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The rapid growth of digital commerce has encouraged companies to integrate digital and physical marketing channels to create seamless and consistent customer experiences. This study aims to analyze the role of integrated marketing channels through omnichannel strategies, the utilization of Artificial intelligence (AI), and their impact on customer experience in the context of digital commerce. The study employs a Systematic literature review (SLR) approach by examining relevant scholarly articles related to omnichannel marketing, AI technologies, and customer experience. The findings indicate that integrated marketing channels supported by AI enhance service personalization, customer engagement, operational efficiency, and the quality of interactions between companies and customers. Furthermore, the implementation of omnichannel strategies contributes to higher customer satisfaction and loyalty by providing a more connected experience across multiple customer touchpoints. However, the implementation of integrated marketing channels still faces several challenges, including fragmented channel integration, technological complexity, high investment requirements, and concerns regarding customer data privacy and security. Therefore, effective management of integrated marketing channels is essential for improving customer experience while creating sustainable competitive advantages for companies in an increasingly dynamic digital era.

The development of the digital economy has driven increased customer interaction through online chat services, making customer satisfaction a key factor in business success. Response speed and chat service quality are two important aspects in shaping the customer experience, but previous research has tended to examine them separately. This study aims to analyze the influence of online chat services and response speed on customer satisfaction partially and simultaneously. The method used is a qualitative approach with a literature review of 12 scientific articles from 2020–2025 obtained from academic databases such as Google Scholar and SINTA. The analysis technique used is descriptive-critical through the identification, comparison, and synthesis of previous research findings. The results show that online chat services have a positive effect on customer satisfaction, primarily through interaction quality such as information accuracy, ease of use, and problem-solving ability. Response speed has also proven to be an important determinant, where a fast response significantly increases customer satisfaction. However, speed without quality has the potential to decrease satisfaction. The discussion shows that the two variables have a complementary and inseparable relationship. Online chat services function as a medium for interaction, while response speed is a quality attribute that determines the effectiveness of the service. Therefore, the integration of both in one model is the main contribution of this research in filling the literature gap, especially in the context of e-commerce in Indonesia.

Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

The coffee shop industry in Indonesia is experiencing rapid growth that requires business owners to optimize data-driven strategies. This study aims to analyze customer preferences at Semanis Coffee and Resto using data mining methods  to support more effective business decision-making. The method used is Market Basket Analysis with the FP-Growth algorithm for association rule mining and the K-Means algorithm for customer segmentation. The research data consists of 672 sales transactions during the March-May 2025 period. The results of the association analysis with a minimum support of 0.004 and a minimum confidence of 0.2 resulted in five valid rules with a lift ratio above 1. The strongest rule is the combination of Americano→Milk Choco with a confidence of 42.9% and an elevator ratio of 5.229, indicating a strong linkage between products. The most popular products are Milk Choco (10.8%) and Americano (8.5%). Customer segmentation analysis identified three clusters: Cluster 0 (Loyal Customers) 80% with high frequency but low transaction value; Cluster 1 (Occasional Customers) 10% with low activity; and Cluster 2 (Large Buyers) 10% with high transaction value but low frequency. This study concludes that product bundling strategies, loyalty programs, reactivation campaigns, and premium services can be applied to increase the effectiveness of coffee shop businesses.

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.

Purpose: This study examines the effect of service communication on usage intention of Holistic Homecare for Elderly services, with customer trust serving as a mediating variable. Design/Methodology/Approach: Employing a quantitative explanatory approach with cross-sectional survey design, this research involved 120 family respondents with elderly members (≥60 years) in urban areas of Jakarta and Bandung, selected through stratified purposive sampling. Data were collected using structured questionnaires with 5-point Likert scales that had been tested for validity (Pearson correlation > 0.30) and reliability (Cronbach's Alpha > 0.70). Data analysis utilized Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 to examine measurement and structural models, along with bootstrapping mediation tests. Findings: Service communication demonstrated significant positive effects on customer trust (β = 0.712; t = 12.456; p < 0.001) and usage intention (β = 0.324; t = 4.213; p < 0.001). Customer trust significantly influenced usage intention (β = 0.498; t = 6.847; p < 0.001) and functioned as a partial mediator (indirect effect = 0.354; BootCI [0.248; 0.471]). Originality/Value: This research integrates Commitment-Trust Theory and Social Support Theory within the context of home-based elderly care services in a developing country, revealing the critical role of empathetic, responsive, and transparent communication as the foundation for trust-building that subsequently drives service adoption. Practical Implications: The findings emphasize the necessity of therapeutic communication training for caregivers and information transparency to establish a trustworthy homecare service ecosystem in Indonesia.

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

Pemanfaatan teknologi informasi menjadi kebutuhan penting bagi usaha jasa persewaan untuk meningkatkan efisiensi dan kualitas layanan. Toko Akasa Teknik sebagai usaha persewaan mesin vacuum cleaner masih menghadapi permasalahan dalam pengelolaan data penyewaan, transaksi, dan pelaporan yang dilakukan secara manual sehingga berpotensi menimbulkan kesalahan pencatatan dan keterlambatan informasi. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi persewaan berbasis web yang mampu mengintegrasikan seluruh proses bisnis secara terpusat. Metode pengembangan sistem yang digunakan adalah model Waterfall yang meliputi tahap analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Sistem dikembangkan menggunakan bahasa pemrograman PHP dengan arsitektur Model–View–Controller (MVC) dan basis data MySQL. Pengujian dilakukan menggunakan metode Black Box Testing untuk memastikan setiap fungsi berjalan sesuai dengan kebutuhan pengguna. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efisiensi pengelolaan data, mempercepat proses penyewaan, serta memudahkan pelanggan dalam melakukan pemesanan dan memantau status sewa. Dengan demikian, sistem informasi ini diharapkan dapat meningkatkan kualitas layanan serta mendukung daya saing Toko Akasa Teknik di era digital.

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 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.

This research aims to identify both the partial and simultaneous positive and significant effects of e-service quality, e-trust, and brand image on the customer loyalty of Byond by BSI users in Balikpapan City. The study adopts a quantitative correlational method. A total of 100 respondents were selected using a purposive sampling technique. Data were collected through online questionnaires distributed via a Google Form link. The analytical procedures utilized include correlational analysis, instrument tests, classical assumption tests, multiple linear regression analysis, and hypothesis testing using IBM SPSS Statistics version 24. The results of the study indicate that, partially, e-service quality and e-trust variables each have a positive and significant effect on customer loyalty among users of the Byond by BSI application in Balikpapan City. Meanwhile, brand image variable has a positive but not significant effect on customer loyalty among users of the Byond by BSI application in Balikpapan City. Furthermore, when examined simultaneously, the three variables collectively show a positive and significant effect on customer loyalty of Byond by BSI applications users in Balikpapan City.

This study aims to analyze the influence of customer experience and perceived value on tourist satisfaction among users of the Sidoarjo City Tour Bus as a form of public tourism transportation service. The Sidoarjo City Tour Bus represents a regional government service innovation intended to support tourism development while improving urban tourism accessibility. This study employs a quantitative approach using a survey method involving tourists who used the Sidoarjo City Tour Bus. Data were collected through questionnaires distributed to 99 respondents and analyzed using multiple linear regression with the assistance of SPSS software. The results indicate that customer experience does not have a significant effect on tourist satisfaction, suggesting that the experiences perceived by users are still subjective and not consistently felt by all service users. In contrast, perceived value has a positive and significant effect on tourist satisfaction, making it the most dominant variable in influencing satisfaction among Sidoarjo City Tour Bus users. These findings indicate that tourist satisfaction is more strongly determined by perceptions of benefits, service quality, and the suitability between sacrifices made and benefits received rather than by emotional experience alone. Therefore, this study recommends that the management of the Sidoarjo City Tour Bus and the local government enhance service value through improvements in facilities, information systems and registration processes, as well as optimization of comfort and schedule certainty. These efforts are important to increase tourist satisfaction and to support sustainable tourism development in Sidoarjo Regency.

Penelitian ini bertujuan untuk menganalisis strategi pengelolaan arus kas dalam mendukung keberlanjutan usaha kue tradisional dengan mengambil studi kasus pada Toko Roti Ganjel Rel Ambarawa. Pengelolaan arus kas yang efektif merupakan faktor krusial dalam menjaga keberlangsungan usaha kecil dan menengah, khususnya pada industri makanan tradisional yang menghadapi tantangan fluktuasi permintaan musiman dan persaingan dengan produk modern. Metode penelitian yang digunakan adalah kualitatif deskriptif dengan pendekatan studi kasus melalui observasi langsung, wawancara mendalam dengan pemilik usaha, serta analisis dokumen keuangan selama periode enam bulan. Hasil penelitian menunjukkan bahwa Toko Roti Ganjel Rel Ambarawa menerapkan beberapa strategi kunci dalam pengelolaan arus kas, meliputi sistem pencatatan keuangan harian yang terstruktur, pengelolaan persediaan berbasis permintaan historis, diversifikasi produk untuk menstabilkan pendapatan, serta penerapan kebijakan kredit yang ketat kepada pelanggan. Strategi-strategi tersebut terbukti efektif dalam menjaga likuiditas usaha dengan rasio kas rata-rata sebesar 1,8 dan periode perputaran kas selama 15 hari. Temuan penelitian ini memberikan kontribusi praktis bagi pelaku usaha kue tradisional dalam merancang sistem pengelolaan arus kas yang berkelanjutan serta kontribusi teoretis dalam pengembangan model manajemen keuangan untuk usaha mikro kecil dan menengah sektor kuliner tradisional.

This study aims to examine the effects of service quality and product quality on customer satisfaction and purchase decisions, as well as the mediating role of customer satisfaction. The population consists of Shopee users in Semarang City (January–May 2025). Using the Lemeshow formula, 97 respondents were selected through purposive sampling. Data were collected using a 5-point Likert questionnaire and analyzed with SEM-AMOS. The findings show that both service quality and product quality significantly increase customer satisfaction. Service quality and product quality have positive but insignificant direct effects on purchase decisions, while customer satisfaction has a significant positive effect on purchase decisions. Customer satisfaction also significantly mediates the influence of service quality and product quality on purchase decisions. These results underline the crucial mediating role of satisfaction in shaping consumer purchasing behavior on digital platforms like Shopee.

This study analyzes customer attrition in barbershops through the application of the 4P marketing mix, which includes Product, Price, Place, and Promotion. The research was conducted using a questionnaire distributed to 30 barbershop customers, which aimed to assess their level of satisfaction regarding each element of the marketing mix. The findings revealed that while customers were generally satisfied with the quality of the haircuts and the cleanliness of the tools, there was an important demand for improvement in the quality and variety of additional products offered. The pricing strategy was considered affordable by most customers; however, some expressed dissatisfaction, indicating the need for better communication regarding the value of the services provided. Demographic analysis showed that the majority of respondents were young adults aged 18-25, highlighting a specific target market for barbershops. Overall, this study emphasizes the importance of effectively implementing the marketing mix to improve customer retention and satisfaction in the barbershop industry.

The rapid development of information and technology brings changes in many fields. One of them is in the field of trading which is now carried out online through marketplace. Price discount, E-Trust, and E-Satisfaction are three aspects that play an important role in online business. Therefore, this research was conducted to understand and analyze the impact of Price Discount, E-Trust, and E-Satisfaction on Repurchase Intention in Shopee Marketplace among the students of DIV Marketing Management Study Program class of 2019-2020. This research was a quantitative research. There were three measurements used in this research, namely Price Discount, E-Trust, and E-Satisfaction with Repurchase Intention. The sample obtained by applying nonprobability sampling technique with purposive sampling approach. In analyzing the data, the researcher used descriptive analysis, validity test, reliability test, classical assumption test, multiple linear regression test, coefficient of determination, and hypothesis testing. The result of data analysis shows that, there is an impact between Price Discount, E-Trust, and E-Satisfaction with Repurchase Intention on the customers of Shopee marketplace partially or simultaneously. In conclusion, to increase Shopee customers’ loyalty, the marketplace must be aware that price discount, trust, and satisfaction of customers become the most important aspects that determine the marketplace's success.

In an increasingly competitive business world with ever-increasing customer demands for the quality of services and goods, customer satisfaction and loyalty have emerged as crucial issues in today's marketing management. Under such conditions, business entities must identify the key elements that shape satisfaction and how these contribute to building customer loyalty. This paper aims to examine the nature of study results on the determinants of satisfaction and loyalty through an evaluation of several recent empirical studies. Two key questions are addressed: which elements most strongly influence customer satisfaction, and what is the intermediary function of satisfaction in explaining the relationship between marketing variables such as service quality, cost, brand image, and trust with customer loyalty. The technique employed is a qualitative literature review through a search of articles using Google Scholar and Publish or Perish (PoP). Key terms used include customer satisfaction, customer loyalty, service quality, and trust. Of the 50 articles found in the initial phase, 15 met the inclusion standards and were thoroughly reviewed. The evaluation findings reveal three dominant patterns: service and product quality are the most stable factors driving satisfaction; cost and brand image serve as supporting factors; and trust acts as an important intermediary variable. This evaluation confirms that customer satisfaction is a key component in strengthening loyalty. Future studies are recommended to expand the scope of business sectors, use a more comprehensive structural framework, and utilize a wider variety of methodological techniques.

Penelitian ini bertujuan merancang dan mengembangkan Sistem Informasi Penjualan berbasis web untuk Gerai Oleh-Oleh Bali yang selama ini masih menggunakan pencatatan manual dengan buku besar dan perhitungan menggunakan kalkulator. Sistem manual tersebut menyebabkan risiko kesalahan pencatatan, lambatnya proses pencarian data, serta kesulitan dalam pengarsipan data penjualan, pembelian, pemesanan, dan pengelolaan stok barang. Penelitian menggunakan metode pengembangan perangkat lunak Waterfall dengan pendekatan pemodelan Unified Modeling Language (UML). Tahapan yang dilakukan meliputi analisis kebutuhan melalui wawancara dengan pemilik gerai, perancangan sistem (Use Case Diagram dan Class Diagram), implementasi, pengujian unit, pengujian sistem menggunakan metode black-box testing, serta tahap pemeliharaan (maintenance). Sistem yang dibangun mencakup fitur login, dashboard, pengelolaan data master (supplier dan barang), transaksi penjualan dengan dukungan scan barcode, transaksi pembelian, laporan penjualan dan pembelian, serta pengelolaan user. Hasil pengujian black-box menunjukkan seluruh test case berstatus Valid dan sistem berfungsi sesuai harapan. Pada tahap maintenance dilakukan contoh corrective maintenance dengan perbaikan bug pada query laporan penjualan harian.Sistem informasi penjualan berbasis web yang dihasilkan mampu mempercepat proses transaksi, mengurangi kesalahan manusia, meningkatkan akurasi data, serta memudahkan pengelolaan stok dan pembuatan laporan secara real-time. Implementasi sistem ini memberikan solusi efektif bagi Gerai Oleh-Oleh Bali dalam meningkatkan efisiensi operasional dan interaksi dengan pelanggan.