Version 1
: Received: 29 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (09:10:08 CEST)
How to cite:
Spadacini, D. Navigating Change and Driving Innovation: Leveraging Big Data for Enhanced User Behavior Analysis and Strategic Decision-Making. Preprints2024, 2024072434. https://doi.org/10.20944/preprints202407.2434.v1
Spadacini, D. Navigating Change and Driving Innovation: Leveraging Big Data for Enhanced User Behavior Analysis and Strategic Decision-Making. Preprints 2024, 2024072434. https://doi.org/10.20944/preprints202407.2434.v1
Spadacini, D. Navigating Change and Driving Innovation: Leveraging Big Data for Enhanced User Behavior Analysis and Strategic Decision-Making. Preprints2024, 2024072434. https://doi.org/10.20944/preprints202407.2434.v1
APA Style
Spadacini, D. (2024). Navigating Change and Driving Innovation: Leveraging Big Data for Enhanced User Behavior Analysis and Strategic Decision-Making. Preprints. https://doi.org/10.20944/preprints202407.2434.v1
Chicago/Turabian Style
Spadacini, D. 2024 "Navigating Change and Driving Innovation: Leveraging Big Data for Enhanced User Behavior Analysis and Strategic Decision-Making" Preprints. https://doi.org/10.20944/preprints202407.2434.v1
Abstract
With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.
Keywords
User Behavior Analysis; Big Data; Machine Learning; Natural Language Processing; Livestreaming E-Commerce; Text-Mining; Targeted Marketing; Strategic Decision-Making; Ethical AI; Sentiment Analysis.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
