What needed to be understood.
The decision could not be solved by maximizing unit margin alone. Higher prices changed volume, and complementary offerings created additional downstream demand and profit effects.
How the analysis was built.
- Modeled price-response relationships and calculated expected demand at alternative price levels.
- Built revenue, cost, and profit logic in Excel.
- Used Solver to search for a profit-maximizing price rather than selecting a price manually.
- Extended the model to include complementary-product demand and markup scenarios.
What the evidence showed.
- The most profitable decision depended on the full demand curve, not simply the highest feasible price.
- Complementary-product effects materially changed the economics of the primary pricing decision.
- Scenario analysis exposed the trade-off between unit margin, volume, and total portfolio profit.
What should happen next.
- Use a portfolio-profit objective when products influence one another.
- Retest demand assumptions with observed sales data before deploying the optimized price.
- Add sensitivity ranges for costs, conversion rates, and cross-product attachment rates.
What this project does—and does not—prove.
The optimization is only as reliable as the assumed demand equations, cost inputs, and complementary-demand relationships used in the academic workbook.