This paper explores how Discrete Choice Models (DCMs) help market researchers optimize product portfolios by simulating market scenarios to predict consumer preferences. We present a two-stage approach: First, use algorithms like simulated annealing to find near-optimal portfolios; second, refine these solutions by testing all possible remaining SKU combinations. This method balances mathematical optimization with real-world constraints, providing a practical, adaptable solution for both simple and complex market situations, and delivering actionable insights for business strategy optimization.
