Analysis / Sales pipeline and forecasting
Find deals that keep slipping into later quarters
Use Proforma to find deals that keep slipping into later quarters. Get deal slippage review table from your business data with this copyable prompt.
Sales leader · Revenue operations manager
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Your starting prompt.
Identify opportunities whose expected close dates moved repeatedly during [period]. Count pushes, total delay and changes in forecast category, then group the exposure by owner and deal size. Show the highest-value slipping deals with their date history and compare historical outcomes for similar cases. Keep date corrections separate from genuine slips where change reasons exist, and prepare a forecast review list.
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Method and review
Keep the assumptions visible.
Use a dated pipeline snapshot and stage history. State the target period and probability method, and distinguish expected pipeline from committed revenue.
For this task, check
A corrected data entry may look like slippage unless the change reason is available.
Before you begin
- Uploaded exports or permitted Data covering the listed inputs
Work with permitted files or queryable data. Verify the result against the original records.
Synthetic illustrative example
See the method in practice.
Synthetic input. Illustrative output checked locally; this is not a live Proforma run or customer result. Close-date history only; historical outcomes and reasons are available for one correction, not for prior won/lost cohorts.
These invented records explain the method. They are not a recorded Proforma run.
| Deal | Original close | Later dates | Recorded reason |
|---|---|---|---|
| D-1 | 2026-09-30 | 2026-12-31 → 2027-03-31 | No reason supplied |
| D-2 | 2026-09-30 | 2026-10-31 | Customer review delay |
| D-3 | 2026-09-30 | 2026-09-29 | Date-entry correction |
| Deal | Later pushes | Net delay | Review |
|---|---|---|---|
| D-1 | 2 | 182 days | Repeated slips; ask owner |
| D-2 | 1 | 31 days | Known review delay |
| D-3 | 0 | −1 day | Correction, not a later slip |
Method
Count moves to a later expected close date; net delay compares latest with original date in UTC calendar days. Keep an explicitly recorded correction separate. Historical outcome comparison requires additional won/lost histories.
What the result tells you
D-1 belongs in the forecast review queue. Repeated slippage signals uncertainty, not proof the deal will be lost. Date records cannot explain unrecorded buyer intent.
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Make it your investigation.
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