How a sales leader measures slip count from weekly pipeline snapshots: the number of times each open deal's close date has been pushed, the close rate by slip count from the team's own history, the weighted forecast that discounts multi-slip deals by that rate, the reps whose pipeline carries the most slips, and why a deal that slipped three times is a different object from a deal that slipped once.
A deal has been in the commit for three quarters. Each quarter its close date moved to the next. Each quarter it was counted at 75 percent. The weekly pipeline snapshots know exactly how many times it moved, and the team's own history knows what that means. This guide sets out slip count, the close rate by slip count, the discounted forecast, and the per-rep list.
Per deal:
Slip count = number of weeks in which the close date moved to a later period than the prior week's Slip age = weeks since the first slip
From history, per slip count:
Close rate = deals that closed won ÷ deals that reached an outcome, among deals with that slip count at the same point
Per rep:
Forecast at CRM probability, forecast at slip-adjusted probability, and the difference
Opportunity and rep identifiers only. A CRM with no snapshot history cannot produce this; a weekly export to a file can.
From four quarters of history.
| Slip count | Deals | Closed won | Close rate |
|---|---|---|---|
| 0 | 620 | 291 | 47% |
| 1 | 340 | 102 | 30% |
| 2 | 150 | 24 | 16% |
| 3+ | 110 | 6 | 5% |
A deal that has slipped three times closes at a ninth of the rate of a deal that has never slipped. The CRM has both at 75 percent.
| Rep | Open deals | CRM-weighted forecast | Slip-adjusted forecast | Difference | Deals slipped 3+ | Value in them |
|---|---|---|---|---|---|---|
| R-04 | 31 | $1.20m | $0.74m | $460,000 | 6 | $610,000 |
| R-11 | 22 | $0.90m | $0.81m | $90,000 | 1 | $80,000 |
| R-17 | 14 | $0.75m | $0.71m | $40,000 | 0 |
Rep R-04's forecast is carrying six deals that have slipped three or more times, worth six hundred thousand dollars at face and thirty thousand at the team's own close rate for that count. The conversation is about those six deals by name.
Σ deals' CRM-weighted value = CRM forecast Σ deals' slip-adjusted value = slip-adjusted forecast
And every open deal has a slip count, including zero. A deal in the snapshot with no prior snapshot is new, and its count starts this week.
No snapshot history. The CRM overwrites the close date and the slip is gone.
In-period moves counted as slips. Every deal slips; the table is flat.
Close rate from a benchmark. This team's deals slip for this team's reasons.
Adjusted forecast applied silently. Show both, and the difference per rep.
Mapped once, the weekly pipeline snapshots and the outcomes produce slip counts, the close-rate table, both forecasts and the per-rep list every week. Covirage builds this from the exports as they are. The forecast analysis solution describes the setup, and the forecast bias guide covers the rep-level calibration that slip count explains much of.
It is recorded, and the report shows in-period and out-of-period slips separately. A move from the 10th to the 24th of the same month is a scheduling change; a move from March to June is the kind that predicts. The close-rate table uses out-of-period slips by default, stated.
Weekly snapshots over at least four quarters, so that the close-rate-by-slip table is built on deals that have reached an outcome. Until then, the slip count is shown per deal and the discount is not applied.
One slip is normal. The table says what the second and third mean at this team. The point is not to punish the slip but to stop a deal that has slipped three times being carried in the forecast at the same probability as one that has never moved.