GTM Metrics and KPIs: What to Track, by Role and by Stage

Why most KPI lists fail

Most metrics advice hands you a list of forty things to track and leaves you to guess which five matter this quarter. The list is not the hard part. Every metric on it is defensible somewhere. The hard part is that each one silently assumes something about your data, and if that assumption is false the metric still returns a number. It just returns a wrong one, confidently, on a dashboard, in front of your board.

Win rate assumes your losses get marked lost. Sales cycle length assumes stage timestamps mean something. Pipeline coverage assumes your close dates are not all set to the last day of the quarter out of habit. Lead conversion rate assumes marketing and sales agree on what a lead is.

So before any metric goes on a dashboard, put it through four questions. If any answer is no, you are not ready for that metric, and adding it will cost you more than skipping it.

1. Can two people compute this independently and get the same number?
2. Is every field it depends on filled in more than 90% of the time, without someone being chased?
3. If this number moves 20% next month, do you know which lever to pull?
4. Is there a decision that changes based on this number? Name it.

Question four disqualifies more metrics than the other three combined. A number nobody acts on is not a metric, it is decoration. In the tables below, the level column refers to the ladder: level 1 needs almost nothing, level 4 needs years of clean history.

What KPIs should I track for my sales team?

Different KPIs for each role, because each role controls different things. Track outcomes as targets and activity as a diagnostic. Calls and emails tell you why an outcome moved. The moment they become a quota, they get inflated and stop telling you anything.

Account executives

SDRs and BDRs

Sales managers

Sales leaders: VP Sales and CRO

What marketing metrics actually matter?

The ones that connect marketing to pipeline and to cost. Volume metrics such as leads, sessions and followers are useful for diagnosis, but they are not how marketing should be judged, because they can rise while revenue falls.

If you use MQLs, treat them as a diagnostic, not a headline target. The moment MQL volume becomes the goal, the definition starts drifting downward. The better question for a marketing leader is "how much qualified pipeline did we create, and what did each opportunity cost?" Settling arguments about lead quality starts with one written lead definition that both teams sign.

How do I calculate NRR and GRR?

Take the customers you had at the start of a period, usually twelve months, and compare their recurring revenue at the end with what it was at the start. New customers won during the period are left out of both measures.

An illustration with made-up numbers: a cohort starts the year at $1,000,000 in ARR. Over twelve months it expands by $150,000, contracts by $30,000 and churns $70,000. NRR is $1,050,000 ÷ $1,000,000, or 105%. GRR is $900,000 ÷ $1,000,000, or 90%. GRR can never be above 100%, because it ignores expansion. That is the point of it: it shows how much revenue you keep before upsell papers over the leaks.

The usual mistakes:

• Including new customers in the ending number, which inflates NRR.
• Booking renewals and upsells as new business in the CRM, so expansion never shows up as expansion.
• Mixing monthly and annual contracts without annualising the monthly ones.
• Reporting logo churn only. Losing ten small customers and one large one are very different quarters.

Alongside retention, two more measures are worth the effort: renewal forecast accuracy, measured the same way as the sales forecast, and a health score test. Check whether accounts scored red churned more often than accounts scored green over the last year. If they did not, the score is decoration and your CSMs already know it.

Which revenue metrics does finance need from the CRM?

Finance needs the CRM to agree with billing, and needs a handful of efficiency metrics calculated the same way every time. Most disagreements between the sales number and the finance number come from three words used as if they meant the same thing.

Efficiency metrics, with the formula to write down so everyone calculates them the same way:

Three rules keep these honest. Use fully loaded cost, including salaries, tools and agencies; a CAC that excludes salaries is a media efficiency ratio. Use gross margin, not revenue, in payback. And be wary of LTV to CAC early on: lifetime value needs years of stable churn data, and with less it is a guess with a decimal point.

What metrics should we report to the board?

Report the metrics your data has earned, and no more advanced than that. A board deck with Level 4 metrics on Level 1 data invites questions you cannot answer. Stage is a rough guide; your data maturity matters more than your funding round.

Whatever the stage, show each metric against the plan and the prior period, and keep the definitions on one page at the back of the deck. Boards lose trust when a number's definition changes quietly, not when the number is bad.

Which metrics has your data earned? The four-level ladder

The useful question is not "what should we track". It is "what have we earned the right to track". This is the ladder we use to answer it.

Level 1, Counting

This is where everyone starts, and the mistake here is not tracking the wrong things. It is leaving too early.

Track new revenue booked, revenue lost, customer count, opportunity count by stage, and raw activity volume by channel. That is close to it. All of these are counts, all can be verified by hand, and none require your process to be mature.

The trap at Level 1 is the ratio. Ratios feel like real analytics, and a ratio computed on twelve data points is noise wearing a suit. If you closed three of thirteen deals last month your win rate is not 23%. Your win rate is unknown, and your sample is three.

You have earned Level 2 when your funnel stages have written definitions, your team follows them without being reminded, and you are closing enough deals per quarter that a percentage means something.

It is worth being precise about how few that is. At thirty closed opportunities and a 30% win rate, the 95% confidence interval around that rate runs roughly 14% to 46%. Thirty deals is the point where a win rate stops being noise, not the point where it becomes precise. Below thirty you are reading tea leaves, and the lumpier your deal sizes the longer that stays true.

What each metric actually requires

The table below is the short version of everything that follows. Every metric on it is defensible. The right-hand column is where they die.

Level 2, Converting

Counting tells you how much. Converting tells you where it stops, which is the first genuinely actionable thing a metric can do.

Track stage-to-stage conversion, overall win rate, average deal size by segment, and lead to opportunity rate. Now you can see that you are not short of pipeline, you are losing 70% of it between demo and proposal, which is a different problem with a different fix.

A word on what a win rate is supposed to look like, because this is the metric people benchmark themselves against most and understand least. Across roughly 655,000 opportunities analysed in 2025, the average B2B win rate came in around 19%, down sharply from the year before, and most healthy teams sit somewhere between 20% and 35%. Deal size moves it more than anything else: under $50K, 35% to 45% is normal, while above $100K it drops to 15% to 25% as the buying committee grows. If your reported win rate is well above the band for your deal size, the likeliest explanation is not that you are exceptional. It is that your losses are not being marked.

What this level demands is a funnel your team follows. Not a documented funnel. A followed one. Stages have to be written as exit criteria, meaning a condition someone else can check, rather than as feelings. "Interested" is not a stage. "Has confirmed budget and named an economic buyer" is a stage.

The failure mode here is subtle and common. When stages are subjective, reps batch their updates. A deal sits in stage two for six weeks and then moves through three, four and five in the same afternoon because the rep is tidying up before a forecast call. Your conversion rates still calculate. They are describing a data entry habit rather than a sales process, and no amount of dashboard work will reveal that.

Fixing this is field-level work rather than reporting work, and it is the same job as cleaning up the fields the metric depends on. Our CRM Data Hygiene Playbook covers the audit and the validation rules that make a stage field trustworthy in the first place.

Also at this level, decide what a loss is. Companies that do not mark losses cleanly develop a category of deal that is neither won nor lost nor open, and it grows until win rate becomes meaningless. If you have opportunities with close dates more than two quarters in the past and no outcome, you have this problem right now.

You have earned Level 3 when your stage timestamps reflect when things actually happened, your losses are marked with a reason from a picklist, and your marketing spend can be tied to something downstream of the click.

Level 3, Diagnosing

Here the questions become expensive to answer and worth answering. What does an acquired customer cost? How long does the cycle take, and where does the time go? Which segment actually pays back?

Track customer acquisition cost by channel and segment, sales cycle length by segment, pipeline velocity, CAC payback period, net revenue retention, and cost per qualified opportunity.

Two of those need care.

Pipeline velocity multiplies opportunity count by win rate by average deal size, then divides by cycle length. It is useful precisely because it is a compound metric: it moves when any of four inputs move, which makes it an excellent early warning and a terrible diagnosis. Never report it without the four components beside it.

CAC payback is the number most often computed wrong in early-stage SaaS, usually by taking revenue instead of gross profit, or by counting only paid media as acquisition cost. If your CAC excludes salaries it is not CAC, it is a media efficiency ratio, and calling it CAC will make your unit economics look roughly twice as good as they are.

The prerequisite for this whole level is a closed loop. Spend has to be attributable to source, source has to survive onto the opportunity, and the opportunity has to survive to revenue with the source still attached. Most CRMs lose this at the second step.

The failure mode at Level 3 is precision theatre. A CAC figure quoted to the dollar, built on a source field filled in 60% of the time, is not more accurate than a range. It is less honest than one.

You have earned Level 4 when you have at least four quarters of clean history, enough volume that a cohort is more than a handful of accounts, and a team that has already acted on Level 3 numbers at least once.

Level 4, Predicting

Prediction is where most of the interesting work is, and it is also where every unpaid debt from the levels below comes due at once.

Track forecast accuracy against actuals, cohort retention curves, propensity or lead scoring, multi-touch attribution, and expansion revenue by cohort.

The rule that governs this entire level: a model is a compression of your history. If the history is inconsistent the model learns the inconsistency and repeats it back to you with a confidence score attached, which is worse than having no model, because the confidence score buys it trust it has not earned.

Two specific cautions.

Model accuracy is not a meaningful number on its own. If 5% of your leads convert, a model that predicts "no" for every single lead scores 95% accurate and is worth nothing. Ask for precision and recall at the threshold you will actually use, or ask what it beats. Accuracy alone is a number people quote when the real numbers are unflattering.

Multi-touch attribution is oversold at every company size below several hundred deals a year. Below that volume the models are fitting noise, and the output tends to confirm whatever the person who configured it already believed. Single-touch attribution plus a self-reported "how did you hear about us" field is less sophisticated and considerably more truthful.

Forecast accuracy is the one metric at this level almost every company should track, and almost nobody does. Record the forecast, wait, compare it to what happened, and keep the history. It costs nothing, it needs no model, and it is the fastest way to find out whether any of the rest of this is working.

Which metrics should we stop reporting?

Any metric that fails the readiness test. Six fail it more often than not: email open rates, demo counts used as a quota, MQLs as a headline target, website sessions and follower counts, response time with no outcome attached, and any number reported without a denominator. Two are worth acting on today.

Email open rates stopped being trustworthy in 2021, when Apple's Mail Privacy Protection began prefetching tracking pixels for Apple Mail users whether or not the message was ever read. Clicks and replies survived that change. Opens did not, and a rising open rate now tells you about your audience's mail client rather than your subject line.

Number of demos booked is a real metric that becomes a vanity metric the instant it becomes a quota, because demo count is the easiest number in the funnel to inflate.

And one metric almost nobody tracks that belongs at Level 1, because it costs nothing and predicts a great deal: median time from inbound form fill to first human contact. The long-running MIT and InsideSales work on this found the odds of qualifying a lead fall by roughly 80% after the first five minutes. The published cross-industry average response time is somewhere around 42 hours. Most teams have never measured their own, and the number is usually worse than the guess.

Metrics that look advanced and usually are not

Some numbers signal maturity without producing decisions. They are not always wrong, but they earn their place far less often than they appear on dashboards. Open rates and demo counts are covered above. Four more.

Marketing qualified leads as a headline metric rewards volume over fit, and the moment it becomes a target the definition starts drifting downward. This is Goodhart's law arriving on a dashboard: a measure that becomes a target stops being a good measure.

Website sessions and follower counts move for reasons that have nothing to do with revenue, and no one has ever changed a go-to-market decision because sessions rose 8%.

Average response time, unless it is tied to an outcome. Fast routing to the wrong owner is still the wrong owner.

Any metric reported without a denominator or a comparison. "We generated 340 leads" answers nothing on its own, and the absence of the denominator is usually deliberate.

How to install the right metrics in 30 days

Keep reading: the pipeline and forecast guide, the CRM data hygiene playbook, and why your CRM data is costing you revenue.