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All Journal Aptisi Transactions on Management Journal of Computer Science and Technology Application
Fitzroy, Arabella
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Computer Science & IT Decision Sciences, Operations Research & Management Other

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International Business Expansion Strategies: A Data-Driven Approach with IBM SPSS Williams, Tane; Kallas, Evelin; Garcia, Emily; Fitzroy, Arabella; Sithole, Precious
APTISI Transactions on Management (ATM) Vol 8 No 2 (2024): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i2.2275

Abstract

This paper presents a structural framework to enhance time management proficiency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The methodology involves the application of the Eisenhower Matrix, Pareto Principle, and time-blocking techniques, supported by case studies in diverse professional settings. Results indicate a 20% improvement in project completion times, a 25% reduction in project turnaround time, and a 30% increase in project visibility. These findings underscore the framework’s effectiveness in enhancing time management and achieving long-term success. Implications include recommendations for continuous refinement and integration of emerging technologies.
Leveraging Big Data Analytics for Strategic Marketing Optimization: Insights and Impacts Fazri, Muhammad Faizal; Ramadhan, Tarisya; Apriliasari, Dwi; Julianingsih, Dwi; Fitzroy, Arabella
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.39

Abstract

In the digital era, Big Data Analytics has emerged as a crucial tool for optimizing marketing strategies. This research explores the integration of Big Data into marketing, aiming to identify effective analytical techniques and their impact on marketing outcomes. The study utilized secondary data from various sources, including sales transactions, social media interactions, customer demographics, and web analytics. The analysis process involved data cleaning, integration, predictive modeling, clustering, sentiment analysis, and data visualization. The findings reveal that promotional campaigns and seasonal discounts significantly boost sales, with customer segmentation identifying three key groups: discount hunters, loyal customers, and occasional shoppers. Sentiment analysis shows positive customer feedback, though logistics-related issues warrant improvement. These results underscore the importance of targeted and personalized marketing strategies driven by data insights. The research contributes to marketing theories by providing empirical evidence on the effectiveness of Big Data Analytics in enhancing marketing strategies. Further research is recommended to explore its applicability across different industries, incorporate more diverse data sources, and utilize advanced analytical techniques to refine marketing strategies.
Co-Authors Apriliasari, Dwi Fazri, Muhammad Faizal Garcia, Emily Julianingsih, Dwi Kallas, Evelin Ramadhan, Tarisya Sithole, Precious Williams, Tane