A driver-based forecast builds revenue from what causes it, instead of growing last year's total by a percentage:
- units x price;
- average stores open x sales per store;
- market size x market share x price;
- cafes x days x transactions per day x average ticket.
Each driver is an input someone can question and own: sales argues about volume, marketing about share, finance about price. When revenue misses, the drivers show which assumption was wrong.
Two edge cases come up all the time. Stores opened during the year trade for only part of it, so revenue uses the average number of stores, (start + end) / 2, not the year-end count. And seasonality weights copied from last year rarely add up to exactly 100%. Divide each month's weight by the total of the weights, so the months add back to the annual figure.
Units 1,800 =B5*(1+C6) 1,908
Price $1,450 =B7*(1+C8) $1,494
Revenue 2,610,000 =C5*C7 2,849,598
6% more units at a 3% higher price grows revenue by about 9.2%.