Beer Price Simulator
Pick a brand and move the price slider to see how weekly volume and revenue would respond, based on price elasticities estimated from a year of weekly sales.
Synthetic data · parody brand names · see the full analysisRevenue change at every price change
Each line is a brand. Lines that rise to the right are brands that gain revenue from a price increase. The shaded band is the selected brand's 95% confidence range.
Weekly units: actual vs. at your price
Weekly shelf price: actual vs. at your price
Dips are promotions. Your change is applied to every week.
Show scenario as a table (all brands)
How this works
Elasticity is the % change in units sold for a 1% change in price. Brands below −1 are elastic (a price increase loses revenue); brands between 0 and −1 are inelastic (a price increase gains revenue).
Elasticities come from a log-log regression, ln(units) = brand + week + elasticity × ln(price), where the week term removes seasonality and holiday effects shared by all brands (R² = ). For a price change p, units scale by (1+p)e and revenue by (1+p)1+e. Projections assume other brands' prices stay the same, and they're most reliable within the ±20% range seen in the data.