Sales Pipeline Hygiene Checklist
Every rule scored on the win-rate gap between the deals that pass it and the deals that fail it.
Free download · No account needed
Eight rules, 1,064 closed deals, one column that decides. Base win rate 31.4%.
The bottom two are on every hygiene checklist published and had been enforced here for three years. They fire on more than half the book and separate by roughly one point. The top three separate by more than twenty and none of them can be computed from a report the platform ships with.
Every pipeline hygiene checklist published is a list of fields that should not be blank. Next step, contact role, amount, close date in the future. Circulate it, nudge the reps, watch the completeness percentage climb past ninety. The percentage climbing is not the same thing as the forecast getting better, and on a real book those two numbers move independently. Nobody publishes the win-rate gap behind any of those rules, which is the only number that would say which ones matter.
Ravensgate Software scored all eight rules it enforced against four quarters of closed deals. Next step blank fired on 51.3% of the open book and separated win rates by 1.2 points. Contact role fired on 55.2% and separated by 0.6. Both had run for three years. The three rules that separated by more than twenty points were invisible to every standard report. The largest was a date nobody typed: a deal created in an open stage with no close date gets the last day of the month.
So this pack scores each rule before anybody enforces it, and the three survivors read field history rather than field values. A close date with an edit count of zero. Every push counted, not only the ones crossing a reporting boundary. Staleness at twice each stage's own median. Reweighted at their measured rates, a $9.4M commit becomes $6.57M. It pairs with the forecast call that reads that number, with reconciling a rep's own sheet against the export and with two revenue views that agree.
What the pack does that a hygiene checklist does not
Scores every rule on measured win-rate separation
Each rule applied retroactively to four quarters of closed deals, evaluated at the state each deal was in while open. The gap between the pass side and the fail side is the score, and everything else is context.
Cuts the rules that measure tidiness, by name and with the number
Next step blank at 1.2 points on 51.3% of the book. Contact role at 0.6 points on 55.2%. Both stay on the standard permanently with their figures, because a retired rule with no number beside it comes back.
Proves a close date was written by the platform, not a person
Two conditions joined: the date equals the last day of the creation month, and the field has no edit recorded against it, ever. That is certainty rather than inference, and it was 37.2% of open pipeline value.
Counts every push, not just the ones crossing a boundary
A platform push report only fires when a date leaves the selected timeframe, so 65.7% of the pushing here was invisible to it. Counting edits in the field history turns a binary flag into a curve from 44.3% down to 5.6%.
Sets staleness from each stage's own median
A flat thirty-day rule fired on 561 deals, 339 of them in stages where thirty days is normal, and missed 17 negotiation deals worth $1.18M stuck past twice their stage's median. Those 17 were the urgent ones.
Corrects the forecast as a union rather than a sum
The defect sets overlap, so the correction subtracts the intersections: 128 deals worth $6.98M against a naive sum of 193. Reweighted at measured rates, a $9.4M commit becomes $6.57M.
Reports completeness by value beside completeness by count
The team read 88.7% complete by record count and 61.6% by value. One rep sat at 94.0% by count and 38.0% by value, which by count made them look like the second cleanest book on the team.
How the pack runs
- 1
Send the exports
The open deal or opportunity export, closed deals from the trailing four quarters, and the close-date and stage field history. The history is the one worth pushing for, because two of the three rules that matter cannot be computed without it.
- 2
Score every rule against closed history
Candidates and the rules already in force together, each with the share of the book it fires on and the win-rate gap it produces. The cut list usually arrives first and is the most immediately useful output.
- 3
Run the three that survive
Defaulted close dates proven from an edit count of zero, pushes counted per deal and bucketed with their win rates, and per-stage staleness thresholds computed from each stage's own median dwell time.
- 4
Correct the commit and brief the managers
The union of the flagged deals reweighted at their measured rates, then one list per manager sorted by value at risk, with platform-written values routed upstream instead of onto a rep's list.
Frequently asked questions
Why cut the next-step rule? Everyone enforces it.
Because it fired on 51.3% of the open book and separated win rates by 1.2 points. Deals with a blank next step closed at 30.8% against 32.0% for deals with one. There may be good reasons to want the field filled in. Forecast accuracy measurably is not one of them.
How can a close date be wrong if nobody touched it?
That is exactly why it is wrong. HubSpot documents that a deal created in an open stage with no close date gets the last day of the month. Nobody committed to it. Every rule checking whether the close date is in the future passes all 468 of them, which was 37.2% of open value here.
Our CRM already reports pushed deals. What is missing?
Two things. The report's own definition only counts a close date moving from inside the selected timeframe to outside it, so three pushes inside one quarter show as nothing. And it is binary, so one push and six pushes arrive identical when they close at 33.2% and 5.6%.
Why not just use a 30-day stale rule?
It fails in both directions at once. Ours fired on 561 deals, 339 in stages where thirty days is normal, so nobody read the list. It also missed 17 negotiation deals worth $1.18M past twice the eleven-day median for that stage. No single threshold serves a 34-day stage and an 11-day stage.
Does this work on Salesforce, or only HubSpot?
Both, and any warehouse table holding field history. The three mechanisms are platform independent: an edit count of zero on a date field, a count of edits moving a date later, and time in stage against that stage's own median. Confirm your platform's creation default before trusting the month-end match.
We do not have field history. What can still be done?
Current-state findings, and the pack says plainly which two rules are blocked rather than producing a thinner pass and calling it complete. Field histories are commonly retained for a fixed window, so check yours before trusting a four-quarter figure. A CRM audit covers what the export alone can show.
Is this the same as fixing our stage definitions?
Different job, and worth doing first if your stages have no exit criteria, because a median dwell time per stage means nothing while reps disagree on what each stage is. That is lifecycle stage definition work. This pack takes the stages as they are and measures against them.
Find out which of your hygiene rules earn their place
Send an opportunity export, four quarters of closed deals and the close-date field history, and get every rule scored on what it separates.
Get the template