Stage History and Velocity
Every time a record changes stage, the move is recorded: which stage it left, which it entered, when, how long it spent there, and who moved it.
Where to find it
Architect Panel → Commercial:
- Boards & Pipeline — the pipeline that generates the history
Architect Panel → ERP - Trading & Analytics:
- Analytics — reporting over the accumulated history
What is recorded
- The board and the record.
- The stage moved from and to.
- When it entered and exited.
- Days in stage.
- Who changed it.
It accumulates automatically. There is nothing to switch on and nothing for anybody to fill in.
Why duration is the useful number
Conversion rates tell you where deals are lost. Durations tell you where they stick — which is usually a different stage and a more fixable problem.
A stage with a high conversion rate and a 40-day average is not working well: those deals close eventually, and the delay is capital, capacity and forecast accuracy.
What to look at
- Average days per stage — where does time actually go?
- The distribution, not just the average — a stage averaging 12 days made of mostly-3 and a few-60 is two different processes.
- Backwards moves — a card returning to an earlier stage means something was misjudged, and a pattern of it means the entry criteria are unclear.
- Time to won versus time to lost — if losing takes longer than winning, you are spending your most time on deals you do not get.
The most valuable question
How long does a deal you eventually lose spend in the pipeline before you admit it? In most organisations the answer is uncomfortable, and it is the single best argument for qualifying harder.
You can only ask it because losing is an explicit stage with recorded history.
Use it to set expectations
Once you know a typical deal spends three weeks in Proposal, a deal sitting there for eight is visibly stuck rather than a matter of opinion. That turns pipeline reviews from judgement into observation.
It also validates your probabilities
Stage history plus won and lost flags gives you the actual conversion rate from each stage. Compare that against the probabilities you configured, and correct them. Most pipelines are set up once with estimated figures and never revisited.
Report on it properly
The history is a datastore like any other, so Analytics can report over it — average days by stage, by owner, by period. Build that once and it answers the same questions every quarter.
Worked example
Six months of history shows deals averaging 9 days in Qualified, 31 in Proposal and 6 in Negotiation. Proposal is the constraint, and the cause turns out to be waiting for a technical sign-off nobody owned. Assigning that step cuts the average to 12 days. Separately, the data shows only 22% of Qualified deals are won against a configured probability of 40% — so the forecast had been overstated by nearly half.
Recommendations
- Look at durations, not just conversion.
- Check the distribution, not the average alone.
- Compare actual conversion against configured probability and correct it.
- Measure how long losing takes.