Customer Retention Strategies Research Articles

BAYESIAN NETWORKS AND STRUCTURAL EQUATION MODELLING TO INVESTIGATE THE PASSENGERS’ PERCEPTIONS IN HIGH-SPEED RAIL SYSTEMS

Ensuring sustainability in the global world today depends on perception management as well as financial management. In order to manage the perceptions, which are inherently latent variables as they are measured indirectly through their indicators, they must be accurately handled and modelled comprehensively. In the present study, a hybrid technique combining Bayesian Networks (BN) and Structural Equation Modelling (SEM), which are regarded as causal models, was used to investigate the perceptions of High-Speed Rail System (HSRS) passengers. In order to provide insight into the customer retention strategy for HSRS, the analyses were performed on the survey data gathered from the frequent users of HSRS operating between 2 cities of Turkey. After the measurement model of the perception variables through SEM was established, the relationships between the variables were learned using BN knowledge extraction algorithms. As a result, relationships from image to trust and loyalty, from trust to perceived value, from perceived value to satisfaction, and from satisfaction to loyalty were determined. Final interpretations were made in terms of risk management with the help of the probabilistic predictive ability of the BN by setting evidence on the satisfaction levels of the perceptions.

Analysing the Customer Retention Strategies of Subscribers of "Over-The-Top" (OTT) Media Streaming Services: Capturing the Insights of Urban Consumers using Data Analytics

Analysing the Customer Retention Strategies of Subscribers of "Over-The-Top" (OTT) Media Streaming Services: Capturing the Insights of Urban Consumers using Data Analytics

Double-Compressed Artificial Neural Network for Efficient Model Storage in Customer Churn Prediction

In the rapidly evolving field of Artificial Intelligence (AI), efficiently storing and managing AI models is crucial, particularly as their complexity and size increase. This paper explores the strategic importance of AI model storage, focusing on performance, cost-efficiency, and scalability within the realm of customer churn prediction, utilizing model compression technologies. Deep learning networks, integral to AI models, have become increasingly large, necessitating millions of parameters. These parameters make the models computationally expensive and voluminous in storage requirements. Addressing these issues, the paper discusses the application of model compression techniques—specifically pruning and quantization—to mitigate the storage and computational challenges. The experimental results demonstrated the effectiveness of the proposed method. These techniques reduce the physical footprint of AI models and enhance their processing efficiency, making them suitable for deployment on resource-constrained devices. Using these models in customer churn prediction in telecommunications illustrates their potential to improve service delivery and decision-making processes. By compressing models, telecom companies can better manage and analyze large datasets, enabling more effective customer retention strategies and maintaining a competitive edge in a dynamic market.

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.

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.

Service Provision Strategies and Performance of Cleaning Organizations in Embu County, Kenya

The business world of the twenty-first century is defined by rapid and significant changes in market trends, technological advancements, globalization, as well as a change in consumer needs and preferences. Consequently, the changes in the business environment, have forced organizations’ management to realize that the current strategies are ineffective and thus the need for new effective strategies in response to the changing environment. This study sought to examine the influence of service provision strategies on performance of cleaning services organizations in Embu County, Kenya. The study was anchored on the resource-based view, Porter’s generic strategies model and the expectancy disconfirmation theories. The study adopted a descriptive research design and purposive sampling to get the sample size. The research employed primary data which was collected through the use of semi-structured questionnaires. The questionnaires were picked after they had been filled by the respondents. The Cronbach’s alpha test was used to check the reliability of the questionnaire with a level of 0.7 and above confirming the internal consistency. Descriptive and inferential statistics were used to analyze the data using SPSS software. The study findings showed that customer retention strategy, cost leadership strategy, customer retention strategy and customer innovation strategy have a positive and significant influence on firm performance. It was concluded that having effective customer feedback strategies (complaints management system, online and non-online surveys and social media engagements) contributes to the improved performance of the cleaning service companies. It was also concluded that that adoption of economies of scale, economies of scope and enhancing operational efficiency can contribute to a more competitive and resilient business, positively impacting the performance of the cleaning service companies. The study recommended that customer feedback mechanisms should be enhanced through the integration of real-time feedback mechanisms to capture customer sentiments immediately after service delivery allowing for prompt issue resolution. It was further recommended that the cleaning firms should further adopt and implement cost-effective measures. The cleaning firms should focus on strategies that enhance customer loyalty.

Evaluating consumer insights in water services: perspectives on health benefits, pricing tolerance, and continuous service demand

Abstract This study investigates the relationship between consumers and water services, focusing on the global challenge of providing clean and reliable water. Employing structured questionnaires, the research explores customers’ perceived health benefits, willingness to pay (WTP) for potential price increases, and service expectations. The study, primarily quantitative in nature, gathered data from both customers and non-customers of water services. The findings reveal that customers are more likely to recognize health benefits from water services compared to non-customers, emphasizing the role of health perceptions in service acceptance. A significant majority of respondents showed a willingness to accept modest tariff increases. However, this willingness sharply declines as the proposed increase grows, indicating a clear threshold in consumers’ financial flexibility. A strong preference for 24-h water service emerged, underscoring the importance of continuous service in consumer satisfaction. Additionally, factors such as water consistency, cleanliness, and pressure were identified as key determinants of service adoption and satisfaction. These insights are crucial for water service providers and regulators, suggesting the need for a balanced approach to pricing, service quality, and continuity. The study highlights the importance of communication about the health benefits of water services, offering guidance for enhanced customer engagement, service adoption, and retention strategies.

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.

A Study to Know Impact of AI on CRM

This research paper delves into the dynamic intersection of Artificial Intelligence (AI) and Customer Relationship Management (CRM), exploring the profound effects of AI technologies on modern business practices. As AI continues to evolve, it reshapes how organizations manage and nurture their relationships with customers, presenting new opportunities and challenges. The study investigates the role of AI in personalizing customer experiences, emphasizing the utilization of advanced algorithms to analyze vast datasets and derive actionable insights. AI's influence on data management, analysis, and the subsequent enhancement of customer insights are scrutinized, providing a comprehensive understanding of its impact on informed decision-making. Furthermore, the paper examines the integration of AI-powered chatbots and virtual assistants in CRM systems, evaluating their effectiveness in providing real-time support, streamlining interactions, and improving overall customer satisfaction. The automation of repetitive CRM tasks through AI technologies is also explored, highlighting the resulting efficiencies and the potential for human resources to engage in more strategic aspects of customer relationship management. Sales forecasting, customer segmentation, and sentiment analysis emerge as key focal points, illustrating how AI contributes to more accurate predictions, targeted marketing strategies, and proactive reputation management. The impact of AI on cross-selling, upselling, and customer retention strategies is scrutinized, offering insights into how businesses can leverage AI to optimize revenue and foster enduring customer loyalty. As businesses navigate the rapidly evolving landscape of AI in CRM, this research paper aims to provide a comprehensive overview of the transformative dynamics at play. By understanding the nuanced influence of AI on customer relationships, organizations can adapt their strategies to align with the evolving expectations and demands of the contemporary market.

Consumer Behavior in the E - Commerce Era

This research investigates the relationship between factors such as accessibility, social media influence, personalization, user satisfaction, customer retention strategies, and consumer behavior in the context of e-commerce. Through a systematic literature review, the authors explore how these factors influence the way consumers search for information, evaluate options, and make purchasing decisions in an electronic commerce environment. The results show that there is a significant relationship between these factors and consumer behavior in e-commerce, with relevant implications for companies' marketing and sales strategies in the digital era.

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.

Telecom customer churn prediction model : Analysis of machine learning techniques for churn prediction and factor identification in telecom sector

This research project leverages exploratory data analysis (EDA) on a telecom company’s customer data to predict user churn. Utilizing Python and its libraries, including Pandas, NumPy, Matplotlib, and Scikit-Learn, it identifies key parameters crucial for accurate predictions. The results and predictions are visualized using the Flask framework and Power BI analytics tool. In the highly competitive telecommunications sector, customer churn poses a significant challenge, with an annual churn rate of 15-25%. This study addresses the escalating phenomenon of consumers freely switching between providers, leading to financial losses for businesses. By employing machine learning models, it aims to forecast potential churners, enabling companies to focus targeted retention efforts and mitigate losses. The tool’s efficacy lies in its ability to analyze patterns among churned users, offering a solution to a pressing issue in the telecom industry. Beyond its immediate application, this tool can be extended to other industries, providing valuable insights for customer retention strategies.

Marketing Сommunication during the Crisis: Strategies for Customer Recovery and Retention

"During an economic crisis, customers’ purchasing ability decreases, and company revenues become unpredictable. However, the crisis is not always a negative phenomenon and a profit decline but a chance to improve the company’s performance. Innovativeness, client orientation, customer retention, and close interaction with them are key to business development success. The article complemented the theoretical aspects of crisis management. It also substantiates the importance of strategic marketing and the formation of marketing strategies in crisis management. The authors highlighted the causes of customer outflow and the increasing importance of customer retention. The paper identified and substantiated the basic principles and methods of building an anti-crisis marketing strategy for customer recovery and retention. The authors indicated the main goal of the strategy, which is to increase profits and reduce the cost of attracting new customers. They also described the indicators for evaluating the customer retention process. The study analyzed the concepts, strategies, and tools for customer recovery, retention, and acquisition and identified their features. Their application in the practical activities of a company during a crisis period was grounded in overcoming crises and promoting successful company operations in the current and future periods."

EXPLORING THE IMPACT OF TRUST ON CUSTOMER LOYALTY IN THE TELECOMMUNICATION INDUSTRY

This study explores the impact of brand experience, service quality, customer satisfaction, and trust on customer loyalty within the mobile telecommunications sector in Batam City. The participants of this research were residents of Batam who utilized cellular telecommunications services. Data was collected through an online questionnaire via Google Forms, targeting 300 respondents who were customers of mobile telecommunications services. The data analysis revealed several key findings: 1. A significant positive relationship exists between brand experience and customer loyalty. 2. Service quality is strongly linked to customer satisfaction. 3. There is a notable connection between brand experience and customer trust. 4. Customer satisfaction significantly influences customer loyalty.5. Service quality indirectly affects customer loyalty through customer satisfaction. 6. Interestingly, the study found no direct significant relationship between trust and loyalty, nor between brand experience and loyalty when mediated by trust. These results suggest that while brand experience, trust, and customer satisfaction are important factors influencing loyalty, trust does not directly contribute to building customer loyalty, nor does brand experience impact loyalty through trust. Moreover, maintaining high service quality is emphasized as a fundamental requirement for telecommunications service providers. This research provides valuable insights into the dynamics of customer loyalty in the mobile telecommunications context, offering practical implications for businesses in this industry aiming to enhance their customer retention strategies.

A Journey From Customer Acquisition to Retention: An Integrative Model for Guiding Future Gaming Marketing Research

Casinos operate in a competitive environment. Relationship marketing has become a central focus of casino marketing practice. However, the literature lacks any clearly identified marketing strategies for customer acquisition and retention. This article employs a systematic literature review method to identify and delineate acquisition and retention strategies for the casino sector. The findings revealed measures and antecedents for customer acquisition, retention, casino consumption, and loyalty. The study draws on these findings to present a conceptual model that categorizes customer acquisition and retention strategies. Suggestions and recommendations are highlighted for practitioners and researchers.

Analysing Health Insurance Customer Dataset to Determine Cross-Selling Potential

Health insurance cross-selling refers to the practice of offering additional or complementary insurance products to existing policyholders. Insurance providers leverage cross-selling, offering customers additional policies like dental or life insurance when they already have a basic health insurance plan. This study is conducted to focus on the application of machine learning techniques to predict health insurance cross-selling opportunities among South African customers. The aim of this study is to develop a cross-selling predictive machine learning model that can assist health insurance companies to identify potential customers for cross-selling probabilities. To achieve this goal, a quantitative research methodology is adopted, focusing on extracting a comprehensive dataset of health insurance consumer information and employing various machine learning algorithms using the Python programming language, including Random Forest, K-Nearest Neighbours, XGBoost classifier, and Logistic Regression algorithms to build the cross-selling predictive machine learning model. The experimental results obtained demonstrate the accuracy scores of four different machine learning algorithms trained using 1,000,000 customer dataset with 17 features, logistic regression is considered as the top-performing model. It achieved an accuracy score of 0.83 and an F1 score of 0.91. The analysis indicates that customers aged 30-60, with prior insurance, and longer service history are more likely to buy additional health insurance products. The findings of this research can help health insurers boost revenue by improving their customer targeting and retention strategies.

Promoting perceived service quality and organisational performance through customer retention strategies: the moderating role of ICT

PurposeThe major objective of the study is to investigate the effect of selected customer retention strategies (fair pricing, online marketing and frequent communication) on perceived service quality and organisational performance within the retail sector in Zimbabwe. Also, the study sought to understand the moderating role of ICT on the effect of customer retention strategies on perceived service quality and organisational performance.Design/methodology/approachA cross-sectional survey of 280 employees within Zimbabwe's retail sector was adopted and respondents were selected using simple random sampling method. A structured questionnaire with Likert type questions was used to gather data.FindingsThe study findings indicate that the performance of organisations within the retail sector is influenced by superior service quality, selected customer retention strategies and also moderated by the use of ICT.Originality/valueThe study contributes to the business management body of knowledge by assessing the effect of selected customer retention strategies (fair pricing, online marketing and frequent communication) on perceived service quality and organisational performance within the retail industry of an emerging economy. The study is also unique in that it used ICT to moderate the effect of selected customer retention strategies on perceived service quality and organisational performance.

Social Listening – A Review and its Use for Customer Engagement & Customer Retention by Organizations

In today's digital age, social media has emerged as a vital platform for businesses to connect with their customers. As of July 2023, there are 5.19 billion internet users worldwide, which amounted to 64.6 percent of the global population. Of this total, 4.88 billion, or 59.9 percent of the world's population, were social media users (Source : Statista ). Tech and policy think tank Esya Centre has found that daily engagement of Indians in terms of time spent is the highest for social media at 194 minutes a day. This number for OTT and Online Gaming stands at 44 minutes and 46 minutes, respectively. Thus it has become important for organizations to be available on these social media platforms to connect with customers. Presence of organizations on Social media platforms has helped companies connect with common people , end users. Social listening, the practice of monitoring and analyzing online conversations and discussions, has gained significant attention as a strategic tool for understanding customer sentiments and preferences. This research article presents a comprehensive review of the concept of social listening and its pivotal role in enhancing customer engagement and retention strategies. To provide a comprehensive perspective, the article also mentions about various organizations that have successfully integrated social listening into their customer-centric strategies. In sum, this research article sheds light on the role of social listening in customer engagement and retention by making use of the insights derived from social listening. Keywords: Social Listening, Social Media, Customer Engagement, Ethics

Sentiment Analysis of Opinions about Online Education in the Kurdistan Region of Iraq during COVID-19

Sentiment analysis is widely used in various areas and has versatile applications. For example, it is used in market research, customer retention strategies, and product analysis, to name a few. Although a few works on the topic exist for the Kurdish language, similar to other fields in Kurdish processing, it is not well-studied, and particularly it suffers from data inadequacy. In this paper, we present research we conducted to analyze the sentiments of learners/educators toward online education during COVID-19 in the Kurdistan Region of Iraq. We collected the data from tweets tweeted in Kurdish (Sorani) up to March 2022. We used four Machine Learning algorithms: Naïve Bayes, SVM, Random Forest, and Logistic Regression, and analyzed their performance on our dataset. We retrieved about 600 tweets, which after preprocessing, yielded 511 items. We conducted five experiments, four of which included testing all algorithms using two scenarios of balanced and unbalanced datasets of positive/negative items, each using 80/20 and 90/10 training/testing data splitting methods. The fifth experiment included four parts, setting a limit for feature selection starting at 500 features and increasing it to 500 at a time until 2000 features, testing for both 80/20 and 90/10 data splitting approaches. The results showed that the best algorithm to build a sentiment analysis model is SVM, with an accuracy of 89% and a maximum feature selection of 1000. The dataset is publicly available for non-commercial use under CC0 1.0 Universal license.