The Challenge

A leading global pharmaceutical company conducts large-scale HCP (Health Care Practitioner) surveys across various therapeutic areas every semester. These surveys collect detailed insights into which treatments are being used in the field, and the patient share associated with each brand.

However, business teams found it difficult to extract meaningful and timely insights from the survey results. The analytical processes behind the scenes were often seen as black-box, requiring significant expertise to interpret. What they needed were clear, actionable, and trustworthy insights, without the complexity.

The Solution

Common Sense AI developed a machine learning-based approach to unlock actionable insights from the HCP loyalty survey data.

We trained a supervised machine learning model that identified the key drivers behind brand preference and loyalty among HCPs. To ensure full transparency, we used an interpretable model and implemented model explainability techniques to reverse-engineer the main influencing factors behind brand usage.

The results were integrated into a self-service dashboard that combined model-driven insights with real-world brand performance, giving business users a clear, intuitive view of what drives HCP loyalty across therapeutic areas. This white-box approach allowed for trustworthy, explainable AI, and was carefully validated to ensure both reliability and relevance.

The Result

Thanks to this solution, business teams are now able to:

• Understand which KPIs influence HCP loyalty and brand performance

• Take timely, informed actions to improve market position

• Explore insights without the need for technical training

• Align brand strategy with practitioner behavior

With continuous access to explainable insights, the company is better positioned to grow brand share, retain loyalty, and compete confidently in a fast-moving pharmaceutical landscape.