Plain definition
Driver-based planning builds a financial plan from a small set of measurable variables that cause the result, rather than entering a final revenue or cost total without its logic. A driver can be demand, conversion, price, units per customer, staffing capacity, or another operational fact that changes the outcome.
Illustrative example
Monetary examples use Saudi riyals (SAR). The same principles apply in other currencies.
A service business plans July revenue from 1,200 qualified leads, a 25% conversion rate, and an average first order of . The plan produces : 1,200 × 25% × 800. The sales lead then reports that an advertising change is likely to reduce qualified leads to 1,000 while conversion and price remain unchanged. The revised revenue outlook is , a reduction.
The explicit driver model gives management a choice: restore 200 qualified leads, improve conversion, raise price where the market allows, or cut an expense that assumed . It also shows that a vague instruction to ‘grow sales’ is not an action plan.
| Qualified leads | |
|---|---|
| Conversion factor (25%) | |
| Average first-order value | |
| Planned revenue |
A driver is useful because it has a plausible causal relationship with the result and can be observed. A convenient number that merely happens to move with revenue is not necessarily a planning driver.
Why it matters
A total-only plan hides which operating change would repair a miss. Teams can then debate the final number without testing the information that produced it. Driver-based planning makes assumptions visible early enough to allocate marketing, capacity, and cash with a clearer view of the trade-offs.
Practical consequence
Use only the few drivers that materially explain the result, define each one consistently, and assign a source for its actual measurement. Compare planned and actual drivers each period, then update the model when evidence changes. Test sensitivities for the drivers most likely to move cash or capacity. Do not confuse a correlation with a cause: validate the link with operating history and override the model where a one-off event makes the usual relationship unreliable.