Sales capacity planning is the math that tells you how many reps you need to hit the number. Most plans only count headcount. This guide models what your reps can actually produce, measures what ramp and attrition really cost, and tests whether fixing the selling system beats signing another offer letter.
I wrote this after one more board meeting where the fix for a missed quarter was "add four AEs." Read it, then we'll do the math.
Hiring more reps won't fix a broken selling system. It just adds more people to fail inside it.
When a number misses, the reflex is to add bodies or push harder. But a great rep dropped into a broken system produces broken results a little faster.
Selling starts before the rep ever dials. It starts with clean routing so leads reach the right person while they're still warm. Playbooks that make the next move obvious. A CRM that captures the deal instead of fighting the rep. Enablement that ramps new hires in weeks, not quarters.
Get the system right and average reps hit quota. Get it wrong and your best closer spends half the week on admin and guesswork, and you conclude you have a talent problem when you have an infrastructure problem.
Talent is a multiplier. But a multiplier on a broken system is still a broken number.
Fix the system reps sell inside of, then talk about hiring.
Most plans don't miss because the math is hard. They miss because headcount gets counted as capacity, while the system the reps sell in is leaking.
Ten reps with $1M quotas is $10M of quota. At 43% average attainment it is $4.3M of productive capacity (Lative, 2026). If the plan multiplies seats by quota, the miss is built in before Q1 starts.
New AEs now take 6.2 months to ramp on average, the longest in The Bridge Group's study history (2026). A rep hired in March barely contributes that fiscal year, yet many models count new hires at half capacity from day one.
AE attrition runs about 30% a year (Optifai, 2025-26). Each exit costs the months the seat is empty plus half of the replacement's ramp. Most models just delete the rep and forget the gap.
Reps spend about 40% of their week actually selling (Salesforce State of Sales, 2026). Every new hire inherits the same admin load, the same manual CRM fields and the same routing delays.
A blended win rate can hide a broken segment. A five-point error in win rate swings the pipeline you need by 20 to 30% (Lative, 2026). Hiring into the leaky stage just pours more deals through it.
Grade your inputs before you trust your output. If two or more rows land in "weak," fix the system before you open a req.
| Metric | Weak | Acceptable | Strong | Benchmark reference |
|---|---|---|---|---|
| Share of AEs at or above quota | Under 40% | 40 to 55% | 60%+ | Median 48% at quota (Bridge Group, 2026) |
| New AE ramp time (mid-market) | Over 7 months | 4 to 6 months | Under 4 months | 6.2-month average (Bridge Group, 2026) |
| Share of rep time spent selling | Under 30% | 30 to 40% | 45%+ | About 40% (Salesforce State of Sales, 2026) |
| Annual AE attrition | Over 40% | 25 to 35% | Under 20% | 30% for AEs, 45% for SDRs (Optifai, 2025-26) |
| Quota-to-OTE ratio | Under 4x | 4 to 5x | 5x+ | 4.6x median, $960K quota on $200K OTE (Bridge Group, 2026) |
| Time to fill an AE seat | Over 90 days | 60 to 90 days | Under 45 days | About 60 days (DePaul Sales Lab, via Gangly 2026) |
Sources: The Bridge Group AE Models, Motions and Metrics (2026, 158 companies); Salesforce State of Sales (2026, 4,000+ sellers); Optifai sales turnover survey (Q2 2025 to Q1 2026, 939 companies); RepVue Cloud Sales Index (2025, 43% of SaaS reps at quota, via Qobra). Weak, acceptable and strong bands are JayOh operating ranges set around these medians.
Nine steps, in order. Steps 3 to 6 are where the system gets fixed. Skip them and you're just budgeting for more people to fail inside the same process.
Trailing four quarters of closed-won per ramped rep, by segment.
Quota is a comp tool. Capacity is what the team actually produces.
Productive capacity per rep = quota x average attainment.
HubSpot: create a Forecasted revenue goal per user (Reporting, Goals, Create goal from template). Then build a single-object Deals report filtered to Closed won, grouped by Deal owner and Close date, and add the goal from the Goal dropdown so actual and target sit on one chart. Salesforce: an Opportunities report (Closed Won, last 4 fiscal quarters) grouped by Opportunity Owner, compared against Collaborative Forecasts quotas. On the Sales Planning add-on, use Quota Attainment Planning instead.
Closed-won by month of tenure for your last two hire cohorts.
Flat ramp assumptions overstate first-year output, often by a full quarter of productivity.
A month-by-month percent-of-quota curve per segment.
Keep a governed rep roster: owner, segment, start date, status, quota. In Salesforce, add a Start_Date__c date field on User and report Opportunities with Owner fields. In HubSpot, hold the roster in a custom object (Enterprise) or a locked sheet keyed to owner ID, then join it to the closed-won export. Rebuild the curve every quarter.
Hours per rep per week on selling versus admin, research and data entry.
Recovered selling time is the cheapest capacity you'll ever buy. No ramp, no recruiter fee.
A ranked list of admin tasks to automate or delete.
Run a two-week time study with three or four reps. Pair it with activity data: in HubSpot, an Activities report by owner (calls, meetings, emails logged); in Salesforce, an Activities with Tasks and Events report. Audit every field reps must fill in by hand to move a stage. If nobody reports on a field, remove the requirement. This is where CRM adoption problems turn into lost capacity.
Stage-to-stage conversion and stage aging, by segment and source.
A new hire inherits your worst stage. Fix that stage and every existing rep produces more.
The single stage with the biggest drop-off, with an owner and a fix.
HubSpot: a deal funnel report (Reporting, Reports, Create report, Funnels, Deals) split by pipeline, with stage conversion and time in stage. Salesforce: Historical Trending on Opportunities, or an Opportunity History report (backed by OpportunityFieldHistory) to measure stage-to-stage movement. Then set coverage per segment with the pipeline coverage ratio math (1 / qualified win rate), not a blanket 3x.
Time from hand-raise to first rep touch, and how many leads sit unowned.
Warm demand that waits goes cold. No amount of headcount fixes a routing queue.
An SLA with automatic assignment and escalation.
HubSpot: a contact or deal workflow using the Rotate record to owner action by team or territory, plus a Create task step with an SLA due date and an if/then branch that reassigns if the task is still open. Salesforce: Lead Assignment Rules or a record-triggered Flow that assigns by territory with a queue fallback. Measure it weekly. The speed-to-lead playbook covers the full build.
A 30/60/90 plan with certifications, deal shadowing and a seeded sandbox.
Every month you cut from ramp adds capacity for every future hire.
Time to first closed-won tracked as a KPI per cohort.
Gate each phase with something you can observe: a recorded discovery call graded on your qualification framework by day 30, a solo-run demo by day 45, first opportunity created by day 60. Put playbooks inside the CRM (HubSpot Playbooks on the record; Salesforce Path guidance per stage) so the next move is obvious. See how we approach sales enablement.
Expected departures x (vacancy months + ramp months / 2).
A 30% attrition rate on a 10-rep team can wipe out most of one rep's annual output.
An attrition drag line in the model, plus a backfill recruiting trigger.
Use the trailing 12 months of exits by segment from your HRIS. Hire in H2 for next year's capacity (OnlyCFO's rule of thumb). Start backfill sourcing the day a rep gives notice, not when the seat is empty.
Four leading indicators that must be true before a new req opens.
Hiring after a miss scales the problem that caused it.
A written hiring policy that Finance and Sales both sign.
Open reqs when: 60%+ of ramped reps hit quota over four quarters; qualified pipeline per rep covers quota at 1 / win rate; selling time is 40%+; and your ramp curve comes from real cohorts. Otherwise the budget goes to steps 3 to 6 first.
One capacity dashboard reviewed in the monthly operating meeting.
An annual model is stale by Q2. Small input drift compounds fast.
A re-baseline whenever trailing attainment moves 2+ points from plan.
Build it as a HubSpot dashboard (goal reports plus the deal funnel) or a Salesforce dashboard (attainment, stage conversion, activity). RevOps owns it and Finance co-signs. Use the same inputs for the forecast so forecasting pipeline accurately and capacity planning never disagree.
The best capacity models are boring and refreshed often. The expensive mistakes all treat a hire as a fix.
Put in your numbers. The left panels model today. The middle panel tests system fixes. The orange box shows how many reps you still need after the fixes.
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Method: capacity = ramped AEs x quota x attainment, minus attrition drag (AEs x attrition x (vacancy + ramp / 2) / 12 x capacity per AE). A new hire starting on day one of the year yields capacity per AE x (12 - ramp / 2) / 12. Hire cost = OTE x 1.25 for benefits and tools, with no recruiting fees included. Attainment after fixes is capped at 100%. Defaults are illustrative and sit near the Bridge Group 2026 medians.
Copy the input sheet into your planning doc. Then hand your real numbers to the Claude skill and get the hire-or-fix answer in one pass.
SALES CAPACITY PLAN - [Company] - FY[Year] Owner: RevOps Co-signer: Finance Refresh: monthly, rolling 4 quarters 1. TARGET [ ] New bookings / new ARR target by segment: SMB $___ MM $___ ENT $___ 2. CAPACITY INPUTS (per segment, trailing 4 quarters) [ ] Ramped AEs: ___ Ramping AEs (with start dates): ___ [ ] Annual quota per AE: $___ [ ] Average attainment of ramped AEs: ___% Share of AEs at quota: ___% [ ] Ramp curve from last 2 cohorts (% of quota by month 1-12): ___ [ ] Annual AE attrition: ___% Avg vacancy before backfill: ___ months [ ] Qualified win rate: ___% Required coverage (1 / win rate): ___x [ ] Selling time (time study): ___% of the week 3. MATH Capacity per ramped AE = quota x attainment = $___ Attrition drag = AEs x attrition x (vacancy + ramp/2) / 12 x capacity per AE = $___ Net capacity = (ramped AEs x capacity per AE) - drag + ramping AEs on curve = $___ Gap = target - net capacity = $___ First-year yield per new hire = capacity per AE x (12 - ramp/2) / 12 = $___ 4. FIX-FIRST LEVERS (price each in equivalent reps) [ ] Selling time recovered: ___ pts -> +$___ (= ___ reps) [ ] Worst-stage conversion fix: ___ -> ___% -> +$___ (= ___ reps) [ ] Ramp shortened: ___ -> ___ months -> +$___ (= ___ reps) [ ] Routing / speed-to-lead SLA in place (Y/N) 5. HIRING TRIGGERS (all must be true to open a req) [ ] 60%+ of ramped AEs at quota over 4 quarters [ ] Qualified pipeline per AE covers quota at 1 / win rate [ ] Selling time at or above 40% [ ] Ramp curve built from real cohorts, not assumptions [ ] Backfill sourcing starts on notice, not on vacancy 6. DECISION Reps to hire after fixes: ___ Start month: ___ Loaded cost: $___ Re-baseline if trailing attainment drifts 2+ points from plan.
sales-capacity-planner
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--- name: sales-capacity-planner description: Builds a sales capacity plan from a team's own numbers and decides how much of the gap to close by fixing the selling system versus hiring reps. Use when someone asks how many sales reps they need, wants to check a headcount plan, is setting next year's quota and hiring plan, or pastes a rep roster, quota, attainment, ramp or attrition data and asks whether the team can hit the number. --- # Sales Capacity Planner You turn a team's real numbers into a capacity plan, and you test "fix the system" before "hire more reps". You use the method from the JayOh post Sales Capacity Planning: Why Hiring More Reps Won't Fix a Broken System. ## Inputs to collect Ask for any of these that are missing. Never fill a gap with a guess. 1. The revenue target for the plan period (new bookings or new ARR), by segment if possible. 2. The rep roster: name or ID, segment, start date, status (ramped or ramping), and quota. 3. Trailing four quarters of closed-won by rep, or average attainment by segment. 4. Ramp history: months until a new rep reaches full productivity, ideally from the last two hire cohorts. 5. Annual rep attrition and the average months a seat sits empty before a backfill starts. 6. Selling time as a percent of the week, from a time study or activity data. 7. Win rate (qualified opportunity to closed-won) by segment. 8. Fully loaded cost per rep (OTE plus benefits and tools). ## Method Run every step per segment. Never use one blended rate across segments. 1. Productive capacity per ramped rep = quota x average attainment. 2. Current capacity = ramped reps x productive capacity per rep. 3. Attrition drag = ramped reps x attrition rate x (vacancy months + ramp months / 2) / 12 x productive capacity per rep. 4. Net capacity = current capacity minus attrition drag. Add ramping reps at their position on the ramp curve, not at 50 percent. 5. Gap = target minus net capacity. 6. First-year yield of a new hire = productive capacity per rep x (12 - ramp months / 2) / 12, assuming they start on day one of the plan. Reduce it for later start dates. 7. Hire path: reps needed = gap / first-year yield, rounded up. Cost = reps needed x loaded cost. 8. Fix path: model three system levers before hiring: - Selling time recovered (admin removed, routing automated, CRM fields cut). Count only part of the gain as output, 50 percent by default. - Win-rate lift at the stage with the worst conversion. - Ramp months removed through structured onboarding. Recalculate attainment, net capacity and the gap, then the reps still needed. 9. Report the fixes as an equivalent number of reps: (fix-path net capacity minus current net capacity) / first-year yield of a new hire. ## Benchmarks to grade inputs against - Share of AEs at or above quota: weak under 40 percent, acceptable 40 to 55, strong 60 plus (Bridge Group 2026 median 48 percent). - New AE ramp: weak over 7 months, acceptable 4 to 6, strong under 4 (Bridge Group 2026 average 6.2 months). - Selling time: weak under 30 percent, acceptable 30 to 40, strong 45 plus (Salesforce State of Sales 2026, about 40 percent). - Annual AE attrition: weak over 40 percent, acceptable 25 to 35, strong under 20. - Quota to OTE: weak under 4x, acceptable 4 to 5x, strong 5x plus (Bridge Group 2026 median 4.6x). ## Hiring triggers Recommend hiring only when all of these are true. Otherwise recommend fixing the system first. - 60 percent or more of ramped reps hit quota over the trailing four quarters. - Qualified pipeline per rep covers the quota at the ratio the win rate requires (1 / win rate). - Selling time is 40 percent or more. - The ramp curve is documented from real cohorts. ## Output format 1. Capacity summary table: segment, ramped reps, ramping reps, productive capacity per rep, attrition drag, net capacity, target, gap. 2. Input grades: each benchmark with its grade (weak, acceptable or strong) and the input it came from. 3. Hire path: reps needed, start-date assumption, cost. 4. Fix path: each lever, the capacity it adds, and reps still needed. 5. Recommendation: fix first, hire now, or both, in three sentences or fewer, tied to the hiring triggers. 6. Missing evidence: every input you did not have and how it would change the answer. ## Guardrails - Quote the user's own inputs as evidence for every number you report. - Never invent attainment, ramp, attrition or win-rate figures. If one is missing, list it under Missing evidence and show the result as a range. - Keep quota and capacity separate. Raising quota does not raise capacity. - Say clearly that the selling-time-to-output conversion is an assumption. - Do not name or compare individual reps beyond what the user provided. Built by JayOh (jayoh.io). Method from https://jayoh.io/sales-capacity-planning/
The questions operators ask us most. Short answers, no hedging.
Sales capacity planning is the process of calculating how much revenue your sales team can realistically produce in a period, then deciding how to close any gap to target. The gap can be closed by hiring, by fixing the selling system, or both. It accounts for quota, real attainment, ramp time, attrition and vacancy, not just headcount.
Multiply ramped reps by quota by average attainment to get gross capacity. Subtract attrition drag: reps x attrition rate x (vacancy months + ramp months / 2) / 12 x capacity per rep. Then add ramping reps at their point on a ramp curve built from real cohorts. Do it per segment and sum the results.
Divide the gap between target and net capacity by the first-year yield of a new hire. That yield is capacity per rep x (12 - ramp months / 2) / 12 if they start on day one. Before you hire, test system fixes, because recovered selling time and a better win rate often close part of the gap for far less than an OTE.
A healthy plan has 60% or more of ramped reps at or above quota. The Bridge Group's 2026 AE report puts the median at 48%, and RepVue's 2025 Cloud Sales Index found about 43% of SaaS reps at quota. Below 40%, the plan or the system is broken, not the people.
The Bridge Group's 2026 average is 6.2 months, the longest in its study history. Mid-market AEs typically ramp in 4 to 6 months, enterprise in 6 to 12. Under 4 months for mid-market is strong and usually comes from structured 30/60/90 enablement and playbooks built into the CRM.
Multiply expected departures by the months of output lost per exit, which is vacancy time plus about half of the replacement's ramp. Then convert that to dollars at capacity per rep. With 30% AE attrition, a 2-month vacancy and a 6-month ramp, each exit costs about 5 months of a ramped rep's output.
Fix first if fewer than 60% of ramped reps hit quota, if selling time is under 40%, or if one funnel stage converts far below the rest. A new rep inherits the same leaks. Hire when those triggers are healthy and qualified pipeline per rep already covers quota at 1 / win rate.
We build the system your reps sell inside: routing that hits warm leads in minutes, a CRM that captures the deal instead of fighting the rep, stage definitions that make the forecast boring, and enablement that ramps in weeks. Then the capacity model tells you, with real numbers, when the next hire actually pays back.
If your plan says "add four AEs" and nobody can explain the attainment spread, start with a capacity diagnostic, not a req.
Further reading: The Bridge Group 2026 AE report, HubSpot: report on goals, Salesforce Trailhead: Sales Planning, and OnlyCFO on sales capacity models.
We engineer growth systems. JayOh builds the RevOps architecture, GTM execution and data infrastructure that turn messy revenue operations into predictable pipeline for B2B SaaS and PE-backed teams.
