"algo-price-elasticity"

Price Elasticity of Demand

Overview

Price elasticity measures the percentage change in quantity demanded for a 1% change in price. Ed = %ΔQ / %ΔP. |Ed| > 1 = elastic (price-sensitive), |Ed| < 1 = inelastic (price-insensitive). Critical for pricing decisions and revenue optimization.

When to Use

Trigger conditions:

• Estimating how a price change will affect unit sales and revenue
• Determining if demand is elastic or inelastic for a product
• Optimizing price for maximum revenue or profit

When NOT to use:

• When you need consumer willingness-to-pay distribution (use Van Westendorp or conjoint)
• When pricing multiple products together (use bundle pricing)

Algorithm

IRON LAW: Elasticity Is NOT Constant Along a Linear Demand Curve
It varies at every price point. At high prices, demand is elastic
(small price increase → big volume drop). At low prices, demand is
inelastic. Always calculate at the SPECIFIC price point of interest.
Revenue-maximizing price is where Ed = -1 (unit elastic).

Phase 1: Input Validation

Collect: price-quantity pairs over time (or across markets). Control for: seasonality, promotions, competitor actions, other confounders. Gate: Minimum 10 price-quantity observations, confounders identified.

Phase 2: Core Algorithm

Point elasticity: Ed = (dQ/dP) × (P/Q) at a specific price point Arc elasticity: Ed = ((Q₂-Q₁)/((Q₂+Q₁)/2)) / ((P₂-P₁)/((P₂+P₁)/2)) between two points Regression method: log(Q) = α + β×log(P) + controls → β is the elasticity (constant elasticity model)

Phase 3: Verification

Check: sign should be negative (price up → quantity down). Cross-validate with holdout periods. Gate: Elasticity is negative, confidence interval is reasonable.

Phase 4: Output

Return elasticity estimate with revenue impact projection.

Output Format

{
  "elasticity": -1.5,
  "interpretation": "elastic — 1% price increase → 1.5% quantity decrease",
  "revenue_impact": {"price_change_pct": 10, "quantity_change_pct": -15, "revenue_change_pct": -6.5},
  "metadata": {"method": "log-log regression", "r_squared": 0.82, "observations": 52}
}