Compare revenue to the same period last year
Compares the last six months of revenue against the same period a year earlier and reads the trend behind the movement.
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You are a senior FP&A analyst preparing a revenue trend read for a monthly review.
Context: revenue is in the Amount field and the reporting date is {date_field}.
Compare the last six months to the same six months one year prior.
{revenue_data}
Task: produce a month-by-month comparison of current vs. prior-year revenue,
then summarize the trend.
Output format:
- A table with columns: month, current-year revenue, prior-year revenue,
variance in {currency}, variance %.
- A total row.
- Below the table, two sentences: the overall trend direction, and the single
month with the largest year-over-year swing.
Run it in four steps
- Export six months of revenue from the Amount field, plus the same six months one year prior, on one consistent fiscal calendar.
- Paste it into
{revenue_data}, set{date_field}to your reporting date, and set{currency}. - Run it for the month-by-month comparison and trend read.
- Before reading into the trend, confirm both periods use the same revenue definition and that no prior-year month was still open when the data was pulled.
When to reach for this prompt
Use this at the start of monthly review, before opening the budget, when someone wants a fast read on revenue direction. It is the lightest form of trend work there is, one variable across two periods, which also makes it a good first test of whether your reporting periods actually line up year over year.
What you can expect back
Revenue, last 6 months vs. prior year (Amount)
| Month | This yr | Last yr | Variance | % |
|---|---|---|---|---|
| Jan | 1,840K | 1,610K | +230K | +14.3% |
| Feb | 1,910K | 1,655K | +255K | +15.4% |
| Mar | 2,050K | 1,720K | +330K | +19.2% |
| Apr | 1,880K | 1,690K | +190K | +11.2% |
| May | 1,920K | 1,705K | +215K | +12.6% |
| Jun | 1,820K | 1,600K | +220K | +13.8% |
| Total | 11,420K | 9,980K | +1,440K | +14.4% |
Revenue is up roughly 14% year over year and has trended upward fairly consistently across the six months. The largest swing was March (+19.2%), which may warrant a closer look to confirm the increase is organic.
This prompt has real limitations you should understand.
A two-period comparison is the easiest chart to over-read, because the arithmetic is trivial and the result looks definitive. The comparison only holds if "revenue" meant the same thing in both periods, and the Amount field rarely guarantees that. If it mixes bookings, billings, and recognized revenue across rows, the trend is the sum of three different measures and nothing in the output reveals it.
The prior-year side of the comparison is the more common failure. A month that was still open or closed late a year ago shows up as a year-over-year "decline" that is really just missing data, and the percentage column reports it with full confidence. The same risk applies to the period boundary itself: if the reporting date is not anchored to one consistent fiscal definition, "the same six months last year" can quietly span five or seven actual months.
For a comparison like this to be trustworthy in production, both periods need to draw on a single, consistently defined revenue measure and a fiscal calendar that aligns the current and prior windows to the same boundaries. That alignment is a property of the data layer feeding the prompt, not of the prompt itself, and no amount of prompt tuning fixes a year-over-year comparison built on two different definitions of revenue.
What your data needs to look like
- A revenue figure in the Amount field at monthly grain
- A single revenue definition (recognized, billed, or booked) applied to every row
- A reporting date that covers both the current and prior-year window on the same fiscal calendar
- No mid-window change to how revenue is classified
See how FinanceOS handles this prompt on real financial data.
Book a 20-minute walkthrough. We’ll run this exact prompt against a sample dataset reconciled through FinanceOS, and show you what changes when the data underneath is right.
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