of predictor variables on churn timing, accounting for censoring and time-varying covariates. Through a review of existing literature, case studies, and practical applications, we explore the strengths and limitations of these methodologies in customer churn prediction. We also discuss challenges in data collection, feature engineering, and model evaluation, along with future research directions to enhance churn analysis effectiveness. By leveraging logistic regression with Kaplan-Meier and the Cox model, telecom companies can develop robust churn prediction models to improve customer retention strategies and mitigate revenue loss. Keywords: Customer churn analysis, Telecom industry, Logistic regression, Kaplan-Meier survival analysis, Cox Proportional Hazards Model, Retention strategies.

AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn

Understanding consumer churn is pivotal for companies in the USA to develop efficient strategies for consumer retention and reduce its negative effects on revenue and profitability. To start with, understanding client churn entails pinpointing the factors that contribute to it. This research paper delved into the application of machine learning algorithms such as Random Forests and Decision Trees for designing churn prediction models and exploring key factors that churn probabilities. The dataset used in this study was sourced from the prominent UCI repository of machine learning databases, preserved at the University of California, Irvine. This dataset provided extensive information on a total of 3333 clients, facilitating in-depth analysis and insights. Models performance evaluation comprised examining the model's efficiency using a confusion matrix. Random Forest seemed to be a relatively better performing model than Decision Tree for this specific classification task. In particular, Random Forest attained higher accuracy (96.25%), precision (91.49), Recall (83.49%), F-measure (0.87), and Phi coefficient (0.85). By deploying Random Forest and Decision Tree models, government companies can get an in-depth comprehension of the factors that lead to consumer churn. As a result, this information may enable them to tailor targeted retention strategies and interventions. By effectively retaining consumers, government organizations can maintain a stable customer base, leading to sustained revenue and economic growth.

Telco Customer Churn Prediction

In recognizing the significance of retaining current consumers to thrive in this competitive landscape, this paper aims to predict customers' churn probability based on the background and behaviors of previous customers. The paper utilizes churn probability as a proactive means to identify customers at a high risk of leaving, serving as a reference for Telco to make informed decisions and take actions aimed at enhancing customer loyalty. This paper pre-target customers who have a high risk of leaving based on our churn probability with at least over 80% accuracy rate. And then use the churn probability as the reference to help Telco make better decisions and take actions to retain customers, such as providing a private discount or coupon. This paper limits the choice of customers in the services provided by telecom companies. However, customer attrition decisions may also be related to the quality of the service and the level of price, which the study was unable to quantify. The study addresses two primary challenges: determining models that accurately predict customer churn and identifying the characteristics of customers more prone to churn.

Using machine learning methods to predict future churners: an analysis of repeat hotel customers

Purpose This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand. Design/methodology/approach Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix. Findings The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups. Research limitations/implications This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics. Originality/value Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

Hybrid black-box classification for customer churn prediction with segmented interpretability analysis

Customer retention management relies on advanced analytics for decision making. Decision makers in this area require methods that are capable of accurately predicting which customers are likely to churn and that allow to discover drivers of customer churn. As a result, customer churn prediction models are frequently evaluated based on both their predictive performance and their capacity to extract meaningful insights from the models. In this paper, we extend hybrid segmented models for customer churn prediction by incorporating powerful models that can capture non-linearities. To ensure the interpretability of such segmented hybrid models, we introduce a novel model-agnostic approach that extends SHAP. We extensively benchmark the proposed methods on 14 customer churn datasets on their predictive performance. The interpretability aspect of the new model-agnostic approach for interpreting hybrid segmented models is illustrated using a case study. Our contributions to decision making literature are threefold. First, we introduce new hybrid segmented models as powerful tools for decision makers to boost predictive performance. Second, we provide insights in the relative predictive performance by an extensive benchmarking study that compares the new hybrid segmented methods with their base models and existing hybrid models. Third, we propose a model-agnostic tool for segmented hybrid models that provide decision makers with a tool to gain insights for any hybrid segmented model and illustrate it on a case study. Although we focus on customer retention management in this study, this paper is also relevant for decision makers that rely on predictive modeling for other tasks.

A Decade of Churn Prediction Techniques in the TelCo Domain: A Survey

This work surveys the research contributions of the last decade to the prediction of customer churn and adds a perspective toward what is yet to be reached. The main objective of this article is to report on (1) the methods and algorithms studied, the evaluation metrics adopted, and the results achieved, (2) the data used, and (3) the issues and limitations identified. Furthermore, the work highlights the gaps in the current literature and suggests a direction for future research.

Customer Churn Prediction Using Machine Learning Algorithms

In today’s highly competitive industries, retaining customers is vital for sustaining business growth and profitability. Customer churn, the phenomenon where customers switch from one service provider to another, poses a significant challenge for different companies. Predicting churn can help these companies take proactive measures to retain valuable customers. This study explores the application of machine learning algorithms for predicting customer churn in different industries like Telecom Industry, IT and Banking Sector. Additionally, the research contributes to the existing body of knowledge in the field of customer churn prediction, showcasing the potential of machine learning algorithms in addressing complex business challenges. The project begins with data collection and preprocessing, involving the extraction and cleaning of relevant features from diverse sources such as customer interactions, transaction history, and demographic information.The predictive modeling phase employs state-of-the-art machine learning algorithms, including but not limited to logistic regression, decision trees and random forest, feature engineering is employed to enhance the model's ability to capture nuanced customer behavior. The dataset is split into training and testing sets to evaluate model performance accurately.The outcomes of this project have practical implications for businesses aiming to proactively manage customer retention strategies. By identifying potential churners early, companies can implement targeted interventions, personalized marketing strategies, and loyalty programs to mitigate the risk of customer loss and foster long-term relationships.

Predicting churn with filter-based techniques and deep learning

Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.

Customer Churn Prediction Using Machine Learning

Customer churn, the phenomenon where customers cease their relationship with a business, is a critical concern for e-commerce platforms striving for sustained growth and profitability. Predicting churn in advance can empower businesses to implement proactive retention strategies, thereby mitigating revenue loss and enhancing customer satisfaction. In this study, we propose a machine learning-based approach to predict customer churn in e-commerce settings. We begin by collecting extensive data encompassing various customer attributes, transactional history, browsing behavior, and engagement metrics. Leveraging this rich dataset, we employ state- of-the-art machine learning algorithms such as logistic regression, random forests, gradient boosting machines, and neural networks for predictive modeling. Feature engineering techniques are applied to extract meaningful patterns and insights from the raw data, enhancing the predictive performance of the models. By deploying the developed predictive model into production environments, businesses can proactively identify at-risk customers and tailor targeted retention strategies to mitigate churn. The results demonstrate the effectiveness of machine learning in accurately predicting customer churn in e-commerce, enabling businesses to proactively implement retention strategies and enhance customer engagement. Index Terms— Machine learning, E-commerce, Logistic re- gression, Decision tree, Random forest algorithm.

Data mining models in telecom churn prediction

Customer attrition has become one of the biggest problems facing the telecommunications industry due to its fast growth. According to telecom studies, acquiring new customers is more expensive than keeping current ones. Telecom companies may use the information gleaned from telecom data to understand the causes behind customer churn and take steps to maintain their current clientele. This study explores the popular data mining methods for spotting trends in loss of clients. Principal component analysis is used in the survey to reduce the dimension of the attributes. Some conclusions about the relationship between costumer usage data their churn can be summarized through the exploratory data analysis. And three prediction techniques (Logistic Regression, SVM Regression, Random Forest Regression) are applied in the customer churn prediction. This paper compares the accuracy and performance of these models. The result shows, among these models, SVM regression has the highest accuracy. It has an accuracy of 0.92 and a precision of 0.48.

Bank Customer Churn Prediction Using SMOTE: A Comparative Analysis

In today's market, customers have a plethora of options available to them when deciding where to invest their money. Consequently, customer churn and engagement have emerged as prominent concerns. With an increasing number of service providers targeting the same customer base, it is imperative for providers to understand evolving customer behavior and heightened expectations to retain their clientele. Numerous studies have addressed the issue of customer churn, with data mining frequently employed to predict bank customer attrition. While many researchers have proposed various approaches for predicting customer churn, some machine learning (ML) algorithms have struggled to deliver the required performance in identifying customer churn accurately most especially when the dataset is imbalance data. Therefore, this paper presents an application of Synthetic Minority Over Sampling Technique (SMOTE) on bank churn dataset. The SMOTE algorithm was employed to address the problem of data imbalance and Genetic Algorithm (GA) was applied to select most informative features from the original dataset. The selective features were evaluate using four (4) different classification algorithms: Random Forest (RF), K-Nearnest Neighbor (KNN), Artificial Neural Network (ANN) and Adaboost algorithms. The KNN model demonstrated superior performance compared to other models in terms of accuracy (96%), precision (96%), and F-measure (96%) respectively. Furthermore, we compared our results with existing models that utilized the same dataset, and our proposed strategy outperformed them.

SAP: Fiori Application and its Integration with AI

Abstract: Bridging the Gap Between Data and Decision: SAP Fiori's AI Integration Revolutionizes Business Processes. SAP Fiori, the sleek user interface for SAP solutions, is undergoing a metamorphosis, empowered by the transformative potential of Artificial Intelligence (AI). This integration transcends the traditional data visualization approach, weaving intelligence directly into the user experience. Imagine a purchase order form intelligently suggesting optimal suppliers based on past performance and market fluctuations, or a sales dashboard predicting customer churn with uncanny accuracy. This is the future of SAP Fiori, where AI acts as a co-pilot, guiding users through complex tasks, surfacing relevant insights, and automating repetitive processes. By seamlessly integrating AI, SAP Fiori empowers users to make informed decisions faster, optimize workflows, and unlock new levels of business agility. These abstract paints a picture of the exciting possibilities that emerge when SAP Fiori and AI join forces, transforming the way businesses operates. S/4HANA Cloud migration and AI integration is a powerful combination that can help businesses of all sizes to achieve their transformation goals and stay ahead of the competition. By migrating to S/4HANA Cloud and integrating AI features, businesses can improve their operational efficiency, increase their agility, accelerate their innovation, and reduce their costs. In this paper, author has explained the requirement of SAP S/4HANA Cloud RISE Enterprise Solution Automation with Artificial Intelligence features – a current business transformation needs to achieve business goal.

A neural network-based predictive decision model for customer retention in the telecommunication sector

Acquiring a new customer is far more expensive than retaining a customer. Hence, customer retention is a key aspect of business for a firm to maintain and improve on its market share and profit. The paper analyses customer retention strategies by employing an artificial neural network-based decision model to a real-life dataset collected from 311 mobile service users in India. Seven linear and non-linear adaptive models are developed using features related to customer dissatisfaction (DSF), customer disloyalty (DLF) and customer churn (CF). Findings of this study suggest that non-linear models are most efficient in predicting customer churn, and both DSF and DLF variables significantly affect the retention strategy. Three groups of customers are discussed in this study in the order of least likelihood of churning to most likelihood. Finally, a priority matrix based on key performance indicators is proposed to help service providers target potential customers to retain.

Predicting customer abandonment in recurrent neural networks using short-term memory

Customer retention, a critical business priority, has become a growing concern, especially in the telecommunications industry. This study addresses the need to anticipate and understand customer churn through the application of Deep Learning models. The central focus of the research was the development and evaluation of a short-term memory model (LSTM) specifically designed to predict customer leakage. The choice of LSTM as the mainstay of the research is based on its proven ability to model long-term dependencies in sequences, its resilience to recurrent challenges in neural networks, and its success in various sequence prediction tasks. The model implementation, configured sequentially with Keras, comprised of an initial LSTM layer of 64 units, followed by a 20% removal layer to mitigate overfitting. The second LSTM layer, with 32 units, was supplemented with another elimination layer. Model training was conducted using a dataset consisting of 20 attributes and 4250 records. The model evaluation was based on crucial measures such as precision, accuracy, sensitivity and F1 count, revealing exceptional results with 95% performance on all metrics. This study, therefore, highlights the effectiveness of the LSTM model in predicting customer churn, providing companies with a valuable tool to improve retention and mitigate associated losses.

Customer Churn Prediction using Machine Learning Models

Customer churn is a critical concern for the telecommunication industry. Understanding and predicting customer churn can lead to more effective retention strategies and an increase in profitability. Predicting customer churn allows telecommunication companies to identify potentially dissatisfied customers early on and take proactive measures to retain them. Due to a large client base, the telecom industry generates a large volume of data on a daily basis. Decision makers and business analysts stressed that acquiring new customers is more expensive than retaining existing ones. Business analysts and customer relationship management (CRM) analysts must understand the reasons for customer churn as well as behaviour patterns from existing churn data. This paper proposes a churn prediction model that uses classication and clustering techniques to identify churn customers and provides the factors that contribute to customer churning in the telecom sector. The results presented shows that XBoost and Random Forest achieved higher prediction accuracy when compared to K- Nearest Neighbours, Support Vector Machines and Decision Trees in terms of accuracy, precision, F1-Score and recall.

Customer churn prediction model in enterprises using machine learning

Customer churn prediction model in enterprises using machine learning

Imbalanced customer churn classification using a new multi-strategy collaborative processing method

The rapid advancement of big data and artificial intelligence heralds a dual-edged era of opportunities and challenges for the banking sector. Indeed, enhancing a model's capability to accurately classify imbalanced datasets represents a critical challenge within the field of customer churn prediction (CCP). In this paper, to address the challenges presented by the problem of imbalanced customer classification, a new multi-strategy collaborative processing method named IADASYN-FLCatBoost is proposed from dual perspectives: data and algorithm. At the data level, the traditional Adaptive Synthetic (ADASYN) sampling is improved, that is, the LOF (Local Outlier Factor) algorithm is introduced to eliminate outliers, and the classification features are specially processed to synthesize new minority class samples, thus an improved ADASYN (IADASYN) algorithm is obtained. At the algorithm level, the Focal Loss is embedded into the CatBoost ensemble learning framework to form a new Focal Loss-CatBoost (FLCatBoost) to make a focal-aware, cost-sensitive version of imbalanced customer churn prediction. Moreover, the empirical analysis is conducted in conjunction with the credit card customer dataset obtained from the Kaggle platform. The results of the staged comparison experiments show that the proposed method IADASYN-FLCatBoost in this paper shows the best prediction performance. Comparing the proposed method with 5 other imbalanced classification algorithms and 20 classifiers composed of classical sampling methods and ensemble learning algorithms, it is verified that the classification effect of the proposed method performs best, and the values of Recall, F1 score, G-mean and Area under Precision-Recall curve (AUPRC) have been significantly improved. In addition, further verification of the model also proves that the proposed method has certain generalizability and is still valid for other banks and customer churn datasets of other industries.

Customer churn prediction in imbalanced datasets with resampling methods: A comparative study

Customer churn presents a significant challenge for businesses in the era of subscription-based services because retaining customers plays a key role in sustained growth. Existing techniques for automatic churn prediction suffer from a primary challenge inherent in datasets as their significant disproportion between majority and minority classes, which may result in model bias favoring the dominant class. This study presents a comprehensive analysis of Customer Churn Prediction (CCP) with a focus on three public highly imbalanced datasets. The explored datasets span diverse business sectors, including telecommunications, online retail, and banking. We employ a comparative analysis regarding fourteen distinct classification methods considering popular resampling strategies, namely the Synthetic Minority Over-sampling Technique (SMOTE) and the Adaptive Synthetic Sampling (ADASYN). In particular, we investigate a specific configuration that combines a novel two-phase resampling method predicated on both clustering and ensemble techniques in conjunction with Long Short-Term Memory (LSTM) networks. Our findings demonstrate competitive effectiveness, underscoring its potential for effective imbalance correction by further enhancing prediction accuracy. Achieved results suggest that in almost all instances, the integrated approach outperforms the standalone methods across different scenarios in the three datasets, particularly in terms of the Area Under the Curve (AUC). This research represents a significant contribution to the field of churn prediction for addressing class imbalance.

A framework to improve churn prediction performance in retail banking

Managing customer retention is critical to a company’s profitability and firm value. However, predicting customer churn is challenging. The extant research on the topic mainly focuses on the type of model developed to predict churn, devoting little or no effort to data preparation methods. These methods directly impact the identification of patterns, increasing the model’s predictive performance. We addressed this problem by (1) employing feature engineering methods to generate a set of potential predictor features suitable for the banking industry and (2) preprocessing the majority and minority classes to improve the learning of the classification model pattern. The framework encompasses state-of-the-art data preprocessing methods: (1) feature engineering with recency, frequency, and monetary value concepts to address the imbalanced dataset issue, (2) oversampling using the adaptive synthetic sampling algorithm, and (3) undersampling using NEASMISS algorithm. After data preprocessing, we use XGBoost and elastic net methods for churn prediction. We validated the proposed framework with a dataset of more than 3 million customers and about 170 million transactions. The framework outperformed alternative methods reported in the literature in terms of precision-recall area under curve, accuracy, recall, and specificity. From a practical perspective, the framework provides managers with valuable information to predict customer churn and develop strategies for customer retention in the banking industry.

Mathematical Modelling and Deep Learning: Innovations in E-Commerce Sentiment Analysis

This research explores e-commerce dynamics, focusing on the challenge of predicting customer churn using deep learning [65]. It integrates and analyses both textual and transactional data, including social media posts and customer feedback [59]. The approach uses an advanced deep learning model, involving data collection, pre-processing, and feature extraction [40]. Novel methods fuse data to create a detailed customer profile combining sentiment analysis with behavioural insights derived from transaction data [25]. The deep learning architecture is designed to analyse and predict customer sentiments and purchasing behaviours, informed by the latest research [65]. This study is significant as it provides an innovative solution for predicting customer churn in e-commerce, aiding sustainability [45]. It also enables targeted retention strategies and personalized customer engagement [59]. Additionally, it contributes insights to big data analytics and customer relationship management in e-commerce, showcasing deep learning's potential in transforming business practices and enhancing customer experience [40].