Strong B2B SaaS teams at $5M–$15M ARR convert more than 26% of MQLs to SQLs, win over 25% of qualified opportunities, hold 3–4x pipeline coverage, keep NRR above 108% and GRR above 90%, recover CAC in under 12 months and touch new leads in under 5 minutes.
A band tells you where to spend the next sprint. It is not a grade. One weak metric upstream usually explains three mediocre ones downstream.
The metric is costing you pipeline or retained revenue right now. In our experience a weak band is almost always a system problem (definitions, routing, data capture), not an effort problem.
ActWhere most companies we work with land. Not urgent unless a board target depends on it. Improve it after the weak metrics are fixed.
WatchWhat well-run revenue systems sustain for several quarters. A strong metric is an asset: do not let a CRM rebuild or comp change elsewhere break it.
ProtectDefault segment: unless a table says otherwise, ranges are for B2B SaaS companies at $5M–$15M ARR with a sales-assisted motion and ACV between $15K and $75K. Metrics that move meaningfully with scale (MQL→SQL, sales cycle, speed to lead, NRR, forecast accuracy, logo churn) get an ARR-band table. All ranges are JayOh guidance, explained in the methodology.
MQL to SQL: a good MQL to SQL conversion rate for B2B SaaS at $5M–$15M ARR is 12–26%. Above 26% is strong; below 12% means the MQL definition is too loose or sales is not working the leads.
Lead to MQL: 10–25% of net-new leads reaching MQL is acceptable. Below 10% usually means junk volume or a scoring model that only rewards one behavior.
SQL to opportunity: 30–50% of SQLs should become a qualified opportunity; above 50% is strong. Lower means reps are accepting leads they later decline to work.
Demo request rate: 1.2–2.8% of website visitors to high-intent pages requesting a demo is acceptable; above 2.8% is strong.
Meeting show rate: 60–80% of booked first meetings held is acceptable; above 80% is strong, and almost always comes from instant booking plus reminders rather than rep chasing.
| Metric | Weak | Acceptable | Strong | How we measure it |
|---|---|---|---|---|
| Lead → MQL | < 10% | 10–25% | > 25% | Created-date cohort, net-new leads only, 2 quarters |
| MQL → SQL | < 12% | 12–26% | > 26% | MQL-date cohort; SQL = sales accepted and qualified |
| SQL → opportunity | < 30% | 30–50% | > 50% | Opportunity created with amount and close date |
| Lead → opportunity | < 2% | 2–5% | > 5% | End-to-end, same cohort |
| Demo-request rate (high-intent pages) | < 1.2% | 1.2–2.8% | > 2.8% | Demo submits / sessions on pricing, product, demo pages |
| First-meeting show rate | < 60% | 60–80% | > 80% | Held / booked, inbound meetings |
| MQLs never worked (no touch in 7 days) | > 25% | 10–25% | < 10% | MQLs with zero logged activity by day 7 |
| ARR band | Weak | Acceptable | Strong |
|---|---|---|---|
| $5M–$15M | < 12% | 12–26% | > 26% |
| $15M–$30M | < 14% | 14–30% | > 30% |
| $30M–$50M | < 16% | 16–34% | > 34% |
Why the bar rises with scale: larger companies typically run tighter ICP definitions, enrichment and a lead scoring model with negative scores, so fewer junk MQLs enter the denominator. If your MQL→SQL looks strong but lead→opportunity is weak, your SQL definition is probably too easy. Build the model with our lead scoring guide and the stage definitions in the lifecycle stages guide.
Pipeline coverage: strong B2B SaaS teams start the quarter with 3–4x coverage of the remaining bookings target in qualified pipeline. Below 2.5x is weak. Coverage above 5x is rarely good news: it usually means stale deals that should have been closed out.
Win-rate-adjusted coverage: the coverage you need is roughly 1 divided by your win rate. A team winning 25% of qualified opportunities needs about 4x; a team winning 33% needs about 3x.
Pipeline created vs plan: creating at least 100% of the pipeline target each month is strong; 80–100% is acceptable; below 80% for two months in a row is a leading indicator of a missed quarter.
Expansion share of pipeline: 10–22% of new pipeline coming from existing customers is acceptable at $5M–$15M ARR; above 22% is strong.
Stale pipeline: fewer than 15% of open opportunities sitting longer than 1.5x the median time-in-stage is strong; above 30% is weak.
| Metric | Weak | Acceptable | Strong | How we measure it |
|---|---|---|---|---|
| Pipeline coverage (quarter start) | < 2.5x | 2.5–3x, or > 5x (usually inflated) | 3–5x, target 3–4x | Qualified open pipeline closing this quarter / remaining target |
| Pipeline created vs plan | < 80% | 80–100% | > 100% | Monthly, by opportunity created date |
| Expansion share of new pipeline | < 10% | 10–22% | > 22% | Expansion + upsell opps / all new pipeline $ |
| Stale opportunities | > 30% | 15–30% | < 15% | Open opps > 1.5x median time in current stage |
| Close-date pushes per opp | > 2.0 | 1.0–2.0 | < 1.0 | Average close-date changes per closed opp |
| Opps with next step + date | < 60% | 60–85% | > 85% | Open opps past stage 1 |
Coverage is only meaningful when stages are buyer-verified gates. If your stages describe rep activity rather than buyer commitments, coverage inflates and forecasts miss. See sales stage architecture and how to forecast pipeline accurately.
Win rate: a good B2B SaaS win rate on qualified opportunities is 15–25%; above 25% is strong. Measure it from the first qualified stage, not from opportunity creation, or junk opps will drag it down.
Win rate by source: inbound-sourced opportunities should win at 18–30% (strong above 30%); outbound-sourced at 10–20% (strong above 20%). Blending the two hides which engine is broken.
Late-stage win rate: deals that reach proposal or negotiation should close at 40–60%; above 60% is strong. Below 40% means late stages are entered too early.
Sales cycle: at $5M–$15M ARR, a median cycle of 55–120 days from opportunity creation to close is acceptable; under 55 days is strong; over 120 days is weak for that segment.
| Metric | Weak | Acceptable | Strong |
|---|---|---|---|
| Qualified opp → won | < 15% | 15–25% | > 25% |
| Inbound-sourced | < 18% | 18–30% | > 30% |
| Outbound-sourced | < 10% | 10–20% | > 20% |
| Proposal → won | < 40% | 40–60% | > 60% |
| Competitive deals | < 20% | 20–35% | > 35% |
| ARR band | Weak | Acceptable | Strong |
|---|---|---|---|
| $5M–$15M | > 120 days | 55–120 days | < 55 days |
| $15M–$30M | > 150 days | 70–150 days | < 70 days |
| $30M–$50M | > 180 days | 90–180 days | < 90 days |
| Annual contract value | Weak | Acceptable | Strong |
|---|---|---|---|
| Under $15K | > 60 days | 30–60 days | < 30 days |
| $15K–$50K | > 120 days | 60–120 days | < 60 days |
| $50K–$100K | > 150 days | 90–150 days | < 90 days |
| Over $100K | > 210 days | 120–210 days | < 120 days |
Flag any deal sitting longer than 1.5x the median time for its stage. Cycle length and win rate move together: a 10-point win-rate lift and a 15% shorter cycle compound into sales velocity. Model it in the velocity calculator on Sales Stages.
Net revenue retention: good NRR for a SaaS company at $5M–$15M ARR is 95–108%; above 108% is strong. The bar rises with scale: above 112% is strong at $15M–$30M and above 118% at $30M–$50M.
Gross revenue retention: GRR of 85–90% is acceptable for mid-market SaaS; above 90% is strong; below 85% means churn and contraction are eating the expansion engine before it starts.
Gross logo churn: at $5M–$15M ARR, losing 8–18% of customers a year is acceptable; under 8% is strong.
NRR below 100% means the business shrinks every year without new logos. In our engagements, most retention problems trace back upstream to qualification and the sales-to-CS handoff, not to customer success effort.
| Metric | Weak | Acceptable | Strong |
|---|---|---|---|
| Net revenue retention | < 95% | 95–108% | > 108% |
| Gross revenue retention | < 85% | 85–90% | > 90% |
| Gross logo churn (annual) | > 18% | 8–18% | < 8% |
| Time to first value | > 60 days | 30–60 days | < 30 days |
| Renewals forecast 90+ days out | < 50% | 50–85% | > 85% |
| ARR band | NRR weak | NRR acceptable | NRR strong | Logo churn strong |
|---|---|---|---|---|
| $5M–$15M | < 95% | 95–108% | > 108% | < 8%/yr |
| $15M–$30M | < 100% | 100–112% | > 112% | < 6%/yr |
| $30M–$50M | < 103% | 103–118% | > 118% | < 5%/yr |
Formulas: NRR = (starting ARR + expansion − contraction − churn) / starting ARR, for the same customer cohort over 12 months. GRR = (starting ARR − contraction − churn) / starting ARR, capped at 100%. For the enterprise-value math behind NRR, see Net Revenue Retention and RevOps for CS leaders.
CAC payback: a good CAC payback period for B2B SaaS is 12–24 months on a gross-margin-adjusted basis; under 12 months is strong; over 24 months is weak unless NRR is well above 110%.
Magic number: 0.5–1.0 is acceptable; above 1.0 is strong and usually a signal to invest more in acquisition; below 0.5 means spend is outrunning the system that converts it.
LTV to CAC: 3:1 or better is strong; 2–3:1 is acceptable; below 2:1 is weak.
Cost per MQL: at $5M–$15M ARR, $180–$420 per MQL is acceptable; under $180 is strong. Judge it next to MQL→SQL: cheap MQLs that never convert are the most expensive kind.
| Metric | Weak | Acceptable | Strong | Formula |
|---|---|---|---|---|
| CAC payback | > 24 months | 12–24 months | < 12 months | S&M spend / (new ARR × gross margin) × 12 |
| Magic number | < 0.5 | 0.5–1.0 | > 1.0 | (Quarter net new ARR) / prior-quarter S&M spend |
| LTV : CAC | < 2:1 | 2–3:1 | > 3:1 | (ARPA × GM / churn rate) / CAC |
| Cost per MQL ($5M–$15M) | > $420 | $180–$420 | < $180 | Program + media spend / MQLs, same period |
| Cost per MQL ($15M–$30M) | > $520 | $220–$520 | < $220 | Same |
| Pipeline per $1 of marketing spend | < $3 | $3–$6 | > $6 | Marketing-sourced pipeline $ / program spend |
Efficiency metrics are lagging. If CAC payback is weak, the cause is almost always visible earlier in this report: a weak MQL→SQL rate, slow speed to lead or a low win rate. Fix those and payback follows. For portfolio-level comparison across companies, see Portfolio GTM Metrics.
Speed to lead: strong B2B teams make first human contact with a demo request in under 5 minutes, measured as a median. 5 minutes to 4 hours is acceptable at $5M–$15M ARR; more than 4 hours is weak.
Routing accuracy: more than 94% of leads landing with the right owner on the first assignment is strong; under 82% is weak.
Lead SLA compliance: more than 85% of MQLs worked inside the agreed SLA is strong; under 60% means the SLA exists on paper only.
Forecast accuracy: landing within ±8% of the commit call made at the start of the quarter is strong at $5M–$15M ARR; a miss of more than ±20% is weak.
Attribution coverage: more than 80% of closed-won deals carrying a usable original source is strong; under 50% means channel decisions are guesses.
| Metric | Weak | Acceptable | Strong | How we measure it |
|---|---|---|---|---|
| Speed to lead (median) | > 4 hrs | 5 min–4 hrs | < 5 min | Form submit to first logged call or email, business hours |
| Routing accuracy | < 82% | 82–94% | > 94% | Leads not reassigned within 48 hours |
| Lead SLA compliance | < 60% | 60–85% | > 85% | MQLs touched inside SLA / all MQLs |
| Forecast accuracy | > ±20% | ±8–20% | < ±8% | Week-1 commit vs actual bookings |
| Attribution coverage | < 50% | 50–80% | > 80% | Closed-won with original source not Direct/Unknown |
| Reporting lag | > 72 hrs | 12–72 hrs | < 12 hrs | Event to dashboard |
| Duplicate records | > 10% | 3–10% | < 3% | Contacts and companies, by email and domain |
| Required opp fields complete | < 70% | 70–90% | > 90% | Amount, close date, stage, source, next step |
| ARR band | Speed weak | Speed acceptable | Speed strong | Forecast weak | Forecast strong |
|---|---|---|---|---|---|
| $5M–$15M | > 4 hrs | 5 min–4 hrs | < 5 min | > ±20% | < ±8% |
| $15M–$30M | > 6 hrs | 10 min–6 hrs | < 10 min | > ±15% | < ±6% |
| $30M–$50M | > 8 hrs | 15 min–8 hrs | < 15 min | > ±12% | < ±5% |
Speed to lead is the cheapest benchmark on this page to move: it is a routing and notification fix, not a hiring plan. The 9-step HubSpot and Salesforce routing build is in Speed to Lead; territory logic is in Account & Territory Planning.
Pick your ARR band first. Six of these metrics change with scale; the rest hold across bands.
Scoring: strong = 2 points, acceptable = 1, weak = 0, divided by the maximum for the metrics you entered. Nothing you type leaves this page.
Observations from JayOh engagements, not survey findings. They explain why two companies with the same number can need opposite fixes.
These are JayOh practitioner ranges, not survey statistics. We say so plainly because a benchmark you cannot trace is a benchmark you should not plan against.
Source. The ranges are JayOh guidance drawn from more than 100 RevOps and GTM engagements since 2016, mostly B2B SaaS and PE-backed software companies between roughly $3M and $50M ARR, running HubSpot, Salesforce or both. In those engagements we baseline these metrics directly from CRM and billing data before changing anything.
How bands are set. “Acceptable” reflects the range where most systems we baseline sit. “Strong” reflects what well-run systems sustain across several quarters. “Weak” is the level at which, in our experience, the metric is actively costing pipeline or retained revenue. We cross-check bands against publicly available industry benchmark sets and adjust where our experience consistently differs.
What this is not. This is not a statistically sampled survey, and we do not publish sample sizes or percentiles. Treat the ranges as directional targets for planning and diagnosis, then set your actual targets from your own trailing cohort data.
Segment and definitions. Default segment is sales-assisted B2B SaaS at $5M–$15M ARR with $15K–$75K ACV. PLG, transactional (sub-$5K ACV) and enterprise (over $250K ACV) motions behave differently; for self-serve conversion benchmarks see The Math Behind PLG.
Measurement rules. Conversion rates use created-date cohorts, not same-period ratios. Times are medians, not averages. Win rate starts at the first qualified stage. Retention uses a fixed 12-month customer cohort.
Refresh. We review these ranges annually. This edition: October 2026. Interactive, ARR-segmented scoring lives in the JayOh Benchmarks Database; the full 48-KPI system is in GTM KPIs for CROs.
How to cite this report JayOh (2026). State of GTM Benchmarks Report 2026. Joe LaGrutta. https://jayoh.io/gtm-benchmarks-report-2026/| Metric | Definition | Common mistake |
|---|---|---|
| MQL | A lead that clears both a fit threshold and an engagement threshold, or a fit lead that raised a hand | Counting every form fill as an MQL |
| SQL | An MQL that sales has accepted and qualified against written criteria | Using “meeting booked” as SQL |
| Pipeline coverage | Qualified open pipeline with a close date this period, divided by remaining target | Including stage-1 and stale opps |
| Win rate | Closed-won / (closed-won + closed-lost), from the first qualified stage | Counting open deals in the denominator |
| Sales cycle | Median days from opportunity created to closed-won | Using the average, which a few long deals distort |
| NRR / GRR | 12-month cohort ARR retained with (NRR) and without (GRR) expansion | Mixing new-logo ARR into the cohort |
| CAC payback | Months of gross margin from new ARR needed to recover sales and marketing spend | Ignoring gross margin |
| Speed to lead | Median minutes from form submit to first human touch | Counting the automated email as the touch |
Every term above is also defined in the RevOps & GTM Glossary.
JayOh baselines your metrics straight from the CRM, scores every band, and puts a pod on the most upstream gap.
We engineer growth systems. JayOh builds the RevOps architecture, GTM execution and data infrastructure that turn messy revenue operations into predictable pipeline for B2B SaaS and PE-backed teams.
