Create advanced Works with: Any AI

Revenue Model and Assumptions Register

Json output Has example

Make every number in the forecast traceable.

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Create an assumptions register for my financial forecast. Business and revenue streams: [BUSINESS_AND_REVENUE_STREAMS]. Forecast horizon: [HORIZON]. The assumptions I am making (prices, volumes, growth, conversion, costs, hiring): [ASSUMPTIONS_LIST]. Evidence I have: [EVIDENCE].

Return valid JSON only: an array of objects, one per assumption, with keys "id", "stream_or_category", "assumption" (plain statement), "value", "unit", "period", "evidence_type" (actual data, quote, benchmark, interview, estimate), "evidence_detail", "confidence" (low, medium, high), "sensitivity" (what happens to year-1 revenue or profit if the value is 20 percent worse, described as a qualitative or approximate effect, flagged as an estimate), "owner", "how_to_validate", and "review_date".

Then, below the JSON, in plain text: rank the ten most sensitive assumptions, propose the cheapest test for each of the top three, and describe how to link the register to a spreadsheet so that each forecast cell references an assumption by id. Mark any assumption lacking evidence as low confidence rather than inventing support. Add a rule: change the register first, then the forecast, and keep a version log.

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Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.

[{"id": "A-01", "stream_or_category": "Repairs", "assumption": "Average repair invoice", "value": 118, "unit": "GBP", "period": "per job", "evidence_type": "actual data", "evidence_detail": "Mean of last 400 invoices", "confidence": "high", "sensitivity": "If 20 percent lower, year-1 revenue falls roughly 20 percent (estimate)", "owner": "finance", "how_to_validate": "Recalculate quarterly", "review_date": "2027-01-15"},
{"id": "A-07", "stream_or_category": "Service plans", "assumption": "Plan conversion rate", "value": 0.08, "unit": "ratio", "period": "per repair customer", "evidence_type": "estimate", "confidence": "low"}]
Top sensitive: A-07, job volume, parts cost. Test A-07 with a two-month pilot by offering the plan to the next 50 repair customers and recording how many accept. Rule: change the register first, then the forecast, and keep a version log; assumptions without evidence stay low confidence.

• assumptions