A quota that is too easy breeds a mediocre team and a comp budget that pays for underperformance. A quota nobody can reach drives reps out and kills good pipeline habits. Setting the right number is a modeling job with a clear goal: a healthy attainment distribution across the whole team.
Before You Start: Find Out Why Last Year Missed
If last year's data is unreliable, work out why before you reuse any of it. Each cause needs a different fix:
| What happened last year | What it does to the data | What to change |
|---|---|---|
| High turnover | Average tenure fell, so the team looked less productive | Build ramp by position and region, and look at productivity over about three years |
| The market shifted | Buyers delayed decisions and cycles lengthened | Revisit market potential and cycle assumptions |
| A new competitor arrived | Pricing pressure pulled deal sizes down | Revisit average sales price, then quota or the comp plan |
Step 1: Anchor to the Company Revenue Target
Start from the top. What revenue does the company need this period? The team's total quota has to cover it.
Most companies set total quota above the target. The buffer covers reps who leave, reps still ramping, and the fact that no team produces 100% of quota. Size it from your own attainment history and your mix of ramped and new reps.
Write down the target, the total quota and the buffer. If leadership changes the target later, rerun the model.
Step 2: Run the Bottoms-Up Build
The top-down number tells you what you need. The bottoms-up build tests whether it is achievable.
For each territory or rep slot, estimate what can realistically be sourced and closed given:
- Total addressable accounts in the territory. - Historical win rates in that segment. - Average selling price for the segment. - Expected pipeline generation rate for the period.
Territories differ. A rep in a dense, well-worked enterprise patch faces a different job from a rep opening a new region. The bottoms-up build shows this, so score territories first.
If the bottoms-up total falls well short of the target, you have three choices: add headcount, change the target, or accept that the plan is underfunded. Raising each rep's quota to close the gap does not work.
Step 3: Apply Territory Fairness Adjustments
Flat quotas across unequal territories are unfair by design. Reps in rich territories overachieve. Reps in thin ones miss however hard they work. Both hurt retention and the forecast.
Give each territory a potential score, then set quota from the score rather than at one flat rate.
| Territory Tier | Potential Index | Quota Adjustment |
|---|---|---|
| Tier 1 (high density) | 1.20 | Above base |
| Tier 2 (standard) | 1.00 | Base quota |
| Tier 3 (developing) | 0.80 | Below base |
Step 4: Account for Ramp and Role Differences
New hires should not carry a full annual quota from day one. In ORM customer data it can take a seller 15 to 18 months to confidently hit quota, and the ramp differs by role and region. A blended assumption such as 25%, 50%, 75% and then fully productive is almost never true across a whole company. Assign quota in proportion to the ramp curve for that role and region. A rep hired mid-year on a six-month ramp should carry a prorated quota that reflects their productive capacity for the period, not a full-year number halved.
Different roles need different quota designs too. A new-logo hunter and an expansion rep produce different things. One design for both puts the comp plan at odds with the job.
Step 5: Run the Attainment Distribution Test
This is the check that matters most. Once quotas are set, model the likely spread of attainment from past performance.
Think of quota as a curve. ORM's COO plans for average attainment around 75% to 80% of quota, with a tail of reps below that and a tail above it. A well-calibrated design produces a distribution roughly shaped like this:
- A small top tier of reps (roughly the top quartile) consistently exceeds quota. - The largest group attains in a range that reflects strong but not universal quota achievement. - A meaningful portion misses, indicating areas where performance management is warranted. - Very few reps at or near zero, which signals pipeline problems or hiring failures rather than quota problems.
If nearly everyone beats quota, the number is too low. If most reps fall well short, it is out of touch with the market. Read the shape of the spread along with the average.
Here is a hypothetical check. A team of ten reps carries $1 million each, $10 million in total, against a $10 million revenue target:
| Average attainment | Revenue produced | Against the $10 million target |
|---|---|---|
| 75% | $7.5 million | $2.5 million short |
| 80% | $8.0 million | $2.0 million short |
| 100% | $10.0 million | On target, but only if every rep averages quota |
What If the Plan Still Comes Up Short?
Find out why before you pick the fix. Sometimes the gap is the product. Look at the product in its market and ask whether it is still competitive. If it has slipped, the support goes to product.
Sometimes the answer is a smaller number. Call down your number, or decline to sign up for a larger one with the board, when the operating assumptions cannot support it.
Either way, say it early. Bad news doesn't get better with time.
What Are Typical Sales Quotas?
For SaaS account executives, annual quotas usually sit between $500,000 and $1 million. Enterprise sellers typically carry $1 million or more. The ranges stay relatively stable, which is why raising quota is a weak way to close a gap in the plan.
Changing quota does make sense when the goal is to shape behavior. A company pushing a strategic product can offer quota relief or pay a multiple on it. That is a different decision from raising everyone's number because the company wants more revenue.
For the underlying definitions, see quota planning and quota attainment. Territory adjustments connect directly to the fair share quota method.
Common Mistakes
Setting quotas without territory data. Flat quotas across different territories are unfair and make attainment data misleading. Territory potential has to be an input. Copying last year's quota. If your segment mix, headcount or market changed, last year's number is the wrong place to start. Rebuild from the target. Skipping the attainment test. Setting quota without modeling the likely spread is guesswork. The spread shows problems before they reach the field. Setting quotas late. Reps need their number at the start of the period. A late quota slows pipeline building and causes comp and legal headaches.Frequently Asked Questions
What makes a sales quota aggressive but achievable?
Most reps can hit it with strong effort, and some fall short. If nearly everyone hits it, it is too low. If most reps miss every year, it is out of touch with the market. The spread of attainment across the team tells you which.Should quotas be set top-down or bottoms-up?
Both. Start with the company target so the total adds up. Then check it against what each rep and territory can really produce. If the gap is large, change the hiring plan, the territory design or the target, not the math.How do you adjust quotas for territory differences?
Give reps in territories with less opportunity a lower quota. Score each territory on its market, account density, competition and travel, then set quota from that score.Frequently Asked Questions
What is a typical sales quota for a SaaS account executive?
Usually between $500,000 and $1 million a year. Enterprise sellers typically carry $1 million or more. Those ranges move little from year to year.
What average quota attainment should you plan for?
Around 75% to 80%, with a tail of reps below and above. If the revenue target only works when the average rep hits 100%, or would push average attainment toward 50%, you are short of selling capacity.
What makes a sales quota aggressive but achievable?
Most reps can hit it with strong effort, and some fall short. If nearly everyone hits it, it is too low. If most reps miss every year, it is out of touch with the market. Look at the spread of attainment across the team to tell.
Should quotas be set top-down or bottoms-up?
Both. Start with the company target so the total adds up. Then check it against what each rep and territory can really produce. If the gap is large, change the hiring plan, the territories or the target, not the math.
How do you adjust quotas for territory differences?
Give reps in territories with less opportunity a lower quota. Score each territory on its market, account density, competition and travel. Then set quota as a share of that score, so a strong rep in any territory has a similar chance to hit it.
What should leadership do if the team cannot support the revenue target?
Work out why first. If the product has lost ground in its market, the investment belongs in product. If the operating assumptions cannot support the number, call it down or decline a larger one with the board. Either way, say so early, because bad news does not get better with time.
See how ORM turns these insights into action
ORM builds custom revenue forecast models for B2B SaaS companies. Not dashboards. Prescriptive analytics that tell you what to do next.
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