Every pipeline has a layer of opportunities nobody has touched in a long time. They carry value, they count toward coverage, and they are not going to close.
The share is larger than most teams assume. It varies by customer, but more than 10 percent of pipeline has typically not been touched in 12 months.
What untouched means
The definition does the work here. Meaningful activity means a change in stage, close date, or amount.
Not a logged call. Not an email. Those record effort, and effort is easy to generate against a deal that is never going to move. A deal can accumulate touches for a year while sitting in the same stage, at the same amount, and satisfy any activity-based hygiene rule you write. See what counts as meaningful deal activity.
Defining staleness on the three fields that record progression is what makes the measurement mean something.
Why 10 percent matters more than it sounds
Stale pipeline is not inert. It occupies the numerator of every ratio built on pipeline value.
| Metric | Effect of a 10 percent stale share |
|---|---|
| Pipeline coverage | Overstated by roughly the stale share |
| Weighted forecast | Stage weights applied to dead deals |
| Close-probability model | Curves flattened by a long dead tail |
| Rep capacity planning | Territory potential overstated |
Sizing it in your own data
Straightforward, and usually a surprise the first time.
1. For every open opportunity, find the date of the last change to stage, close date or amount. Not the last activity date. 2. Bucket by age since that change: under 90 days, 90 to 180, 180 to 365, over 365. 3. Sum the value in each bucket and express as a share of total open pipeline. 4. Recompute coverage excluding the over-365 bucket and compare it to the number you have been reporting.
The gap between those two coverage figures is the honest measure of how much your reporting has been overstating the quarter.
Setting the threshold
Twelve months is the rule ORM applies for most customers, and it is defensible because close curves rarely carry meaningful expectation past 52 weeks. Very few deal groups do.
It is also too generous for a fast transactional motion. Derive your own from your close curves rather than adopting it, and set it separately for new business, expansion and renewal, since they do not share a shape. See the 12-month opportunity aging rule.
What to do with what you find
Classify rather than delete. Marking an opportunity stale removes its value from coverage reporting, excludes it from close-probability modeling, and produces a working list.
That list then needs a decision per deal, and only two answers are acceptable. Revive it with a real next step and a validated close date, or close it out. Leaving it open because nobody wants to be the person who removed it is how the layer accumulated in the first place.
Expect resistance. Stale pipeline makes coverage look better, and removing it makes a number worse without changing any underlying reality. The counter is that the reality was already what it was, and the reporting was the only thing that changed. For definitions see stale pipeline and pipeline hygiene.
Frequently Asked Questions
How much stale pipeline is typical?
It varies by customer, but more than 10 percent of pipeline has typically not been touched in 12 months. That share still counts toward coverage at full value, which is one reason coverage overstates how well resourced a quarter is.What counts as untouched?
No change in stage, close date, or amount. Logged calls and emails are activity in the CRM sense, but they do not indicate the deal moved, so a rule built on them can be satisfied without any progression.Should stale pipeline be deleted?
No, classified. Marking it stale removes it from coverage reporting and from close-probability modeling, and produces a working list for a deliberate decision to revive or close. Deleting removes the record you would learn from.How do I size stale pipeline in my own data?
For every open opportunity find the date of the last change to stage, close date or amount, bucket by age since that change, sum the value in each bucket, then recompute coverage excluding the over-365 bucket and compare it to the number you have been reporting.Why does stale pipeline distort the model as well as the ratio?
Because a deal group containing a large dead tail produces a longer, flatter close curve than the live deals in it actually have, so every remaining deal inherits a pessimistic timing estimate.Why do teams resist removing it?
Because stale pipeline makes coverage look better, and removing it makes a number worse without changing any underlying reality. The reality was already what it was, and only the reporting changed.Frequently Asked Questions
How much stale pipeline is typical?
It varies by customer, but more than 10 percent of pipeline has typically not been touched in 12 months. That share still counts toward coverage at full value, which is one reason coverage overstates how well resourced a quarter is.
What counts as untouched?
No change in stage, close date, or amount. Logged calls and emails are activity in the CRM sense, but they do not indicate the deal moved, so a rule built on them can be satisfied without any progression.
Should stale pipeline be deleted?
No, classified. Marking it stale removes it from coverage reporting and from close-probability modeling, and produces a working list for a deliberate decision to revive or close. Deleting removes the record you would learn from.
How do I size stale pipeline in my own data?
For every open opportunity find the date of the last change to stage, close date or amount, bucket by age since that change, sum the value in each bucket, then recompute coverage excluding the over-365 bucket and compare it to the number you have been reporting.
Why does stale pipeline distort the model as well as the ratio?
Because a deal group containing a large dead tail produces a longer, flatter close curve than the live deals in it actually have, so every remaining deal inherits a pessimistic timing estimate.
Why do teams resist removing it?
Because stale pipeline makes coverage look better, and removing it makes a number worse without changing any underlying reality. The reality was already what it was, and only the reporting changed.
See how ORM turns these insights into action
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