In the highly specialized women’s health sector, demand forecasting is complex. Brands face divergent regional adoption rates, varying regulatory landscapes, and demographic-specific preferences (e.g., ethnicity, income, and age-driven demand shifts). Traditional national-level forecasts fall short; they mask critical local variations, leading to misaligned promotions, excess inventory costs, and missed growth opportunities.
This challenge was particularly pressing for a Top 50 pharma company. Its diverse women’s health portfolio, spanning multiple therapeutic areas and lifecycle stages, demanded a detailed, event-driven forecasting approach. With promotional strategies hinging on regional nuances (e.g., higher product uptake among specific ethnic groups), the company needed to move beyond averages and predict demand at a sub-national level with scientific accuracy.
Key highlights from the case study
- Less than average prediction error: Machine learning models delivered unprecedented precision in regional demand forecasting.
- Hyper-local insights: Identified high-potential demographics (age, income, ethnicity) to optimize promotional spend.
- Event-driven forecasting: Incorporated market events, regulatory changes, and promotions for real-world accuracy.
- Data-driven strategy: Enabled smarter inventory planning, targeted marketing, and agile decision-making.
Why does this matter for your business?
- Eliminate guesswork: Move beyond national averages with AI-powered sub-national forecasts.
- Boost ROI: Allocate promotions and inventory to the right regions, at the right time.
- Outpace competitors: Leverage predictive insights to anticipate demand shifts before they happen.
