Product Line Optimization (Discrete Choice / Conjoint)
Design the lineup and price that maximize demand and margin.
When to use it
- Too many SKUs/features with unclear roles; risk of cannibalization.
- Pricing/pack changes on the table; scenario planning needed pre‑launch.
- Investment decisions for premium vs. value tiers.
What you get
- Interactive simulator for mixes, prices, and features
- Optimal portfolio & price structure by audience/segment
- Attribute importance & willingness‑to‑pay
- Cannibalization matrix and volume/revenue projections
How we do it
- Rigorously map real-world market options to a representative product and feature set for quant research.
- Execute conjoint/choice with realistic constraints and competitive sets, across a variety of possible market scenarios.
- Integrate costs/operational constraints to deliver feasible recommendations.
- Use qual when it’s the right answer to define features, bundles, and language before fielding.
Selected results
- Streamlined portfolio from 14 to 9 SKUs while holding revenue and improving margin.
- Priced a premium variant that expanded revenue without eroding core product revenue.