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INDONESIA
Journal of Digital Market and Digital Currency
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30480981     DOI : https://doi.org/10.47738/jdmdc
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
Journal of Digital Market and Digital Currency publishes high-quality research on: Digital Marketing Digital Currencies Cryptocurrency Trends Blockchain Applications Fintech Innovations Our goal is to provide a platform for researchers, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address the challenges and opportunities presented by the Journal of Digital Market and Digital Currency.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2024): Regular Issue December" : 5 Documents clear
Segmenting Walmart Customers for Personalized Marketing Strategies Using MiniBatchKMeans Clustering and Decision Trees: An Analysis of Purchasing Behavior Buchdadi, Agung Dharmawan
Journal of Digital Market and Digital Currency Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i3.18

Abstract

This study explores the application of MiniBatchKMeans clustering and decision tree analysis to segment Walmart customers for personalized marketing strategies. Using a dataset of 550,068 customer transactions, including variables such as User_ID, Product_ID, Gender, Age, Occupation, City_Category, Stay_In_Current_City_Years, Marital_Status, Product_Category, and Purchase, we identified five distinct customer segments. These segments were characterized by unique demographic and purchasing behaviors. Segment 1 included older customers (mean age: 55+) with high and consistent spending, primarily on premium products. Segment 2 comprised middle-aged customers (mean age: 36-45) with moderate to high spending levels, favoring household and family-related products. Segment 3 consisted of young adults (mean age: 18-25) with variable purchasing patterns, focusing on low to mid-range priced items. Segment 4 included young families (mean age: 26-35) with significant spending on a variety of products, and Segment 5 featured middle-aged to older customers (mean age: 46-55) with steady but moderate spending habits. The MiniBatchKMeans clustering algorithm effectively handled the large dataset, identifying clear customer segments. Decision tree analysis provided insights into the key features driving each segment, with Purchase amount, Age, and Occupation being the most significant. The decision tree model achieved an accuracy of 100%, with precision, recall, and f1-scores of 1.00 for all segments, indicating robust classification. These findings have significant implications for personalized marketing strategies. For instance, premium product promotions can be directed at high-spending older customers, while family-oriented discounts and bundles can be tailored for young families. Digital marketing efforts can be optimized to engage younger segments through social media and personalized recommendations. This study highlights the importance of data-driven decision-making in retail, emphasizing the need for continuous data collection and analysis to stay competitive. Future research should incorporate datasets from different retail contexts and explore alternative clustering techniques and additional features to provide a more holistic view of customer segmentation.
Optimizing Publisher Revenue in Digital Marketing Using Decision Trees and Random Forests Irfan, Muhamad
Journal of Digital Market and Digital Currency Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i3.19

Abstract

This study explores the optimization of reserve prices in real-time first price auctions within digital advertising using decision tree and random forest algorithms. The dataset used includes 567,291 entries covering various variables such as impressions, bids, prices, and revenue, providing a comprehensive view of auction dynamics over a full year. The decision tree model achieved a Mean Squared Error (MSE) of 0.1347 and an R² score of 0.731, indicating a reasonable level of accuracy in predicting reserve prices. In contrast, the random forest model significantly outperformed the decision tree model with an MSE of 0.0789 and an R² score of 0.842, demonstrating superior predictive power and robustness. The analysis revealed that the application of these machine learning models significantly enhances the accuracy and reliability of reserve price predictions, helping publishers to optimize their revenue. The findings show that by setting optimal reserve prices based on the models' predictions, publishers can minimize the risk of underselling ad inventory and maximize revenue, as evidenced by a 15% increase in revenue observed in a case study after implementing the random forest model. The study also provides insights into bidder behavior, particularly bid shading strategies, highlighting how bidders adjust their bids in response to different reserve price settings. Higher reserve prices tend to reduce bid shading, resulting in more competitive and balanced auctions. The practical implications for digital marketing include enhanced strategic decision-making for publishers and a more transparent and predictable bidding environment for advertisers. Despite the promising results, the study acknowledges limitations such as reliance on historical data from a single ad exchange platform and the assumptions inherent in the models. Future research should expand the dataset to include multiple platforms and explore more advanced machine learning techniques to further improve reserve price optimization. Overall, this research underscores the potential of leveraging data science and machine learning to transform digital advertising strategies, driving higher revenue and efficiency in the industry.
Predicting Ad Click-Through Rates in Digital Marketing with Support Vector Machines Sangsawang, Thosporn
Journal of Digital Market and Digital Currency Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i3.20

Abstract

This study investigates the effectiveness of Support Vector Machines (SVM) in predicting click-through rates (CTR) in digital marketing campaigns. Utilizing a dataset comprising user demographic and behavioral data, the research aims to develop a predictive model to forecast ad clicks accurately. The primary objectives include conducting exploratory data analysis (EDA), preprocessing data, training the SVM model, and evaluating its performance using standard metrics. The dataset includes features such as Daily Time Spent on Site, Age, Area Income, Daily Internet Usage, and Gender. Key findings from the EDA reveal that "Daily Time Spent on Site" and "Daily Internet Usage" are significant predictors of CTR, with notable correlations. The SVM model, trained on this data, demonstrated exceptional performance, achieving an accuracy of 97.65%, a precision of 98.58%, a recall of 96.53%, and an F1-score of 97.54%. These results confirm the model's robustness and reliability, indicating its potential for optimizing digital marketing strategies. The study's significance lies in its contribution to the fields of digital marketing and predictive analytics by showcasing the applicability and advantages of SVM in predicting user behavior. These insights can help marketers optimize ad placements, enhance user engagement, and improve return on investment. Practical implications include strategies for targeted and personalized marketing based on key user demographics and behaviors. Despite the promising results, the study acknowledges limitations such as the dataset size and scope of features. Future research should focus on utilizing larger and more diverse datasets, incorporating additional features, and exploring other advanced machine learning algorithms. This research encourages further exploration of machine learning applications in digital marketing to enhance predictive accuracy and campaign effectiveness. By addressing these aspects, this study aims to advance the academic understanding and practical implementation of predictive analytics in digital marketing, providing a robust framework for future applications.
Analyzing the Determinants of User Satisfaction and Continuous Usage Intention for Digital Banking Platform in Indonesia: A Structural Equation Modeling Approach Pratama, Satrya Fajri
Journal of Digital Market and Digital Currency Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i3.21

Abstract

This study investigates the factors influencing user satisfaction (US) and continuous usage intention (UI) of the digital banking platform in Indonesia. Utilizing a quantitative research approach, structural equation modeling (SEM) via SmartPLS was employed to analyze data from 376 users. The study integrates key constructs, including Task-Technology Fit (TTF), System Quality (SQ), Performance Expectancy (PE), US, and UI, into a comprehensive model. The findings confirm that TTF, SQ, PE, and US significantly influence UI. Specifically, higher TTF and SQ directly enhance PE (path coefficient = 0.871, t-value = 92.895) and US (path coefficient = 0.798, t-value = 47.957), positively impacting UI. Performance Expectancy emerged as a stronger predictor of UI (path coefficient = 0.559, t-value = 12.800) compared to the US (path coefficient = 0.245, t-value = 5.229), underscoring the critical role of perceived performance benefits in driving continuous usage. All five hypotheses were supported: TTF positively affects UI (path coefficient = 0.250, t-value = 7.154); SQ positively influences PE and US; PE positively impacts UI; and US positively affects UI. The Sobel test results indicated that PE significantly mediates the relationship between SQ and UI (Z = 12.60), and US also significantly mediates this (Z = 5.19). The R-squared values indicate the explanatory power of the model: PE (0.758), UI (0.956), and US (0.637), demonstrating that the model explains a substantial portion of the variance in these constructs. The study contributes to the literature by validating the integrated model, extending existing models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), and highlighting the importance of technical and perceptual factors in technology adoption. Practically, the results offer actionable insights for digital banking providers. Enhancing TTF and maintaining high SQ are crucial for fostering positive user experiences and encouraging continuous usage. Providers should also emphasize the performance benefits of their platforms to improve PE and UI. Despite its contributions, the study has limitations, including sample size and reliance on self-reported data, which may affect generalizability. Future research could expand the sample size, incorporate objective usage data, and explore additional factors such as social influence and facilitating conditions. Overall, the study provides a robust framework for understanding user behavior in digital banking and offers practical strategies for improving user satisfaction and retention in the industry.
Using K-Means Clustering to Enhance Digital Marketing with Flight Ticket Search Patterns Sukmana, Husni Teja; Oh, Lee Kyung
Journal of Digital Market and Digital Currency Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i3.22

Abstract

This study explores the application of K-Means clustering to enhance digital marketing strategies by analyzing flight ticket search patterns. Utilizing a dataset containing 4,000 search engine results related to flights to Hong Kong, the research identifies five distinct user clusters based on search terms, titles, snippets, and other relevant features. The dataset's key features include search terms, ranks, titles, snippets, display links, and direct links, providing a comprehensive view of user interactions and preferences. The cluster analysis reveals significant variations in user intent and preferences across the identified segments. For instance, Cluster 1 is characterized by users searching for "cheap flights" and "discount tickets," indicating a price-sensitive segment. In contrast, Cluster 2 users prefer "premium flights" and "business class," highlighting an interest in luxury travel options. The study also examines the behavioral patterns within each cluster, such as Cluster 3 users who search for flights well in advance and prioritize flexible booking options. The findings underscore the effectiveness of K-Means clustering in enhancing digital marketing strategies. By leveraging the insights from the clustering analysis, marketers can design highly targeted advertising campaigns and personalized offers. For example, budget airlines can target Cluster 1 with promotions and discounts, while premium airlines can focus on Cluster 2 with exclusive service highlights. This targeted approach is expected to improve user engagement and conversion rates significantly. The study also highlights the advantages of behavior-based segmentation over traditional demographic methods, offering a more accurate representation of user preferences and intentions. The identified clusters provide a framework for understanding different user groups, enabling more efficient resource allocation and campaign design. Future research should explore the integration of additional data sources, such as social media interactions and user reviews, to enhance clustering accuracy. Additionally, advanced clustering techniques like hierarchical clustering and Gaussian Mixture Models could be investigated to provide further insights. The ongoing refinement and enhancement of segmentation processes are crucial for maintaining effective and impactful digital marketing strategies in the dynamic travel industry. Key results include the identification of five user clusters, the importance of personalized marketing strategies, and the potential for improved engagement and conversion rates through targeted advertising and offers.

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