AI use case · Is your data ready for revenue forecasting?A forecast is only as honest as the deals behind it.
Forecasting tools learn from your pipeline history. If Closed Won in the CRM doesn’t match what finance invoiced, the model learns a version of your revenue that never happened, and forecasts it with confidence.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What revenue forecasting needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostAccuracyConsistencyConnectivityThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where revenue forecasting breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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CRM ↔ Billing
Closed Won deals with no matching invoice
What it does to the AIThe model learns from wins that never became revenue, so it over-forecasts.
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CRM ↔ Finance
Deal amounts that differ from what was billed after discounts and credits
What it does to the AIThe forecast is in bookings; the board reads it as revenue.
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Spreadsheets ↔ Finance
A forecast adjusted by hand in a spreadsheet nobody versioned
What it does to the AIThere is no honest history of past forecasts to measure a new one against.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
Which number do you treat as revenue: the deal amount, or the invoice?
If it’s the CRM amount, you are forecasting bookings, and the first person to compare it with the ledger will lose faith in it.
- Q2
How do you handle deals whose close date moved?
A model that only sees the final close date learns that deals close faster than they do.
- Q3
What accuracy did you measure, on whose data, against what baseline?
Ask for the comparison with a simple run-rate forecast. If they haven’t run one, that is your answer.
- Q4
What happens when we change our sales process?
A stage redesign breaks the history the model learnt from. Ask how, and how quickly, it re-learns.
How a Truth Score Read tests it
Revenue forecasting, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Match won deals to invoices
Every Closed Won opportunity in your CRM export, matched to your billing or ledger export by ID, name and domain. The ones that don’t match are usually the first red flag.
- 02
Test the history
How far back the pipeline goes, how often close dates moved, and whether stage definitions changed part-way through.
- 03
Rebuild the reported number
If there is a board pack, whether its revenue figure can be reproduced from the ledger export alone.
- 04
Give the verdict
A Leverability read on the forecasting proposal (which AI ideas you can use now, after fixes, or not at all), with the records that decide it.
Two systems are enough to start. We send the exact field list after the call.
How the Truth Score Read worksWhat you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Revenue forecasting, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callHow much history does AI revenue forecasting need?
Enough complete cycles to show your seasonality and your real sales cycle; as a rule of thumb, two to three years. Consistency matters more than length: three years with a stage redesign in the middle can be worth less than eighteen clean months.
Why compare the CRM with finance for a forecast?
Because the CRM records what sales believed and finance records what was billed. If the two disagree, a model trained on the CRM learns optimism. Matching won deals to invoices across the two exports shows how far apart they are before you buy a tool that assumes they agree.
Can we forecast if our pipeline is stale?
You can, but the forecast inherits the staleness. Open deals that are already dead inflate the pipeline and teach the model the wrong win rate. We measure how much of the open pipeline is past its close date or untouched, so you know what the forecast is standing on.
Will you tell us which forecasting tool to buy?
No. We sell no software and take no referral fees from anyone. We tell you whether your data can support the tool you are considering, and what has to change first.
Is your data ready for revenue forecasting? Find out before you sign.
Exports from two or more systems in. A signed verdict on this use case within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.