How to Calculate Pipeline Aging and Find Stale Deals

How do you calculate the average age of open pipeline?

Subtract each open opportunity's creation date from today, sum the days, and divide by the number of open opportunities.

``` Average pipeline age = Sum of (today - creation date) across open deals / Count of open deals ```

Run a value-weighted version alongside it by multiplying each deal's age by its value, summing, and dividing by total pipeline value. The two numbers usually differ, and the difference is informative. When the value-weighted age runs well above the count-based age, your largest deals are your oldest deals, which is a specific risk worth naming.

Neither figure is useful as a single headline. Average age compresses a distribution that matters, and the distribution is where the decisions live.

Which date should you measure from?

Measure two clocks: days since creation and days since the last meaningful change.

Age from creation tells you how long a deal has occupied the funnel. Days since meaningful activity tells you whether it is still a live deal. They diverge constantly, and the second one carries more predictive weight.

Meaningful activity means a change in stage, close date, or amount. Those three fields represent deal movement. Logged calls, emails, and meeting notes represent seller effort, which is worth knowing but is not the same thing. A deal with 40 logged touches and no change to any of the three core fields in four months has not progressed, and counting the activity log as evidence of life is how stale pipeline survives review after review.

A 300-day-old opportunity that changed stage last week is healthy. A 90-day-old opportunity with no field change since creation is not.

What does a pipeline aging report look like?

Bucket open pipeline by days since last meaningful activity, then attach a treatment to each bucket. The bottom three rows total 20% of pipeline value in this example. Coverage calculated on the full $16,700,000 reads meaningfully higher than coverage calculated on the $13,300,000 that is actually in motion. Across ORM customers, 10% or more of open pipeline has shown no activity in 12 months, so the over-365 row is rarely empty.

Report the buckets by rep as well. Aged pipeline is usually concentrated rather than evenly spread, and the concentration points at where the cleanup work belongs.

When should a deal be considered stale?

A 12-month rule holds for most B2B SaaS pipelines, and shorter windows apply to faster segments.

The reasoning comes from close curves rather than from a round number. When opportunities are grouped by their shared characteristics, each group carries a predictable curve for how long it takes to close. In ORM models those curves run from 1 to 80 weeks, with most of the expectation landing before week 12 and very few groups showing expectation past 52 weeks. A deal sitting past the point where its group's curve has effectively flattened is not slow. It is finished.

That gives you a defensible threshold. For a segment where most closes happen inside 12 weeks, a 12-month rule is generous. For a long enterprise motion, 12 months may be reasonable. Set the threshold per segment against your own close history rather than applying one number across a pipeline that contains several different motions.

How much does aged pipeline distort your coverage?

Proportionally, and it inflates every metric built on the same denominator.

If 12% of open pipeline is stale, a reported 3.5x coverage is really 3.1x. Weighted pipeline is overstated by the same share. Sales velocity is overstated twice, once through an inflated opportunity count and again through a stretched cycle length, since aged deals eventually close as losses with enormous day counts attached.

The correction is to filter before you calculate rather than adjusting afterward. Exclude anything past your stale threshold from every pipeline metric, then report the excluded value separately so nobody thinks it vanished. A coverage ratio that never mentions its aging filter is not comparable across quarters, since the filter is doing silent work. The base metric is defined in pipeline coverage, and the argument against reading it without context is in why the 3x pipeline coverage rule is wrong.

Why does pipeline age instead of closing?

Because deals stall for buyer-side reasons that never produce a CRM field change, and nobody is required to close them out.

The buyer loses budget, the champion changes roles, a competing priority takes the quarter, or the evaluation quietly ends without anyone saying so. From the seller's side the pattern is recognizable: emails stop being returned, calls stop being taken, and the last real conversation was months ago. The opportunity stays open because marking it lost feels like a decision and leaving it open does not.

Compensation design contributes as well. When pipeline volume is measured and pipeline quality is not, closing out a dead deal has a cost and leaving it open does not. Fix the incentive and most of the aging problem resolves itself.

What is the operating process for aged pipeline?

Run a monthly cleanup with a fixed rule, and make the rule automatic rather than discretionary.

Pull every open opportunity with no change to stage, close date, or amount inside your threshold. Give the owning rep a short window to either produce a documented buyer-side next step or close the record out. Absent that, close it automatically. Deals that come back to life can be reopened, and reopening a real deal costs far less than carrying a hundred dead ones.

Then measure the result. Track stale share of pipeline as a standing metric next to coverage, and watch whether it rises between cleanups. A stale share that climbs steadily during a quarter is an early sign that deals are stalling faster than they are being worked, and that signal shows up before win rate or forecast accuracy reacts to it. Feeding a clean pipeline into the model is the precondition for everything in sales forecasting best practices.