Most unit-economics spreadsheets are read once, by the analyst who built them, and never again. The board wants a story (what changed, why, and what to do), not a wall of cells. This workflow turns the numbers into a half-page narrative the CEO can read in 90 seconds and act on the same day.
The task
You have a unit-economics workbook with CAC by channel, contribution margin per cohort, LTV computed two ways, payback period, and a deck slot due tomorrow morning. You want the AI to read the numbers, find the one or two cohort shifts that actually matter this quarter, and write a narrative that says “paid social CAC went from 42 to 71, payback stretched from 7 to 14 months, the rest is noise”, not a recap of every cell.
When this is the right job for AI
AI is strong at reading a structured table, comparing periods, and producing a narrative in your tone. It is weak at deciding which metric you actually care about and at catching unit errors (mixing gross and net, mixing trial-converted and total signups). Hand it a clean sheet, name the metric definitions inline, and verify every headline number against the source row before sending. If the sheet is messy, fix the sheet first; the model will dutifully narrate garbage.
What to feed the AI
- The unit-economics table, at minimum CAC, gross-margin-adjusted LTV, payback in months, by channel and by cohort
- Two comparison points: last quarter, and the same quarter last year
- Your metric definitions, e.g., “LTV here is 24-month, gross-margin-weighted, churn-adjusted”
- The audience (board, exec team, or finance review); each wants a different lens
- The decision the narrative should support, e.g., “should we cut paid social spend”
- Any known one-offs: a holiday promo, a pricing test, a campaign you paused mid-quarter
Copy-ready prompt
You are reading a unit-economics workbook for a SaaS / consumer business.
Write a half-page narrative for the audience and decision below.
Audience: <board / exec team / finance review>
Decision the narrative should support: <one sentence>
Metric definitions (use exactly these, do not redefine):
CAC: <definition>
LTV: <definition, including window and margin treatment>
Payback: <definition>
Known one-offs this quarter: <list, or "none">
Data (current quarter / last quarter / same quarter last year):
"""
<paste the table — keep column headers>
"""
Return:
1. Headline (one sentence) — the single most important shift this quarter.
2. Three bullets: what changed, by how much, and in which channel or cohort.
Use the metric definitions above. Do not invent any number not in the table.
3. One paragraph on root cause — only what the data supports.
Mark anything you are inferring as [hypothesis].
4. One paragraph on the decision: what the data suggests we should do,
and what we would need to see to reverse the call.
5. A short "verify" footer listing the 3-5 numbers a reader would want to
double-check in the source before quoting.
Tone: direct, plain-English, no MBA hedging. Numbers in absolute terms first,
percent change second. Do not paraphrase a 12% move as "meaningful."For the follow-up pass: “Now rewrite the headline three ways: one for the board, one for the exec team, one for the finance committee. Same numbers, different lens.”
Sample output structure
Headline: Paid social payback stretched from 7 to 14 months; everything else held.
- CAC on paid social: $42 → $71 (+69%), Q4 vs Q3, all cohorts.
- LTV on paid social: $620 → $585 (-6%), driven by a 9% drop in month-3 retention.
- Payback: 7.2 → 13.8 months. Organic and partner channels unchanged.
Root cause: CPM inflation on the two largest creative sets we have not refreshed since Q2 [hypothesis — confirm with the ads team]. LTV drop is too small to fully explain payback; the issue is CAC.
Decision: Cut paid social spend to a 30% floor pending creative refresh. Reverse if 4-week rolling CAC drops below $55 with retention flat.
How to refine
- If the narrative recaps every cell: “Cut to the one shift that moves the decision. Everything else is a footnote.”
- If it invents a number: “Use only numbers in the table. If you want to cite a derived metric, show the arithmetic.”
- If it hedges every sentence: “Strip ‘may,’ ‘could,’ ‘potentially.’ Either the data supports it or mark [hypothesis].”
- If root cause is hand-wavy: “Name the specific cohort or channel. ‘Engagement dropped’ is not a root cause; ‘month-3 retention on the Sept paid-social cohort dropped from 64% to 55%’ is.”
Common mistakes
- Mixing gross and net CAC across periods without flagging the change
- Letting AI compute LTV from raw inputs rather than reading your sheet’s definition
- Hiding the one big shift inside a paragraph of small ones
- Forwarding to leadership without the verify footer: bad numbers ruin credibility for a quarter
- Treating a 4-week blip as a trend; require two consecutive periods to call a shift
FAQ
- Should I paste the whole workbook?: No. Paste the summary table plus 2 comparison points. Detail tabs are noise.
- What if cohorts overlap with promos?: Flag the promo in the “known one-offs” line; ask the model to caveat the affected cohort in the narrative.
- Can AI compute payback from scratch?: Yes, but verify; rounding and gross-vs-net errors are common. Feed your own computed payback whenever possible.
- How often should this run?: Monthly internal, quarterly for the board. Re-use the same prompt and definitions for clean period-over-period comparison.
Related
- Board deck narrative AI: pair this with deck building
- Financial report summary AI: adjacent 10-K summarisation workflow
- Business data analysis AI: broader business-data narrative
- Pricing experiment AI: pricing test design
- AB test summary AI: translate experiment output to narrative
- Chart table explanation AI: make the underlying table digestible