Price elasticity quantifies how demand responds to price changes. With the right controls, it helps teams optimize pricing, plan promotions, and forecast revenue impact.

Data Format

  • Required: price, quantity (or revenue)
  • Recommended: date/time, product/store (for segments), promo flags, competitor price, seasonality effects
  • Granularity: transactional or aggregated by day/week; aim for ≥ 500 observations per item/segment

Estimation Basics

  • Log‑log regression: elasticity ≈ coefficient on log(price)
  • Panel fixed effects: control for stable item/store effects and time shocks
  • Controls: promotions, holidays/seasonality, marketing, inventory constraints
  • Uncertainty: report confidence intervals and robust standard errors

Validation & Pitfalls

  • Endogeneity: price may respond to demand. Prefer instruments or experiments for causal interpretation.
  • Confounding: include key drivers (promos, seasonality, competition) to reduce bias.
  • Regime changes: watch for structural breaks; re‑estimate when conditions shift.
  • Heterogeneity: segment by product, region, or customer to capture different elasticities.

Using the Results

  • Simulate revenue/profit curves to identify recommended price ranges
  • Compare elasticity across segments to guide differentiated pricing
  • Run what‑if scenarios (promo on/off, competitor moves) for planning
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