Operating model
Revenue drivers
Forecast a retailer's revenue from store count and revenue per store, with price and traffic separated.
Learning objectives
- Decompose revenue into volume and price drivers
- Derive a historical driver (revenue per average store) from reported figures
- Build a driver-based revenue forecast and explain the growth it implies
"Revenue grows 8%" is an assumption nobody can test. "Rawdah opens three stores, closes one, and sales per store grow with 2% price increases and 1% more transactions" can be debated, benchmarked and stress-tested. Driver-based forecasts make the assumptions explicit, so management, lenders and investors can argue about the right things.
Every revenue line is volume × price. For a retailer, volume is the store estate (and traffic through it); price is the average ticket. Rawdah Home Retail Co. is a fictional retailer; all figures are SAR millions and illustrative.
Revenue = Average stores × Revenue per average store · LFL growth = (1 + price growth) × (1 + traffic growth) − 1
- Average stores
- (opening + closing) ÷ 2, because stores open and close through the year
- Revenue per average store
- last year's value × (1 + LFL growth)
- LFL
- like-for-like growth: sales growth in stores open in both years
Included with a Formula Quest membership
The rest of this lesson is for members. Members get the full lesson, including its 3 graded exercises, and every other Finance Quest lesson.