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All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain
Quba Siddique
Institute of Banking and Finance, Bahauddin Zakariya University Multan, Pakistan
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Optimizing Pricing Strategies for Female Fashion Products Using Regression Analysis to Maximize Revenue and Profit in Digital Marketing Quba Siddique
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.28

Abstract

This study explores optimal pricing strategies for the female fashion sector through the application of advanced data science methodologies. Utilizing a dataset of 4,272 entries, comprising various attributes such as original prices, promotional prices, and discount percentages, we employed regression models to predict promotional pricing. The research highlights Ridge Regression as the most effective model, balancing high accuracy with reduced overfitting. The model achieved an R-squared (R²) value of 0.9999999999999678, a Mean Absolute Error (MAE) of 4.31×10−6, and a Mean Squared Error (MSE) of 4.89×10 −11, demonstrating its robustness and reliability. The study's findings indicate that dynamic pricing and tailored discount strategies can significantly enhance revenue and profitability. High-value items are best priced with moderate discounts, maintaining higher promotional prices, while low-value items benefit from aggressive discounting to drive sales volume. Sensitivity analysis further supported these strategies by showing that a 10% increase in original prices proportionally increased promotional prices, while a 10% increase in discount percentages led to lower promotional prices, affecting sales performance differently across product categories. Practical implications for e-commerce businesses include implementing dynamic pricing, developing targeted discount strategies, and timing promotions strategically. Regular sensitivity analysis and continuous model validation are recommended to adapt to market changes effectively. Future research should consider broader datasets, advanced modeling techniques, external market factors, and customer segmentation to enhance the generalizability and applicability of pricing strategies across different sectors. This research underscores the importance of data-driven approaches in optimizing digital marketing strategies, offering actionable insights that can significantly boost revenue and profitability in the female fashion sector.
Anomaly Detection in Blockchain Transactions within the Metaverse Using Anomaly Detection Techniques Henderi; Quba Siddique
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.17

Abstract

The rapid expansion of blockchain technology and its integration into the Metaverse has brought about significant opportunities, but also new challenges, particularly in ensuring the security and integrity of transactions. This study explores the application of anomaly detection techniques, specifically the Isolation Forest algorithm, to identify unusual and potentially fraudulent transactions within a blockchain dataset. The analysis focuses on detecting anomalies across various transaction types, such as sales and scams, and regions including Asia and Africa. The dataset, comprising 78,600 transactions, revealed that 3,930 (approximately 5%) were flagged as anomalies. "Sale" and "Scam" transactions were found to be particularly vulnerable, accounting for the majority of anomalies. Geographical analysis highlighted that Asia and Africa had the highest average risk scores, indicating a higher prevalence of high-risk transactions in these regions. Visualizations further emphasized the distribution of anomalous activities, providing valuable insights into regional and transaction-specific risks. The study demonstrates the effectiveness of Isolation Forest in detecting anomalies within blockchain transactions and underscores the importance of targeted security measures. The findings suggest that focusing on high-risk transaction types and regions can enhance blockchain security. Future research is encouraged to explore additional anomaly detection methods and integrate network analysis to further refine the detection of suspicious activities in decentralized networks. This research contributes to the growing body of knowledge on blockchain security, offering practical insights for improving the detection and mitigation of risks in the increasingly complex and interconnected world of the Metaverse.
Co-Authors Henderi