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This study explores digital marketing strategies employed by Small and Medium Enterprises (SMEs) to enhance customer engagement in an increasingly competitive business environment. By leveraging tools such as social media, email marketing, content marketing, and search engine optimization (SEO), SMEs can establish stronger connections with their target audience. The research highlights key practices, including personalized communication, interactive content, and data-driven decision-making, which contribute to customer loyalty and retention. Furthermore, challenges faced by SMEs, such as limited budgets and technological expertise, are discussed alongside potential solutions. The findings provide actionable insights for SMEs to optimize their digital marketing efforts and foster long-term customer relationships.  

This study aims to determine the performance of KCU Pintu Air by using the balanced scorecard method. This research method is a quantitative descriptive method. The data in this study were obtained from documentation in the form of financial reports and distributing questionnaires. The population in this study was KCU Pintu Air with a sample of a financial perspective in the form of financial reports, a customer perspective with a sample of 44 members, an internal business process perspective and a growth and learning perspective with a sample of 27 employees. The findings of this study show that the performance of KCU Pintu Air when measured using the balanced scorecard method results in a rating scale of 0.2 which lies between 0-0.6 which indicates that the cooperative's performance is quite good. The results of perspective calculations in the balanced scorecard are financial perspective calculations with unfavorable results, this is because most of the PEARLS ratio calculation results are in the bad category. The customer perspective shows good results, which are obtained from indicators of member retention and member acquisition which experience fluctuations and indicators of member satisfaction as much as 79% of members are satisfied with cooperative services. The internal business process perspective shows very good results, which are obtained from the results of the questionnaire with an operational efficiency indicator of 80% and an indicator of competitive position of 83%, which means that the respondents stated that they were very satisfied. The growth and learning perspective shows good results obtained from the results of employee satisfaction indicators as much as 91% of employees who express attitudes towards very high satisfaction, employee retention indicators that show good results and employee productivity indicators that experience fluctuations during the study period.

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.

This research implements RFM (Recency, Frequency, Monetary) analysis to perform customer segmentation and evaluate the effectiveness of marketing campaigns in a retail company. Using a Kaggle dataset, this study identifies customers based on purchasing behaviour and assesses marketing campaign responses for each segment. The analysis reveals that Loyal, VIP, and New Customer segments showed the highest responses, especially in Campaign 6. The findings emphasize the importance of targeting resources on effective segments and campaigns to optimize marketing strategies and maximize ROI. Personalized campaigns based on segmentation can enhance customer retention and align product offerings with customer needs.

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..

This study aims to optimize marketing strategies through RFM (Recency, Frequency, Monetary) analysis on a retail transaction dataset obtained from Kaggle. The dataset contains 64,682 transactions from 5,242 SKUs involving 22,625 customers over one year. Data cleaning and RFM analysis were conducted to segment customers based on recency, frequency, and monetary values. The findings reveal that customers were segmented into groups such as Champions, Loyal Customers, and At Risk. These segments provide valuable insights for developing targeted marketing strategies, such as loyalty programs for high-value customers and retention campaigns for at-risk customers. The study demonstrates that RFM analysis is effective in identifying valuable customer segments and optimizing marketing efforts based on customer behavior. This approach can increase customer retention and improve the return on investment (ROI) in marketing campaigns.

This study explores the significant role of digital trust and transparency in fostering customer loyalty in online banking services. As the financial sector increasingly shifts towards digital platforms, understanding the dynamics of customer retention becomes crucial for banking institutions. The research adopts a quantitative approach, using a survey questionnaire distributed to over 200 participants with experience in digital banking services. The findings reveal that both digital trust and transparency are pivotal in shaping customer loyalty. Specifically, digital transparency-in areas such as data security, privacy, and operational clarity-was found to have a stronger correlation with customer retention than trust alone. The study emphasizes that transparency not only boosts customer confidence but also strengthens long-term loyalty by reducing perceived risks and uncertainties. Additionally, the research highlights that customer engagement, driven by transparent communication and reliable services, plays a significant role in mediating the relationship between transparency and loyalty. The study further discusses the implications for banking institutions, suggesting that focusing on transparent practices, clear communication about security measures, and data usage can lead to enhanced customer satisfaction and long-term retention. Future research could investigate the impact of cultural differences on digital trust and transparency in banking, as well as explore the role of emerging technologies like blockchain in furthering transparency and trust in financial transactions.

This study aims to analyze the effect of service quality, perceived value, and customer satisfaction on customer loyalty at Tanjung Decoration. Service quality, perceived value and customer satisfaction are key factors influencing purchase decisions and customer retention in the decoration service industry. However, there has not been much research specifically examining the relationship between these variables in the context of Tanjung Decoration. This research is worth conducting for several reasons. First, Tanjung Decoration is a major player in the fast-growing decoration industry, and an understanding of the factors that influence customer loyalty will help them improve their marketing and service strategies. Second, with a focus on service quality, perceived value, and customer satisfaction, this research will provide valuable insights for managers and practitioners in developing more effective programs to increase customer loyalty. Third, this research will complement the literature on marketing management and consumer behavior by providing a more in-depth understanding of the factors that influence purchasing behavior and customer loyalty in the context of the decoration services industry. By combining appropriate research methodology with a strong theoretical framework, this research is expected to make a significant contribution to the development of knowledge in the field of marketing management and consumer behavior. The results of this study are expected to be a valuable reference source for researchers, practitioners, and other stakeholders interested in understanding and improving customer loyalty in the decoration service industry.    

This study investigates the level of customer loyalty in e-commerce in the United States in 2020. Using e-commerce customer data from 2020 available on Kaggle, this research analyzes cohort retention to understand the number of customers from each cohort who continue to make purchases in subsequent months. Additionally, this study evaluates the implementation of data analysis to assess the effectiveness of promotional strategies and their impact on customer loyalty and retention. The results show that retention percentages decline over time, with the number of active customers decreasing after registration. However, some cohorts exhibit better retention performance. For instance, the April cohort saw a significant increase from 18.46% to 29.23%, and the May cohort experienced the highest increase from 14.29% to 30.16%. On the other hand, the January cohort showed the most drastic retention drop from 100% to 10.42% after the first month, with the largest monthly decline from 23.96% in April to 7.29% in May. These findings provide practical guidance for e-commerce companies in developing more effective strategies to improve customer retention. Further analysis is recommended to understand the specific causes of these retention trends and to develop more holistic strategies to support better business decisions and increase long-term profitability.

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.

Management Information System (MIS) is an important tool for companies to manage information and support business processes. However, there are still many companies that have not utilized SIM optimally to improve customer service. This research uses a qualitative case study method to collect data. This research examines the role of Management Information Systems (MIS) in improving customer service quality, building customer loyalty, and achieving competitive advantage in the digital era. The findings show that SIM plays an important role in improving the five dimensions of customer service quality, namely reliability, responsiveness, assurance, empathy, and form. SIM is also proven to help build customer loyalty through increased satisfaction, trust, and commitment. In addition, SIM can help companies achieve a competitive advantage by increasing customer retention, attracting new customers, and improving the company's image.      

Maintaining the best possible relationship with customers is part of Customer Relationship Management (CRM). The goal is to keep customers loyal and make the company make more money. Leading companies have incorporated CRM into their marketing strategies in an effort to increase sales. The Farmers Market company has implemented a customer relationship management (CRM) system; however, there has been no evaluation of their CRM model to identify profitable marketing strategies or customer retention opportunities. To conduct this investigation, a survey had to be conducted on a sample of 55 current Farmers Market consumers. Using the partial least squares method, the analysis will be conducted with the help of the SmartPLS 2.0 program. The findings of the analysis show that, overall, CRM variables relating to the impact of human resources on customer loyalty and customer satisfaction provide satisfactory results. Customer satisfaction is significantly influenced by each HR variable. While all other variables did contribute to the results, they were not statistically significant. This section of the study concludes with an analysis of the impact of Farmers' Market management and suggestions for improvement.

This theoretical review aims to explore the potential influence of free shipping, customer experience, and perceived risk on repurchase intentions in the context of e-commerce. e-commerce has experienced significant growth globally, emphasizing the need to understand the factors that influence consumer repurchase behavior. Based on existing literature, this paper examines the theoretical basis and research results regarding the influence of free shipping interest, customer experience, and risk perception and their potential impact on consumers' willingness to repurchase on e-commerce platforms. This review highlights the importance of the factors free shipping, customer experience and risk perception in shaping consumer behavior and suggests avenues for future empirical research to validate the proposed relationships. Understanding these dynamics can help e-commerce businesses in formulating effective marketing strategies to increase customer retention and loyalty.

This research investigates the implementation of Customer Relationship Management (CRM) in the Micro, Small and Medium Enterprises (MSME) and startup sectors. Through a qualitative approach, data was collected from a number of MSMEs and startups to analyze the factors that influence the adoption and success of CRM implementation. The findings show that limited resources, lack of knowledge about the benefits of CRM, and dependence on technological infrastructure are the main challenges in adopting CRM systems. However, CRM has also been found to have great potential in increasing customer retention, developing more suitable products, and expanding into new markets. Practical implications of these findings include recommendations for appropriate resource allocation, employee training, and more effective digital marketing strategies. The contribution of this research lies in a deeper understanding of CRM implementation in the context of MSMEs and startups as well as practical guidance for improving business performance through effective CRM strategies.

In a competitive business environment, understanding customer behaviour and improving retention strategies are critical to a company's success. Many companies struggle to identify valuable customers, understand their needs, and develop effective marketing strategies. One method that has proven effective is Recency, Frequency, and Monetary (RFM) analysis, which measures customer value based on three dimensions: when the customer last made a purchase, how often they transact, and how much money they spend. This research focuses on applying the RFM method with Python for customer segmentation in a retail company. By analysing customer transaction data, this research shows how RFM analysis can provide deep insights into customer behaviour and assist in the development of more targeted marketing strategies. The ultimate goal is to improve customer retention and maximise the return on investment (ROI) of marketing activities. This research offers practical solutions to common challenges in customer relationship management and contributes to the development of more efficient data-driven marketing methods.

This research applies Marketing Funnel and SWOT Analysis to the Graha Office marketing strategy at PT ABC. Funnel stages involve Awereness, Consideration, Purchase, Retention, and Advocacy. The analysis data, including query popularity, market stage, clicks, impressions, and product CTR, are taken from Microsoft Excel. The focus of the research was the internal and external factors affecting the marketing of Graha Office. SWOT analysis identified Strengths, Weaknesses, Opportunities, and Threats. Results show the effectiveness of the Marketing Funnel strategy with full expectations. Achieving customer expectations involves process, product, location, employee attitude, friendliness, convenience, and product arrangement. The SWOT analysis highlights internal components, including management and product strategy, that influence marketing success. The findings form the basis for developing Graha Office's marketing strategy, strengthening its position in the competitive market. The implications of this study serve as a guide in strategic decision-making to deal with market dynamics.