Top Pharma Leverages Vamstar’s AI-Powered Pricing Insights for
Product Lifecycle Management & Forecasting

The pharmaceutical company faced challenges in using traditional pricing data sources to predict net pricing and comprehend market dynamics. They required a more comprehensive understanding of the hospital market in a specific disease area, particularly the institutional business and the gross-to-net pricing process, to accurately assess the target company’s future earnings potential.

Vamstar gathered 5 years of historical data on net prices from 15 countries and all publicly announced tender awards. This data came from Vamstar’s AI-powered pricing and tendering database. Our team of analysts also calculated the average unit net price by combining publicly available contract notices, awards, and documents, then deriving the total demand from those contracts in 20 more countries.

Finally, the team estimated net prices in markets with limited visibility using modelling techniques based on real data points from the original 15 countries. Additionally, the team combined this data with import-export datasets and other publicly available supply chain and volume data to gain a comprehensive understanding of value and volume in each of the top 50 markets. 

This data, including insights from upcoming launches and the price erosion insights from similar comparable products, was used to construct an AI-led model with more than 300 variables to forecast prices moving forward. These models were then tested using A/B testing methodologies to ensure that any insights are always thoroughly validated. Through reinforcement learning, the AI system can learn from pricing teams’ actions, making it a valuable tool for decision-making.

Armed with Vamstar’s data-driven recommendations and insights, the pharmaceutical firm gained a strong understanding of the market’s scale, size, gross-to-net evolution, and future forecasts. They identified discount patterns across regions and recognised promising opportunities for differentiation. The AI-powered modelling allowed them to quickly test market assumptions and simulate price movements for the next 3 to 5 years based on their current operations.

Vamstar’s expertise in AI-driven market analysis, net pricing insights, and detailed modelling capabilities provided the pharmaceutical firm with actionable insights into the market. This empowered them to navigate the complexities of gross-to-net pricing and overall competitive dynamics, including its evolution in the coming years.

This case study demonstrates how Vamstar’s platform can guide pricing teams in the industry through strategic decision-making, leading to successful insights into the true market value of the asset, from a current and future perspective.