Our client, a global consumer health company, was exploring a white-label opportunity in a new market. While the prospective partner’s product appeared to be widely used, the client needed to apply their high pharmacovigilance standards before making any decisions. The central concern was quality. They needed an independent, data-backed view of adverse event trends and product sentiment, without relying on reports from the manufacturer.
We conducted a large-scale digital evidence review using advanced data science methods. Our team scraped thousands of online reviews from foreign e-commerce platforms for the product in question and several direct competitors. We used natural language processing to extract and cluster recurring issues, ranging from packaging faults to suspected side effects.
We then applied statistical analysis to benchmark sentiment, complaint frequency, and severity against other brands, creating a clear, comparative view of risk. By blending quantitative signals with qualitative context, we helped the client understand not only what was being said, but the implications for safety, trust, and brand alignment.
The client received an unbiased, data-driven risk profile for the product and manufacturer. This included:
The insights allowed the client to make an informed, defensible decision, protecting their brand reputation while opening the door to expansion. What could have been a risky move became a transparent, evidence-based assessment with clear commercial logic.
We are continuing to support the client with market monitoring tools that track product sentiment and issue emergence in real time, giving them early warnings for future business decisions.
