Reduce Customer Churn Research Articles

Customer churn data analysis using data mining

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].

Predictive Customer Analytics: Machine Learning for Churn Prediction and Retention

Customer churn presents a big challenge in the industry. Businesses have to deal with the problem of customers stopping using their products and services due to dissatisfaction, competitive offers, more affordable alternatives, or changing needs. Churn can be damaging to businesses since it causes revenue loss and higher costs. To address this issue, our research aimed to develop a prediction model that helps predict customer churn. We started with getting the data set about telecommunication. Our analysis and model development were based on this dataset. Then we did data visualization to gain a better understanding of the data through multiple charts. After that, we performed data preparation. First, we did data transformation, data cleansing to address missing values and outliers; feature selection was done, and finally, in this step, the data set was split into testing and training sets. Multiple machine learning algorithms were used for modeling, such as decision trees, random forests, logistic regression, support vector machines, Naïve Bayes, and neural networks. Following model development, we evaluated the model performance with each algorithm using tune model hyperparameters. The decision tree algorithm performed the best with %96.7 accuracy, %96.9 precision, %99.3 recall, and %98.1 F1-score. These findings showed how effective decision tree algorithms are in predicting customer churn. This predictive model will enable telecommunication businesses to predict potential churn, make retention strategies, reduce customer churn and increase customer retention rates.

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.

Smart Customer Churn Management System Using Machine Learning

While the speed of digitalization is rapidly evolving, the importance of the competition and customer experience is simultaneously gaining prominence. It is crucial for banks to understand the needs of customers and offer special products or opportunities accordingly, through developing automated systems that monitor and analyse reasons for customer churn and predict customers who are likely to leave in the future. The system aims to improve customer satisfaction by reducing customer churn, which is a term used to describe the loss of clients or subscribers for whatever reason from an organisation. This research seeks to leverage on the core processes of machine learning involved in building such a system, in order to address the business question ‘How to prevent customer attrition or churn?’ The adopted methodology involves four different phases, namely, data preparation and processing, feature extraction, dataset and testing, performance evaluation. Six classification algorithms are applied and compared against each other, and the best performing model is selected for the system implementation. In comparing the models, random forest classifier is selected with the accuracy of 87% and precision of 85%.

A STUDY OF THE INTERRELATIONSHIP BETWEEN SWITCHING COSTS AND CONSUMERS RESISTANCE TO SWITCHING BRAND LOYALTY IN INDIAN MOBILE MARKET

To keep customers loyal to their brand is the main problem for companies in the mobile telecommunications market today. This research examines how and why consumers resist switching from one brand to another when they want to maintain loyalty, but there are many drawbacks associated with this. Interviews will be scheduled for qualitative data while quantitative data will also be collected through surveys so as to establish what makes consumer loyalty tick by understanding the impact of monetary and non-monetary switching costs on brand-switching behaviour across various industries including Indian’s Mobile Market. The researchers discover that although some customers might switch brands due to costs related to contract termination, handset subsidy, among others; however non-monetary switching expenses such as inconvenience and perceived risk tend to play an equal role in influencing customers’ willingness to shift. Additionally, it provides insights on how switching costs interact with consumer resistance towards changing brands, thereby highlighting intricate dynamics at work. Therefore, it is a significant study that contributes both theoretical knowledge and practical aspects of enhancing brand loyalty and reducing customer churn in a highly competitive Indian telecom industry. As a result, marketing managers can use the knowledge about interaction between consumer behaviour and switching costs as an input into their decisions making process. KEYWORDS: Switching Costs, Brand loyalty, Consumer resistance, Mobile marketing, Indian marketing, Customer Behaviour, Competitive advantage, Marketing strategy.

ХАРАКТЕРИСТИКА ЧИННИКІВ, ЯКІ ВПЛИВАЮТЬ НА МАРКЕТИНГОВІ ВЗАЄМОВІДНОСИНИ ТА КОНКУРЕНТУ МАРКЕТИНГОВУ СТРАТЕГІЮ

The article is devoted to the characteristics of factors that affect marketing relationships and competitive marketing strategy. Every day, companies face the issue of paying close attention to the factors that affect marketing communication with the client, the formation of competitive advantages in the market, and the development of such a marketing strategy that would provide the company with a leading position in the market. The article defines the main principles of marketing relationships that underlie the marketing competitive strategy. If a company builds relationships with a customer based on these principles, the overall company wins, because this approach helps increase customer satisfaction, increase their trust in the brand, and reduce customer churn. The main types of marketing strategies used by companies in the market are defined: cost leadership strategy, differentiation strategy, focus strategy, aggressive strategy, defense strategy. In general, a marketing strategy is a plan of action developed by a company to ensure its competitive advantage in the market. The listed types of competitive marketing strategies prove that the choice of a competitive marketing strategy depends on many factors, such as the size of the company, its resources, market position, characteristics of the target audience and the actions of competitors. The classification of influencing factors aimed at the formation and maintenance of longterm relations between the company and clients has been carried out. Such factors are defined as: product or service quality, value for the client, satisfaction of the client's needs and expectations, communication, client service, trust and reputation, loyalty, technologies. Having analyzed the factors influencing marketing relationships and the competitor's marketing strategy, attention is focused on their positive impact on the company's activities. Keywords: marketing, marketing relationships, marketing strategies, influencing factors, company, strategy

Enhanced data augmentation for predicting consumer churn rate with monetization and retention strategies: a pilot study

Customer retention and monetization have since been the pillar of many successful firms and businesses as keeping an old customer is far more economical than gaining a new one – which, in turn, reduce customer churn rate. Previous studies have focused on the use of single heuristics as well as provisioned no retention strategy. To curb this, our study posits the use of the recen-cy-frequency-monetization framework as strategy for customer retention and monetization impacts. With dataset retrieved from Kaggle, and partitioned into train and test dataset/folds to ease model construction and training. Study adopt a tree-based Random Forest ensemble with synthetic minority oversampling technique edited nearest neighbor (SMOTEEN). Various benchmark models were trained to asssess how well each performs against our proposed ensemble. The application was tested using an application programming interface Flask and integrated using streamlit into a device. Our RF-ensemble resulted in a 0.9902 accuracy prior to applying SMOTEENN; while, LR, KNN, Naïve Bayes and SVM yielded an accuracy of 0.9219, 0.9435, 0.9508 and 0.9008 respectively. With SMOTEENN applied, our ensemble had an accuracy of 0.9919; while LR, KNN, Naïve Bayes, and SVM yielded an accuracy of 0.9805, 0.921, 0.9125, and 0.8145 respectively. RF has shown it can be implemented with SMOTEENN to yield enhanced prediction for customer churn prediction using Python

Customer Churn Prediction

In today’s highly competitive business landscape, customer retention revenue preservation, customer experience improvement, and marketing optimisation are critical factors for sustained growth and profitability. Customer churn predictionis discontinuing their services or purchases, which presents asignificant challenge for businesses across various industries. This project focuses on developing a predictive model to expect customer churn in the banking sector using machine learning techniques. The project involves the collection and analysis of historical customer data, confined account activity, transaction history, demographic information, and customer service interactions. By extracting the right features from this data, a machine learning model is trained to forecast which bank customers are at the highest risk of churning. A critical step in this study was the selection of relevant features that influence customerchurn. Feature selection was guided by domain knowledge and feature importance analysis. The different classifiers were used and then trained on the training dataset further ensuringthe model’s optimal performance. The model’s performance is assessed through various evaluation metrics, including accuracy, precision, and recall. Additionally, the project explores a model illustration to uncover the influential factors contributing to customer churn within the banking context. This project’s outcomes can empower banks to take proactive measures in retaining customers, enhancing their overall experience, and thereby preserving revenue streams. By addressing customer churn, banks can foster long-term relationships, reduce customer acquisition costs, and boost their competitiveness in the financial industry. The results of this project are expected to assist businesses in proactively retaining customers by targeting those at the highest risk of churning. Ultimately, reducing customer churn can lead to increased customer satisfaction, revenue, and long-term business sustainability.

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.

Customer retention in commercial banking as a classification task in machine learning

Introduction. Customer churn is a common problem for many industries, particularly the banking sector. To thrive, banks need to attract new customers, as each lost customer leads to a decrease in profit and requires time and effort to acquire a new one. Customer churn occurs when a client ceases to use a bank's product or service. Retaining customer interest is more beneficial and cost-effective than attempting to attract new ones. Therefore, reducing customer churn becomes one of the key tasks for businesses. Banks that can retain and attract new customers have significantly higher chances of success. Hence, the use of machine learning methods becomes one of the key tools for addressing the task of reducing customer churn. These methods have the potential to help banking institutions optimize their processes and increase profitability. Purpose. The aim of the study is to assess the effectiveness of using machine learning methods for customer retention in a bank, including their construction, testing, and evaluation of the economic impact. Method (methodology). This article investigates the issue of retaining customers of a commercial bank by determining the probability of customer churn using classification methods of machine learning. Logistic regression models (GLM), decision trees (Decision Trees), random forests (Random Forest), as well as support vector machines (SVM), k-nearest neighbors (k-NN), and naive Bayes algorithm will be constructed for this purpose. The quality of the constructed models will be evaluated using a confusion matrix. Results. The obtained results revealed high accuracy of the constructed models and their ability to effectively identify bank customers prone to churn. The conclusions of this article may be valuable for developing customer retention strategies not only for commercial banks but also for various business sectors where customer attrition is a relevant issue.

Customer Churn Analysis

Abstract: Customer churn analysis is a critical process for businesses to understand the reasons why customers stop using their products or services. It involves examining customer data and identifying patterns that can indicate potential churn. By analyzing this data, businesses can take proactive measures to retaincustomers, such as improving the quality of their offerings or targeting specific customer segments. Ultimately, effective customer churn analysis can help businesses reduce customer churn and increase profitability.

Early warning of telecom enterprise customer churn based on ensemble learning.

Analyzing customers' characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

The Effectiveness of Membership-Based Free Shipping: An Empirical Investigation of Consumers’ Purchase Behaviors and Revenue Contribution

Membership-based free shipping (MFS) has been increasingly adopted by online retailers. However, its effectiveness is understudied. This study leverages a consumer transaction data set provided by an online retailer and uses a stacked difference-in-differences approach to quantify the enrollment effect of MFS on consumers’ purchase behaviors and their net revenue contribution to the retailer. Interestingly, MFS enrollment does not lift average consumers’ spending at the beginning of the enrollment, as they break large orders into smaller ones. Retailers do not gain incremental net revenue due to the increased shipping costs during this period. However, the free shipping benefit may build a switching barrier that motivates consumers to purchase more often with larger order sizes over time. It eventually leads to increased spending and revenue contribution. MFS also increases members’ purchase variety and impulse purchases. In addition, the authors find that there is a greater increase in net revenue contribution after enrollment for light buyers, variety seekers, and those who are willing to pay shipping fees before enrollment. Surprisingly, MFS has a negative impact on the revenue contribution of heavy buyers, the conventional best-value consumers. Moreover, MFS can effectively strengthen consumer–retailer relationships by reducing customer churn.

Data Visualization and Prediction for Telecom Customer Churn

With the deepening of telecom industry reform and the intensification of competition, the customer churn rate of telecom enterprises is gradually increasing. How to predict and effectively reduce customer churn is directly related to the survival and development of telecom enterprises. In order to effectively deal with unbalanced classification and improve the accuracy of high-value customer churn prediction in telecom industry, this paper uses telecom customer data set from kaggle platform to analyze people's use of telecom services, and help telecom operators find out the reasons for customer churn, and establish churn prediction model to reduce customer churn rate. In this paper, firstly, the data set is imported, and then the data visualization analysis is carried out. Then, the random forest model, SVM model and GBDT model are introduced for comparison. Experiments show that random forest has better classification performance than other methods, and improves the accuracy of high-value customer churn prediction.

Reducing Customer Churn in Self-Serve Ad Platforms: A Data-Driven Approach to Customer Success

Reducing Customer Churn in Self-Serve Ad Platforms: A Data-Driven Approach to Customer Success

Churn Rate Modeling for Telecommunication Operators Using Data Science Methods

The telecommunication company functioned in the market with extremely high competitiveness. Attracting new customers needs 5-10 times more expenses than maintaining an existing one. As a result, effective customer churn management and analysis of the reasons for customer churn are vital tasks for telecommunication operators. As a result, predicting subscriber churn by switching on the competitors becomes very important. Data Science and machine learning create enormous opportunities for solving this task to evaluate customer satisfaction with company services, determine factors that cause disappointment, and forecast which clients are at a greater risk of abandoning and changing services suppliers. A company that implements data analysis and modelling to develop customer churn prediction models has an opportunity to improve customer churn management and increase business results. The purposes of the research are the application of machine learning models for a telecommunications company, in particular, the construction of models for predicting the user churn rate and proving that Data Science models and machine learning are high-quality and effective tools for solving the tasks of forecasting the key marketing metrics of a telecommunications company. Based on the example of Telco, the article contains the results of the implementation of various models for classification, such as logistic regression, Random Forest, SVM, and XGBoost, using Python programming language. All models are characterised by high quality (the general accuracy is over 80%). So, the paper demonstrates the feasibility and possibility of implementing the model to classify customers in the future to anticipate subscriber churn (clients who may abandon the company’s services) and minimise consumer outflow based on this. The main factors influencing customer churn are established, which is basic information for further forecasting client outflow. Customer outflow prediction models implementation will help to reduce customer churn and maintain their loyalty. The research results can be useful for optimising marketing activity of managing the outflow of consumers of companies on the telecommunication market by developing effective decisions based on data and improving the mathematical methodology of forecasting the outflow of consumers. Therefore, the study’s main theoretical and practical achievements are to develop an efficient forecasting tool for enterprises to control outflow risks and to enrich the research on data analysis and Data Science methodology to identify essential factors that determine the propensity of customers to churn.

Churn Customer Estimation Method based on LightGBM for Improving Sales

Churn customer estimation method is proposed for improving sales. By analyzing the differences between customers who churn and customers who do not churn (returning), we will conduct a customer churn analysis to reduce customer churn and take steps to reduce the number of unique customers. By predicting customers who are likely to defect using decision tree models such as LightGBM, which is a machine learning method, and logistic regression, we will discover important feature values in prediction and utilize the knowledge obtained through Exploratory Data Analysis: EDA. As results for experiments, it is found that the proposed method allows estimation and prediction of churn customers as well as characteristics and behavior of churn customers. Also, it is found that the proposed method is superior to the conventional method, GradientBoostingClassifier: GBC by around 10%.

A Novel Telecom Customer Churn Analysis System Based on RFM Model and Feature Importance Ranking

Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets and reduce capital outflows. Future Research: The research is also applicable to other fields, such as education, banking, and so forth. We will make more new attempts based on this system.

헤어미용서비스 소비자가 지각하는 인적서비스품질이 전환비용 및 충성도에 미치는 영향

Hairbeauty industry is a field which is highly dependent upon human services. This study attempted to investigate the influence of the quality of hairdressing services on switching costs and customer loyalty to help beauty salons overcome current crisis by preventing customer churn. For this purpose, this study collected and analyzed 338 questionnaire data from experienced men and women who visited hair salons in Gwangju. The questionnaire survey was performed with the following measuring tools by referring to previous studies: ‘reliability’, ‘professionalism’, ‘authenticity’, ‘switching costs’, ‘cognitive loyalty’, ‘affective loyalty’. The collected data were analyzed by multiple regression analysis, using SPSS 26.0, and the results found the followings: First, ‘reliability’, ‘professionalism’ and ‘authenticity’ had a positive influence on ‘switching costs’ with statistical significance. Second, ‘reliability’, ‘professionalism’ and ‘authenticity’ revealed a positive effect on ‘cognitive loyalty’ with statistical significance. Third, ‘reliability’, ‘professionalism’ and ‘authenticity’ showed a positive influence on ‘affective loyalty’ with statistical significance.
 Fourth, ‘switching costs’ had a positive effect on ‘cognitive loyalty’ with statistical significance. Fifth, ‘switching costs’ revealed a positive influence on ‘affective loyalty’ with statistical significance. The above results confirm that switching costs are recognized by the quality of services and that high switching costs enhance customer loyalty.
 Therefore, there is a need to create and supplement new services compared to other hair salons such as development of service manuals suitable for hair salons and efforts to maintain good relationships. With differentiated switching costs, it would be possible to enhance customer loyalty by reducing customer churn, anticipating creation of new consumers. significant influence was observed in ‘personnel’, ‘reward’ and ‘business factor’ (p<.001). In predicting social cognitive factors, ‘personnel’, ‘reward’ and ‘business factor’ demonstrated the explanatory power of 11.4%.

Winning your customers’ minds and hearts: Disentangling the effects of lock-in and affective customer experience on retention

Building barriers to lock in customers and improving the affective customer experience are two key strategies employed by firms to enhance customer retention. Although pursuing the same goal, these strategies work differently: the former relies more on a calculative, cost–benefit approach to the exchange, while the latter promotes affective aspects of the relationship. Integrating experiential learning theory with social exchange theory, we provide a conceptual framework to understand the impact of lock-in and affective customer experience on customer retention, and the moderating role of relationship depth. Using a comprehensive data set for a sample of 13,761 customers covering all firms in one telecom market for two different services, we empirically test the framework via multinomial logit modeling. The results offer novel insights into the interplay between the two strategies. For poor affective customer experience (i.e., a score below five on a 0–10 scale), lock-in helps firms reduce customer churn (between 49.03% and 47.86%). However, the impact of lock-in decreases when affective customer experience improves and turns to be insignificant once the experience reaches the “acceptable level” (i.e., a score above seven on a 0–10 scale). Importantly, the separate and joint effects of the two strategies are stronger when there is a low relationship depth, and weaker when heavy relationships are established. The findings offer useful practical advice to manage these strategies in an efficient and optimal way.