Revenue operations exists to make the revenue engine measurable and predictable. That starts with a shared set of metrics, each with one agreed formula, calculated the same way every month. Many companies have dashboards full of numbers that each team calculates differently: marketing counts pipeline one way, sales another, finance a third. The result is meetings spent arguing about numbers instead of acting on them. This article lists the core metrics for a RevOps dashboard, grouped into pipeline, conversion, efficiency and retention, with a formula for each, the data source, and the calculation mistakes that most often make the numbers misleading.
Principles for a RevOps dashboard
Before choosing metrics, agree three principles. One definition per metric, written down and stored where everyone can find it, so the number means the same thing in every meeting. One source of truth per metric: pipeline and conversion from the CRM, revenue and retention from billing or finance, spend from finance and ad platforms. Trends and plan, not snapshots: every metric is shown over time and against the target, because a single month's number rarely tells you whether things are improving. Keep the leadership view short, around a dozen metrics, and put the detail in drill-down reports for each team.
Pipeline metrics
Pipeline created: the total value and count of new qualified opportunities created in the period, split by source such as inbound, outbound, partner and existing customers. Pipeline coverage: open pipeline expected to close in the period divided by the remaining target for the period. Coverage needs to be read against your own historical win rates; the right ratio depends on how much of your pipeline typically converts. Pipeline velocity: the number of qualified opportunities multiplied by win rate and average deal size, divided by sales cycle length in days, which gives revenue per day moving through the pipeline. The pipeline velocity article explains how to use it to find where to improve. Common mistake: counting pipeline at different stages in different reports, or including unqualified early-stage deals in coverage.
Conversion metrics
Lead-to-opportunity rate: qualified opportunities created divided by leads or MQLs in the same cohort. Stage conversion rates: the share of deals that reach one stage and go on to reach the next, measured by cohort. Win rate: deals won divided by deals won plus deals lost, measured for deals closed in the period or, more accurately, for a cohort of deals created in the same period. Sales cycle length: the median number of days from opportunity creation to close for won deals. Common mistakes: calculating win rate as won divided by all deals including open ones, which understates it; mixing cohorts so that a month's win rate reflects deals created long ago; and using the mean cycle length, which a few very long deals can distort.
Efficiency metrics
Customer acquisition cost (CAC): total sales and marketing cost to acquire new customers in a period divided by new customers in that period. Report both fully loaded CAC, including salaries, tools and agency fees, and paid CAC by channel. CAC payback: CAC divided by monthly gross-margin-adjusted revenue from a new customer, giving months to recover acquisition cost. Quota attainment: share of reps at or above quota, and the distribution, not just the average. Sales capacity: ramped rep equivalents multiplied by productivity per rep, compared with the plan. Common mistakes: including only media spend in CAC; ignoring gross margin in payback; and comparing attainment across reps at different stages of ramp.
Retention metrics
Gross revenue retention (GRR): recurring revenue at the start of a period from existing customers, minus churn and contraction, divided by starting recurring revenue. GRR cannot exceed 100 per cent. Net revenue retention (NRR): the same, plus expansion revenue from those customers. Logo churn: customers lost divided by customers at the start of the period. Expansion pipeline: open upsell and cross-sell opportunities. Common mistakes: including revenue from new customers acquired during the period in NRR, which inflates it; and mixing monthly and annual cohorts in the same calculation.
Formula reference table
Use this as the definitions page of your dashboard. Adjust the source column to your systems.
| Metric | Formula | Source |
|---|---|---|
| Pipeline created | Sum of value of qualified opportunities created in period | CRM |
| Pipeline coverage | Open pipeline due in period ÷ remaining target | CRM and plan |
| Pipeline velocity | (Opportunities × win rate × average deal size) ÷ cycle days | CRM |
| Lead-to-opportunity rate | Qualified opportunities ÷ leads, same cohort | CRM |
| Win rate | Won ÷ (won + lost), same cohort | CRM |
| Sales cycle length | Median days from opportunity created to won | CRM |
| CAC | Sales and marketing cost ÷ new customers | Finance and CRM |
| CAC payback (months) | CAC ÷ (new monthly recurring revenue per customer × gross margin) | Finance and billing |
| Gross revenue retention | (Starting recurring revenue − churn − contraction) ÷ starting recurring revenue | Billing |
| Net revenue retention | (Starting recurring revenue − churn − contraction + expansion) ÷ starting recurring revenue | Billing |
| Forecast accuracy | 1 − |forecast − actual| ÷ actual | CRM and finance |
Building the dashboard
Build in layers. The leadership view shows the dozen core metrics with trend lines and targets. Below it, a marketing view shows pipeline by source, lead-to-opportunity rates and cost per qualified opportunity; a sales view shows stage conversion, cycle length, win rate and attainment by team; and a customer view shows retention and expansion. Calculate everything from systems, not manual entry, and document each metric's definition alongside the chart. CRM reporting is often enough at first; a separate BI tool helps once you need to combine CRM, billing and spend data reliably. The article on building a RevOps reporting layer the CEO and CFO trust covers this in more depth.
Reviewing the dashboard
A dashboard is only useful if it is reviewed with a consistent rhythm. Weekly, sales and marketing leaders look at pipeline created, coverage and stage movement. Monthly, the leadership team reviews the full set against plan and decides on actions. Quarterly, the team reviews definitions and targets. In each review, ask what changed, why, and what will be done about it. A metric that has not led to a decision in several months is a candidate for removal from the leadership view.
Adapting the metrics to your model
The core set suits most B2B companies with a sales team and recurring revenue, but some models need adjustments. In a business that sells one-off projects or services rather than subscriptions, replace recurring revenue retention with repeat purchase rate and revenue from returning clients, and watch utilisation or delivery capacity alongside pipeline, because winning work you cannot deliver creates its own problems. In a product-led company where users sign up before talking to sales, add activation rate and product-qualified accounts to the pipeline group, and measure the conversion from free or trial to paid. In a company that sells through partners or distributors, add partner-sourced pipeline and partner win rates, and agree how deals registered by partners are counted so that the same opportunity is not credited twice. Whatever the model, keep the principle of one definition and one source per metric.
FAQ
Pipeline created and coverage, pipeline velocity, stage conversion, win rate, sales cycle length, CAC and payback, quota attainment, and gross and net revenue retention, each with one agreed formula.
Divide deals won by deals won plus deals lost, ideally for a cohort of deals created in the same period. Including open deals in the denominator understates it.
Gross revenue retention counts churn and contraction only and cannot exceed 100 per cent. Net revenue retention also adds expansion from the same customers.
It depends on your historical win rate for pipeline at the start of a period. Calculate the ratio you actually need from your own conversion data rather than using a generic figure.
Not at first. CRM reports often suffice. A BI tool helps when you need to combine CRM, billing and spend data reliably in one view.
Around a dozen, each with a trend and a target. Put team-level detail in drill-down reports, and remove metrics that have not led to a decision in several months.
Usually because they count pipeline at different stages, use different date fields or apply different source rules. Agree one definition and calculate it from one system.
Lead with sourced pipeline as the accountable number, and show influenced pipeline alongside it as context.