Update a 12-Month Rolling Forecast with New Actuals

Rolling forecasts only deliver value if they're actually rolled — re-cut each month with fresh actuals so the 12-month horizon never shrinks. In practice that monthly refresh is tedious: pull actuals, compare to the old forecast, re-project, and write up what moved. This prompt compresses that into one structured pass. The emphasis on comparing new versus prior forecast is deliberate. Leadership cares less about the absolute number than about what changed since last month and why. By forcing a materiality threshold, the prompt keeps you from drowning in noise and surfaces only the movements worth a conversation. It also produces a CFO-ready narrative, so the same exercise that updates your model also drafts your board pre-read. Feed it clean actuals and honest assumption updates, and treat the projected periods as planning estimates that you still sanity-check before circulating.

What you can expect back

Rolling forecast refresh — base period May 2027 (estimates)

Material moves vs. prior forecast:
- New-business revenue: June revised down ~$90K as two enterprise deals shifted into Q3; full-year impact is timing, not lost pipeline. Q3 steps up correspondingly.
- Headcount cost: trimmed ~$40K across June–August from the extended freeze; reverts in Q4 as planned roles reopen.
- Total opex: down ~$55K near-term, tracking the headcount change.
- Expansion revenue: unchanged, within threshold.

Window now runs Jun 2027–May 2028.

CFO narrative: The May refresh is largely a timing story. Two enterprise deals slipped a quarter, moving roughly $90K of new ARR from June into Q3 without changing the annual total. The extended hiring freeze lowers near-term burn by about $55K. Full-year revenue and margin outlook are intact; the main watch item is closing the slipped deals in Q3.

Illustrative example — your results will vary by tool and inputs.

Get sharper results

• 01Paste the prior forecast and the new actuals in clearly labeled blocks so the model knows which is which when computing deltas.
• 02Distinguish timing shifts from real changes in your assumptions — the model will echo that distinction in the narrative, which executives appreciate.
• 03Keep the same line names and order every month so you can diff outputs across refreshes.
• 04Ask for the output as a table plus narrative; the table feeds your model and the narrative feeds your deck.

Adapt it for your case