Funnel conversion, sales cycle, retention and efficiency benchmarks for B2B SaaS — every figure carrying its source, sample size and panel, and every metric carrying the definition it was actually measured on. Built so that when someone asks "are we in the fairway?", you can tell whether you're even comparing the same number.
Two credible sources put B2B SaaS win rates at 43% and 19% in the same year. Both are right. They are measuring different denominators — and almost every benchmark fight is that fight, unnamed.
A B2B SaaS benchmark is only comparable when the denominator, the panel, the statistic and the time basis all match. Run these five checks in order — if a comparison fails any one of them, the gap you are looking at is measurement, not performance. Most do fail, usually on the first or the third.
The same metric name covers genuinely different formulas. Win rate, NRR, CAC payback and conversion rate each have three or more live conventions. Settle the formula before the number.
ARR band, ACV band, funding type, GTM motion, geography. A $60K-ACV enterprise seller and a $600-ACV self-serve product share almost no structural DNA. ACV is the strongest single cut.
Median, mean, upper quartile. The single most common benchmarking error in the wild is comparing your median against somebody's published top quartile and concluding you're broken.
Cohort (records that entered in the period, tracked to outcome) versus snapshot (records sitting in the stage now). These produce different numbers from identical data.
Sample size collapses when a survey cuts by ACV. A report with n=150 can have n=9 in the band you care about. Read the per-chart n before you quote the per-report n.
So what
Benchmarks answer "are we structurally normal?" They do not answer "what should we fix?" For the second question, the only useful comparison is a company against its own trailing cohorts — which is also the only comparison where the definition is guaranteed to be constant.
This is the part of the corpus that does the work. For each metric: the canonical formula, the variants that circulate, what each variant does to the resulting number, and which published source uses which convention. Risk pills flag how often the metric is the actual cause of a mismatch.
Most common published formula
closed-won ÷ (closed-won + closed-lost)
— measured on opportunities that reached a terminal outcome in the period.
The decision-only denominator is the single largest inflator in B2B SaaS benchmarking. In enterprise, no-decision is frequently the largest loss category — excluding it can roughly double the reported rate. This is most of the distance between the 43% and the 19% in the banner above.
Source conventions — ICONIQ states it explicitly in a footnote:
(# closed-won ÷ (# closed-won + lost)) × 100, and reports 43%
average for sales-sourced opportunities (2026). The Bridge Group reports a 19% median win
rate (2024) on a broader denominator. Neither is wrong; they are not comparable.
"Does your win rate denominator include deals that went nowhere?"
Canonical (cohort basis)
records that advanced past stage N ÷ records that ENTERED stage N in the period
— tracked forward to outcome, not counted where they sit today.
Snapshot conversion is biased optimistic, because the deals that never advance accumulate in the stage and are counted as denominator only once while winners recycle through. Immature cohorts bias pessimistic. Together they make period-over-period comparison on an unstated basis close to meaningless.
Source conventions — Survey-based sources (ICONIQ, Benchmarkit) ask respondents for a rate and do not enforce a basis, so the published averages blend both conventions. This is a structural limitation of every funnel benchmark in this corpus and the reason the funnel section carries wider error bars than the retention section.
"Is that cohort-based or a snapshot? And what happens to open deals?"
There is no industry definition These are locally defined thresholds, not standard objects. An MQL is whatever score or action a given company decided counts.
The MQL→SQL rate is primarily a measure of how loose your MQL threshold is, and only secondarily a measure of lead quality. It is the least portable metric in this corpus. Benchmark it against your own history, not against anyone else's panel.
Source conventions — Benchmarkit publishes MQL→SAL at a 20% median. ICONIQ publishes New Lead→MQL at 28% and MQL→SQL at 30% (2026). The stage names do not refer to the same objects across the two panels.
"What has to be true for a record to become an MQL here?"
Canonical
close date − opportunity created date, median, measured on won deals only.
Start-point choice is the dominant term. Two companies with identical processes can publish cycle lengths a factor of two apart purely on where they start the clock. Always convert to opportunity-created → closed-won, median before comparing.
Source conventions — ICONIQ reports average weeks and does not publish a start-point definition, so its by-ACV curve should be read as a shape rather than as absolute values. The Bridge Group reports medians on AE-owned opportunities.
"Does the clock start at opportunity creation or at first touch?"
Canonical
open pipeline for the period ÷ quota (or target) for the period, measured at period start.
The classic "3× coverage" rule is only meaningful with the stage set attached. The right coverage number is simply 1 ÷ your own stage-to-close conversion rate, which means a company with a genuine 25% conversion needs 4×, and one at 33% needs 3×. Borrowing the rule without the conversion rate behind it is guesswork.
Source conventions — Benchmarkit currently recommends 4:1 against a historical 3:1, and notes coverage requirements correlate strongly with ACV.
"Which stages count as pipeline in that ratio?"
Canonical
reps at or above 100% of quota ÷ total reps, over a full fiscal year.
Ramped-only plus survivor-only can add 10–15 points against an all-reps, all-holders basis. This explains most of the gap between the two figures cited opposite.
Source conventions — ICONIQ reports the share of ramped AEs achieving quota: 62% for 2025. The Bridge Group reports 48% for 2026 on a broader rep base. Both are credible; the ICONIQ figure is the more flattering basis.
"Ramped reps only, or everyone who carried a number?"
Three different metrics, routinely used interchangeably
ACV = total contract value ÷ contract years ·
ASP = average value of a closed deal ·
ARR/customer = total ARR ÷ customer count
This matters more than it looks, because ACV is the primary segmentation axis in this entire corpus. Choosing ARR/customer instead of new-deal ACV can move you a full band up the table and hand you the wrong benchmark for everything else.
Source conventions — KeyBanc reported a $62K median ACV (2024 survey). The Bridge Group reported a $47K median ASP (2024) and a $50K median ASP in its 2025 SDR panel. SaaS Capital and High Alpha both band by ACV as respondents self-report it.
"Is that new-deal ACV, or ARR divided by customers?"
Canonical — SaaS Capital's published formula, the clearest in circulation
MRR in Dec Y2 from customers who were customers in Dec Y1 ÷ total MRR in Dec Y1
Includes upsell, cross-sell, price increases, downgrades and churn. Can exceed 100%.
The two failure modes both push the same way: up. A company reporting 115% NRR with new customers in the denominator and churned logos dropped may be running 95% on the canonical formula. When an NRR figure looks anomalously strong for its ACV band, check these two things first.
Source conventions — SaaS Capital publishes the formula above explicitly. High Alpha defines NRR as "annual net revenue retention (after churn, inclusive of upsells and expansion) seen in cohorts". ICONIQ reports NDR from portfolio operating data rather than survey self-report, which is why its figures run higher and are published as quartiles.
"Are churned customers still in the numerator at zero, and are new customers out of the denominator?"
Canonical Same cohort formula as NRR, with each customer's ending value capped at its starting value. Captures churn and downgrade only. Cannot exceed 100%.
Renewal-base GRR is the flattering variant and is common in board decks at companies with multi-year contracts, because contracts that never came up for renewal cannot churn. A GRR above 100% is definitionally impossible — if you see one, the cap is being applied in aggregate.
Source conventions — SaaS Capital and High Alpha both use the whole-book cohort convention. Both land on ~90–92% as the all-company median, which is the strongest agreement between any two panels in this corpus.
"Is that against the whole book, or only against what came up for renewal?"
Two different metrics with one name
Logo churn = customers lost ÷ customers at period start ·
Revenue churn = revenue lost ÷ revenue at period start
The monthly-to-annual conversion error is small at low churn and large at high churn: at 5% monthly, linear gives 60% and the correct compounding answer is 46%. Any SMB benchmark quoted as an annual figure should be checked for which way it was derived.
Source conventions — the sources in this corpus report revenue retention rather than churn, and on an annual cohort basis. SaaS Capital states plainly that it considers dollar-based retention the more important metric and surveys on that basis.
"Logos or dollars? And was the annual number compounded or multiplied?"
Canonical — gross-margin-adjusted
fully-loaded S&M cost to acquire a customer ÷ (new MRR × subscription gross margin)
— expressed in months.
Every common variant pushes payback down. The sub-$1M ARR median of 5 months in the High Alpha data is not a real 5 months — the publisher itself warns that early-stage companies under-allocate founder salaries, support costs and onboarding into CAC.
Source conventions — High Alpha defines it as "months of subscription gross margin to recover the fully-loaded cost of acquiring a customer" — the strict version — and publishes quartiles by ARR band.
"Is that gross-margin-adjusted, and does the denominator include expansion?"
Three ratios, and they are not interchangeable
New CAC ratio = total S&M ÷ new customer ARR
Blended CAC ratio = total S&M ÷ (new customer ARR + expansion ARR)
Expansion CAC ratio = expansion-attributed S&M ÷ expansion ARR
Benchmarkit put the median New CAC ratio at $2.00 — two dollars of sales and marketing per dollar of new customer ARR — up 14% year over year, with the fourth quartile at $2.82.
Source conventions — Benchmarkit is the source that maintains this family of definitions most rigorously and segments them by ARR, ACV, GTM motion and VC- vs PE-backed.
"New or blended?"
Canonical
YoY growth rate (%) + profitability margin (%) ≥ 40
Companies choose the flattering combination, and it is rarely disclosed. Treat any single-company Rule of 40 claim as unverifiable unless both terms are stated.
Source conventions — High Alpha defines it as "year-over-year ARR growth percentage plus last twelve months free cash flow margin or EBITDA margin", explicitly permitting either margin.
"Growth of what, plus which margin?"
Canonical
function spend including headcount ÷ ending ARR for the period.
Function-boundary choices dominate. Before comparing an S&M ratio, confirm whether CS and RevOps sit inside it — that alone can account for the entire apparent gap.
Source conventions — High Alpha uses spend including headcount as a percentage of ending ARR. LeanScale's own RevOps investment study works through the same denominator problem for RevOps specifically and reaches the same conclusion: pick the denominator before the number.
"Percent of ending ARR or of revenue, and is CS inside the numerator?"
Canonical (quarterly)
(current-quarter ARR − prior-quarter ARR) × 4 ÷ prior-quarter S&M spend
Magic number and CAC ratio are reciprocals of each other under matching conventions — a New CAC ratio of $2.00 is a magic number of 0.5. If a company's two figures do not reconcile, the conventions differ somewhere.
"Prior-quarter spend, and net new or new-customer ARR?"
Canonical
ending ARR ÷ full-time employees at period end
Cleanest metric in the corpus definitionally, and increasingly the one people most want, because it is the crispest read on whether AI leverage is real.
Source conventions — High Alpha counts full-time employees at quarter end and publishes medians and upper quartiles by ARR band.
"Are contractors and offshore staff in that headcount?"
Canonical
(subscription revenue − subscription COGS) ÷ subscription revenue
Inference cost is the live issue. High Alpha's 2025 data shows early-stage gross margins declining year over year while mature-company margins held, consistent with AI-native products carrying real variable cost of goods.
"Subscription-only or blended with services, and is inference in COGS?"
The strongest by-ACV funnel data available comes from ICONIQ's annual GTM survey. Read every figure here against the definition cards above — particularly win rate and stage conversion, where the published averages blend measurement conventions.
Average weeks · ICONIQ Growth, State of Go-to-Market 2026 (Jan 2026 survey, 157 respondents on this chart)
Per-band n: 9 · 30 · 35 · 83. The <$10K cell is thin and should be read as directional only. Shape: the cycle roughly doubles for each ~5× step in ACV. Overall average cycle fell from 25 weeks (H1 2025) to 19 weeks (H2 2025), against 23 weeks in 2024 — but over the same window the share of new-logo contracts under one year rose from 2% to 13%. Customers are signing faster for shorter terms.
Average · ICONIQ Growth, State of Go-to-Market 2026 · n=147 (2026), n=167 (2025)
| Stage transition | 2026 | 2025 | Change |
|---|---|---|---|
| New lead → MQL | 28% | 25% | +3 |
| MQL → SQL | 30% | 27% | +3 |
| SQL → closed-won | 28% | 25% | +3 |
| Demo → closed-won | 38% | 36% | +2 |
| Free trial / POC → paid | 50% | 36% | +14 |
| The POC jump is the largest single-year move in the dataset. ICONIQ attributes it to POCs being run as a disciplined motion rather than an open trial — support level scales with ACV, with a dedicated solutions architect on 49% of POCs above $100K ACV versus 25% below $50K. | |||
Average · closed-won ÷ (closed-won + closed-lost) · ICONIQ Growth · n=145 (2026), n=165 (2025)
| Opportunity source | 2026 | 2025 |
|---|---|---|
| Customer success | 52% | — |
| Sales | 43% | 38% |
| Channel / partner | 39% | 35% |
| Marketing | 27% | 23% |
| This denominator excludes no-decision outcomes entirely — see the win-rate definition card. Use these figures for the relative ranking by source, which is robust, rather than as an absolute target. | ||
Share of reps achieving quota · two sources, two bases
| Basis | Figure | Source | Note |
|---|---|---|---|
| Ramped AEs, 2025 | 62% | ICONIQ | Up from 58% (2024) and 59% (2023) |
| SMB AEs | 68% | ICONIQ | n=71 |
| Mid-market AEs | 59% | ICONIQ | n=113 |
| Enterprise AEs | 64% | ICONIQ | n=125 |
| Strategic AEs | 61% | ICONIQ | n=106 |
| All AEs, 2026 | 48% | Bridge Group | Down from 51% (2024), 66% (2022) |
| All AEs, high AI engagement | 57% | Bridge Group | vs 39% in the lowest tercile |
The Bridge Group — 2026 AE Research (n=158) and 2025 SDR Research (n=351; 83% B2B SaaS, $47M median revenue, $50K median ASP)
| Metric | Current | Prior | Direction |
|---|---|---|---|
| AE median quota | $960K | $800K (2024) | Quota CAGR ~2.4% |
| AE median OTE | $200K | $190K (2024) · $167K (2022) | OTE CAGR ~4.9% |
| Quota-to-OTE ratio | 4.6× | 4.2× (2024) | Rising |
| AE ramp time | 6.2 mo | 5.7 mo (2024) | Highest in study history |
| Experience required at hire | 3.7 yrs | 2.7 yrs (2022) | Rising sharply |
| AE win rate | 19% | 23% (2022) | 2024 figure; broader denominator |
| SDR : AE ratio | 1 : 2.4 | 1 : 2.4 | Flat since 2018 |
| SDR ramp | 3.0 mo | — | Lowest since 2010 |
| SDR tenure | 1.9 yrs | — | Highest since early 2010s |
| SDR monthly quota (meetings held) | 10 | — | Down ~40% since 2018 |
| SDR daily activities | 112 | — | 44 phone · 41 email · 19 LinkedIn · 8 other |
| SDR quality conversations / day | 4.1 | — | First rebound in study history |
| Pipeline generated per SDR | $3.78M | $2.83M (2022) | Rising |
| SDR median OTE | $80K | $80K (2022) | 68:32 base:variable |
| SDR annual attrition | 40% | — | 13% involuntary · 11% voluntary · 16% promotion |
| ACV is the largest single determinant of AE quota in the Bridge Group data: the gap between a sub-$25K-ACV seller and a $250K+ seller is roughly 2.5×. | |||
Retention is the best-instrumented area in this corpus: two independent panels of 800–1,500 companies, both publishing by ACV, both stating their formulas. Where they agree, confidence is high. Where they disagree — the top of the ACV range — the disagreement is itself informative.
Median · SaaS Capital, 2023 B2B SaaS Retention Benchmarks (12th annual survey, 1,500+ private B2B SaaS companies, excludes <$1M ARR)
| ACV band | NRR — 25th | NRR — median | NRR — 75th | GRR — median |
|---|---|---|---|---|
| Less than $12K | 93% | 100% | 105% | 90% |
| $12K – $25K | 96% | 102% | 108% | 90% |
| $25K – $50K | 97% | 103% | 111% | 92% |
| $50K – $100K | 98% | 105% | 113% | 93% |
| $100K – $250K | 101% | 107% | 118% | 93% |
| More than $250K | 103% | 110% | 120% | 93% |
| All-company medians: NRR 102%, GRR 91%. SaaS Capital's own guidance: below $25K ACV, 90% GRR is the norm; above it, benchmark to 93%. GRR is treated as table stakes — below 90% and growth falls under the population median. | ||||
Why ACV is the right cut. In SaaS Capital's words, companies that share a similar selling price have the most in common — more than company age, revenue level or industry. They organise similarly, go to market similarly, and support customers similarly.
Median · High Alpha, 2025 SaaS Benchmarks Report (9th annual, 800+ respondents)
| ACV band | % of panel | YoY growth | GRR | NRR |
|---|---|---|---|---|
| Less than $1K | 4% | 30% | 83% | 98% |
| $1K – $5K | 11% | 25% | 88% | 97% |
| $5K – $10K | 8% | 25% | 85% | 94% |
| $10K – $15K | 10% | 40% | 88% | 107% |
| $15K – $25K | 11% | 38% | 88% | 104% |
| $25K – $50K | 16% | 30% | 92% | 105% |
| $50K – $100K | 18% | 44% | 94% | 104% |
| $100K – $250K | 12% | 30% | 91% | 102% |
| More than $250K | 9% | 32% | 92% | 105% |
| High Alpha's reading: performance converges in the mid-market. Companies between $10K and $100K ACV show the strongest blend — growth above 30%, GRR near or above 90%, NRR above 104%. Sub-$10K contracts churn and cannot expand; at the top end growth slows as cycles lengthen and concentration rises. | ||||
Cross-check
The two panels agree closely on GRR — roughly 90% below $25K ACV rising to 92–94% above it — which is the strongest corroboration in this corpus. They disagree on the shape of NRR at the top end: SaaS Capital finds NRR rising monotonically to 110% above $250K, while High Alpha finds it peaking in the $10K–$100K range and flattening above. Treat the direction (higher ACV, higher retention) as well-established and the exact top-end value as unsettled.
Median and quartiles · High Alpha 2025 (n=800+)
| ARR band | NRR — lower | NRR — median | NRR — upper | GRR — median |
|---|---|---|---|---|
| Less than $1M | 78% | 100% | 116% | 92% |
| $1M – $5M | 91% | 104% | 110% | 92% |
| $5M – $20M | 95% | 103% | 115% | 88% |
| $20M – $50M | 98% | 103% | 110% | 90% |
| Greater than $50M | 101% | 101% | 108% | 88% |
| Note the spread, not just the median: sub-$1M companies range from 78% to 116%, and the range tightens steadily with scale. Early-stage NRR is a weak signal. | ||||
Median · SaaS Capital (n=1,500+)
| Cut | Group | NRR | GRR / other |
|---|---|---|---|
| Growth rate by NRR | NRR below 90% | — | 20% median growth |
| NRR 90–100% | — | 26% | |
| NRR 100–110% | — | 35% | |
| NRR 110–120% | — | 40% | |
| NRR 120–130% | — | 49% | |
| NRR above 130% | — | 70% | |
| Contract length | Month-to-month | 100.0% | 89.5% |
| Annual | 101.0% | 90.0% | |
| Multi-year | 105.5% | 95.0% | |
| Funding | Bootstrapped | 100% | 91% |
| Equity-backed | 103% | 91% | |
| Product shape | Horizontal | 102% | 90% |
| Vertical | 101% | 92% | |
| Company age | Under 3 years | 102% | 92% |
| 3–5 years | 103% | 94% | |
| 6–14 years | 101–102% | 90% | |
| Population median growth in that survey was 34%. Two cautions: young-company GRR is artificially elevated because customers have not yet had a chance to churn — the decline from 94% to 90% between years 3–5 and 6+ is a measurement artefact maturing, not performance decaying. And the multi-year retention premium may carry the same artefact: contracts that have not come up for renewal cannot churn. | |||
Median [lower quartile – upper quartile] · High Alpha, 2025 SaaS Benchmarks Report (n=800+)
| Metric | <$1M ARR | $1–5M | $5–20M | $20–50M | >$50M |
|---|---|---|---|---|---|
| YoY growth rate | 100% [29–300] | 50% [24–100] | 31% [15–72] | 30% [16–41] | 16% [10–29] |
| CAC payback (months) | 5 [2–8] | 8 [5–14] | 14 [8–22] | 20 [11–27] | 17 [13–22] |
| Gross revenue retention | 92% [80–100] | 92% [83–95] | 88% [82–95] | 90% [85–95] | 88% [84–90] |
| Net revenue retention | 100% [78–116] | 104% [91–110] | 103% [95–115] | 103% [98–110] | 101% [97–108] |
| S&M spend (% of ARR) | 28% [20–50] | 30% [20–50] | 30% [22–53] | 29% [24–34] | 25% [20–40] |
| R&D spend (% of ARR) | 43% [23–76] | 38% [24–50] | 31% [20–54] | 32% [23–40] | 30% [25–40] |
| Software gross margin | 74% [60–80] | 77% [60–85] | 80% [70–86] | 78% [71–84] | 79% [70–83] |
| Rule of 40 | 46% [25–98] | 33% [10–80] | 20% [−3–35] | 24% [11–41] | 30% [15–38] |
| Employees (median) | 9 [5–12] | 22 [15–35] | 66 [48–92] | 131 [100–223] | 361 [263–729] |
| ARR per employee — median | $55,556 | $136,364 | $166,667 | $268,235 | $277,778 |
| ARR per employee — upper quartile | $100,000 | $200,000 | $220,588 | $350,000 | $396,635 |
| Panel: ARR <$1M 23%, $1–5M 27%, $5–20M 31%, $20–50M 10%, >$50M 9%. 69% US. ICP: enterprise 37%, mid-market 35%, SMB 19%. 50% of respondents were CEO or founder. CAC payback caution from the publisher itself: early-stage companies frequently fail to allocate founder salaries, support costs and onboarding into CAC, artificially lowering the reported payback period. | |||||
Each of these is a real conflict between reputable panels. In every case the disagreement resolves to measurement rather than to one source being wrong — which is exactly why the definition layer exists.
ICONIQ's published formula excludes every deal that did not reach a won/lost decision. In enterprise sales, no-decision is routinely the largest single loss category, so removing it lifts the rate substantially. The Bridge Group measures on a broader opportunity base closer to what a CRO experiences on a forecast call.
Panel composition compounds it: ICONIQ's respondents skew to $100K+ ACV growth-stage companies with mature qualification, and 42% of the panel sits in the $100K–$500K ACV band.
ICONIQ measures ramped AEs only. At a company hiring into growth, a meaningful share of the rep base is ramping at any moment and structurally cannot attain — excluding them raises the figure. The two also cover different years, and the trend is downward.
This is not a disagreement at all — it is the most common benchmarking error in circulation. SaaS Capital and High Alpha publish medians across their whole panel. ICONIQ publishes top-quartile net dollar retention from portfolio operating data. Comparing your median against a published top quartile will always make you look broken.
SaaS Capital's own top quartile reaches 118–120% above $100K ACV — which reconciles cleanly with the ICONIQ figure once you compare like with like.
A genuine, unresolved disagreement between two large panels on the same cut. SaaS Capital finds NRR rising monotonically with ACV; High Alpha finds it peaking in the $10K–$100K range and flattening or dipping above. Both agree GRR rises with ACV.
Plausible reconciliations: different survey years and macro conditions; High Alpha's panel skews earlier-stage, so its high-ACV cell may capture young enterprise sellers rather than mature ones; and per-band sample sizes at the top end are small in both.
ICONIQ's average cycle fell six weeks across 2025. The Bridge Group's 2026 AE research reports that near-majorities saw increases in stakeholder count, cycle length, discounting pressure and deal slippage against Q1 2025.
The likely reconciliation sits in ICONIQ's own data: over the same period the share of new-logo contracts under one year rose from 2% to 13%, and three-year contracts fell from 36% to 23%. Shorter, smaller, more reversible commitments close faster. That is a change in what is being bought, not necessarily an improvement in how it is sold.
Every figure in this corpus traces to one of these. All seven are free at time of writing. Cite the publisher, the edition year and the sample — never this page alone.
The State of Go-to-Market · annual
Best for: sales cycle and funnel conversion by ACV. The only source publishing a clean by-ACV cycle-time curve.
Bias: portfolio-adjacent and upmarket. Flatters anyone below $25K ACV. Per-chart n falls as low as 9 in the lowest ACV band. Publishes averages, not medians, for funnel metrics.
iconiq.com/growth/reports/state-of-go-to-market-2026Annual survey + retention research briefs
Best for: retention by ACV. The only source with enough n to publish NRR quartiles by ACV band, and it states its formula explicitly.
Bias: a growth-debt lender, so the respondent pool skews toward companies with real revenue and reasonable retention. Self-reported.
saas-capital.com/researchSaaS Benchmarks Report · annual · 9th edition
Best for: the full operating picture at the early and growth stages, with quartiles on every metric and an explicit definitions page.
Bias: earlier-stage and PLG-weighted; the natural complement to ICONIQ's upmarket panel. This is the continuation of the OpenView benchmarks — Kyle Poyar took the report to High Alpha after OpenView wound down.
highalpha.com/saas-benchmarksAE and SDR Metrics research · biennial · since 2007
Best for: rep-level economics. Quota, ramp, attainment, OTE, SDR:AE ratio, activity levels — nothing else covers this ground, and the time series runs to 2007.
Bias: the most conservative panel here; its win rate and attainment figures run well below the VC-published surveys. Arguably the most representative of an ordinary company.
bridgegroupinc.com/researchSaaS Performance Metrics + B2B Marketing Benchmarks · annual
Best for: definitional rigour on efficiency metrics. Maintains the new/blended/expansion CAC ratio family properly and has an interactive filter by company profile.
Bias: per-chart sample sizes are the least transparent of the seven. Strong on finance metrics, thinner on funnel.
benchmarkit.aiPrivate Company SaaS Survey · annual · 16th edition
Best for: the P&L context around the funnel — CAC ratio, magic number, S&M as a share of revenue, retention, and the longest-running private SaaS operating time series.
Bias: an investment bank's survey; respondents skew toward companies of banking interest. Headline findings are public; the full detail requires requesting the report.
key.com · SaaS surveyField studies from a delivery panel of 40–65 B2B software companies
Best for: what companies actually run, as opposed to what they report on a survey. The only non-self-reported panel in this corpus.
Bias: LeanScale's own customer base — growth-stage B2B software that has engaged a RevOps partner. Smaller n than the survey houses.
knowledge.leanscale.team01
No LeanScale customer data underlies the benchmark figures on this page. Every number is attributed to a named third-party publisher, edition and sample. Where LeanScale's own measurement appears it is labelled as such.
02
Figures were taken from the publishers' own PDFs, research briefs and press releases. Search results for "SaaS benchmarks" are now dominated by AI-generated aggregator sites that invent precise-sounding numbers with no survey behind them; none were used, and none should be.
03
Survey panels are opt-in, and companies having a bad year do not fill out the survey. Assume a consistent upward bias across all of it. Billing-data sources read low for the opposite reason; bracketing the two is better than trusting either.
04
SaaS Capital, High Alpha and ICONIQ publish formulas; those are quoted. Where a publisher does not define a metric, the definition card says so and the figure is flagged as convention-blended rather than presented as precise.
05
Where a publisher discloses per-chart n, it is carried through — including where it is uncomfortably small, such as the nine companies behind the sub-$10K ACV sales-cycle figure.
06
Cite the original publisher, edition year and sample — for example: "ICONIQ Growth, State of Go-to-Market 2026 (January 2026 survey, n=157)". This corpus is a finding aid and a definition layer, not a primary source.
The standing recommendation
Use this corpus to answer "are we structurally normal for our price point?" For "what should we fix?", compute your own stage conversion on an entered-cohort basis and compare the company against its own trailing quarters. That comparison is the only one where the definition is guaranteed constant — which is the whole argument of this page.