Customer Churn Research Articles

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.

Research on Marketing Strategy Management based on Customer Retention

This paper aims to explore marketing strategy management based on customer retention to enhance a company's competitiveness in a highly competitive market. In today's business landscape, where competition is fierce and customer acquisition costs are high, customer retention has emerged as a crucial strategy for businesses to maintain profitability and achieve sustained success. Through a comprehensive literature review and empirical research, we delve into the concept of customer retention and its significance in marketing strategy management. Customer retention refers to the ability of a company to retain existing customers and foster their loyalty over time. It goes beyond simply acquiring new customers and recognizes the value of nurturing and maintaining relationships with existing ones. By examining the interrelationship between customer retention and marketing strategy management, we shed light on the various strategies and tactics that companies can employ to enhance customer retention. This includes not only focusing on the initial sale but also on building long-term customer relationships. We delve into the importance of understanding customer needs, preferences, and behaviors to develop targeted marketing approaches that resonate with customers on a personalized level. Based on the literature review and empirical research, we propose a theoretical framework for analyzing the key factors and influencers of customer retention. This framework takes into account factors such as customer satisfaction, loyalty programs, and customer service, which have been identified as critical drivers of customer retention. By understanding and addressing these factors, companies can implement effective strategies to improve customer retention rates. Ultimately, the adoption of effective customer retention strategies can lead to sustainable competitive advantages for businesses. By reducing customer churn, increasing customer loyalty, and maximizing customer lifetime value, companies can differentiate themselves in the market, increase market share, and achieve long-term profitability.

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.

Telco Churn Anticipation Engine – A Predictive Attrition Model using Deep Learning

The Telecommunication Sector has emerged as one of the world’s fastest-growing industries, prioritizing customer satisfaction for business success. With a global reach, consumers have abundant service options, with pricing, flexibility, and customizability influencing their choices. Telecom companies strive to meet these demands through policies and services to attract and retain customers. However, customer churn remains a significant challenge, prompting telecom companies to monitor customer behaviors to predict subscription terminations. Churn prediction, facilitated by various algorithms, is crucial for organizational success. Therefore, there is a growing need to forecast potential churners to implement retention measures and maximize company revenue. . Key Words: Churn, Exploratory data analysis, RNN, CNN, DNN

Data-Driven Strategies for Fuel Distribution in Indonesia: A Case Study of PT Pertamina Patra Niaga

Fuel oil or what is often referred to as BBM is one of the basic needs to drive all community activities. So the government appointed PT Pertamina as a single company which is a state-owned company to facilitate fuel needs for all levels of society. However, with increasing demand, the government formed a new policy to allow private companies to come in to meet all fuel demand. With this, PT Pertamina is no longer the only fuel supplier in Indonesia and must continue to develop mature strategies so that profits do not fade. One way is by examining sales data and predicting customer loyalty. The RFM method followed by the decision tree algorithm and k-means clustering is applied in this research, with the output being able to determine the level of customer loyalty, the level of salesman performance, as well as predicting the potential for customers to churn and its correlation with the salesman's skills. The data used as a reference for the research is sales transaction data obtained from PT Pertamina Patra Niaga Regional Jatimbalinus. And from the research, results showed that the majority of PT Pertamina Patra Niaga Regional Jatimbalinus customers are loyal customers. With a salesman, performance is divided into good performance and less good performance. This grouping is obtained based on the salesman's overall performance track record. As for customer churn predictions, it was found that there was 1 group of customers who were predicted to churn heavily, but this was not influenced by salesman performance, as evidenced by transaction track records in existing data

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.

Risk assessment of customer churn in telco using FCLCNN-LSTM model

Telco players are constantly evaluating their loss in customer revenue or customer churn, especially on the prevention, control, and risk assessment of churn. In this paper, a domain-order based shallow fusion deep learning model, the Fully Connected Layer Convolutional Neural Network - Long Short-Term Memory (FCLCNN-LSTM) is designed and applied to a decision support system to assess the risk of customer churn. First, a majority least absolute shrinkage and selection operator (Maj-LASSO) algorithm is proposed to identify churn predictors that have an important impact on classification. The proposed Maj-LASSO algorithm is different from previous studies that solved the study of unbalanced data and feature selection separately, but solved both as one problem to give the feature selection method under unbalanced data. Then, a fully connected layer based on multiple ReLU neurons is used to balance the importance of the features. Doing so addresses the problem where some key features are overlooked due to their unequal contribution to the risk assessment of telco customer churn. Third, a 2D convolutional neural network based on domain information and an LSTM model based on ordinal information are combined to learn the complex mapping between the features and labels, to improve the accuracy and generalizability of the predictive model. The proposed FCLCNN-LSTM model is different from the previous single spatial model or temporal model, but improves the network classification feature extraction capability from both temporal and spatial dimensions, thus innovatively solving the problem of model development for large-scale and high-dimensional data, and applying it in the field of telecommunication customer churn prediction. Finally, using three public customer churn datasets from the Kaggle and UCI platform, the FCLCNN-LSTM model is compared against other classification models, namely, Logistic Regression, Support Vector Machine, Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Network, and Long Short-Term Memory. The experimental results inform that the accuracy of the FCLCNN-LSTM is 3.43% higher than that of the other comparison models, while the AUC is 4.84% higher. Thus, the FCLCNN-LSTM model provides a better decision-making reference for telco players to identify potential churners.

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.

Optimizing Customer Retention in the Telecom Industry: A Fuzzy-Based Churn Modeling with Usage Data

Churn is a serious challenge for the telecommunications industry because of the much higher costs of gaining new customers than maintaining existing ones. Therefore, efforts to increase loyalty and decrease customer churn are the focus of telecom’s retention departments. In order to direct antichurn activities, profitable clients who have the highest probability of churning need to be identified. The data used to identify churners are often inaccurate and vague. In this paper, a fuzzy approach to modeling churn intent based on usage data in mobile telecommunications is presented. It appreciates the uncertainty of the data and provides insights into churn modeling. The goal of the study was to evaluate the applicability of the Mamdani and Sugeno models for building a churn model based on a limited but real-world dataset enriched with feature engineering. The additional goal was to find features most usable for churn modeling. Four metrics—accuracy, recall, precision, and F1-score—were used to estimate the performance of the models. The developed fuzzy rule-based systems show that to generalize possible churn identification factors with fuzzy rules, it is advisable to begin with features such as the change in the total amount of the invoice in the last period before the churning compared to the previous one, the total amount of the invoice in the period preceding the churning, the total amount of subscription in two months before the churning, the time of cooperation with the operator, and the number of calls out of the last quarter before leaving.

Optimising customer retention: An AI-driven personalised pricing approach

Customer churn has become one of the most important challenges that telecom companies have to deal with. Churn cases not only cause revenue losses but also impose extra costs of finding new customers. To overcome this issue, they develop various strategies to retain their customers. In this regard, this study presents an integrated artificial intelligence-based model that can meet the expectations of these companies not only to profile the customer churn, but also to predict a service fee that is likely to be accepted by the customers. The model first identifies the customers who are likely to churn and then offers the customers a personalised service fee that is likely to be acceptable. In this study, the K-nearest neighbours, Decision Tree, Random Forest, and Support Vector Machine methods are adapted as classifiers for churn prediction, and regression models of the same methods are utilised to predict the most optimum “personalised” service fee for potential customer churns. Additionally, to reduce the cost of data collection for companies, the feature selection method is used to determine the most optimal feature subset in churn analysis and service fee prediction. The results show that the Random Forest method is superior to other methods in both churn and price predictions and has resulted in as much as a predictive accuracy of 94% and AUC of 98%. The outcome of this comprehensive analysis using four artificial intelligence methods over three diverse telecom datasets, suggests that the proposed “personalised” pricing model in the telecom sector could prevent the churn and increase the profitability by up to 36%. In addition, the model based on SVM suggests that it is possible to reduce the number of required data to be collected by as much as 20%. As the robustness and generalisation ability of the models has been demonstrated over three diverse data sets, it can be further adapted in different sectors.

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.

A priority queueing-inventory approach for inventory management in multi-channel service retailing using machine learning algorithms

PurposeNowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.Design/methodology/approachThis paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.FindingsTo investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.Originality/valuePrioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Customer Churn Construct: Literature Review and Bibliometric Study

Customer Churn Construct: Literature Review and Bibliometric Study

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].

Bank Customer Churn Prediction with Machine Learning Methods

This paper examines and analyses customer churn prediction in the banking sector using the data from ABC Bank. The analysis conducted will document the determinants of bank customer churn and provide insights to the most important factors which influence the customers decision to quit utilizing the services of a bank. The investigation is based on the results of two machine learning algorithms with k-fold-cross-validation and same boosting methods. The result of the analysis reveals that out of logistic regression and random forests algorithms, the random forest methods show a higher accuracy score which corresponds with the literature review studied. Furthermore, the statistic of the research indicates that customers age has the highest association with the likelihood of customer churning, while whether the customer has a credit card at the bank has the lowest interconnection. The results of this research may provide valid explanations to customer churn in the banking sector and bring further intuitions of the advantages which machine learning methods may provide to future financial analysis.

Customer Churn Analysis Using Deep Reinforcement Learning Approach

Customer churn analysis is a critical component of business strategy in industries where retaining customers is essential for profitability and growth. This study aims to investigate the factors influencing customer churn in the telecommunications sector, utilizing a comprehensive dataset of customer interactions and demographics.

Profit-driven weighted classifier with interpretable ability for customer churn prediction

Customer churn prediction methods aim to identify customers with the highest probability of attrition, improve the effectiveness of customer retention campaigns, and maximize profits. However, previous studies have relied on a single classifier, leading to suboptimal predictive results. To address this issue, we propose a novel profit-driven weighted classifier that integrates a weighted strategy with multiple profit-driven ensemble members. We employ an artificial hummingbird optimization algorithm to determine the optimal weight coefficients of the profit-driven ensemble members based on the expected maximum profit criterion. We then calculate the Shapley additive explanation value to further improve the interpretability of the proposed weighted classifier. We conducted experiments and statistical tests on eight real-world datasets from different industries. The results show that the proposed weighted classifier significantly improves profits compared with comparative classifiers and provides strong interpretability based on the Shapley additive explanation value.