Using Publicly Accessible Data for Competitive Intelligence in the U.S. Analgesic Market

This thesis develops and demonstrates a competitive intelligence model for the U.S. over-the-counter analgesic market built exclusively on publicly accessible U.S. government data sources. The research follows a Design Science Research methodology and uses the CI lifecycle as the theoretical framework for the proposed model. The model draws on eight data sources maintained by the FDA, NIH, and CDC, integrated through ten standardized collection templates and guided by five key intelligence topics and twelve key intelligence questions covering portfolio structure, label positioning, adverse event signals, clinical trial activity, and demand context. The model was applied to three leading OTC analgesic brands, Tylenol, Advil, and Aleve, representing three distinct active ingredient classes and three distinct parent companies. The application produced structured competitive intelligence across all five dimensions analyzed. The most significant findings relate to clinical trial activity, where a pronounced asymmetry was identified across the three brands, and to the gap analysis, which compared population level pain condition prevalence against publicly visible brand activity across portfolio, label, and clinical trial evidence. The thesis contributes to the CI literature by extending the CI lifecycle framework to the consumer health sector using public data and by demonstrating cross source triangulation as a structured CI methodology. Practically, the model offers a reproducible workflow and highlights the value of publicly accessible regulatory and clinical research databases as underused sources of competitive intelligence in the pharmaceutical sector.