Privacy and Security Implications of Big Data Applications in Consumer Behavior Analysis for Fashion Retail

Abstract

The use of big data analytics has become pervasive in the fashion retail industry to understand and influence consumer behavior. By collecting and analyzing different types of customer data from various sources, fashion retailers are able to gain valuable insights into shopping patterns, brand preferences, sizing information, and other consumer trends. However, the increasing use of consumer data also raises significant privacy and security concerns. This research article provides a comprehensive overview of common big data applications in fashion retail for consumer behavior analysis and discusses their privacy and security implications. Specifically, it examines practices like targeted advertising, recommendation engines, customer micro-segmentation, predictive analytics, and development of customer profiles based on data mining. The potential privacy invasive harms like exposure of sensitive information, price discrimination, and manipulation of consumer choices are analyzed. The research also covers security vulnerabilities of big data systems which can lead to breaches exposing consumer data or opening pathways for larger cyber-attacks. Solutions like consent requirements, opt-out mechanisms, de-identification, security audits, governance models, and legislation around consumer data are considered. Overall, while big data enables smarter marketing and better customer experiences in fashion retail, it also opens up many ethical, privacy and security risks which companies need to thoughtfully assess and mitigate.