Deal age distribution

Deal age distribution is the breakdown of your open pipeline by how long each deal has been alive, grouped into age bands such as 0 to 30 days, 31 to 60, 61 to 90, and 90 plus. Rather than reporting a single average age, it shows the shape of the pipeline so you can see at a glance how much of your forecast rests on fresh, active deals versus old ones that have stopped moving. A pipeline can show a healthy total value while half that value sits in deals older than 90 days.

It matters because deal age is one of the most reliable predictors of whether a deal will close. Opportunities that move steadily through stages tend to convert. Opportunities that stall and age in place rarely recover, yet they keep inflating the pipeline and the forecast until someone removes them. Watching the distribution, not just the headline number, is how you separate a real pipeline from a padded one.

The distribution is also a coaching and process tool. A cluster of deals piling up in one age band often points at a specific bottleneck: a procurement step that always drags, a stage with no clear exit criteria, or a rep holding onto dead deals to keep their pipeline looking full. The shape tells you where to look.

Deal age distribution is built from a simple per-deal calculation rolled up into bands. The work is less in the arithmetic and more in choosing bands that match your sales cycle, so the distribution actually distinguishes healthy deals from stalled ones.

1. 1

   Calculate each deal age

   Subtract the creation date from today for every open opportunity. Use only open deals: closed-won and closed-lost belong in win rate and cycle length analysis, not in a distribution of live pipeline.

2. 2

   Set age bands to your sales cycle

   Choose bands relative to your typical cycle length. A 30 day cycle needs tight bands such as 0 to 15 and 16 to 30. A six month enterprise cycle needs wider ones. Bands that ignore your cycle make every deal look either fresh or ancient.

3. 3

   Count and value each band

   For each band, total both the number of deals and their combined value. Value matters more than count: ten small stale deals are a tidiness problem, one large stale deal is a forecast problem.

4. 4

   Compare against expected cycle length

   Flag any band that sits well beyond your average sales cycle. A deal older than your typical cycle has, by definition, already missed the path most won deals take.

5. 5

   Segment the distribution

   Split the bands by rep, segment, and source. A distribution that looks fine in aggregate can hide one rep whose pipeline is entirely stale or one lead source that never progresses past the first month.

Read the result as a shape, not a single figure. A healthy distribution is weighted toward the younger bands with a tail that thins out as age increases. A pipeline bulging in the oldest band is carrying dead weight that will eventually be purged, taking the forecast down with it. The earlier you see that bulge, the earlier you can act on it.

A metric tree decomposes deal age into the stages where time accumulates and the causes that make deals sit there, then connects each cause to the person who can clear it. This turns an ageing pipeline from a worry into a list of specific, owned actions.

The first level splits age by where the time is being spent: early-stage drag, mid-stage drag, and late-stage drag. Each branch then decomposes into the real reasons deals slow down. Early drag is usually weak qualification or slow first response. Mid drag is often a missing champion or a stalled evaluation. Late drag is typically procurement, legal, or budget approval sitting outside the rep control.

This structure lets you diagnose the bulge rather than just notice it. A pile-up in the 90 plus band might be deals that should never have been qualified in, or genuine deals trapped in legal review. Those are opposite problems with opposite fixes, and they belong to different owners. The tree forces you to name which one you are looking at.

Healthy deal age depends entirely on your sales cycle, so the bands below are expressed relative to your own typical cycle length rather than as fixed day counts. A deal at half your cycle length is on track, a deal at twice your cycle length is almost certainly stuck. Read the distribution against your cycle, not against another company.

A simple health check is the share of open pipeline value sitting beyond 150 percent of your cycle length. If it is in low single digits, your pipeline is fresh and your forecast is credible. If it climbs toward a quarter of pipeline value, the forecast is resting on deals that statistically will not close, and a clean-up is overdue.

Improving deal age distribution means shifting the weight of the pipeline toward the younger bands and keeping the old bands clear. That comes from two things at once: helping live deals move faster, and being honest about the ones that have stopped. Both are needed, because faster deals without honest hygiene just hides the problem.

The metric tree approach starts by reading where in the distribution the value is trapped and which branch is responsible, because clearing late-stage procurement drag and fixing weak early-stage qualification are entirely different jobs owned by different people.

KPI Tree lets you model this by connecting each age branch to the team and the action that moves it. Reps own qualification rigour and pipeline hygiene. Sales leadership owns stage exit criteria and the stale deal review cadence. Deal desk owns the procurement and legal drag at the bottom. With RACI ownership on each node, the accountable owner is named, and when the oldest band starts growing the alert reaches them rather than surfacing in a forecast review three weeks too late. The verified impact loop then checks whether the new exit criteria actually pulled the distribution toward the younger bands, so you know the change worked.

1. 1

   Reporting average age instead of the distribution

   A single average hides the shape. A pipeline with a healthy average can still be carrying a heavy tail of stale deals, and the average will not show it until they are purged and the forecast drops.

2. 2

   Using bands that ignore the sales cycle

   Fixed day bands borrowed from another team make a fast pipeline look ancient or a slow one look fresh. Set bands relative to your own cycle length or the distribution tells you nothing useful.

3. 3

   Counting deals instead of value

   Ten tiny stale deals matter far less than one large one. Weight the distribution by value so you focus attention on the deals whose age actually threatens the forecast.

4. 4

   Confusing deal age with time in stage

   An old deal that just reached the final stage is healthy. Tracking only total age flags it as a problem when the real signal is how long it has sat in its current stage.

5. 5

   Letting stale deals linger to pad pipeline

   Deals kept open to make a pipeline look full distort every downstream number. Without a hygiene cadence and an owner, the oldest band keeps growing and quietly erodes forecast accuracy.