Public benchmark dashboard with anonymized industry performance data for sales benchmarks 2026.
Win rate
25%
Sales cycle
58d
Avg deal size
$16,200
Quota attainment
67%
Team-size, region, and motion-specific benchmark slices are available for signed-in users.
The Sales Metrics Dashboard shows anonymized sales benchmarks by industry so you can see how win rates, sales cycle length, average deal size, and quota attainment compare across different types of businesses. It answers the question every founder eventually asks: are our numbers normal?
Why it matters: without a reference point, a 20% win rate is just a number. Against an industry benchmark it becomes a signal — either you are qualifying well and competing hard, or leads are leaking somewhere between first contact and close. Benchmarks turn vague unease about sales performance into specific questions you can act on.
The dashboard is public and free. It is designed for small business owners and sales leaders who want context for their own pipeline reviews, not enterprise analysts.
Filter the dashboard to the industry closest to yours. Cross-industry averages hide huge differences — compare like with like.
Healthy businesses exist across the whole benchmark range. What matters is whether you fall inside it or well outside it.
From your CRM (or spreadsheet), calculate the same four metrics for the last quarter: deals won / deals closed, average days from creation to close, average won-deal value, and percent of reps hitting target.
Pick the one metric furthest from benchmark and dig into stage-by-stage pipeline data to find the cause. Fixing one real bottleneck beats nudging four metrics at once.
Usually a qualification or follow-up problem: too many poor-fit deals entering the pipeline, or warm deals going quiet between touches. Check speed-to-lead and how many follow-ups a typical lost deal received.
Find the stage where deals sit longest. Often it is proposal-sent-awaiting-response — a follow-up cadence and deal-risk alerts shorten it more than any pitch change.
Look at discounting frequency and whether reps quote the smallest viable package by default. Sometimes the fix is pricing structure, not selling harder.
When everyone misses, the quota or the pipeline math is wrong — not the reps. Check whether pipeline coverage (open pipeline ÷ target) actually supports the number you set.
The owner feels deals drag but has no reference. The dashboard shows his sales cycle runs far past the industry range. Stage analysis reveals proposals wait untouched for two weeks on average — a three-step follow-up cadence pulls the cycle back toward normal.
Before assigning targets to two new reps, the sales manager checks quota attainment and deal-size benchmarks for her industry, then sets quotas her actual pipeline volume can support instead of copying a SaaS blog's advice.
Ahead of an annual planning session, the founder pulls benchmark ranges for win rate and cycle length, puts the agency's own CRM numbers beside them, and turns a vague "we should sell better" discussion into two specific fixes.