Use of Big Data Analytics to Understand Consumer Behavior

The purpose of this study is to investigate the meaning and characteristics of big data and to examine aspects of consumer behavior within the framework of big data research. The results show that although external factors and internal perception are the primary determinants of consumer decision making, big data also affects customer perception through external factors. Data is too large to be handled and analyzed by standard database management system techniques (Latvia_ ESRD 43_2016, n.d.). In order to give their businesses a competitive edge, marketers can use analytics to better understand consumer behavior. This article explores the characteristics of the big data phenomenon (Ramanathan, U., Subramanian, N., Yu, W., & Vijaygopal, R., 2017). Data is gathered from clients who receive business and technical training under controlled conditions. Apart from going over technical aspects like architecture, infrastructure, logic, theory, and environment building, this study will also cover consumer behavior modeling (Erevelles, S., Fukawa, N., & Swayne, L, 2016) The first part of the literature review examines concepts related to trust in consumer behavior. It investigates the psychological foundations of consumer trust as well as the ways in which perceptions of risk, confidence, and trust influence the decision-making process (Sousa, R., & Voss, C., 2012) Furthermore, it runs counter to the idea of empowered firms by highlighting how legitimacy affects consumer trust, brand integrity, and trust. However, it looks at why customers are suspicious of unlicensed services and discusses the problems and reasons behind that mistrust. In (Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A., 2019). The recommendation engine essentially suggests numerous products based on a variety of factors, such as the user's age and previous purchases. This kind of data filtering technology uses machine learning algorithms to recommend the best products to a particular customer. The purpose of this study is to segment related product reviews and analyze user sentiment in order to create a product recommendation system (Ertemel, A. V., 2015).