Overview
Most price optimization relies on correlation between historical prices and volumes — which is systematically biased. When price rises, volume falls, but when price fell in the past it was usually because of a promotion, which biases the measured elasticity toward overstated response. Models trained on this data recommend over-discounting.
The Price Optimization Model uses causal methods (price A/B tests, regression discontinuity, instrumental variables where available) to estimate elasticity without bias, maps the demand curve, identifies profit-maximizing prices factoring in marginal cost, and handles cannibalization across substitutes/complements.
What you get: - Causal elasticity estimation (not naive correlation) - Demand curve per SKU/segment - Profit-maximizing price with marginal cost factored in - Cross-price elasticity for substitutes and complements - Cannibalization matrix across product portfolio - Price A/B testing framework with statistical power analysis - Price-change rollout playbook (gradual vs. immediate) - Customer perception guardrails (anchoring, fairness)
Built for: pricing managers, revenue optimization teams, e-commerce merchandisers, and SaaS monetization specialists who need elasticity estimates that are not biased by promotional history.