The distinction matters because the two numbers drive opposite responses. High slippage with high recovery is a close date accuracy problem, and the fix is dating discipline. High slippage with low recovery is a qualification problem, and the fix sits much earlier in the funnel.
Building the cohort
Freeze a list of every open deal carrying a close date inside a period, then follow that list forward until every deal has resolved as won or lost. Deals still open at the time you run the report belong in a separate bucket, because counting them as failures understates recovery and ignoring them overstates it.
| Cohort segment | What it reveals |
|---|---|
| Slipped once, closed won in the next period | Timing error, dating discipline problem |
| Slipped twice or more, closed won | Long buying cycle mismatched to your stage model |
| Slipped and closed lost | Qualification failure, look at entry criteria |
| Slipped and still open past 12 months | Effectively dead, ORM applies a 12 month rule here |
Why recovery falls with every push
ORM identifies the rep changing a close date as the best available slippage signal, and a deal that moves from one quarter into the next is less likely to close even when it sits in commit. Each additional push compounds that. The buyer that could not decide in the first window is now further from the trigger that started the evaluation, the champion has had more time to change roles, and the budget that was allocated has had more time to be spent elsewhere.
The earliest warning is the absence of a signal rather than the presence of a bad one. ORM points to no activity, no data changing, and no notes on the record as the first thing to look for. A slipped deal with a silent record is not recovering.
Using recovery to set forecast policy
Once you know your recovery rate by segment, the treatment of slipped deals becomes mechanical. A segment that recovers well can keep its slipped deals in the next period's forecast at a discounted value. A segment that rarely recovers should have slipped deals dropped from the forecast and reworked as new opportunities.
That policy does more for forecast accuracy than tightening the commit call, because it removes the argument entirely. It also gives an honest read on win rate, since a pipeline carrying many unresolved slipped deals reports a win rate that has not been settled yet. Pair the number with your deal slippage trend and you can see both how much revenue is leaving each quarter and how much of it is coming back.
Frequently Asked Questions
How do you calculate slipped deal recovery rate?
Identify every open deal that carried a close date inside a period and did not close in that period. Follow that cohort forward until each deal has resolved, then divide the number that closed won by the size of the cohort. Give the cohort enough time to resolve fully, otherwise unresolved deals will inflate the result.
How is recovery rate different from slippage rate?
Slippage rate counts how many deals leave a period. Recovery rate tells you what happened to them afterward. A team can have a high slippage rate and still hit its annual number if most slipped deals close one period later, and a team with modest slippage can be in serious trouble if almost none of its slipped deals ever come back.
Do slipped deals close at their original value?
Usually less. A pushed deal gives the buyer more time to negotiate and more chances for the business case to lose its sponsor. ORM's point is that most deals close for less than the value carried in the CRM, so recovery should be measured in dollars as well as in deal count.
What recovery rate should a team expect?
Build the number from your own closed history rather than borrowing one. What holds is the direction. ORM finds that a deal which slips from one quarter to the next is less likely to close even when it sits in commit.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like slipped deal recovery rate into prescriptive action for your team.
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