What is a good sales close rate? Benchmarks by industry

There is no single good sales close rate, and the three most-quoted figures are not comparable. HubSpot publishes 20% across industries. RAIN Group publishes 47%, but only of opportunities that reached a proposal. Ebsta’s 4.2-million-opportunity 2024 dataset publishes no absolute rate at all — only year-on-year change.

• Cross-industry anchor: 20% (HubSpot, denominator stated two different ways on the same page).
• Proposal-stage anchor: 47% average, 62% for top performers (RAIN Group, self-reported survey of 472 sellers).
• Largest CRM dataset: 4.2 million opportunities, 530 companies, $54bn (Ebsta and Pavilion) — reports win rates as −18% year on year, not as a level.
• Australia: no close-rate dataset exists. We looked.
• The rule that matters: a close rate without its denominator is not a number.

What counts as a good sales close rate?

If you need one number to hold against yourself today: 20% of qualified opportunities is the most widely published cross-industry figure, and 40–47% of issued proposals is the most widely published late-stage figure. Those are not two views of the same team’s performance — they are two different fractions, and a business can report both truthfully in the same month.

Here is every close-rate benchmark we could trace to a named publisher with a stated method, checked at source on 7 September 2026, with what each one actually measures and what it does not tell you.

The most useful sentence on any of those pages is HubSpot’s own: “The most meaningful benchmark that sales leaders have is their own internal data.” The publisher of the 20% figure says do not lean on the 20% figure.

The three-denominator rule: why close-rate benchmarks are not comparable

The three-denominator rule: never quote or compare a close rate without saying which of three denominators it uses — leads created, qualified opportunities, or proposals issued. A close rate carrying no denominator is a ratio missing half its definition, and comparing two of them is arithmetic with different units.

A worked illustration, using round numbers rather than measured data. One team, one month:

• 400 enquiries received
• 150 marked as qualified opportunities
• 60 proposals issued
• 18 deals closed won

Close rate of leads: 18 ÷ 400 = 4.5%. Close rate of qualified opportunities: 18 ÷ 150 = 12%. Close rate of proposals issued: 18 ÷ 60 = 30%. Same team, same month, same eighteen deals, and the honest answer spans a factor of 6.7. A manager comparing that 4.5% against RAIN Group’s 47% is comparing leads to proposals and concluding the reps are ten times worse than average. They are not. Choosing and holding one denominator is a page of its own in this series; what matters here is that the benchmark you were about to use almost certainly does not share yours.

Close rate by industry: what is actually published, and what is not

Two publishers put close rates on the record by industry, and neither discloses a sample size. HubSpot covers three — software 22%, finance 19%, biotech 15% — and First Page Sage covers 27, from HVAC at 29% down to biotech at 11%, measured sales-qualified lead to closed won across its own client base from 2019 to 2025. Beyond those two, every industry table we could trace either recycles HubSpot’s three figures, restates a traffic-conversion dataset as a sales one, or supplies numbers with no publisher attached.

Neither of the two survives the test this page applies to everyone else, and the failure is the same one in both cases: no sample size. HubSpot attributes its figures to a “2024 survey” without saying how many companies answered. First Page Sage discloses its method plainly — its own internal sales data plus clients it has worked with, augmented by interviews with client contacts — but never says how many clients or how many deals sit behind a row, which is why a 29% and an 11% in the same table cannot be given a margin of error or told apart from sampling noise. Use First Page Sage for the shape of the spread, on its own SQL denominator, and not as a target.

There is a further complication that the industry tables tend to omit. RAIN Group, which surveyed across industries and company sizes, reported that “though there were slight variations among industries and company sizes, their win rates were similar” — their finding is that industry is a weak explanatory variable at the proposal stage. Ebsta’s data cuts by deal size and stakeholder count rather than by industry, which points the same way: how big the deal is and how many people have to agree explains far more of the variance than what sector you are in.

The practical consequence: if you sell $8k services, you have more in common with anyone else selling $8k services than with a “professional services average” that also contains $2m engagements.

Where no credible close-rate benchmark exists at all

We went looking for benchmarks rather than assuming they exist, and the absences are as informative as the figures. Method: full-text search of each published report on 7 September 2026, searching for “win rate” and “close rate” in the source document rather than trusting a summary of it.

• B2B SaaS has no published win rate in its flagship benchmark. The 2025 B2B SaaS Performance Metrics Benchmarks reports on N = 583 participants and covers growth rate, CAC, retention, expansion and headcount efficiency. The phrase “win rate” appears zero times in the report.
• Neither does the main AE benchmark. The Bridge Group’s 2026 AE Models, Motions & Metrics research covers 158 B2B companies and publishes quota attainment, OTE, ramp time and AI adoption. It does not publish a win rate.
• Australia has no close-rate dataset. Not from the ABS, not from an industry body, not from a vendor. Any “Australian close rate benchmark” you find is a global figure with a flag on it.
• Trades, home services, fitness, and most local B2C verticals have nothing credible. Quoted numbers in these categories trace back to vendor blog posts citing other vendor blog posts.
• The most-shared 2026 figure could not be verified at source. Ebsta’s own public summary of the 2025 GTM Benchmarks states that “win rates have improved from −18% in 2024 to −10% in 2025” — a rate of change. Secondary write-ups render this as an absolute “19%, down from 29%”. That absolute figure does not appear in Ebsta’s public summary, and the full report is behind a form, so we could not confirm it. We have not used it on this page.

How much can a close rate even be trusted at your volume?

Below about 30 opportunities in the period, your close rate’s margin of error is wider than the entire published gap between industries. This is not an opinion about small businesses; it is the binomial arithmetic.

Take a measured close rate of 20%. The 95% confidence interval under the normal approximation is 1.96 × √(0.2 × 0.8 ÷ n):

If you close fewer than about 40 deals a quarter, that is the band you are in. At that volume the honest management move is to report counts — opportunities created, proposals issued, deals won — and to treat the ratio as a trailing twelve-month figure only.

Is close rate a rep metric or a lead-flow metric?

Close rate is the only pipeline metric that four different functions can each move without touching the sales team. It is habitually treated as a scorecard for closers, and that is why it gets managed badly: a manager who coaches objection handling when the real cause is a fourteen-hour first-response time will get nothing for the effort.

Only the last row is genuinely the rep’s, and even there Ebsta’s dataset found the drivers are procedural: opportunities that skip a stage are 46% less likely to close, close dates moved more than three times cut win rates by 77%, and discounting raised before the negotiation stage dropped win rates by 39%. Our companion pages on lead response time benchmarks for Australia and on why sales conversion is a follow-up problem rather than a lead problem cover the upstream inputs in detail.

Why your close rate can fall while your revenue rises

Any improvement that widens the top of the funnel dilutes the denominator, so close rate falls even as the number of deals goes up. This catches out a lot of teams six weeks after they fix their follow-up, and it is worth understanding before you set a close-rate target as a KPI.

Suppose the team above doubles the number of qualified opportunities from 150 to 300 by contacting leads it previously never reached. The newly reached leads are, on average, colder than the ones who put their hand up twice. Say they close at half the rate: 18 deals from the original 150 plus 9 from the new 150 = 27 deals, a 50% increase in revenue, on a close rate that has fallen from 12% to 9%. Both numbers are correct. Only one of them pays wages.

Report close rate alongside the volume it was measured on, always. A close rate is a quality signal about a fixed cohort, not a performance score, and it moves for reasons that have nothing to do with whether anyone got better at closing.

What we see when a 2020-era sales operation moves to 2026 operations

The honest framing for our own numbers: these are operator observations from our client work, not a study. There is no published n, no fixed window and no dataset behind them, and we would not accept them from someone else without those things. We are stating them plainly and labelling them, because they are the reason the upstream rows in the table above matter more than the rep row.

In our own client work, a business still running 2020-era sales operations — a human checking a form inbox during business hours, two or three follow-ups, no structured objection capture — typically sees roughly a 3× improvement in conversion from paid ads after those operations are rebuilt. On individual levers, we typically see speed-to-lead alone worth about 3×, doubling contact rate worth about 2×, and doubling appointment set rate worth about 2×.

Those numbers do not multiply, and we say so. 3× × 2× × 2× is 12×, which we have never seen. The levers overlap heavily: responding in sixty seconds is a large part of how contact rate doubles, and contact rate is a large part of how set rate doubles. They are three views of one improvement, which is why the headline is around 3× rather than the product of the parts. Keeping our own claim visibly separate from published research is the same discipline this page applies to everyone else’s benchmarks — and the one figure of ours that does have a published method, the 7× average sales lift and how it is calculated, discloses that its median is closer to 4×.

The method itself is not a secret and a competent operator can run it in-house: first contact inside sixty seconds, seven days a week including evenings; six to eight touches across call, SMS and email over about fourteen days; every objection captured as structured data rather than free text; and no-shows routed back into the sequence rather than marked lost.

The arithmetic worth doing is what that costs to run. Sixty-second response outside business hours means rostered after-hours coverage or an automation layer that can hold a real conversation. Six to eight touches across three channels is roughly fifteen to twenty-five minutes of handling per lead, so a hundred leads a week is most of a full-time role before anyone gets on a sales call. And the whole thing degrades quietly: the first thing to break at volume is the after-hours window, and nobody notices, because leads that were never contacted never enter the close-rate denominator.

That is the trade to weigh: run that layer in-house, or run it as AI appointment setting or as a full AI outbound sales function. This page sits in our sales pipeline stages series, which indexes each stage of the pipeline — lead, contact, appointment set, show, proposal, close — as its own benchmark, diagnostic and calculation page.

Frequently asked questions

What is a good sales close rate?

The most widely published cross-industry figure is 20%, from HubSpot’s close rate benchmarks. It is only meaningful if your denominator is qualified opportunities. Measured against proposals issued, the comparable figure is 47% from RAIN Group. Measured against all leads created, a healthy business can sit in single digits and be performing well.

Is close rate the same as win rate?

No, and the difference is the denominator. HubSpot’s page states that close rate measures “deals closed against all leads fed into the pipeline” while win rate measures deals won against opportunities that reached a final decision stage, then gives a close-rate formula of deals won divided by total qualified opportunities, which is a third definition again. RAIN Group defines win rate as the percent of opportunities proposed or quoted that were won. Pick one, write it down, and never compare across definitions.

What is the average close rate by industry?

Two publishers put one on the record, and neither discloses a sample size. HubSpot gives three industries — software 22%, finance 19% and biotech 15% — and First Page Sage gives 27, from HVAC at 29% down to biotech at 11%, measured sales-qualified lead to closed won across its own client base from 2019 to 2025. Other industry tables online are not traceable to a publisher at all. RAIN Group’s survey of 472 sellers found only slight variation between industries, and Ebsta’s data segments by deal size and stakeholder count instead, which suggests deal size explains more of the difference than sector does.

Do the large B2B datasets publish an average win rate?

Mostly no. The Ebsta and Pavilion B2B Sales Benchmark Report 2024 analysed 4.2 million opportunities across 530 companies and reports win rates only as a year-on-year change of minus 18%, never as a level. The 2025 B2B SaaS Performance Metrics Benchmarks, with 583 participants, does not contain the phrase “win rate” at all.

What is a good close rate in Australia?

There is no Australian close-rate dataset from the ABS, an industry body or a vendor, so any figure presented as an Australian benchmark is a global number relabelled. Use your own trailing twelve months instead, and if you want an Australian comparison on an input you can actually measure, lead response time is the one with local data behind it.

Why did my close rate drop after we improved our follow-up?

Because reaching leads you previously never contacted adds colder records to the denominator. If 150 opportunities producing 18 deals becomes 300 opportunities producing 27 deals, revenue rose 50% while the close rate fell from 12% to 9%. Always report close rate next to the opportunity count it was measured on.