Quantify demand sensitivity to price, control for confounders, and simulate revenue‑optimal prices with confidence intervals
Log-log regression for point elasticity, arc elasticity across price quantiles, rolling window analysis for temporal changes. Includes time trend and seasonal components (sin/cos for 12+ observations).
Finds revenue-maximizing price using 100-point simulation. Calculates cross-price elasticity if competitor prices available. Segment-level elasticity for heterogeneous markets.
Pre-computed scenarios for -20% to +20% price changes showing expected quantity and revenue impacts. Demand and revenue curve visualizations with optimal price highlighting.
Provide data with price_column and quantity_column. Optional: revenue_column (calculated if not provided), competitor_price_column for cross-elasticity, segment column for heterogeneous analysis.
Algorithm uses log-log regression (default method), calculates arc elasticity at price quantiles, finds revenue-maximizing price through simulation, and generates what-if scenarios from -20% to +20% price changes.
From preparation to validated pricing scenarios
Log-log regression for point elasticity with confidence intervals. Arc elasticity at price quantiles (10th to 90th percentile). Rolling window analysis if sufficient data (20+ observations).
Simulate 100 price points from min to max, predict quantities using elasticity model, calculate revenues, identify maximum. Add time trend and seasonal components (sin/cos) when applicable.
Calculate impacts for 7 price changes (-20%, -10%, -5%, 0%, +5%, +10%, +20%). Show expected quantity and revenue changes. Create demand and revenue curve visualizations.
Price elasticity analysis provides point estimates, arc elasticities across price ranges, and revenue-maximizing prices through simulation of demand curves.
The tool calculates multiple elasticity measures: log-log regression coefficient, arc elasticity at quantiles, rolling window for temporal changes. Identifies optimal pricing through 100-point simulation and provides ready-to-use what-if scenarios from -20% to +20% price changes.
Note: Uses log-log regression by default. Automatically adds seasonal components (sin/cos) for 12+ observations. Cross-price elasticity requires competitor_price_column. Segment analysis requires 10+ observations per segment.
Estimate elasticity and simulate revenue‑optimal price points
Read the article: Price Elasticity