1.8× → 3.4×
Median pipeline coverage across Rob's clients, measured before engagement and again within two quarters of starting work. The lift is the gap between founder-reported coverage and board-defensible coverage.
Methodology · Issue No. 04
The Pipeline Coverage Index, in plain operational terms.
A single ratio that replaced the wishful number on the board slide. Written up because technical buyers asked for the formula, the audit playbook, and the receipts — not the pitch.
- Originated Rob Macklem, 2018. Maintained since.
- Adopted by 200+ RevOps teams on the RevOps Co-op Slack.
- Audit framework open-source, used by 30+ Series A startups.
- Locale Toronto, Canada. Reviewed quarterly.
Algolia·
Calendly·
Loom alumni·
Forty-seven B2B SaaS teams·
RevOps Co-op·
Stanford GSB·
INSEAD·
Algolia·
Calendly·
Loom alumni·
Chapter I
One ratio. Four variables. Zero wishful arithmetic.
PCI
(Weighted Pipeline + Unweighted Pipeline × Stage Probability Adjustment)
÷
(Quota × (1 − Time-to-Close Adjustment))
Result expressed as a multiplier against remaining quarterly quota.
01 — The variables
Pipeline value. Total open opportunity ARR in the period, weighted by stage probability as recorded in your CRM. Not the number your AE puts in the Friday update.
Weighted pipeline. The same set, restated against stage probabilities that have been validated against your own historical close rates — not the vendor default.
Quota. Remaining quota for the period the board is actually looking at, not the annual number divided by four.
Time-to-close. Days between stage advance and closed-won, measured against the cycle you have, not the cycle you wish you had.
02 — Why a single ratio
Most boards receive a pipeline coverage figure that conflates weighted and unweighted opportunity, ignores stage slippage, and treats the annual quota as a quarterly proxy. The result is a number that is either reassuring or terrifying, and almost never falsifiable.
PCI replaces that with one multiplier. It is the same number whether you compute it on Monday or Friday, in HubSpot or Salesforce, at $3M ARR or $30M ARR. The board either has coverage or it does not, and the gap is named in the same units the operator is held to.
Footnotes: the formula is published in full on the RevOps Co-op Slack #methodology channel and in the 2022 benchmark report How Mid-Market SaaS Actually Forecasts.
Reference numbers · as of Q4 2024
Coverage lift
Forecast accuracy
In production use
Three figures the index is measured against.
47% → 89%
Average forecast accuracy against quarterly board targets, measured within 90 days of engagement. Baseline is what the client was reporting before the audit; the figure on the right is what the board receives after.
200+
RevOps teams on the RevOps Co-op Slack now reference the Pipeline Coverage Index in their own operating cadences — a sign it has escaped the originating practice and become a working instrument elsewhere.
These figures are drawn from Rob's published client work between 2018 and 2024 and from the 2022 benchmark report How Mid-Market SaaS Actually Forecasts, downloaded 14,000 times. Median, not best case.
Chapter II
The CRM audit framework, in four inspection layers.
A first-time RevOps hire, a PE operating partner, or a founder between sales leaders can run every layer below without engaging a consultant. The framework is open-source on purpose — what it surfaces is what the diagnostic costs to resolve.
-
01
Stage hygiene
Pull every opportunity closed-won or closed-lost in the last four quarters. Re-bin them against the stage definitions you think you have, not the stage names on the page. The drift between intent and label is where most forecast inaccuracy starts.
Layer · data -
02
Probability calibration
For each stage, compute the actual historical win rate. Compare to the probability your reps apply when forecasting. The delta between the two is the size of the lie on your board slide.
Layer · math -
03
Activity coherence
Cross-reference calls, emails, and meetings logged against the opportunity record against the stage the deal is sitting in. Stage 3 deals with no logged activity in 14 days are not Stage 3 deals.
Layer · behaviour -
04
Forecast lineage
For the last eight weekly forecast calls, trace the named pipeline from "commit" back to the underlying opportunities. A commit number that cannot be traced to a CRM record is not a forecast — it is a hope with a date on it.
Layer · governance
The framework lives on GitHub under an MIT licence and is maintained by Rob personally. 30+ Series A startups have forked it. If you can run these four layers against your own CRM in an afternoon, you are closer to a board-defensible forecast than most $20M ARR companies.
Read the full audit specification →
A short filter
This page is written for four readers, and probably not for the others. Founder-CEOs between $2M and $30M ARR who have hit a sales plateau after initial product-market fit and are tired of running the forecast call themselves. VP of Sales stepping into a leadership seat for the first time and inheriting a CRM that nobody trusts. RevOps modernizers at a scaling SaaS company who want a referenced instrument they can deploy internally without inventing one. PE operating partners prepping a portfolio company for board reporting before a recap or a sale. If none of those describe you, the diagnostic is still useful, but this page is not where the conversation starts.
Next step
Either book the diagnostic, or take the sample home.
A paid 60-minute Revenue Diagnostic is $450, applied to your live CRM, and ends with a written brief on what to fix first, second, and third. The sample PCI audit PDF below is the same artefact, redacted, so you can see what you would be buying before you book.
Book a Revenue Diagnostic
Download the sample PCI audit (PDF, 2.1 MB)
Calendar opens two weeks out. Cancellations inside 48 hours are refunded in full — there is no fine print because there is no contract.