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This study aims to analyze the effect of personalized marketing on customer satisfaction and customer retention, as well as to explain the role of customer satisfaction as a mechanism linking marketing personalization to customers’ decision to remain with a brand. The background of this study is based on the increasing use of data-driven marketing strategies, product recommendations, personalized promotions, and digital communication tailored to customer preferences. In an increasingly competitive business environment, companies are required not only to attract new customers but also to retain existing customers through relevant, convenient, and valuable experiences. This study employed a quantitative explanatory approach, with the population consisting of customers who had received personalized marketing from a particular company or brand. The sampling technique used was purposive sampling, with the criteria that respondents had received personalized promotions and had made repeat purchases. The total sample consisted of 110 respondents. Data were collected using a structured questionnaire with a five-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling. The findings show that personalized marketing has a positive and significant effect on customer satisfaction. This indicates that promotions, recommendations, and marketing messages that are relevant to customer needs can create more positive customer experiences. Customer satisfaction is also found to have a positive and significant effect on customer retention, meaning that satisfied customers are more likely to make repeat purchases, continue using products or services, and avoid switching to competitors. In addition, personalized marketing has a direct effect on customer retention, although its effect becomes stronger when mediated by customer satisfaction. The implications of this study emphasize that companies need to develop personalization strategies that are not only data-driven but also relevant, ethical, non-intrusive, and oriented toward customer value. Therefore, personalized marketing can serve as an important strategy for improving customer satisfaction and maintaining long-term customer retention.

The premium healthcare industry in urban areas is experiencing increasing competition along with the increase in healthcare facilities and the increasing public demand for fast, comfortable, and quality medical services. In this context, Columbia Asia Pulomas Hospital is implementing an expansion strategy by increasing facility capacity, modernizing services, and adding a Center of Excellence (COE) as a service differentiation. This study aims to analyze customer retention strategies within the Balanced Scorecard (BSC) framework, focusing on the relationship between customer perspectives, internal processes, learning and growth, and their application to the financial perspective. The research method uses a document-based policy and strategy analysis approach, field findings, and a synthesis of Balanced Scorecard theory and patient experience.The analysis shows that customer retention during the expansion phase is influenced not only by clinical quality, but also by the assurance of doctor time in practice, speed of service, physical comfort, and digitization of queues and administration. The addition of a COE has been shown to increase the perception of service value and expand market share through service specialist differentiation. Within the BSC framework, the customer perspective serves as a leading indicator for achieving the financial perspective, where increased patient retention contributes to increased revenue, ROI growth, and long-term financial expectations. The research implications emphasize that strategies for strengthening human resources, modernizing internal processes, and service innovation are important foundations in ensuring successful hospital expansion and enhancing competitive advantage.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

The retention of customers in the retail banking sector is a critical economic imperative; however, predictive modeling is frequently hindered by severe class imbalance and the “Black Box” nature of complex algorithms. This study proposes a Heterogeneous Stacking Ensemble framework integrating XGBoost, CatBoost, and Random Forest base learners with a Logistic Regression meta-learner to forecast customer attrition. To overcome the pervasive “Majority Class Bias,” we introduce a “Dual-Imbalance Defense” that synergizes the Synthetic Minority Over-sampling Technique (SMOTE) with algorithmic cost-sensitive penalization. Furthermore, moving beyond standard accuracy metrics, the framework mathematically derives a dynamic classification threshold to guarantee a strict 0.90 recall rate, actively optimizing the capture of at-risk capital. Model opacity is addressed through the integration of a SHapley Additive exPlanations (SHAP) TreeExplainer. This cooperative game theory approach provides localized, patient-level “Reason Codes” for regulatory compliance and reveals global systemic vulnerabilities, including non-linear drivers such as the “Product Paradox.” Achieving a 0.90 recall rate and an AUC of 0.8654, this framework provides a statistically robust and operationally transparent tool for targeted customer retention.

Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

Customer churn is a strategic challenge for digital streaming platforms because it directly Impacts revenue and business sustainability. This study aims to analyze the factors influencing customer Churn and develop a churn prediction model using the Random Forest algorithm. The study uses a Quantitative approach with an explanatory design and utilizes secondary data from the Netflix Customer Churn and Engagement Dataset available on Kaggle. The dataset consists of 1,000 customer data with 16 Variables covering demographic characteristics, service usage behavior, financial condition, and customer Satisfaction level. The data was processed through preprocessing, one-hot encoding, and a 70:30 split Between training and test data. Model performance was evaluated using accuracy, precision, recall, F1 Score, and ROC-AUC metrics. The results show that the Random Forest model produces an accuracy of 53.7%, precision of 56.3%, recall of 63.6%, F1-score of 59.7%, and ROC-AUC of 0.534, indicating Moderate predictive ability and only slightly better than random classification. Feature importanceAn.evealed that user engagement levels, such as viewing duration and frequency of interactions, Were the most dominant factors influencing churn, followed by economic factors and customer satisfaction. The results of this study are expected to provide a basis for streaming platforms to design more effective Customer retention strategies.

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.

This study aims to examine the application of heart-centered communication based on Nonviolent Communication (NVC) Theory in interactions between employees and guests at the Masainn Hotel, Kuta, Bali. NVC, developed by Marshall Rosenberg, stresses empathetic engagement built on four core components: observation, feelings, needs, and requests. Using a descriptive qualitative approach, data were collected through in-depth interviews with guests and direct field observations of daily service interactions. The findings indicate that employees consistently apply empathy-driven communication by offering warm greetings, attentive service, and genuine emotional presence. These behaviors help create a family-like environment that makes guests feel comfortable and emotionally connected to the hotel. Notably, one guest reported returning to the hotel for three consecutive years, having been introduced by a friend who has been a loyal customer for nine years. This demonstrates that NVC-based communication contributes significantly to guest satisfaction, trust, and long-term loyalty. Furthermore, the study highlights the strategic role of emotional intelligence and compassionate communication in shaping service quality within the hospitality industry. By integrating NVC principles into daily service practices, hotels can foster stronger interpersonal relationships, enhance guest experiences, and build sustainable customer retention.

Because it directly impacts revenue, customer loyalty, and long-term business sustainability, customer churn is a critical issue for the e-commerce industry. High churn rates indicate that a business is unable to retain existing customers, which means it is more expensive to acquire new customers. Therefore, a precise analytical approach is needed to identify customer behavior patterns that are likely to churn. Using machine learning methods, this study analyzes and predicts customer churn. For this study, the E-Commerce Customer Churn 2025 dataset, obtained from Kaggle, was used. This dataset consists of 10,000 customer data and contains fifteen variables covering transaction behavior, customer characteristics, and churn status. Data preprocessing, descriptive analysis, exploratory data analysis (EDA), and classification model development using Logistic Regression and Random Forest algorithms were part of the research project. Model evaluation was conducted using a Confusion Matrix and Receiver Operating Characteristic (ROC) Curve to evaluate the model's accuracy and ability to distinguish between churned and non-churned customers. The results showed that the Random Forest model performed better than Logistic Regression, with an ROC-AUC of 1.00. Furthermore, feature importance analysis revealed that the days_since_last_purchase variable was the most dominant factor in predicting customer churn. These findings are expected to help e-commerce companies design more effective, data-driven customer retention strategies.  

This study examined the operational performance of CV Fortis Sportwear Indonesia (FSI) before implementing the Balanced Scorecard approach, identified the factors that hindered each Balanced Scorecard perspective, and analyzed the company's operational performance when measured using this approach.This study employed a descriptive quantitative method, aimed at providing a systematic, factual, and accurate overview of CV FSI's operational performance based on the Balanced Scorecard approach. Data were collected through interviews, questionnaires, observations, and literature review to obtain information related to the four main perspectives of the Balanced Scorecard: financial, customer, internal business processes, and learning and growth.After implementing the Balanced Scorecard, the performance evaluation of CV Fortis Sportswear Indonesia became more comprehensive compared to the previous system, which focused solely on financial aspects. The financial perspective showed improvement, but the customer aspect remained adequate, with declining retention and increasing complaints. Internal business processes still faced inefficiencies and quality issues, despite the introduction of new technology. The learning and growth perspective also improved through training, but employee satisfaction remained moderate. The Balanced Scorecard proved to provide a more comprehensive performance assessment and identified the need for improvements in customer, internal processes, and human resource development.