Title: The Economics of Prediction: Maximizing Cross-Industry Profits Using Buyer Intent Models
Authors: Mayowa Alonge
Volume: 9
Issue: 5
Pages: 64-69
Publication Date: 2025/05/28
Abstract:
In an increasingly data-driven economy, predictive analytics has emerged as a key driver of buyer intent identification and monetization, across industries. This article explores the economics of prediction in terms of how businesses can leverage machine learning models to predict consumer behavior and optimize revenue-generating efforts. By embedding buyer intent models-built from behavioral signals such as search queries, clickstreams, and purchase history-businesses can enhance targeting precision, reduce customer acquisition costs, and gain higher conversion rates. The study discusses case applications in e-commerce, digital advertising, financial services, and B2B sales to illustrate how predictive models reshape competitive advantage and redefine value creation. Additionally, it discusses ethical concerns, data privacy constraints, and how platform monopolies affect the prediction economy. The paper concludes with strategic implications for firms intending to operationalize intent data to drive long-term, cross-industry profitability.