Indegene
Unlocking True Drivers of Vaccine Demand with ML Models
Unlike chronic therapies, where demand builds over time, vaccines operate in short, high-stakes windows. Uptake is driven not just by clinical need, but also by timing, awareness and provider action, often compressed into a few critical weeks ahead of the season. As a result, even well-funded campaigns can deliver uneven outcomes.
Vaccine A required greater accuracy in commercial planning for a top-10 global pharma company. Demand was shaped by multiple, interdependent factors: field engagement, non-personal promotion, healthcare professional behavior and seasonal patterns. However, these signals existed in isolation. Teams could track activity across <a href="https://myappsplus.com/how-to-watch-hungary-vs-georgia-free-streams-tv-channels-for-nations-league-2026-27/” title=”How to watch Hungary vs Georgia: FREE streams & TV channels for Nations League 2026/27″>channels but lacked a clear view of which actions were actually influencing vaccination uptake.
The company needed a clearer understanding of which activities were driving performance, especially as it became clearer that no single dataset could explain vaccine demand. The solution was integrating multiple data sources, then using exploratory data analysis and correlation testing to assess data quality, timing consistency and overlap across variables. Because many commercial variables moved together, it also evaluated regularized approaches and selected Elastic Net regression for greater stability, interpretability and predictive reliability. And it used separate models for retail and non-retail.
These outputs were translated into scenario simulations and a GenAI-enabled interface that allowed users to test decisions, compare channel responses and get simple explanations of what changed, why it changed and what to do next.
For retail channels, this approach increased accuracy by about 18% and boosted ROI by 14%. In non-retail, accuracy improved 16%, with ROI improving by 10%.
Silver
CMI Media Group and PulsePoint
Redefining HCP Media Performance Through AI Precision & Optimization
Not every HCP who treats epilepsy is equally positioned to diagnose, prescribe or adjust therapy at any given moment. So even targeting the most precise HCP audience could miss the exact moment that actually matters. To get beyond “targeting the right doctors” or “optimizing based on past behavior,” this brand needed a way to dynamically identify and prioritize high-intent prescribers as their behaviors shifted. PulsePoint’s Adaptive Optimization model shifted media delivery toward the prescribers with higher clinical relevance and stronger prescribing propensity. It reached fewer doctors but still led to meaningful increases in monthly prescriptions.
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