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All Journal Journal of Digital Market and Digital Currency International Journal Research on Metaverse
Shih Chih Chen
Department of Information Management, National Kaohsiung University of Science and Technology, Taiwan
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Customer Segmentation and Targeted Retail Pricing in Digital Advertising using Gaussian Mixture Models for Maximizing Gross Income Taqwa Hariguna; Shih Chih Chen
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.11

Abstract

This study investigates the application of Gaussian Mixture Models (GMM) for customer segmentation and targeted pricing strategies in the retail industry to maximize gross income. Using a dataset of 1000 transaction records, the analysis focused on attributes such as unit price, quantity, total amount, and payment methods. The dataset was preprocessed to handle missing values, encode categorical features, and scale numerical features. The optimal number of components for the GMM was determined using the Bayesian Information Criterion (BIC), resulting in the selection of 10 clusters. Model training was conducted using the Expectation-Maximization (EM) algorithm, achieving convergence after 18 iterations. Customer segments were identified and analyzed based on their purchasing behaviors and demographic traits. For instance, Segment 0 preferred bulk purchases of lower-priced items, while Segment 1 favored higher-priced items in smaller quantities, resulting in a higher average purchase value of 2274.19. Conversely, Segment 2 showed a high frequency of returns, indicated by a negative average purchase value of -2608.40. Targeted pricing strategies were developed for each segment, aiming to maximize gross income. The effectiveness of the segmentation and pricing strategies was evaluated using metrics such as the silhouette score, with training and testing scores of 0.175 and 0.015 respectively, highlighting areas for improvement in clustering quality. This study underscores the potential of GMM in uncovering distinct customer segments and tailoring pricing strategies to enhance profitability. Future research should explore alternative clustering techniques and extend the analysis to other retail domains and larger datasets to validate and improve the findings. The practical implications for retail businesses include the need for iterative testing and refinement of pricing strategies based on customer segmentation to achieve sustainable growth and customer satisfaction.
Determinants of Virtual Property Prices in Decentraland an Empirical Analysis of Market Dynamics and Cryptocurrency Influence Tri Wahyuningsih; Shih Chih Chen
International Journal Research on Metaverse Vol. 1 No. 2 (2024): Regular Issue September 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i2.12

Abstract

This study explores the emerging virtual property market within the digital world, with a focus on identifying the key factors influencing property prices, market activity, and sales volume. Using a dataset of 2,000 virtual property transactions, the research provides a comprehensive analysis of market dynamics in this new frontier of digital real estate. The findings reveal significant volatility in transaction activity, with a peak of 1,222 transactions in January 2022 followed by a sharp decline to 539 in February 2022 and just 24 in March 2022, indicative of a nascent and speculative market. The analysis identifies land price as the most significant determinant of virtual property values, showing a near-perfect correlation of 0.992 with sales prices. This highlights the critical role of location and land value, similar to traditional real estate markets. Additionally, the study finds that properties attracting more bids tend to sell at higher prices, with a moderate correlation of 0.380 between bids count and sales price, reflecting the impact of competitive bidding in driving up values. However, the market is relatively illiquid, with a mean sales count of just 1.79, indicating that most properties are held as long-term investments rather than frequently traded assets. Interestingly, the research also uncovers a weak negative correlation of -0.051 between sales price and the underlying cryptocurrency, MANA, suggesting that the value of virtual properties may be increasingly decoupled from cryptocurrency volatility as the market matures. These insights provide valuable guidance for investors, developers, and policymakers navigating the evolving landscape of virtual real estate. The study concludes with a discussion of the implications for future market stability and potential areas for further research.
Co-Authors Taqwa Hariguna Tri Wahyuningsih