The reason this matters is that CRM stage probabilities do not decay. A deal in negotiation reports the same likelihood in week 40 as it did in week 4. That gap between the static number and the decaying reality is where inflated forecasts come from.
How the curve is built
At ORM each opportunity is grouped by a machine learning model, and each group gets a predicted curve for how long deals in that group take to close. Those curves run from 1 to 80 weeks. Most of the expectation lands before week 12, and very few groups carry any expectation past 52 weeks. ORM applies a 12 month rule for most customers on top of the curves.
Reading a deal against its own group's curve is the whole exercise. A 20 week old enterprise deal in a group whose expectation extends to week 30 is on track. A 20 week old deal in a group that peaks at week 8 has decayed, even though both records look identical in a stage report.
Activity is the second input
ORM counts meaningful activity as a change in stage, close date, or amount. Deals that age without any of those three changes decay fastest, because absence of movement is the earliest available warning. ORM identifies the lack of a signal as the earliest slippage indicator: no activity, no data changing, no notes.
| Deal state | What decay looks like |
|---|---|
| Inside its group's window, moving | Probability holding, no action needed |
| Inside its window, no field changes | Early decay, inspect before the date arrives |
| Past its window, close date pushed | Steep decay, downgrade the forecast category |
| Past 12 months, untouched | Effectively dead, remove from the forecast |
Using decay instead of arguing about it
Decay converts a subjective pipeline debate into a data question. Instead of asking a rep whether a deal is still alive, compare its age against the closing curve for its group and look at whether anything on the record has changed.
Teams that apply decay systematically stop counting aged opportunities toward pipeline coverage and see their forecast accuracy improve without changing anything about how reps sell. It also gives an honest read on deal slippage, because a decayed deal that slips was never a candidate for the period it was assigned to.
Frequently Asked Questions
What is deal decay?
Deal decay is the loss of close probability that happens as an opportunity ages. It is not the same as a deal being lost. The record stays open and the forecast still counts it, but the real odds of it closing have dropped well below the stage probability the CRM assigns.
How do you measure deal decay rate?
Group historical opportunities by the attributes that drive cycle length, such as segment, product, and source, then plot the share of each group that closed won by week since creation. The slope of that curve after its peak is the decay rate. Any open deal past the peak of its group's curve is decaying.
Why does stage probability fail to capture decay?
Stage probability is static. A deal sitting in negotiation at 80% keeps reporting 80% whether it entered the stage last week or eleven months ago. Age carries information that stage does not, and a model that ignores it will overstate the pipeline every time.
At what age should a deal be removed from the forecast?
Use the closing window of comparable deals rather than a single company-wide number. ORM applies a 12 month rule for most customers, and its predicted close curves run from 1 to 80 weeks with most of the expectation landing before week 12. A deal well past the peak of its own group's curve belongs out of the forecast.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like deal decay rate into prescriptive action for your team.
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