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Advances in digital technology have triggered a major transformation in the way marketing is conducted, particularly through the use of social media as a communication tool for marketing. Instagram has become one of the most popular platforms among businesses due to its focus on visual appeal and the various interactive features it offers that can strengthen the bond between brands and customers. The purpose of this study is to evaluate the content marketing methods used by @PrefekturCoffee's Instagram to increase customer purchase interest. This study was conducted using a qualitative approach and case study. Data collection techniques were carried out through in-depth interviews with the business owner, observation of Instagram content, and documentation. Data analysis was conducted using Miles and Huberman's interactive model, which includes data reduction, data presentation, and conclusion drawing. The results of the study indicate that Prefektur Coffee implements a content marketing strategy that emphasises visual consistency, message relevance, and the utilisation of Instagram features such as feeds, stories, reels, and highlights. This strategy is capable of building attention, interest, information search, purchase action, and encouraging customers to share their experiences in line with the AISAS model (Attention, Interest, Search, Action, Share). It is hoped that this research will contribute theoretically to digital marketing communication research and provide practical guidance for businesses on how to optimise content marketing strategies for Instagram.

This study aims to analyze the effectiveness of Rengginang MSME registration on Google Maps as a strategy to strengthen the economy in Pahang Village, Babata Village. The background of the research shows that most MSMEs still rely on conventional marketing so that business visibility is relatively low. With a qualitative case study approach, this study explores the experience of the owner of Rengginang Azam MSMEs, Mrs. Siti, who registered her business location on Google Maps. The results of the study show that digitalization through Google Maps has a positive impact in the form of increasing accessibility and reach for customers, especially from outside the village. Location registration allows customers to find more complete information about products and locations, increasing the number of visits to the store. The real impact felt is an increase in sales and profits without additional promotional costs. This study concludes that the use of Google Maps as a digital promotional medium is an effective strategy in strengthening the competitiveness of MSMEs in the digital era. In addition, this finding confirms the importance of digital literacy for MSME actors to utilize technology as a means of business development and strengthening the local economy.

This study is motivated by the increasing use of digital technology and the influence of psychological factors, particularly Fear of Missing Out (FoMO), on consumer purchasing decisions, especially in gold investment products. Along with the digitalization of financial services, Pegadaian introduced the Tring! Application as a digital transaction platform expected to increase Galeri 24 gold sales. This study aims to analyze the effect of Fear of Missing Out (FoMO) and the use of the Tring! Application on the increase in Galeri 24 gold sales at Pegadaian Sunter Branch, both partially and simultaneously.This research employs a quantitative method with a descriptive approach. Data were collected through questionnaires distributed to 90 customers of Pegadaian Sunter Branch who have used the Tring! Application. The data were analyzed using multiple linear regression with the assistance of IBM SPSS software, including t-test, F-test, and coefficient of determination. The results indicate that Fear of Missing Out (FoMO) and the use of the Tring! Application have a positive and significant effect on the increase in Galeri 24 gold sales, both partially and simultaneously. These findings highlight the important role of psychological factors and digital application utilization in driving gold product sales growth.

This study examines the implementation of Integrated Marketing Communication (IMC) at NailedShape, a Bali-based nail art micro-enterprise, as a strategy to foster customer loyalty. NailedShape operates under a home-service and home studio model, primarily using Instagram for promotion and customer engagement. The study utilizes a descriptive qualitative method, collecting data through interviews and observations with the owner, customers, and micro-influencers. The research findings show that NailedShape successfully employs IMC techniques, such as visual content, customer testimonials, and collaborations with digital influencers, to capture attention and create strong emotional connections with customers. This integrated approach helps in building a positive brand image and maintaining customer engagement. However, the study also highlights challenges, such as limited resources and frequent changes to social media algorithms, which hinder consistent communication and marketing efforts. Despite these obstacles, the study concludes that a well-executed IMC strategy remains effective in enhancing customer loyalty, reinforcing the importance of an integrated communication approach for small and medium enterprises (SMEs) in the beauty industry. By utilizing digital platforms effectively, NailedShape can compete in a highly dynamic market. The findings suggest that beauty-based MSMEs can benefit greatly from IMC in the digital era, as it not only enhances brand perception but also helps in building lasting customer relationships.

Nowadays, people are more likely to make transactions online. Online transactions are the same as offline transactions, only the online buying and selling process is carried out via the internet rather than offline, where sellers and buyers meet.  In fact, when carrying out online transactions, some customers become disappointed and disadvantaged, such as when the product ordered is not suitable or is not sent by the seller, especially for customers who experience fraud with large amounts of money.  As a result, it is very important to follow Islamic business ethics when making transactions on Tokopedia.  This research uses qualitative descriptive research where researchers use the literature study method to obtain data.  The research results show that Islamic business ethics at Tokopedia fulfill several Islamic ethical principles

The purpose of this study is to describe the marketing strategies and adaptability of “cakar bongkar” (second-hand clothing) traders in responding to market dynamics in Nabire City. This study employs a qualitative approach, as it is appropriate for addressing research problems that require an in-depth understanding of the socio-economic realities in Kalibobo Market and Karangtumaritis Market in Nabire City. The data used in this study consist of primary data as the main source and secondary data as supporting and relevant information. Data collection methods are carried out using the triangulation principle, namely through direct observation, in-depth interviews, and documentation. Informants are selected using snowball sampling and purposive sampling methods to obtain comprehensive and representative information. Data processing and analysis are conducted through the stages of data reduction, data presentation, and systematic conclusion drawing. The results of the study indicate that the informants apply five (5) marketing strategies in selling second-hand clothing (cakar bongkar), namely: (1) sourcing strategies, (2) pricing and promotion strategies, (3) word-of-mouth marketing strategies, (4) strategies for building social relationships with customers, and (5) product display strategies using prototype tables to attract consumer attention.

This study aims to analyze the marketing strategy of Betet Rice at Toko Cendrawasi Fakfak. The research uses a qualitative approach with data collection methods including field research, interviews with managers and employees, observations on sales data management, and documentation. The data were analyzed using marketing mix analysis and SWOT analysis. The results show that Betet Rice is distributed directly to customers through Toko Cendrawasi Fakfak, which acts as a distributor of the product. Toko Cendrawasi has made efforts to provide significant market access for Betet Rice marketing, with products and prices adjusted to customer desires and purchasing power. The marketing strategy used involves the marketing mix of product, price, promotion, and distribution, which has led to an increase in demand for Betet Rice every year. Based on the SWOT analysis, the marketing strategy implemented includes strengths in strong financial conditions and a fleet of ships, weaknesses in the lack of attention to key marketing elements, opportunities in market analysis and customer segmentation, and threats from increasing competition and globalization of competitors offering similar products.

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

A suitable and targeted marketing plan is required because of the intense competition in the retail drinking water sector. Customer segmentation using RFM (Recency, Frequency, and Monetary) analysis is one of the techniques employed. Additionally, K-Means clustering, a clustering technique based on machine learning, is employed. This study's goal is to present the findings in the form of graphs that can be used to examine consumer trends according to their attributes. With a value of 10286, the Calinski Harabaz index is a suitable metric to move on to the segmentation step in this study, which also tests three metrics using the clustering method. An ideal cluster is created for every cluster evaluation by dividing the Calinski Harabaz index into three more manageable clusters. This contrasts with other evaluation metrics that only yield two clusters. For instance, when XYZ drinking water sales transaction data was distributed, it was discovered that, out of the total drinking water sales, woodsale had 422 customers, diamond had 1061 customers, and star diamond had 2005 customers. The management of the XYZ drinking water company and other marketing fields are expected to encounter more intense competition as a result of the study's findings.

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.

The rapid growth of e-commerce has intensified the need to understand transaction patterns and customer purchasing behavior as a foundation for strategic decision-making. This study aims to analyze e-commerce transaction patterns and customer purchasing behavior based on demographic characteristics and transaction timing. By utilizing e-commerce transaction data, this research seeks to provide a more comprehensive understanding of customers’ purchasing tendencies and the factors influencing their behavior. This study employs Exploratory Data Analysis (EDA) as the primary method to descriptively explore data characteristics through various statistical visualizations, including histograms, bar charts, line graphs, and boxplots. The analysis conducted to identify transaction trends, the distribution of purchase values, and behavioral differences across demographic groups and specific time periods. The results indicate that e-commerce transaction patterns tend to increase during certain periods, particularly in the latter part of the observation timeframe, suggesting the influence of seasonal factors and promotional strategies. The distribution of transaction values is asymmetric, with most transactions occurring in the low to medium value range, while high-value transactions are conducted by a relatively small proportion of customers. Furthermore, variations in purchasing behavior are observed across demographic groups in terms of transaction frequency and value, despite relatively balanced transaction volumes. The findings confirm that e-commerce customer purchasing behavior is influenced by a combination of temporal factors and demographic characteristics. These results are expected to serve as a basis for e-commerce practitioners in developing more targeted marketing strategies and as a reference for future research in the field of e-commerce data analytics.

This study aims to analyze the influence of accountability and transparency principles on financial management in feed retail micro, small, and medium enterprises (MSMEs) in West Lampung Regency. Accountability and transparency are considered essential aspects of good governance implementation, which play a crucial role in building trust and improving the financial effectiveness of small enterprises. The research applies a quantitative approach through a survey distributed to customers and farmers using a Likert-scale questionnaire. Data were analyzed using multiple linear regression to examine the effect of accountability and transparency on financial management perception.. The results indicate that both variables have a positive and significant influence on customers’ and farmers’ perceptions. The implementation of accountability and transparency strengthens public trust and enhances the professionalism of financial management practices in MSMEs, particularly in feed retail businesses.

This study aims to determine the influence of price, product quality, and service quality on customer satisfaction at Salon Natayu. The approach used was quantitative, with data collection techniques through questionnaires distributed to Salon Natayu customers, with a total of 53 respondents (using census techniques). The data were analyzed using multiple linear regression analysis with the help of SPSS version 20. The results of the study show that the three independent variables (price, product quality, and service quality) simultaneously have a positive effect on customer satisfaction. However, partially, the price variable has the most dominant influence on customer satisfaction. The conclusion of this study shows that price is the most influential factor on customer satisfaction, followed by service quality, while product quality has the least influence. These findings indicate that a competitive pricing strategy is a crucial aspect for Salon Natayu in maintaining and improving customer satisfaction. In addition, continuous improvement of service quality is also necessary to create a better customer experience. This research is expected to be a reference for salon managers in formulating more effective marketing strategies as well as for future research that examines the factors that affect customer satisfaction in the beauty services industry.

The rapid growth of Indonesia’s banking industry has encouraged various corporate actions, such as mergers, to enhance efficiency and competitiveness. However, post-merger system integration often creates issues for customers, particularly the increase in credit collectability caused by data migration errors. This study aims to analyze the legal protection and liability of banks toward customers adversely affected by the merger process. The research employs a normative juridical approach through legislation review and case study analysis. The findings indicate that customer legal protection operates both preventively and repressively. Preventive protection is regulated under the Financial Services Authority Regulation (POJK) No. 6/POJK.07/2022, emphasizing transparency, education, and data security principles. Meanwhile, repressive protection is carried out through internal bank complaint mechanisms, the Financial Services Alternative Dispute Resolution Institution (LAPS SJK), and civil lawsuits under Articles 1365 and 1243 of the Indonesian Civil Code. The study concludes that banks are legally responsible for restoring customers’ rights by correcting SLIK data, issuing clarification letters, and providing material and immaterial compensation. Upholding prudential principles and consumer protection is essential to maintaining public trust in the post-merger banking system.

In the tvMu news broadcast program, a shooting technique that can produce good video quality is needed to be broadcast. Because this shooting technique is needed in visual communication technology, especially in the field of videography. The purpose of this study is to determine the shooting angle in the tvMu news broadcast program and to determine the image size that determines the quality of the content of the tvMu news broadcast program that is broadcast. This study uses a qualitative method that is used to explain how the shooting technique is in the tvMu news broadcast program. This tvMu news broadcast is presented to obtain information about the association and national that can invite important figures of Muhammadiyah. This was done through in-depth interviews with several informants and triangulation. The results of this study revealed the shooting techniques used in tvMu news broadcasts, including various shooting types, image composition, aspect ratio, frame size, and camera movements.customers.

This study aims to analyze the role of the digital economy in increasing the competitiveness of MSMEs in Medan City. The rapidly developing digital transformation provides opportunities for MSMEs to expand their markets, improve operational efficiency, and strengthen their competitive position. However, the use of digital technology among MSMEs in Medan remains limited due to low digital literacy, limited capital, lack of training, and minimal use of digital platforms such as e-commerce, social media, and Google My Business. This study uses a descriptive qualitative method through a literature review of research conducted in 2024–2025. The results of the analysis indicate that the digital economy plays a significant role in increasing the competitiveness of MSMEs, particularly through digital marketing, technological innovation, and optimization of digital platforms that can increase visibility and interaction with customers. Based on the Resource-Based View (RBV) approach, the competitive advantage of MSMEs is determined by the quality of internal resources, especially digital literacy, innovation capabilities, and technology management. With the support of training and government policies, the implementation of the digital economy has the potential to strengthen the growth and sustainability of MSMEs in Medan City.

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

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.  

The increasing competition in the coffee shop industry in Bandung requires businesses to adopt marketing strategies that emphasize long-term relationships with customers. This study aims to analyze the implementation of relationship marketing strategies in building business networks at Groei Coffee, a community-based café located in Sukapura, Bandung Regency. A descriptive qualitative approach was employed, with data collected through observation, interviews, and documentation. The findings indicate that relationship marketing at Groei Coffee is implemented through consistent product quality, two-way communication with customers, personalized services, and collaboration with campus communities and strategic partners. These strategies have proven effective in enhancing customer loyalty, expanding business networks, and strengthening the café’s competitive position in the food and beverage industry. However, several challenges were identified, including limited resources, price sensitivity among student customers, and dynamic consumer preferences. Therefore, an adaptive and sustainable approach is required to ensure that relationship marketing strategies provide long-term benefits for small and medium-sized enterprises (SMEs).