See what drives loyalty.
Make the next decision clearer.
Explainable machine learning and a self-service dashboard turn healthcare-practitioner surveys into insights business teams can use.
A leading global pharmaceutical company
The challenge
A leading global pharmaceutical company runs large-scale surveys of healthcare practitioners (HCPs) across therapeutic areas every semester. The surveys collect detailed insight into which treatments are used in the field, and the patient share of each brand.
But business teams found it hard to get meaningful, timely insights from the results. The analysis behind them was often seen as a black box that took 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 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. For full transparency, we used an interpretable model and applied explainability techniques to work out the main factors behind brand usage.
The results were integrated into a self-service dashboard that combines model-driven insights with real-world brand performance. Business users get a clear, intuitive view of what drives HCP loyalty across therapeutic areas. This white-box approach makes the AI explainable and trustworthy, and it was carefully validated for reliability and relevance.
The result
Business teams can now see which KPIs drive HCP loyalty and brand performance.
- They take timely, informed action to improve market position
- They explore insights without technical training
- They align brand strategy with practitioner behaviour
With continuous access to explainable insights, the company is better positioned to grow brand share, retain loyalty and compete with confidence in a fast-moving market.