Slip count

The number of times an open deal's close date moved to a later period, from weekly pipeline snapshots. The team's own close rate by slip count discounts multi-slip deals in the forecast; a deal that slipped three times closes at a fraction of the rate of one that never moved.

DefinitionThe number of times an open deal's close date moved to a later period, from weekly pipeline snapshots. The team's own close rate by slip count discounts multi-slip deals in the forecast; a deal that slipped three times closes at a fraction of the rate of one that never moved.

Out-of-period slips

A move within the month is scheduling. A move to the next quarter is the kind that predicts.

Needs snapshots

A CRM overwrites the close date. A weekly export keeps the history.

How it is computed

The number of times an open deal's close date has moved to a later period, from weekly pipeline snapshots.

Example

Deals that slipped twice or more closed at 9 percent on this team, against 31 percent for those that never slipped.

Where it goes wrong

The close date overwritten with no history. A deal pushed five times looks the same as a new one.

Articles that use this term

Coverage and territory · Sales teams

A new rep's first ninety days, from the data: the list that replaces shadowing

What a newly hired rep should be given on day one from the book they inherit: the accounts ranked by revenue with days since last touch, the returning lines that were bought and dropped, the dormant accounts with their cadence and run rate, the open deals with slip counts, and the tier cadence that says who to call first. A ninety-day plan in three lists, the measures that say whether the ramp is on track, and why it beats a week of shadowing.

16 Sept 20263 min read AI and self-service analytics

Account health score: why a computed score beats a colour-coded one

What is wrong with the red-amber-green account health field most CRMs carry, what a computed health score is made of, five inputs from the ledger and the CRM with stated weights, how the weights are set from the company's own churn history rather than opinion, why the score is shown with its inputs and never alone, the identity that every account has one score from the same rule, and how the score is validated against what happened.

16 Sept 20263 min read How-to guides

How often should sales data be refreshed? A cadence per measure, not one for the tool

Why 'real-time' is the wrong answer to how often sales data should be refreshed, the cadence each measure actually needs, weekly for coverage and pipeline, monthly for share of wallet and concentration, quarterly for norms and segments, annually for definitions, what a refresh more frequent than the measure's cadence does to the reader, and the export schedule that follows.

16 Sept 20262 min read Forecast and pipeline · Finance and FP&A teams

How to read a forecast bridge: the lines that sum, and the deal behind each one

A reading guide for a forecast bridge from last period's number to this one: the check that the lines sum before anything is read, the order to read them in, closed, slipped, lost, new, resized, the deal list behind each line as the thing to open, the line with no deals behind it as the one to distrust, the repeat slippers across bridges, and the two lines that set next period's calibration.

16 Sept 20263 min read Forecast and pipeline

How to read a pipeline table: face value, weighted, and the column in between

A reading guide for a pipeline table by rep and stage: why face value is the column to distrust, why weighted by historical conversion is the one to believe, what the slip count column does to both, how to read stage distribution as a shape rather than a total, the needed multiple against coverage, and the two rows to open, the rep whose weighted coverage is lowest and the rep whose face-to-weighted ratio is highest.

16 Sept 20262 min read Forecast and pipeline

Pipeline coverage on ten deals: the whole arithmetic on one page

The complete pipeline coverage calculation on ten open deals small enough to check by hand: the deals by stage and value, the team's historical conversion by stage at this point in the quarter, the face value, the weighted value, the slip counts and the slip adjustment, the remaining target, coverage three ways, the needed multiple, and the deal list that explains the gap, so a reader can reproduce every figure and then run it on their own snapshot.

16 Sept 20263 min read