Challenge

One of our client was launching a new product, but lacked a data-driven approach to set an optimal price. Traditional methods relied on competitor analysis and limited market research, potentially leading to missed opportunities or profit erosion.

Our Approach

• Data gathering: We collected historical sales data of existing sleep medications, competitor pricing strategies.

• Demand modeling: We employed bayesian analysis to estimate the relationship between price and demand for sleep medication. This provided a range of possible demand outcomes at different price points, accounting for uncertainty.

• Cost analysis: We factored in production costs and defined a minimum required profit margin to ensure financial viability.

• Mixed-integer programming (MIP): We built a MIP model to optimize pricing across the entire category medication portfolio. This considered demand-price relationships, cost constraints, and the potential for cannibalization between products.

• Scenario analysis: We explored multiple objective functions, including maximizing total revenue and maximizing gross margin. This allowed our client to understand the trade-off between sales volume and profitability and choose the pricing strategy that best aligned with their business goals.

Impact

• Data-driven pricing: Our client was able to transitioned from subjective pricing to a data-driven approach, ensuring their pricing strategy considered cost structures and market dynamics.

• Profitability optimization: The MIP model identified the optimal price point for several product, balancing market competitiveness with profit maximization. Additionally, portfolio-wide pricing adjustments led to an overall revenue increase while maintaining market share.

• Long-term strategic advantage: By establishing a data-driven pricing framework, our client gained agility and flexibility to adapt their pricing strategy to future market changes and product launches (especialy in a context where inflation is moving up every month).