Pharmaceutical

Pharmaceutical companies contend with intense branded generic competition, overlapping regulations, and high-stakes pricing decisions. Relying on static spreadsheets or simple benchmarks is no longer enough. Our pharma work focuses on three pillars:

1. Product Equivalence Matrices (PEM) at Scale

• Build automated equivalence logic using ingredient, dosage form (including ER/SR), strength, pack size, and therapeutic alignment (e.g., ATC/TA).
• Normalize prices to a standard therapeutic unit (such as DDD or standard units) so you can compare your price architecture to competitors on a true like-for-like basis.
• Deliver transparent similarity scores that make it easy for brand, pricing, and market access teams to trust and use the mapping across countries and portfolios.

2. Causal Elasticity & Demand Modeling

• Apply advanced machine learning and Double Machine Learning (DML) frameworks to separate true price sensitivity from noise created by promotions, competition, supply constraints, and other confounders.
• Estimate own- and cross-price elasticities at the right level of granularity (brand/SKU, segment, or channel) to inform pricing strategy in both non-regulated and semi-regulated environments.
• Translate elasticities into practical guidance: where you can take price up with limited volume risk, where you should defend price, and where targeted reductions can expand access and share.

3. Pricing Quick Wins & Scenario Simulation

• Combine PEM outputs and elasticity insights into a Competitive Price Index (CPI) view that highlights underpriced and overpriced SKUs within each market.
• Build user-friendly simulators that allow country and portfolio teams to test “what-if” price changes and see projected impacts on volume, revenue, and gross profit before any move is executed.
• Prioritize a pipeline of Pricing Quick Wins (PQWs) that can be implemented quickly while laying the foundation for longer-term revenue growth management capabilities.
  
  

Beyond analytics, we emphasize capability building—training your teams to maintain the PEM, refresh models, interpret outputs, and embed pricing analytics into standard business rhythms so value continues to compound after the project ends.