Predictive Analytics in Consumer Buying Behaviour Using Machine Learning

Khushi Bhatia, Shikha Tiwari — Sat, 16 May 2026 00:00:00 +0000

Businesses that want to stay competitive in the digital age need to know how consumers buy things. As more data becomes available from the online world, it is now possible to look more closely at what customers like. Using machine learning and predictive analytics together is a good way to predict what people will do based on past and present data. This study looks at how classification, clustering, and regression machine learning models can be used to predict how people will buy things. It also looks at important factors that affect buying decisions and reviews research that has already been done in this area. The results show that predictive analytics can help businesses better target their customers, personalize their marketing, and make their marketing more effective. Nevertheless, issues like data security, ethics, and model bias should be taken seriously into consideration. Overall, this study has emphasized the significance of predictive analytics as a powerful method to comprehend consumer behaviour and make decisions based on data.

Hierarchical Trust-based Artificial Intelligence Governance Model for Data Protection

Olubodun E. O, Alese B. K. — Thu, 28 May 2026 00:00:00 +0000

The widespread adoption of artificial intelligence in organizations has introduced critical risks for trade secret protection, as AI systems may inadvertently disclose or infer sensitive information. Existing governance frameworks lack empirical validation, hierarchical trade-secret safeguards, and integration with zero-trust principles. This study presents a hierarchical trust‑based governance framework that explicitly prevents AI systems from accessing trade secrets beyond authorized organizational levels. A six-equation mathematical model quantifies trust level, access control strength, decision rights, AI usage eligibility, governance effectiveness, and knowledge advancement. The framework is implemented as the HTF Platform, a web application with two-factor authentication, real-time dashboards, and a policy decision confusion matrix. A real-world deployment with 250 audited access decisions achieved 91.6% accuracy, 94.7% precision, 91.6% recall, and 93.1% F1-score. The system architecture and operational flowchart are presented. The confusion matrix confirms that lower-tier users are correctly denied access to higher-tier trade secrets, while legitimate requests are reliably granted. The HTF Platform provides a practical, verifiable, and adaptive solution for safeguarding trade secrets in AI-driven organizations.

Journal of Big Data Analytics and Business Intelligence

Predictive Analytics in Consumer Buying Behaviour Using Machine Learning

Hierarchical Trust-based Artificial Intelligence Governance Model for Data Protection