Quota Attainment Calculator: Distribution & 60% Rule Diagnostic

At the rep level, attainment is closed bookings divided by quota — the standard percentage every comp plan tracks. At the team level, the metric that actually signals whether the plan is working is the share of reps at or above 100%. A team where 8 of 12 reps cleared quota has a 67% attainment rate; a team where 4 of 14 cleared has 29%. Two very different stories about the same revenue number — and the difference shows up in retention three quarters later, not in the closed-won column today.

The classical practitioner benchmark is the Pavilion 60% Rule: in a healthy SaaS sales org, roughly 55–65% of reps land at or above 100% of quota each quarter. The number is published by the Pavilion compensation community and is widely cited across SaaS comp planning. It is not a hard target. It is a sanity check. If the share is materially above the band, quotas were probably set too low; if it is materially below, quotas were too aggressive and reps will churn out of the comp plan before next planning cycle. Either tail produces the same outcome — a broken team — through opposite mechanisms.

This calculator turns the rule into a one-screen audit. Drop your reps in (or paste from a CRM export), and the histogram immediately shows whether the team is bell-shaped around 100%, right-skewed (mass at low values, tail to the high — quotas too hard), left-skewed (mass at high, tail to the low — quotas too easy), bimodal (two clumps with a hollow middle — broken ramp), or sandbagged (suspicious cluster at 100–105% — comp truncation pattern).

The per-rep formula is trivial: closed_bookings / quota × 100. The team-level formula needs care: count(reps with attainment ≥ 100%) / count(active reps) × 100. The denominator is where most teams get it wrong — including ramp reps under 12 months tenure pulls the rate down by 10–20pp and makes the comp plan look broken when it is not.

Use the Scope filter at the top of the tool to flip between Whole Team, Exclude Ramp, and Tenured Only. The healthy 55–65% benchmark applies to the tenured cohort. A team that reads 38% on the whole-team view but 61% on the tenured view does not have a quota problem — it has a ramp problem. Different fix, different vendor, different cost.

Worked example. Twelve mid-market AEs: three reps at 142, 128, 118; three at 110, 104, 98; three at 92, 88, 76; three at 64, 52, 38 (the last two flagged as ramp). The whole-team rate is 5 of 12 ≥ 100% = 42%. With ramp excluded the denominator drops to 10 active reps and 5 of 10 = 50%. Still under the band, but the verdict shifts from "comp plan probably broken" to "tight but fixable with a small cut".

The shape diagnosis is where this tool earns its keep. The histogram bins every rep into one of five attainment bands — under 60%, 60–80%, 80–100%, 100–120%, over 120% — and the SVG bell curve overlay shows the fitted normal that those bins should approximate. Five outcomes are diagnostically distinct.

Normal. Roughly bell-shaped, centered near 100%, mass dropping off symmetrically. This is what a well-tuned plan looks like. Coaching the middle has a positive return; cuts will overshoot.

Right-skewed. Mass clusters in the under-60 and 60–80 bands with a thin tail of overachievers. Quotas are too aggressive. The fix is a 10–18% cut, not more coaching.

Left-skewed. Mass clusters at 100–120 and over 120 with a thin tail of strugglers. Quotas are sandbagged or undercooked. The fix is a 10–15% raise on the next cycle, not chasing a 12th SDR.

Bimodal. Two peaks separated by a hollow middle — a clump in the 30–60 band and a clump in the 100–140 band, with almost no one in 80–100. Coaching cannot fix this. You have two teams, not one — usually a ramp problem (new hires under-supported) or a segment problem (territories not balanced). Audit ramp programmes and territory assignments before touching the quota number.

Sandbagged. A suspicious spike at 100–105% with thin tails. This is almost always a comp-truncation artifact — reps stop logging deals once they hit the accelerator threshold and pull the rest into next quarter to start fresh. The fix is structural (uncap, or change accelerator boundaries), not behavioral.

The coefficient of variation (CV = stdev ÷ mean) is the sanity-check companion. CV under 0.30 is a tight cluster; over 0.55 means coverage width is chaotic and territories are likely mismatched. Concentration risk is the other companion: when one rep produces more than 35% of revenue, the tool flags a Pareto trap — the forecast is dangerously concentrated, and the next-best move is lifting the middle, not adding capacity at the top.

The 55–65% band is the cross-segment median, but stage and motion shift the target. Industry consensus across SaaS comp surveys produces the following adjusted benchmarks:

• SMB SaaS (transactional): ~65% — faster cycles and higher pipeline volume push base attainment up.
• Mid-Market SaaS: ~60% — the Pavilion classical target; mid-cycle, mid-ACV, blended motion.
• Enterprise SaaS: ~55% — long cycles and fewer deals per rep mean per-rep volatility is high.
• PLG / Sales-Assist: ~70% — pipeline-rich, qualified inbound, easier quotas by design.
• New team < 2 yrs: ~45% — ramp drags the mean; this is acceptable if the tenured-only filter still hits 55%+.
• Mature team > 5 yrs: ~65% — stable performers, well-tuned plan, top of the band.

Top-quartile orgs across all segments run at 65–75% with a CV at or below 0.30. Bottom-quartile orgs run below 40%, or with a CV above 0.55, or with a bimodal shape. The point of the benchmarks is not to copy the number — it is to know which conversation to have at the next QBR.

Three approaches dominate practitioner use, and most teams blend them:

1. Top-down. Start with the revenue target, divide by the rep count, add a coverage buffer (typically 15–25% over plan to absorb misses). Fast and defensible to finance, but blind to per-rep variance.
2. Bottom-up. Start with rep-level account modeling — name the accounts, model deal probability, sum the reps. Slow, but reveals territory imbalance before it shows up in attainment.
3. Account-tier. Weight quotas by account tier (Tier 1 / Tier 2 / Tier 3) so reps with a heavier book carry a larger number. Prevents the sandbag-by-territory pattern.

Whichever you pick, back-test the proposed quota against last year's actuals using this tool's reverse calculator. If the model lands the team at 38% at quota, the cut button is your friend. If the model lands the team at 78%, you are leaving money on the table.

1. Paste your team from a CRM export (name, attainment %, optional revenue).
2. Set scope to Tenured Only if you have ramp reps under 12 months — the benchmark applies to tenured cohorts.
3. Read the hero — % at quota, 60% Rule offset in pp, zone verdict.
4. Read the histogram shape and the bell-curve overlay; flag bimodal or sandbagged immediately.
5. Check the concentration meter — if one rep is over 35% of revenue, flag a Pareto trap.
6. Open the What-If simulator. Pull the four levers (cut, coach, fire, dilute). The combined-effect tile shows the new % at quota and the bookings delta.
7. Save the baseline as Scenario A and the proposed fix as Scenario B. Read the delta verdict.
8. Open Exec Deck (press E), copy the board summary, paste into the QBR doc.

The whole workflow takes under five minutes once the rep list is in. Run it before each quota-setting cycle, after each comp-plan change, and at every QBR. The cumulative trend (last 8 runs) is shown as a sparkline so you can see whether the plan is converging or drifting.