Improve Customer Retention Research Articles

Implementing AI-driven Customer Relationship Management (CRM) systems: Enhancing customer experience in the retail industry of Thailand

This research explores the impact of AI-driven Customer Relationship Management (CRM) systems on enhancing customer experience in Thailand's retail industry. However, quantitative data analysis was conducted to focus on the 'how' of AI technologies, such as chatbots, predictive analytics, and personalized marketing, to improve customer satisfaction, retention, and sales conversion rates. It also denotes from the analysis of customer transaction data, CRM interaction logs, and sales performance metrics that there has been a very significant improvement in operational efficiency and customer loyalty. The findings have shown that chatbots reduce response time and increase first-contact resolution rates; predictive analytics improve customer retention with accurate identification of at-risk customers; and a 30% increase in sales conversion through personalized marketing. These findings present a significant positive relationship between AI-CRM implementation and improved business performance, showing just how important AI can be in driving customer engagement and operational success for retailers. This empirical research supports the idea that AI-powered CRM systems have become imperative for maintaining competitive advantage by retailers within an increasingly digital marketplace.

Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.

Membangun Model Prediksi Churn Pelanggan yang Akurat

Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compared to previously implemented rule-based methods. The findings of this research allow TELCO Company to identify high-risk customers more effectively and implement targeted retention strategies. Results show that the resulting model can identify customers at risk of churn more effectively, enabling more targeted retention actions..

Development of an integrated model for SME marketing and CRM optimization

The development of an integrated model for optimizing marketing and customer relationship management (CRM) in small and medium-sized enterprises (SMEs) presents a crucial advancement in enhancing business competitiveness and customer engagement. This paper proposes a unified model that merges scalable software solutions for social media analytics with AI-driven CRM to provide a comprehensive framework for SMEs to enhance their marketing strategies and customer interactions. Social media platforms have become vital channels for customer outreach, engagement, and feedback, yet SMEs often face challenges in leveraging these platforms due to limited resources and expertise. Integrating social media analytics into CRM systems offers businesses the ability to analyze customer behaviors, preferences, and sentiment on a large scale. The proposed model utilizes artificial intelligence (AI) to streamline customer interactions, enabling personalized communication and improving customer retention. By incorporating AI into CRM, SMEs can automate routine tasks, such as responding to customer inquiries and segmenting audiences based on their social media activity. This allows for more efficient marketing strategies and enables businesses to proactively address customer needs, ultimately driving customer loyalty and long-term growth. Furthermore, the unified model focuses on scalability, ensuring that SMEs can adopt and adapt these technologies according to their evolving needs and resources. This paper also explores the benefits of integrating these technologies, including the potential for improved data-driven decision-making, enhanced customer experience, and increased operational efficiency. In conclusion, this integrated model presents a strategic pathway for SMEs to optimize their marketing and CRM efforts through the adoption of scalable, AI-driven tools that enhance customer engagement and boost business performance. Keywords: SME Marketing, CRM Optimization, Social Media Analytics, AI-Driven CRM, Customer Engagement, Scalable Software Solutions, Business Performance, Personalized Communication, Customer Retention, Data-Driven Decision-Making.

Role of policyholders’ beliefs in life insurance lapses: a model building approach

Purpose This study aims to understand the lapse behavior of life insurance policyholders. Despite being accessible for nearly two centuries, only a small fraction of individuals purchase such policies and many of those who do let them lapse. The belief hypothesis model (BHM) is introduced to elucidate the correlation between policyholders' beliefs and their decisions regarding life insurance lapses. Design/methodology/approach BHM establishes a comprehensive linkage between core beliefs, external data and the lapse behavior exhibited by policyholders. To derive policyholders’ core beliefs about life insurance lapses, the authors conducted a semistructured, in-depth interview with 42 policyholders and 11 insurance advisors, using a grounded theory approach with zero-order, first-order and second-order coding. Findings The study's findings reveal that policy lapsation is influenced by various factors such as policyholders' beliefs about life insurance, process-related attitudes, trust in insurers and advisors and personal financial viewpoints. Policyholders who consider life insurance unnecessary or misunderstand its purpose are likelier to lapse their policies. Cumbersome documentation processes and technical issues also contribute to policy lapsation, emphasizing the significance of simplified procedures. Trust in insurers and advisors, personal financial literacy and payment preferences influence policy lapsation. Practical implications The findings of this research can be practically applied by companies to improve customer retention and by regulatory bodies to encourage policyholders to honor their insurance commitments. Originality/value Distinguishing itself from conventional hypotheses-driven and factor-centric models, BHM integrates consumer beliefs, thus enriching comprehension and decision-making insights.

Struggling Models: An Analysis of Logistic Regression and Random Forest in Predicting Repeat Buyers with Imbalanced Performance Metrics

Predicting repeat buyers is essential for businesses seeking to improve customer retention and maximize profitability. This study examines the effectiveness of logistic regression and random forest algorithms in forecasting repeat buyers, utilizing an e-commerce dataset from Kaggle. Despite the theoretical strengths of these models, our results indicate significant performance challenges. Both models were evaluated on key metrics: accuracy, precision, recall, F1 score, and ROC-AUC. The findings revealed that the models logistic regression and random forest performed poorly, with accuracy hovering around 50%, precision and recall demonstrating imbalanced performance, and ROC-AUC scores barely exceeding random guessing levels. Such metrics highlight the limited discriminative power of these models in identifying repeat buyers. The analysis suggests that issues such as data quality, feature relevance, and class imbalance contribute to these shortcomings. Specifically, the models struggled to effectively learn from the data, leading to suboptimal predictions. These results underscore the need for enhanced feature engineering, better handling of class imbalance, and possibly exploring more advanced algorithms. This study provides a critical assessment of the limitations inherent in using Logistic Regression and Random Forest for predicting repeat buyers, hence implements feature engineering, SMOTE and hyperparameter tuning using RandomSearchCV to get better result.

Customer Churn Analysis Using Machine Learning to Improve Customer Retention on Vissie Net

Customer Churn Analysis Using Machine Learning to Improve Customer Retention on Vissie Net

Factors Influencing Customer Satisfaction in Online Shopping Among Non-Resident Students of UiTM Cawangan Terengganu

People nowadays prefer to do their purchasing online. Customers like to purchase on the internet because it is more convenient than traditional shopping, which is often associated with anxiety, crowdedness, traffic jams, limited time, and parking space, among other things. In Malaysia, the number of internet users has increased, and online shopping has become a growing trend. Therefore, this study will look into the elements that influence customer satisfaction with online buying among university students. Product quality, brand, and shopping experience are three aspects that contributed to this study. The survey approach was utilized in this study, and 364 respondents answered questionnaires. Questionnaires were distributed to non-resident university students to determine the elements that influence their decision to shop online. The researcher employed both the online survey and manual method to obtain data. Statistical Package for Social Science (SPSS) and Microsoft Excel were used to analyze the data gathered. According to the findings, product quality, brand, and shopping experience all have a significant impact on non-resident university students' satisfaction with online shopping at UiTM Cawangan Terengganu. In summary, the findings of this study are essential for building a successful, sustainable, and competitive business. They provide actionable insights that can lead to improved customer retention, increased revenue, and a stronger brand reputation.

Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customers Churn Banks

Abstract This study presents a novel multi-level Stacking model designed to enhance the accuracy of customer churn prediction in the banking sector, a critical aspect for improving customer retention. Our approach integrates four distinct machine-learning algorithms – K-Nearest Neighbor (KNN), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) – at the first level (Level 0). These algorithms generate initial predictions, which are then combined and fed into higher-level models (Level 1) comprising Logistic Regression, Recurrent Neural Network (RNN), and Deep Neural Network (DNN). We evaluated the model through three scenarios: Scenario 1 uses Logistic Regression at Level 1, Scenario 2 employs a Deep Convolutional Neural Network (DNN), and Scenario 3 utilizes a Deep Recurrent Neural Network (RNN). Our experiments on multiple datasets demonstrate significant improvements over traditional methods. In particular, Scenario 1 achieved an accuracy of 91.08%, a ROC-AUC of 98%, and an AUC-PR of 98.15%. Comparisons with existing research further underscore the enhanced performance of our proposed model.

Implementing machine learning techniques for customer retention and churn prediction in telecommunications

This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data quality, model selection, implementation, and ethical considerations in using customer data for predictive analytics. The paper also compares machine learning models with traditional methods, emphasizing the advantages of scalability, accuracy, and real-time processing. Furthermore, it identifies potential innovations, such as improved data integration, interpretable models, and personalized retention strategies. Finally, the paper reflects on future trends, predicting the growing role of AI and machine learning in telecommunications, particularly in customer service automation and network optimization. The review underscores the importance of adopting machine learning to reduce churn and improve customer retention while considering the field's ethical implications and future opportunities. Keywords: Customer Churn Prediction, Machine Learning, Telecommunications, Customer Retention, Predictive Analytics, AI in Telecom

Role of Machine Learning Algorithms in Enhancing Customer Relationship Management Systems in Rwanda

Purpose: The aim of the study was to assess the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Algorithms analyze vast amounts of data, allowing businesses to personalize interactions and anticipate customer needs more effectively. For instance, predictive analytics can forecast customer churn, prompting proactive retention strategies. Moreover, sentiment analysis tools gauge customer emotions from feedback, enabling tailored responses and improved service delivery. Machine learning also automates routine tasks like email marketing segmentation, optimizing resource allocation and enhancing overall efficiency. Consequently, CRM systems empowered by machine learning algorithms foster stronger customer relationships, driving business growth through enhanced customer satisfaction and loyalty. Implications to Theory, Practice and Policy: Technology acceptance model (TAM), resource-based view and diffusion of innovations theory may be used to anchor future studies on assessing the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Businesses should adopt advanced ML techniques, such as Long Short-Term Memory (LSTM) models and ensemble learning, to enhance the accuracy of customer segmentation and churn prediction, leading to more personalized marketing strategies and improved customer retention. Policymakers should develop and enforce stringent data privacy and security regulations to protect customer data used in ML-enhanced CRM systems.

Optimizing Social Media Promotion Strategy to Increase Customer Retention Rate (CRR) with GKG Customer Engagement

In the digital age, businesses are increasingly relying on social media platforms to engage with their customers and foster brand loyalty. This paper presents a comprehensive study aimed at optimizing social media promotion strategies to enhance Customer Retention Rate (CRR) while utilizing the GKG (Get Keep Growth) Customer Engagement framework. By examining the interplay between social media promotion tactics and customer engagement metrics, we investigate how businesses can leverage data-driven insights to improve customer retention. Our research showcases the importance of tailoring social media campaigns to individual customer preferences and behaviour, ultimately leading to increased customer satisfaction and loyalty. The results of the analysis of the development of the Customer Retention Rate graph were produced on FMIPA social media with an average CRR in the base case of 71%. Through a combination of data analysis and case studies, we provide actionable recommendations for businesses seeking to maximize the effectiveness of their social media promotion efforts and elevate their CRR.

The Effect of Service Quality Dimensions, Perceived Value, Customer Satisfaction, and Brand Love toward Customer Retention on First Media’s Subscribers

In the dynamic telecommunications sector, customer retention is of paramount importance for companies like First Media in Indonesia, given the rapid technological advancements and increasing demand for high-quality services. This study explores the factors influencing customer retention, including service encounter communication, internet service quality, service convenience, perceived value, customer satisfaction, and brand love. Service encounter communication, service convenience, and internet service quality are examined in relation to their effect on perceived value, customer satisfaction, and customer retention. A comprehensive survey was conducted using Google Forms, with 49 indicators distributed across these variables, and data analysis was performed using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The results indicate that service encounter communication has a positive and significant effect on perceived value, customer satisfaction, and customer retention. Service convenience positively affects customer satisfaction but does not affect perceived value or customer retention. Internet service quality has a positive influence on perceived value. Perceived value enhances customer satisfaction, leading to improved customer retention and brand love, which in turn positively influences customer retention. This study provides valuable insights for First Media to enhance customer retention and satisfaction. However, it has limitations in terms of variable selection and testing, suggesting opportunities for future research to explore additional factors contributing to customer retention and brand management.

An Analysis of Luckin Coffee's New Marketing Model from the Perspective of Behavioral Finance

With the development of Internet technology, Luckin Coffee has transformed the traditional value creation model, driven by customer value as the goal, and established its brand effect in a short period of time through financing. However, the excessive and rapid expansion led to financial crisis, which caused it to fall into the negative vortex of financial fraud, and finally returned to the consumer market at the cost of delisting and restructuring in the United States. After returning to the market, Luckin Coffee changed its business operation model, and the reform of the new marketing model focusing on products and customers made its financial turnaround and rebuilt its brand value. How does Luckin Coffee take advantage of consumers' psychology and guide their consumption preferences? Based on the perspective of behavioral finance, this paper analyzes how the operation management team of Luckin Coffee reconstructs its marketing methods and paths by utilizing the anchoring effect of consumers, limited attention transactions and herd effect. The research conclusion shows that the new marketing model with consumer demand caused by consumer psychology as the core can effectively and rapidly increase the number of new customers and improve customer retention. Luckin Coffee has also quickly passed the crisis after delisting and restructuring, and its business performance and corporate value have also gradually increased. The brand value has returned to the vision of consumers, and the future is promising.

Integration of business intelligence and marketing: Enhancing corporate reputation in the financial services sector

This investigation extends into the intricate fabric of customer-based corporate reputation within the banking industry, applying advanced analytics to decipher the nuances of customer perceptions. By integrating structural equation modeling, particularly through SmartPLS4, we thoroughly examine the interrelations of perceived quality, competence, likeability, and trust, and how they culminate in customer satisfaction and loyalty. Our comprehensive dataset is drawn from a varied demographic of banking consumers, ensuring a holistic view of the sector’s reputation dynamics. The research reveals the profound influence of these constructs on customer decision-making, with likeability emerging as a critical driver of satisfaction and allegiance to the bank. We also rigorously test our model’s internal consistency and convergent validity, establishing its reliability and robustness. While the direct involvement of Business Intelligence (BI) tools in the research design may not be overtly articulated, the analytical techniques and data-driven approach at the core of our methodology are synonymous with BI’s capabilities. The insights garnered from our analysis have direct implications for data-driven decision-making in banking. They inform strategies that could include enhancing service personalization, refining reputation management, and improving customer retention efforts. We acknowledge the need to more explicitly detail the role of BI within the research process. BI’s latent presence is inherent in the analytical processes employed to interpret complex data and generate actionable insights, which are crucial for crafting targeted marketing strategies. In summary, our research not only contributes to academic discourse on marketing and customer perception but also implicitly demonstrates the value that BI methodologies bring to understanding and influencing consumer behavior in the banking sector. It is this blend of analytics and marketing intelligence that equips banks with the strategic leverage necessary to thrive in today’s competitive financial landscape.

The Impact of Loyalty Programs on Customer Retention in the Retail Industry

This study investigates the impact of loyalty programs on customer retention within the retail industry, focusing on how these programs enhance customer loyalty and influence purchasing behaviors. “Utilizing a mixed-methods approach, the research combines quantitative surveys and qualitative interviews to provide a comprehensive analysis. The primary objectives are to identify the types of loyalty programs used in retail, assess their effectiveness in retaining customers, and explore the psychological mechanisms that underpin customer loyalty. The hypothesis posits that well-designed loyalty programs significantly improve customer retention by increasing satisfaction and fostering repeat purchases. The findings reveal that loyalty programs, particularly those offering personalized rewards and tiered benefits, effectively boost customer retention by creating a sense of value and emotional connection with the brand. Additionally, the study highlights the importance of using customer data to tailor loyalty initiatives, which enhances the shopping experience and strengthens customer relationships. The research contributes to both academic literature and practical applications, offering valuable insights for retail managers aiming to optimize their loyalty strategies. By demonstrating the positive impact of loyalty programs on customer retention, this study underscores the critical role these programs play in achieving long-term success and competitive advantage in the retail industry.

Understanding and predicting lapses in mortgage life insurance using a machine learning approach

Mortgage Life Insurance (MLI) offers lucrative opportunities for insurers. However, customer retention has proven to be a daunting challenge, particularly following the regulatory changes of 2009 in Europe. New market entrants strategically employing low-premium tactics have reshaped the competitive landscape, leading established insurers and banks to grapple with retaining their MLI clientele. Consequently, increasing policy lapses hold critical implications for these financial entities. Responding to this intricate landscape, our research presents a predictive model that pinpoints the MLI policies at risk of lapse and disentangles the underlying factors propelling this risk. The objective is to provide insurers with a practical and strategic tool to improve customer retention, enabling them to identify specific actions to reduce customer attrition, improve financial stability, and strengthen customer loyalty. We used a dataset obtained from an insurance company and its partner bank to build the model. The effectiveness of four machine learning models, namely Logistic Regression, Random Forest, Neural Networks, and XGBoost, is investigated, with XGBoost outperforming the others. SHapley Additive exPlanations (SHAP) were utilized to bolster interpretability, thereby facilitating the conception and explication of the predictive model’s most influential features. Underpinning the benefits of a nuanced exploration, the study’s focus on a solitary insurance protection product and integrating bank data enabled us to apprehend the multifaceted drivers of lapse behavior. The study accentuates the merit of comprehensive data encapsulating a holistic perspective, with the four most influential features originating from bank data. From an insurer’s standpoint, this research provides a strategic vantage point to proactively identify and engage with customers at risk of policy lapse and reformulate their policies to mitigate customer attrition.

Gamificación como herramienta de marketing digital

This study addresses the need to improve customer retention and online interaction for "Flip Flop," a company with a customer base of 150 individuals dedicated to the production and marketing of footwear. The research focuses on implementing gamification as a strategic tool in digital marketing. The main objective is to determine the relationship between gamification and digital marketing to establish advertising strategies. The methodology followed a quantitative approach encompassing exploratory, descriptive, and correlational scopes. Data collection combined literature review with field research. Gamified strategies, including challenges and rewards, were implemented. The results revealed the significant relevance of gamification as a highly effective tool in digital marketing. To assess the impact of gamification on customer retention and online interaction, the level of acceptance of the strategy was evaluated, reaching a considerable 55.3% acceptance among surveyed customers. The quantitative approach, through questionnaires, facilitated data collection from potential customers, highlighting the strategic importance of gamification in generating audience interest and engagement. Gamification proved to be an effective strategy for enhancing the connection between the company and its customers, underscoring its valuable role in digital marketing. These findings emphasize the importance of innovation in this field and provide valuable insights for future research. In conclusion, gamification emerges as a crucial tool for enhancing the company-customer relationship in the context of digital marketing.

Customer Churn Analysis in Telecom Industry using 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.

The Role of Comprehensive Customer Relationship Management (CRM) Systems in Achieving High Retention Rates

High client retention rates were crucial for maintaining growth and profitability in today's competitive business world. CRM solutions played a crucial role in facilitating the effective management of client contacts across several channels for organisations. These systems integrate consumer data and streamline procedures, offering a cohesive perspective of customer interactions and preferences. This paper examined how complete CRM systems may improve customer retention via personalised experiences, streamlined sales processes, and data analytics for informed decision-making. Although the advantages of CRM systems are clear, there are still obstacles to overcome, including data integration, expensive installation expenses, and ongoing adaptation and staff training. The research examined a comprehensive knowledge of CRM utilisation and its influence on customer retention. The results emphasised the significance of a well-planned deployment of customer relationship management (CRM) in promoting customer loyalty, enhancing operational effectiveness, and increasing revenue generation. Suggested strategies for firms include allocating resources towards data integration, providing comprehensive staff training, implementing continuous improvement practices, using sophisticated analytics, and cultivating a customer-centric culture. Keywords: Customer Relationship Management (CRM), Customer Retention, Data Integration, Personalised Customer Experience, Sales Force Automation (SFA).