Sales Pipeline Health

A single-screen read on pipeline coverage, slippage and win rate — the four numbers a sales leader actually asks about in a forecast call.

Gebaut von CRM Analytics Academy · Veröffentlicht 2026-08-28

KPIs und wie sie berechnet werden

Metrisch	Wie es berechnet wird
Pipeline Coverage	`sum(Amount) [open] / sum(Quota) [current quarter]` Coverage under 3x this late in the quarter is the signal to go build pipeline, not to re-forecast.
Win Rate	`count() [IsWon] / count() [IsClosed]` Closed-only denominator. Including open deals makes the rate drift down all quarter for no real reason.
Slipped Amount	`sum(Amount) where CloseDate moved out of the quarter at least once` Needs a dataflow field that snapshots the original CloseDate — CRM Analytics can't see history on its own.
Average Deal Age	`avg(daysBetween(CreatedDate, toDate(now())))` Filtered to open deals only, otherwise closed-won deals drag the average down and it looks healthy.

Wie es gebaut wurde

1
Build the dataset at opportunity grain
One row per opportunity, with Account and Owner brought on as lookups. Never join to line items here — it multiplies rows and every sum doubles.
2
Snapshot CloseDate for slippage
Slippage is a history question and the Opportunity object only stores the current value. Append a dated snapshot each night so you can compare.
3
Lay out four KPIs across the top
Coverage, win rate, slipped amount, deal age. Number widgets with conditional formatting — red below target, not merely coloured.
4
Facet everything to one date toggle
A single quarter selector driving every widget. Two independent date filters on one dashboard is how numbers stop reconciling.

Sales Pipeline Health

This is a worked example, not a community submission — it exists so you can see the shape a showcase entry takes before writing your own. Replace the placeholder image with a real screenshot when you submit yours.

What it measures

A forecast call almost always comes down to four questions: do we have enough pipeline, are we winning what we work, what moved out, and how long is this taking? This dashboard answers those four and deliberately nothing else. Every widget that didn't answer one of them got cut.

Why the grain matters here

The dataset is one row per opportunity. That sounds obvious until someone asks to see products, at which point the temptation is to join OpportunityLineItem into the same dataset. Do that and one opportunity becomes five rows, and every sum(Amount) on the dashboard is silently wrong.

If you need product detail, build a second dataset at line-item grain and link to it from this dashboard. Two datasets with honest grains beat one dataset that quietly double-counts.

The part that is not obvious

Slippage is the only KPI here that CRM Analytics cannot compute from live data. CloseDate holds today's value — the fact that a deal was originally committed to last quarter is not stored anywhere you can query. You need a nightly snapshot appended to a history dataset, then a comparison between the earliest snapshot and the current row.

Budget real time for that one. It is usually the whole reason a "quick pipeline dashboard" takes a week.