A pharmaceutical company launching a new vitamin product line faced a classic pricing dilemma: price too high and lose market share, price too low and leave money on the table. This article walks through the pricing optimization framework I built for this engagement — using Bayesian demand modeling and mixed-integer programming.
The company was launching a new vitamin product line but lacked a data-driven approach to set optimal prices. Traditional methods relied on competitor analysis and limited market research, potentially leading to missed opportunities or profit erosion.
The risks included: - Suboptimal pricing leaving potential revenue unrealized - Product cannibalization within their existing portfolio - Market share erosion due to pricing misalignment
The first step was collecting comprehensive market data including: - Historical sales data of existing vitamin and sleep medication products - Competitor pricing strategies across 15 major brands - Consumer willingness-to-pay surveys (n=2,000) - Market dynamics including seasonality effects
About the author
Cyril Noirot
Lead Data Scientist
Freelance data scientist. I design and ship decision systems — forecasting, pricing, marketing measurement, optimization.