How Price-Sensitive Are Your Customers? Measured From Your Own Sales

How it works

The analysis fits an ordinary least squares regression of log quantity on log price (plus any controls you map). On the log-log scale the slope on price IS the price elasticity of demand: the percent change in quantity associated with a 1% change in price. A 95% confidence interval says how precisely it is pinned down; comparing its magnitude to 1 classifies demand as elastic (price cuts raise revenue) or inelastic (price increases raise revenue). A ten-decile demand curve shows the raw price-quantity relationship the regression summarizes.

Use it when you have historical rows with a price and a quantity — transactions, weekly SKU aggregates, regional sales — and want to know how demand responds to price before changing it.

Not for data where price never varies (no elasticity is identifiable), and not a substitute for a pricing experiment when the stakes are high — observed prices are not randomized.

What data do you need?

Any table with a price and a quantity per row. For example, per-transaction sales with a promotion flag:

Minimum 30 rows · Best with 100-50,000 rows with meaningful price variation

What's in the report?

Standard-library analysis: how sensitive is demand to price? A log-log OLS regression of quantity on price estimates the price elasticity of demand from your transactional or aggregated sales rows, with a 95% confidence interval, an elastic/inelastic classification, the revenue direction the data is consistent with, and a 10-point demand curve. Optionally hold other factors constant (promo flag, season, region).