Ask a room of sales leaders what share of reps should hit quota and somebody will say 60 to 70% before you finish the question. Ask where the number came from and you get a shrug and a link to a blog post that links to another blog post.
The number has a likely origin. It describes a narrower group than most people assume, and recent rep-level data sits below it. More to the point, a single percentage can’t tell you which lever to pull in Q4 planning. The shape of your own attainment distribution can.
What percentage of sales reps should hit quota?
The most-cited target, 60 to 70%, traces to ICONIQ Growth’s sales compensation guide (May 2023), built on a March 2023 survey of 236 GTM executives at B2B SaaS companies, and it describes ramped AEs only. Rep-level data since then runs lower: The Bridge Group’s 2026 AE report (June 2026) found 48% of reps at quota. Treat 60 to 70% as what ramped teams reported, and read your own distribution before you pick a target.
A third figure muddies it further. In a March 2023 Forrester blog post, Seth Marrs wrote that the average quota attainment for B2B sales organizations is only 47%. That measures how much of quota the average organization reaches, which is a different question from how many reps cross the line.
Put all three figures on one slide and they look like a trend line. They come from different years and answer different questions, so they can’t carry that weight in a planning meeting.
Where the 60 to 70% figure comes from, and what it leaves out
ICONIQ Growth’s figure is a leader-reported average for ramped AEs at B2B SaaS companies. It leaves out reps who are still ramping, and the same March 2023 survey found 58% of companies over-assign quotas, typically by 20 to 30%. Both details change what the percentage means for your team: the group being measured is narrower than “all reps,” and the quotas behind it are often set above the company plan.
The survey question itself was “What percent of your organization’s ramped account executives achieve quota?” Executives answered it about their own teams, so the figure is what leaders reported, not a rep-by-rep count. Top-quartile companies in the same guide reported 70 to 80%.
Two things follow for your own number.
If your attainment figure includes reps in their first months, you’re comparing a mixed team against a ramped-only benchmark, and your number will look worse than the comparison deserves.
If your rep quotas add up to more than the company plan, the company can hit its number while a large share of reps miss theirs. That reads very differently in a board deck than it does in a comp review, and it’s worth knowing which story you’re telling before you walk in.
Why one percentage hides the answer
Two teams with the same share of reps at quota can have opposite problems. On one, most reps sit a little under the line. On the other, a few reps carry the number and the rest sit far below it. Ebsta and Pavilion’s B2B Sales Benchmark Report 2024, covering 4.2 million opportunities across 530 companies, found 17% of reps generated 81% of revenue. You only see which team you run by plotting each rep.
The same 2024 report found only 15% of sales teams had more than 50% of reps achieve 80% or more of quota. The Bridge Group’s 2026 report describes the spread moving in the wrong direction: fewer companies in the 50 to 90% range for share of reps at quota, and more in the 0 to 30% range.
Averages move slowly. The spread is where the drag shows up first, and the spread is what a single percentage throws away.
How do you build your own attainment distribution from the CRM?
Pick one closed period, list your ramped reps, and divide each rep’s closed-won value in that period by that rep’s quota for the same period. Sort the reps into bands, from well below quota to at or above it, and count how many land in each band. Your CRM holds both inputs. The plot itself usually ends up in a spreadsheet, which is fine.
- Pick one closed period. Use the last full period you set quota against, quarter or year. Don’t blend periods, because quota changes between them.
- List ramped reps only. Leave out reps hired during the period and reps who moved territory mid-period. Write down who you left out and why, so the people reading the chart know what the denominator is.
- Pull closed-won value for each rep for that period, using the same owner and close-date fields your comp plan uses.
- Pull each rep’s quota for the same period. Where it lives depends on the CRM:
- HubSpot. Goal reports have a User performance tab that shows performance by individual user, filterable by team, user and pipeline. A quota-based goal has to be created with the Forecasted Revenue template to appear on deal-based reports, and the quota shown there is the sum of the user or team goals you set (HubSpot Knowledge Base, last updated July 2026).
- Salesforce. The ForecastingQuota object holds an individual user’s or territory’s quota for a specified period, with QuotaAmount as the revenue quota for that user or territory and period (Salesforce Developers object reference, Winter ‘27). If you run territory quotas, decide up front whether you’re reading attainment by rep or by territory.
- Pipedrive. Goals in Insights include a Won goal that tracks the number or value of deals marked as won, and goals can be assigned to users, teams or the whole company. Viewing goals beyond your own needs access to company statistics and other users’ statistics (Pipedrive Knowledge Base, last updated September 2026). Use the value version if quota is revenue.
- Divide. Closed-won value for the period over quota for the period gives you each rep’s attainment.
- Band and count. For a first pass, three bands are enough: well below quota, close to quota, and at or above quota. Also count how many reps reached 80% or more of quota, the threshold Ebsta and Pavilion used, so you can set your team beside their 2024 finding.
- Plot it. A bar for each band works. So does one dot per rep, sorted from lowest attainment to highest. Look at the shape before you look at the average.
What does each distribution shape tell you?
The shape usually points to one lever. Reps bunched a little under the line often point to quota level. A few reps far above with most far below often points to territory or account split. Two clusters often means two different jobs sharing one quota. Most reps above the line usually points to a quota set low or pipeline held back. Each shape comes with a different first question.
| Shape | What it usually points to | First question to ask |
|---|---|---|
| Most reps bunched a little under quota | Quota set a step too high for the whole team | Do rep quotas add up to more than the company plan, and by how much? |
| A few reps far above, most far below | Territory or account imbalance, or heavy reliance on a few sellers (Ebsta and Pavilion’s 2024 report found 17% of reps generated 81% of revenue) | Do the top reps hold the best accounts or territories, or are they winning comparable books? |
| Two separate clusters | Two different jobs or segments carrying one quota | Do the clusters split by segment, deal size or motion? |
| Most reps at or above quota | Quota set too low, or pipeline held back from an earlier period | Did deals slide into this period that belonged in the last one? |
Shapes overlap, so treat the table as a starting hypothesis to test. If most reps land at or above the line, read how pipeline reviews hide sandbagging before you raise quota.
One caution before any of this goes in front of a board. The chart is only as good as the closed-won data under it. If close dates, deal owners or amounts are off, the shape is off too, which is the same problem behind HubSpot reports that don’t match reality.
What to change once you know the shape
The lever follows the shape: quota level, territory or account split, or the role definition. Change people first and you can lose a full cycle when the shape points to the plan. A team bunched under the line needs a quota conversation. A team where a few sellers carry the number needs a territory conversation first, and a team with two clusters needs a conversation about what the job is before either.
When most reps sit far below the line, the cause often sits upstream of the quota itself, in how little of the week sellers spend selling and in the wider quota attainment crisis. The per-rep data you pulled feeds the forecast too, so the same cleanup sharpens sales forecast accuracy.
You can walk into planning with one percentage and a debate about whether it’s good. Or you can walk in with the distribution, the list of reps you left out, and the lever the shape points to.
One of those ends with a new target. The other ends with a decision.
If you want to start from closed-won data you can trust by rep and by period, the free CRM Diagnostic runs 40+ automated checks on your CRM. Book an intro here.