sales-quota-setting

{
  "skill_name": "sales-quota-setting",
  "evals": [
    {
      "id": 1,
      "prompt": "Board signed off on $24M new ARR for Lattimer Systems next year. We'll have 22 AEs carrying a number - 14 are fully ramped, 5 joined in the last four months, and 3 more start in February. Our ramped reps closed about $750K each last year and roughly 54% of the team hit their number. I was going to give everyone $1.09M ($24M divided by 22) and call it done. Does that hold up?",
      "expected_output": "A capacity-first response that rebuilds the 22-rep team in Ramped Rep Equivalents, applies the attainment-weighted capacity formula, reports the shortfall against $24M, and forces the reconciliation decision into the three honest choices instead of raising per-rep numbers.",
      "files": [],
      "expectations": [
        "Computes team capacity in Ramped Rep Equivalents rather than the nominal 22 headcount",
        "Counts each ramping rep as a fraction of 1 based on tenure/ramp position, producing an RRE total below 22",
        "Applies capacity = RREs x individual quota x average attainment, using the 54% historical attainment term rather than assuming 100%",
        "Reports modeled capacity materially below the $24M committed target",
        "States that a continuously hiring team loses roughly 30% of nominal capacity to ramp at any moment",
        "Names exactly three honest responses to the shortfall: add capacity, revise the target, or knowingly accept an underfunded plan",
        "Explicitly rejects raising individual quotas as a way to close the reconciliation gap",
        "Declines to endorse the flat $1.09M per-rep split as the answer",
        "Asks about each rep's ramp position and the territory structure before producing final numbers",
        "Presents the capacity model for validation before moving to over-assignment and allocation, rather than delivering the whole plan as one finished block"
      ]
    },
    {
      "id": 2,
      "prompt": "Harlow Freight Software here. We're locking the FY plan: 14 AEs at $900K each, against a $12.6M company commitment, and finance wants us to apply the standard 20% over-assignment so the sellers' numbers add up to about $15.1M. Five of the 14 are still inside their first two quarters. Our attainment was 41% last year and 47% the year before. Quick sanity check on the 20%?",
      "expected_output": "A response that treats over-assignment as a contested range rather than a constant, sizes the point from Harlow's own 41%/47% attainment history, and blocks the stacking of a cushion on top of a nominal-headcount capacity base.",
      "files": [],
      "expectations": [
        "States that over-assignment guidance spans a contested 10-25% range, never one settled figure",
        "Refuses to confirm 20% as an industry standard or fixed constant",
        "Ties the recommended point in the range to the org's own 41% and 47% attainment history",
        "Names the trade-off that more cushion raises the odds of landing the company number and raises comp cost per landed dollar",
        "Cites at least two distinct published recommendations spanning the range (for example ~20% typical for enterprise software, no more than 25% sized so a team at 80% attainment still makes plan, 15-25% street quota, 10-20%, or an attainment factor of 75-85%)",
        "Flags that the $12.6M base is built on nominal headcount, not Ramped Rep Equivalents, given five reps are still ramping",
        "States that over-assignment stacked on a nominal-headcount capacity model compounds two overstatements",
        "Directs the user to rebuild capacity in RREs before sizing the cushion",
        "Requires the chosen percentage and its rationale to be written into the plan's over-assignment line"
      ]
    },
    {
      "id": 3,
      "prompt": "Penwright Analytics, B2B SaaS, 26 AEs. Last fiscal year 12 of them hit their annual number - call it 46%. Average team attainment was 44%. Our board keeps quoting that a healthy sales team has 60-70% of reps at or above quota, so they think we're broken and want next year's quotas rebuilt from scratch. What do I tell them, and what distribution should I target?",
      "expected_output": "A re-baselining answer that retires the 60-70% figure, triangulates current published attainment series, places 46% near today's median, and names the real re-baselining thresholds plus the barbell shape.",
      "files": [],
      "expectations": [
        "Identifies 60-70% as a pre-2022 folk benchmark that no longer reflects measured attainment",
        "Cites the Bridge Group attainment series with at least two dated values (for example 66% in 2022, 51% in 2024, 48% in 2026)",
        "Cites at least one additional independent series such as RepVue at roughly 43-44%, Salesforce State of Sales at 28%, or ICONIQ",
        "Triangulates at least three independent published sources rather than relying on one",
        "States that published attainment series diverge by more than 15%",
        "Notes that the ICONIQ figure counts ramped AEs only, a narrower and more forgiving denominator",
        "Concludes 46% sits near the current published median rather than proving the team is broken",
        "Names under roughly 40% of reps at quota as the actual re-baselining trigger",
        "Flags that team attainment around 44-45% is the level where top-quartile rep attrition steepens",
        "Describes the current distribution shape as a barbell - a top decile far over and a thick tail far under - rather than a bell curve",
        "States in the plan which baseline the distribution was graded against"
      ]
    },
    {
      "id": 4,
      "prompt": "Corrimal Auto Group - three dealerships, 31 salespeople, all on straight commission with no salary. I need next year's targets per salesperson. What quota-to-OTE multiple should we use, and what share of the team ought to be hitting target? Also, 9 people join in Q1 who have never sold cars before.",
      "expected_output": "A B2C plan that refuses the B2B SaaS benchmark tables and ratios, sets unit-based monthly quotas validated on the group's own history, and hands draw design to the comp-plan skill while keeping the derivation logic intact.",
      "files": [],
      "expectations": [
        "States that no published rep-level attainment benchmark series exists for B2C",
        "Explains that B2C trade data measures market transactions rather than individual rep performance",
        "Directs validation against the dealership group's own historicals only, and records that in the plan",
        "Declines to apply the 4-6x B2B SaaS quota:OTE band to a straight-commission plan",
        "Explains that quota:OTE is built on a salaried-base-plus-variable B2B comp structure that does not transfer",
        "States that in commission-heavy B2C the quota acts as a performance-management floor, not the comp trigger",
        "Sets the quota in units (cars sold) or activity rather than revenue",
        "Uses a monthly reset cadence rather than an annual or quarterly one",
        "Recommends a draw as ramp support for the 9 new hires, alongside or instead of relieved quota, and routes the draw's design to the sales comp design skill",
        "Confirms that capacity-times-productivity derivation, the three-choice reconciliation, ramp relief mechanics and territory fairness carry over unchanged"
      ]
    },
    {
      "id": 5,
      "prompt": "Vendrell Instruments. Two groups. Six field AEs on geographic patches; our market model values the patches at $18M, $13M, $11M, $9M, $6M and $3M in addressable revenue, and the field team's share of next year's plan is $15M. Separately, four inside reps work a single round-robin inbound queue with a $5M team number. Today everyone in both groups carries an identical quota - feels like the fairest possible setup. Keep it?",
      "expected_output": "A split answer: fair-share weighting for the six unequal patches with the flat quota deleted outright, and the flat quota kept for the round-robin pool, plus the weighting trade-off disclosed.",
      "files": [],
      "expectations": [
        "Rejects the identical quota for the six field patches as structurally unfair",
        "States that flat quota is deleted from the allocation menu for unequal patches rather than ranked last",
        "Applies Rep Quota = Company Target x (territory potential / total market potential)",
        "Produces field quotas proportional to the six potentials, approximately $4.5M, $3.25M, $2.75M, $2.25M, $1.5M and $0.75M against the $15M field plan",
        "Keeps an equal split for the four inside reps on the round-robin inbound queue",
        "Explains that opportunity genuinely equalizes on a shared pool, so no territory weighting is warranted there",
        "Shows the negative case: under a flat $2.5M the $3M-potential rep must capture over 80% of their patch while the $18M rep needs under 15%",
        "Defaults to modified fair share computed from existing CRM history rather than commissioning a new scoring model",
        "Notes that history-based allocation inherits blind spots such as a territory weakened by a prior rep or a recently redrawn patch",
        "Discloses that territory weighting makes attainment a blend of execution skill and scoring-model accuracy",
        "States the model's refresh date next to the allocation table"
      ]
    },
    {
      "id": 6,
      "prompt": "Brightmoor Cloud, calendar fiscal year. New AE Dara Vasquez starts September 15. Our full-year AE quota is $840K, average ACV around $220K, and deals take about 11 months from first meeting to signature. Two questions: what number does Dara carry for the remaining three and a half months, and should I spread the unused portion of her territory's quota across the other seven reps so the team total still adds up?",
      "expected_output": "A single-rep exception handled with ramp relief alone: relieved quota math, relieved denominator for attainment, a cycle-length-derived ramp length, refusal to redistribute, and a handoff for draw design.",
      "files": [],
      "expectations": [
        "Treats this as a single-rep exception and applies ramp relief only",
        "Does not re-derive or re-allocate the other seven reps' quotas",
        "Applies Relieved Quota = Full Quota x Ramp % for the period",
        "States that attainment during ramp uses the relieved quota as the denominator",
        "Derives the ramp length from the 11-month cycle and $220K ACV rather than picking a generic schedule off a ranked menu",
        "Places an enterprise / $200K+ ACV ramp in the 9-15 month band",
        "Rejects redistributing her unassigned quota across the team as an ad hoc mid-cycle change",
        "Routes any mid-cycle territory or account reassignment through a formal carve-out process",
        "Routes income support during ramp - a recoverable draw or guarantee - to the sales comp design skill rather than designing it here"
      ]
    },
    {
      "id": 7,
      "prompt": "Sandfield Robotics. We run 34 territories and reset quotas twice a year. Resource reality: sales ops is one person at maybe 20% of their time on planning, our CRM territory data is messy but usable for historical bookings, and the board wants the H1 plan signed off in five weeks. Politically I have zero room to redraw territories this cycle. What process should we run?",
      "expected_output": "A rung selection that acknowledges the 34-territory promotion condition, then deletes the standing model on the effort ceiling, demotes anything needing new data collection against the five-week deadline, and lands on history-based allocation with the reasoning stated.",
      "files": [],
      "expectations": [
        "Names the 34 territories and twice-yearly reset as the condition that would normally promote to a standing territory-scoring model",
        "Deletes the standing model rather than demoting it, because of the low effort ceiling",
        "States that an unmaintained scoring model produces the unfairness it was built to fix",
        "Says explicitly which rung was struck and why",
        "Demotes any rung requiring new data collection, such as territory-potential scoring, because the deadline sits inside roughly six weeks",
        "Lands on allocation from existing CRM history (modified fair share) for this cycle",
        "Notes that a full derivation cycle standardly starts 3-4 months before the fiscal period begins, and flags the five-week window against that",
        "Presents 2-3 candidate approaches with trade-offs and one explicit recommendation before building anything",
        "Does not propose redrawing territories this cycle"
      ]
    },
    {
      "id": 8,
      "prompt": "Thalvin Data, mid-market B2B SaaS, ACV about $60K, win rate 18%. Nineteen AEs each carrying $1.4M against $175K OTE - the $26.6M came straight down from the board number. Last year 4 of 19 hit quota; our top two finished at 165% and 180% while eleven reps landed under 35%. Our CRO wants me to rebuild the accelerator curve to motivate the middle. Where do I start?",
      "expected_output": "A diagnostic that runs the sanity ratios and distribution check on the shipped plan, identifies a top-down target with no capacity validation, and redirects the fix from comp accelerators back to quota re-derivation with the right handoffs.",
      "files": [],
      "expectations": [
        "Computes quota:OTE of 8x and places it outside the 4-6x B2B SaaS band",
        "Computes implied pipeline coverage of roughly 5.6x from the 18% win rate using coverage = 1 / win rate",
        "States that roughly 5.6x exceeds the 3-4x mid-market coverage band",
        "Notes that the two ratios move together, so a quota breaking one usually breaks the other",
        "Identifies the attainment spread as a barbell rather than a bell",
        "States that a modeled barbell is the signature of a top-down target set without capacity validation behind it",
        "Names pure top-down allocation as a known failure mode",
        "Notes 4 of 19 at quota (about 21%) is below the roughly 40% re-baselining trigger, so the quota process needs re-derivation rather than coaching",
        "Declines to rebuild the accelerator curve and routes comp-plan changes to the sales comp design skill",
        "Routes building the pipeline coverage model itself to the sales pipeline coverage modeling skill"
      ]
    }
  ],
  "trigger_queries": [
    { "query": "Set next year's quotas for our 12 AEs.", "should_trigger": true },
    { "query": "Our board committed to $30M ARR. What does each rep carry?", "should_trigger": true },
    { "query": "My reps say the numbers are unattainable and two are threatening to quit.", "should_trigger": true },
    { "query": "What number should a rep who starts in month 2 of Q3 carry?", "should_trigger": true },
    { "query": "How much quota should an AE carry?", "should_trigger": true },
    { "query": "We're hiring 8 reps mid-year - how do I account for them in the plan?", "should_trigger": true },
    { "query": "Only 4 of our 19 reps hit their number last year. Is that normal?", "should_trigger": true },
    { "query": "How much should I set the team's targets above the company commitment?", "should_trigger": true },
    { "query": "Is 20% over-assignment the standard?", "should_trigger": true },
    { "query": "Our territories are wildly uneven but everyone carries the same number. Problem?", "should_trigger": true },
    { "query": "Help me build the annual sales plan for our 30-person team.", "should_trigger": true },
    { "query": "How do I split a $45M target across 5 regions fairly?", "should_trigger": true },
    { "query": "What should a new AE's target be during their first two quarters?", "should_trigger": true },
    { "query": "annual planning season - need to figure out rep targets", "should_trigger": true },
    { "query": "We've never set targets before. Where do I start?", "should_trigger": true },
    { "query": "How do I know if the number I'm giving each seller is achievable?", "should_trigger": true },
    { "query": "What percentage of a sales team should be hitting their number?", "should_trigger": true },
    { "query": "Our dealership wants monthly unit targets per salesperson for next year.", "should_trigger": true },
    { "query": "Attainment is at 43% across the team. What do I do?", "should_trigger": true },
    { "query": "We redrew territories in Q4 - how do I reset everyone's numbers?", "should_trigger": true },
    { "query": "Should I use headcount or something else to model how much the team can sell?", "should_trigger": true },
    { "query": "What's a reasonable quota to OTE multiple?", "should_trigger": true },
    { "query": "The CFO handed us a revenue commitment and wants it pushed down to the sellers by Friday.", "should_trigger": true },
    { "query": "Do I give the new hires a reduced target while they ramp?", "should_trigger": true },
    { "query": "My top two reps are at 170% and half the team is under 30%. What does that tell me?", "should_trigger": true },
    { "query": "How do I make the targets fair when one rep owns New York and another owns Idaho?", "should_trigger": true },
    { "query": "We're going from 10 to 25 sellers next year. How do I plan the numbers?", "should_trigger": true },
    { "query": "Build me a ramp schedule with reduced targets for a seller's first year.", "should_trigger": true },
    { "query": "Is 60-70% of reps hitting target still the right benchmark?", "should_trigger": true },
    { "query": "Our insurance agents need production targets for next year.", "should_trigger": true },
    { "query": "How far ahead should I start next year's target-setting?", "should_trigger": true },
    { "query": "Can I just divide the revenue plan by the number of sellers?", "should_trigger": true },
    { "query": "quota planning", "should_trigger": true },
    { "query": "Need to justify to the board why the sellers' numbers add up to more than the commit.", "should_trigger": true },
    { "query": "Target setting for our inside team working round-robin inbound leads.", "should_trigger": true },
    { "query": "How do I stop managers from quietly changing people's numbers mid-year?", "should_trigger": true },
    { "query": "Design the accelerator curve above 100% attainment for our AE comp plan.", "should_trigger": false },
    { "query": "What base/variable pay mix should an enterprise AE have?", "should_trigger": false },
    { "query": "Should we offer a recoverable or non-recoverable draw to new hires?", "should_trigger": false },
    { "query": "Build the SPIF for our Q4 push on the new product line.", "should_trigger": false },
    { "query": "What commission rate should we pay on renewals versus new business?", "should_trigger": false },
    { "query": "How much pipeline do we need in the quarter to hit the number?", "should_trigger": false },
    { "query": "Should I use raw coverage or stage-weighted coverage for my forecast?", "should_trigger": false },
    { "query": "What's our seasonality index for Q1 pipeline creation?", "should_trigger": false },
    { "query": "When is the point of no return for in-quarter pipeline?", "should_trigger": false },
    { "query": "What SDR-to-AE ratio should we run at 40 sellers?", "should_trigger": false },
    { "query": "Should we move from an island model to pods?", "should_trigger": false },
    { "query": "How many reps should report to one frontline manager?", "should_trigger": false },
    { "query": "Write a 30-60-90 day ramp plan for our new SDR.", "should_trigger": false },
    { "query": "Build the interview scorecard and question bank for an enterprise AE role.", "should_trigger": false },
    { "query": "Estimate the TAM and SAM for mid-market logistics software in DACH.", "should_trigger": false },
    { "query": "How big is our serviceable obtainable market?", "should_trigger": false },
    { "query": "Define the weighted account fit score from our closed-won data.", "should_trigger": false },
    { "query": "Where should the cutoffs sit between Tier 1 and Tier 2 accounts?", "should_trigger": false },
    { "query": "How many named accounts should a strategic AE cover?", "should_trigger": false },
    { "query": "Write our ideal customer profile with explicit disqualifiers.", "should_trigger": false },
    { "query": "Score this call transcript on discovery and objection handling.", "should_trigger": false },
    { "query": "What should I say in the first 10 seconds of a cold call?", "should_trigger": false },
    { "query": "Our cold emails are landing in spam - check our DKIM and DMARC setup.", "should_trigger": false },
    { "query": "Give me five subject line variants and a test plan.", "should_trigger": false },
    { "query": "Who is the real champion in this deal based on the call notes?", "should_trigger": false },
    { "query": "Review these opportunity notes for qualification red flags.", "should_trigger": false },
    { "query": "Score this deal against MEDDPICC.", "should_trigger": false },
    { "query": "Build the ROI business case and payback period for the Fenwick deal.", "should_trigger": false },
    { "query": "Plan my concessions before the renewal negotiation on Thursday.", "should_trigger": false },
    { "query": "Should we go product-led or stay sales-led?", "should_trigger": false },
    { "query": "Which sales podcasts and benchmark reports should I follow?", "should_trigger": false },
    { "query": "Turn my messy call notes into a recap email with next steps.", "should_trigger": false },
    { "query": "The prospect says we're too expensive. What do I say?", "should_trigger": false },
    { "query": "How many touches should our outbound cadence have and on which days?", "should_trigger": false },
    { "query": "Raise the vCPU quota on our cloud subscription for the east region.", "should_trigger": false },
    { "query": "Plan server capacity for the Black Friday traffic spike.", "should_trigger": false }
  ]
}

# Capacity and ramp math

The bottom-up half of the derivation: how much the team can actually produce, and how new-hire ramp modifies both the team number and the individual quota.

## Ramped Rep Equivalents (RRE)

Nominal headcount overstates capacity whenever part of the team is ramping. Count each rep as their ramp-schedule fraction, not as 1:

```
RRE = Σ over reps of (ramp factor at that rep's tenure-month)
```

On a 0/25/50/75/100% quarterly ramp:

| Tenure       | Ramp factor |
| ------------ | ----------- |
| Month 1      | 0.0         |
| Month 3      | 0.5         |
| Fully ramped | 1.0         |

**Worked example (Dave Kellogg / Kellblog):** 25 nominal reps summed to 17.5 RREs - a 70% effective-to-nominal ratio. A team hiring continuously "loses" roughly 30% of nominal capacity to ramp at any moment. This is why a capacity model on headcount alone systematically overstates the plan.

## Bottom-up capacity formula

```
Sales capacity = RREs × individual quota × average quota attainment
```

(Tomasz Tunguz's formula, with RREs replacing the naive rep count.) Cross-check against the market view: `addressable accounts per territory × historical win rate × average deal size × rep count`. Use your org's own attainment history for the attainment term - not an aspirational 100%.

**Worked example on published B2B SaaS medians** (Bridge Group 2024: median AE quota $800K): a 20-rep team at 16 RREs, $800K quota per ramped rep, 50% historical attainment models to `16 × $800K × 0.5 = $6.4M` - against the `20 × $800K = $16M` a naive plan would book. The gap between those two numbers is the reconciliation conversation.

## Over-assignment: the contested range

Aggregate rep quotas are set above the company commitment so normal miss-rates still land the company number. Published recommendations disagree - treat the range as the finding, never a single figure:

| Source                                                      | Recommendation                                                                  |
| ----------------------------------------------------------- | ------------------------------------------------------------------------------- |
| Dave Kellogg / Kellblog (citing a Zuora CFO Summit dataset) | ~20% typical for enterprise software; observed distribution 0% to 100%+         |
| Drivetrain                                                  | No more than 25%, sized so a team at 80% attainment still makes plan            |
| Withbridges (fractional CFO practice)                       | 15-25% "street quota" above the company target                                  |
| Lative                                                      | 10-20% - flagged by Lative itself as a common but criticized shortcut           |
| RevCast                                                     | An "Attainment Factor" of 75-85% (lower factor = more cushion = more comp cost) |

Working range: **10-25%**. Pick the point from your own attainment history and comp-cost tolerance: more cushion raises the odds of landing the company number and raises comp cost per landed dollar. Over-assignment stacked on top of a nominal-headcount capacity model compounds two overstatements - run RREs first.

## Ramp relief

```
Relieved Quota (period) = Full Quota × Ramp % for that period
Attainment during ramp = Achieved ÷ Relieved Quota
```

There is no universal schedule - sales-cycle length is the primary driver. Documented shapes:

- Canonical quarterly ramp: 0% / 25% / 50% / 75% / 100% across the first four quarters.
- High-velocity (~30-day cycle): 0% / 25% / 50% / 100% by month.
- Mid-market B2B SaaS, 6-month ramp: 30% / 60% / 90% / 100% in quarterly steps.
- Simple variant: first-quarter quota cut ~40%, with a mid-quarter pipeline review.

Ramp-duration benchmarks to sanity-check a proposed schedule:

- SMB: under ~4 months.
- ~$50K+ ACV: around 9 months.
- Enterprise / $200K+ ACV: 9-15 months.

Bridge Group's 2026 AE research (n=158 B2B companies) measured average ramp at 6.2 months - the highest in that series' history - with the average experience bar at hire also rising. Longer cycles and larger deals justify longer, more gradual ramps.

Write the curve and relief rules as explicit policy, applied uniformly - not per-manager discretion. Forecasting, onboarding, and comp payout must all reference the same relieved numbers.

## Draws (boundary note)

Income support during ramp - a recoverable draw (advance repaid from future commission) or a non-recoverable guarantee - is a comp-plan design choice layered beside quota relief, not part of the quota itself. It is the dominant ramp-support mechanism in commission-heavy B2C verticals (solar, auto, insurance), where draw-balance disputes at separation are a known failure. Hand draw design to mbfinotti/sales-skills@sales-comp-design.

# Fair-share allocation

Territories are rarely equal in opportunity, so a flat per-rep quota on patch-based territories is structurally unfair. Fair-share allocation is the dominant documented correction.

## The formula

```
Rep Quota = Company Target × (Rep's Territory Potential ÷ Total Market Potential)
```

Territory potential is a scored estimate combining:

- Addressable market inside the territory.
- Account density.
- Existing penetration and whitespace.
- Competitive intensity.
- Travel burden.

The account-level criteria behind such a score belong to mbfinotti/sales-skills@sales-account-segmentation and mbfinotti/sales-skills@sales-account-tiering - this file covers only the quota-indexing step.

## Worked example - $20M target, 5 reps

| Rep | Territory potential | Share | Fair-share quota |
| --- | ------------------- | ----- | ---------------- |
| A   | $15M                | 30%   | $6.0M            |
| B   | $12M                | 24%   | $4.8M            |
| C   | $10M                | 20%   | $4.0M            |
| D   | $8M                 | 16%   | $3.2M            |
| E   | $5M                 | 10%   | $2.0M            |

**Negative example - the same book split flat:** $4M each. Rep E must now capture 80% of a $5M territory while rep A needs 27% of a $15M one. E's number is structurally unattainable and A's is a layup - the flat quota measured territory luck, not execution, before anyone made a call.

## Modified fair share

Alexander Group's trademarked **Modified Fair Share** applies the same proportional math starting from each territory's _historical results_ instead of a scored potential. It is the efficiency default in the SKILL.md menu because the input already exists in the CRM - no scoring model to build.

It inherits history's blind spots: a territory that underperformed because of a weak prior rep scores as low-potential, and a redrawn territory has no history at all. When history is unrepresentative, move up to potential-based fair share.

## Territory index variant

Some orgs convert scores into an index (baseline 100; a 120 territory carries 1.2× the base quota) and multiply. The proportional-share math is the well-documented core; the index-multiplier mechanics are a firm-by-firm convention, not a published standard - treat any specific index formula as house style.

## Governance for mid-cycle changes

Cichelli's guidance (_Compensating the Sales Force_, the Alexander Group reference text): keep mid-year account and territory changes to a minimum, and route named-account carve-outs and any mid-year reassignment through a formal process, never ad hoc manager adjustment. Every unmanaged move silently rewrites someone's quota.

## The trade-off to disclose

Territory weighting makes performance harder to read: attainment now blends execution skill with the accuracy of the scoring model. Without a credible, periodically refreshed model, weighting produces disproportionate quotas just as easily as fair ones - the inputs matter as much as the act of weighting. Disclose this in the plan, and state the model's refresh date next to the allocation table.

# Validation checks

Two gates a quota plan must pass before it ships:

- The sanity ratios.
- The attainment-distribution model.

All benchmark figures below are B2B SaaS - no equivalent published rep-level series exists for B2C (its trade data measures market transactions, not rep performance), so validate a B2C plan against the org's own historicals.

## Sanity ratios

| Ratio             | Band                                                   | Segment variation                                     |
| ----------------- | ------------------------------------------------------ | ----------------------------------------------------- |
| Quota:OTE         | 4x-6x, ~5x steady state                                | ~3x SMB/early-stage; 7x-10x+ enterprise/multi-product |
| Pipeline coverage | 3x-5x                                                  | SMB 2x-3x; mid-market 3x-4x; enterprise 4x-7x         |
| Commission rate   | ~10% new business, ~5% renewal                         | -                                                     |
| Comp cost of sale | ~20% of new ARR at 5x quota:OTE (~31¢/$1 fully loaded) | -                                                     |

Measured medians: Bridge Group found quota:OTE at 4.2x in 2024 ($800K quota / $190K OTE) and 4.6x in 2026 ($960K / $200K).

**The linkage (Insight Partners' 5x-rule mechanics):** at a 50/50 base-variable split, a 5x quota:OTE ratio implies roughly a 10% commission rate on new business, and required pipeline coverage follows from win rate as `coverage ≈ 1 ÷ win rate`. Quota:OTE, pay mix, and commission rate are one system - fix two and the third is determined.

A quota that pushes quota:OTE out of band therefore breaks the comp plan too: flag it to mbfinotti/sales-skills@sales-comp-design rather than patching the quota alone. Building the coverage model in full is mbfinotti/sales-skills@sales-pipeline-coverage-modeling's job.

Caveat: per-segment granularity beyond the headline multiples is frequently vendor content marketing. Cross-check any specific segment figure against a second independent source before relying on it.

## Attainment distribution: target shape vs. current reality

**Classic target:** a bell curve.

- 60-70% of reps at or slightly above quota.
- 15-20% outperforming.
- 10-15% short.

This shape is what keeps a comp plan cost-effective and a team motivated.

**Current reality - re-baseline before grading against the classic figure.** Attainment structurally collapsed after 2022:

| Source                                                    | Metric                    | Value                                                                                 |
| --------------------------------------------------------- | ------------------------- | ------------------------------------------------------------------------------------- |
| Bridge Group (biennial; n=172 in 2024, n=158 in 2026)     | AEs hitting annual quota  | 74% (2012) → 66% (2022) → 51% (2024) → 48% (2026)                                     |
| RepVue Cloud Sales Index (~47-57K rep ratings, quarterly) | Average attainment        | ~43-44% through 2025                                                                  |
| Salesforce State of Sales (multi-industry survey)         | Reps hitting annual quota | 28% individually - its lowest in six years                                            |
| ICONIQ State of GTM (150+ B2B software cos.)              | _Ramped_ AEs at quota     | 58% (2025) → 62% (2026) - the outlier moving up; narrower, more forgiving denominator |
| WorldatWork / BSC                                         | Best-practice target      | 60% at annual quota (guidance, not measurement)                                       |

The shape changed too: instead of a bell, 2024-2026 data shows a barbell - the top ~20% of reps clearing 120%+ while the bottom third sits under ~40%, with a shrinking middle. Bridge Group's 2026 edition confirms the shift: fewer companies in the 50-90% attainment range, more in the 0-30% zone.

**How to use it:** model the proposed quota's expected distribution from rep-level history. A modeled barbell is the signature of a top-down target with no capacity validation behind it. Grade the model against the re-baselined reality (a well-run team lands roughly in the 60-75% top-quartile band, not the folk 100%-for-everyone), and state in the plan which baseline you graded against.

## Re-baselining triggers (during the cycle)

- **Under ~40% of reps at quota** - the named threshold at which the quota process and comp plan need re-derivation, not coaching.
- **Team attainment under ~45%** - top-quartile rep attrition steepens sharply around this level; a quota failure becomes a retention failure.
- **Underperformance spreading to historically strong reps and territories** - the signal that the target/capacity model is wrong rather than execution being weak. Chronic bottom performers missing is a management question; everyone missing is a planning question.

## Sourcing rules

- Triangulate at least three independent series before setting anything - published sources diverge by more than 15% on attainment.
- Weight by methodology: Bridge Group (transparent biennial sample) and RepVue (largest rep-reported sample, self-selected) over vendor-published segment breakdowns; survey-of-opinion data (Salesforce) is directional only.
- Drop a source that has skipped two publication cycles.

---
name: sales-quota-setting
description: Designs how sales quotas are derived for a team or company - top-down vs bottom-up reconciliation, ramp-adjusted capacity modeling (Ramped Rep Equivalents), over-assignment cushion, territory-weighted fair-share allocation, ramp relief policy, and validation against current attainment benchmarks. A macro planning exercise for VP Sales, CRO, and sales ops, covering B2B and B2C. Use whenever the user mentions quotas, targets, attainment, ramping new hires, annual planning, or "how much quota should an AE carry", even without the word quota. Do NOT use for pipeline coverage math (mbfinotti/sales-skills@sales-pipeline-coverage-modeling) or the comp plan that pays against it (mbfinotti/sales-skills@sales-comp-design).
license: MIT
metadata:
  author: Maya-Beth Finotti
  version: "1.1.1"
---

# Sales Quota Setting

You are a sales-planning advisor to sales leadership. Run the periodic quota-derivation exercise: build a ramp-adjusted capacity model, reconcile the top-down target against it, size the over-assignment cushion, allocate across territories, set the ramp relief policy, and validate the plan against current attainment data before it ships.

Stay at the planning altitude - this skill produces a quota plan, never deal tactics or rep coaching.

- Modeling the pipeline required to cover the quota belongs to mbfinotti/sales-skills@sales-pipeline-coverage-modeling.
- Designing the comp plan that pays against it belongs to mbfinotti/sales-skills@sales-comp-design.

## Invocation examples

Each ask enters at a different point. Run the interview first regardless; these decide how much of the workflow follows.

- _"Set next year's quotas for our 12 AEs."_ - full derivation, steps 1-10.
- _"Our board committed to $30M. What does each rep carry?"_ - the target exists, so reconcile it against capacity (steps 2-4) before allocating; never divide it down untested.
- _"My reps say the quotas are unattainable."_ - diagnostic entry: run the capacity model and the distribution check (steps 2, 8) against the shipped plan, then report which of the failure modes below produced it.
- _"What quota should a rep starting in month 2 of Q3 carry?"_ - exception entry: ramp relief only (step 6), against the existing plan. Do not re-derive the team's numbers.

## Interview

Ask before modeling. One question per message; offer the multiple-choice options where given. Skip anything already answered by prior context.

1. What are you setting quotas for: (a) the whole company's next fiscal period, (b) one team or segment, (c) one rep exception - new hire ramp, leave, mid-cycle territory change, (d) diagnosing a quota plan that isn't working?
2. Is the motion B2B, B2C, or mixed - and what does a rep sell: typical deal size or ticket, and sales-cycle length?
3. Does a top-down number already exist: (a) a board- or investor-committed revenue target, (b) a target you are free to shape, (c) no target yet?
4. Team shape: how many quota carriers, and how many of them are still ramping or will join mid-period?
5. What history exists: (a) 2-3 years of per-rep and per-territory performance, (b) one year or partial, (c) little to none - new team or new market?
6. Last cycle's outcome: roughly what share of reps hit quota, and was the miss spread broadly or concentrated in a few seats?
7. Are territories roughly equal in opportunity, deliberately unequal, or is it a shared pool (pooled inbound, round-robin)?
8. What are OTE and the base/variable split? Only to sanity-check the quota:OTE ratio - designing the plan itself is mbfinotti/sales-skills@sales-comp-design's job.
9. By what date must the finalized quota land, and how far is that from the fiscal-period start? A full derivation cycle typically starts 3-4 months before the period begins.
10. Do you want a one-off fix or a compounding asset: (a) patch this period's numbers, (b) build a repeatable derivation process the org reruns every cycle?
11. What is your effort ceiling: analyst hours, data quality and tooling, and the political capital you can spend with the field on territory or relief changes?

Re-rank the derivation ladder below against answers 9-11 before proposing anything, and say which answer moved what:

- A hard date inside ~6 weeks demotes any rung needing new data collection - territory-potential scoring, tooling setup. Allocation from existing history is what fits the window.
- A compounding mandate (10b) promotes the standing model despite its losing efficiency ratio; a patch mandate (10a) keeps you on the default rung.
- A low effort ceiling **deletes** the standing model rather than demoting it - an unmaintained scoring model produces the unfairness it was built to fix. Say which rung you struck and why.

## Choose the derivation rung

Three rungs, all hybrid - a top-down target validated against a bottom-up build. Pure top-down and pure bottom-up are not on the menu; each is a known failure mode (see Failure modes).

- efficiency: `divide-and-validate > full hybrid > standing model`
- value: `standing model > full hybrid > divide-and-validate`
- effort: `standing model (a standing job) > full hybrid (a planning cycle) > divide-and-validate (a day or two)`

1. **Divide-and-validate.** Allocate the target across teams top-down, then check the aggregate against a ramp-adjusted capacity model (RREs - see [capacity-and-ramp-math.md](./references/capacity-and-ramp-math.md)). Catches the worst failure - a target no capacity model supports - for a day or two of spreadsheet work.
2. **Full hybrid.** Set the top-down target first, build the bottom-up view from territory potential plus RRE capacity, reconcile the two, size over-assignment, weight territories, write the ramp policy. The complete workflow below.
3. **Standing model.** Full hybrid plus a maintained territory-scoring model, multi-scenario attainment modeling, and a re-run cadence with a named owner.

- Default: **divide-and-validate**, for a first-ever quota exercise or a team under ~10 reps.
- Promote to **full hybrid** once the org runs an annual planning cycle and holds 2-3 years of history.
- Promote to **standing model** anyway when the org manages ~20+ territories or reruns quotas more than annually - only a maintained model catches territory drift between cycles.
- What the efficiency order starves: the standing model - high value, high effort, it loses every ratio round; the promotion condition above is what rescues it.

This ordering is a default, not a law. Re-rank it against what you know about the user:

- An org with a planning-tools team already in place gets the standing model near-free.
- A founder setting the first two quotas needs none of it.

## Brainstorm before you model

Quota plans harden fast - once a number reaches the field, changing it costs trust. Surface the assumptions first.

1. After the interview, present 2-3 candidate approaches (drawn from the ladder above, adapted to the answers) with trade-offs and one explicit recommendation. Ask remaining clarifying questions one at a time - prefer multiple-choice.
2. Get explicit approval on the approach before building anything.
3. Build the quota plan section by section, validating each with the user before the next: capacity model → target reconciliation and over-assignment → allocation and territory weighting → ramp and relief policy → validation and governance. A wrong capacity number invalidates everything downstream, so never present the plan as one finished block.
4. Gate finalization on user approval of the assembled plan.

If your harness has persistent memory, store the approved decisions - target, over-assignment level, allocation method, ramp schedule, governance rules - so next cycle's rerun and any mid-cycle exception starts from the recorded plan, not from scratch.

## Workflow

1. **Pull and clean the history.** 2-3 years of performance by rep, territory, and segment, plus average deal size, stage conversion rates, and current headcount with each rep's ramp position. Start 3-4 months before the fiscal period for a full cycle.
2. **Build capacity in Ramped Rep Equivalents, never nominal headcount.** Each rep counts as their ramp-schedule fraction, not as 1. A continuously hiring team commonly loses ~30% of nominal capacity to ramp - the single biggest source of overstated plans. Formula and worked example: [capacity-and-ramp-math.md](./references/capacity-and-ramp-math.md).
3. **Set the top-down target and reconcile.** When the bottom-up capacity build falls short of the target, there are exactly three honest choices: add capacity, revise the target, or knowingly accept an underfunded plan. Raising individual quotas to close the gap is not a fourth option - it manufactures the miss instead of fixing the shortfall.
4. **Size the over-assignment cushion.** Set aggregate rep quotas above the company commitment, so normal miss-rates still land the company number. Published guidance spans a contested 10-25% range - never one settled figure; pick a point in it from your own attainment history and comp-cost tolerance. Source-by-source breakdown: [capacity-and-ramp-math.md](./references/capacity-and-ramp-math.md).
5. **Allocate across territories.** For patch-based territories, rank the methods by efficiency:
   - efficiency: `modified fair share > fair share on potential > standing territory index`
   - value: `standing territory index > fair share on potential > modified fair share`
   - effort: `modified fair share (hours, from CRM history) < fair share on potential (a scoring pass) < standing territory index (a standing job)`

   - Default: **modified fair share** - proportional allocation from historical results the org already holds.
   - Promote to **fair-share on scored potential** when history is unrepresentative: redrawn territories, heavy churn, a market shift.
   - Promote to the **standing index** under the same condition as the standing rung above.
   - What the efficiency order starves: the standing index, for the same reason as above - the promotion condition is what rescues it.

   A **flat quota** appears on none of those three lines deliberately. On unequal patches it is structurally unfair, so delete it rather than park it at the bottom where it silently reappears as scope.

   It is correct and free on a shared pool (pooled inbound, round-robin) - but that is a different menu, because opportunity there genuinely equalizes and no weighting is warranted. Formulas, worked example, and the fairness trade-off: [fair-share-allocation.md](./references/fair-share-allocation.md).

6. **Write the ramp relief policy.** Relieved Quota = Full Quota × Ramp % per period; attainment during ramp uses the relieved number as denominator. The ramp curve is parameterized by sales-cycle length, not chosen from a ranked menu - ranking schedules would be false precision, since a 30-day-cycle team and an enterprise team need different curves for structural reasons.

   Schedules and benchmarks: [capacity-and-ramp-math.md](./references/capacity-and-ramp-math.md). Draw structures that support income during ramp are comp-plan design - hand them to mbfinotti/sales-skills@sales-comp-design.

7. **Run the sanity ratios.**
   - Quota:OTE should land near 4-6x for B2B SaaS (lower for SMB, higher for enterprise).
   - The implied pipeline coverage (roughly 1 ÷ win rate, typically 3-5x) must be plausible against actual pipeline creation.

   These ratios move together - a quota that breaks one usually breaks the other. Values and mechanics: [validation-checks.md](./references/validation-checks.md).

   Building the coverage model itself is mbfinotti/sales-skills@sales-pipeline-coverage-modeling's job.

8. **Model the attainment distribution before shipping.** Classic guidance targets a bell curve with 60-70% of reps at or above quota - but published attainment has structurally fallen below that figure across the methodologically comparable series and keeps moving, so grading a plan against the classic number alone reads a healthy team as broken. Re-baseline the target against current data, not the folk benchmark.

   If the modeled distribution looks like a barbell - top decile far over, a thick tail far under - the target was set top-down without real capacity validation. Current benchmarks, the cross-source table, and the re-baselining thresholds: [validation-checks.md](./references/validation-checks.md).

9. **Write the governance rules into the plan.**
   - Keep mid-cycle territory and account changes to a minimum.
   - Require a formal carve-out process for named accounts and any mid-year reassignment, never ad hoc manager adjustment.
   - Document the ramp curve and relief rules as explicit policy so forecasting, onboarding, and comp payout all reference the same relieved numbers.
10. **Assemble the output** (shape below), run the Measurement check, and iterate until it passes.

## B2B vs B2C

The derivation logic transfers; the calibration data and several conventions do not.

Carries over unchanged:

- Capacity-times-productivity derivation.
- The three-choice reconciliation rule.
- Ramp relief mechanics.
- Territory-fairness allocation.
- Distribution-shape validation.
- The governance discipline.

A car dealership and a SaaS org both fail the same way when the target is divided down with no capacity model behind it.

**Differs:**

- **No published rep-level benchmark series exists in B2C.** The B2B attainment and ratio tables in [validation-checks.md](./references/validation-checks.md) are B2B SaaS data; B2C trade data (insurance, real estate, auto) measures market transactions, not rep performance. Validate a B2C plan against the org's own historicals only, and say so in the plan.
- **The quota:OTE and coverage ratios don't transfer.** They are built on salaried-base-plus-variable B2B comp. Commission-heavy B2C plans (real estate, insurance, solar, auto) pay a percentage of each sale directly, so the quota functions as a performance-management floor, not the comp trigger.
- **Quota units and cadence differ.** B2C quotas are commonly unit- or activity-based (cars, policies, installs) with monthly resets, against B2B's revenue quotas on annual or quarterly cycles.
- **Ramp support is a draw, not only relief.** Commission-heavy verticals support new hires with recoverable or non-recoverable draws alongside - or instead of - a relieved quota; first-year washout is severe, so an unattainable early quota accelerates the attrition it was supposed to measure.

## Quota plan output shape

```
PLAN: fiscal period · altitude (company / team / exception) · committed target and who set it
CAPACITY: quota carriers · RRE total · productivity per ramped rep · modeled capacity
RECONCILIATION: gap vs target · which of the three choices was taken
OVER-ASSIGNMENT: % above commitment · rationale for the point chosen in the 10-25% range
ALLOCATION: method chosen and why · per-team/per-rep quota table
RAMP POLICY: schedule (period × %) · relieved-quota attainment rule · mid-cycle exception rules
SANITY RATIOS: quota:OTE · implied pipeline coverage · pass/fail vs band
DISTRIBUTION CHECK: modeled % of reps at quota · shape vs re-baselined target
GOVERNANCE: carve-out process · review cadence · next re-derivation date
```

## Failure modes

- **Closing the reconciliation gap by raising individual quotas** - the plan now assumes attainment no capacity supports. Fix: return to the three choices in step 3.
- **Capacity from nominal headcount** - overstates a hiring team's output by roughly a third and guarantees later re-baselining. Fix: RREs, always.
- **Pure top-down** - arbitrary numbers with no territory reality behind them; lands as unattainable and reads as leadership not knowing the business.
- **Pure bottom-up** - reps sandbag to protect themselves; the aggregate goes conservative and locks in the current strategy. Both pure forms are why the menu only offers hybrids.
- **Anchoring on the 60-70% folk benchmark** - that era ended around 2022. A plan graded against it will look broken when it is performing at today's median.
- **Single-source benchmarking** - published series diverge by more than 15% on attainment; triangulate at least three before setting anything (see [validation-checks.md](./references/validation-checks.md)).
- **Over-assignment as a folk constant** - "just add 20%" without checking your own attainment history compounds with an inflated capacity model into a plan nobody can hit.
- **Territory weighting off a stale scoring model** - produces exactly the unfairness it was meant to fix, with a veneer of rigor. Refresh the model or drop to modified fair share.
- **Ad hoc mid-cycle changes** - every unmanaged territory or account move mid-year silently rewrites someone's quota. Fix: the carve-out process in step 9.

## Measurement

The plan is not done until all of these pass; iterate until 100%:

- Capacity is stated in RREs with each rep's ramp position visible.
- The reconciliation line names which of the three choices was taken - no silent fourth option.
- Over-assignment names its point in the range and the rationale; it is never presented as a fixed industry number.
- The allocation method matches the territory structure, and any deleted option (flat quota on unequal patches, an unmaintained index) is named as deleted.
- The distribution check compares against current re-baselined data, not the classic bell-curve figure alone.
- Governance rules for mid-cycle changes are written into the plan.

Outcome KPIs to track through the cycle:

- Share of reps at quota vs the modeled share, and the distribution's shape (bell vs barbell).
- Re-baselining triggers: under ~40% of reps at quota, top-performer attrition rising as team attainment sits under ~45%, or underperformance spreading to historically strong reps and territories - the last one signals the model is wrong, not the execution.
- Realized over-assignment vs planned; count of mid-cycle exceptions granted outside the carve-out process.

## References

- mbfinotti/sales-skills@sales-pipeline-coverage-modeling: model the pipeline needed to cover quotas you set here.
- mbfinotti/sales-skills@sales-comp-design: design the comp plan that pays against this quota - pay mix, accelerators, draws.
- mbfinotti/sales-skills@sales-org-structure: headcount and topology decisions that feed the capacity model.
- mbfinotti/sales-skills@sales-hiring: 30-60-90 ramp plans behind the ramp-relief schedule.
- mbfinotti/sales-skills@sales-account-segmentation: account-level criteria that feed a territory-potential score.
- mbfinotti/sales-skills@sales-account-tiering: account-level criteria that feed a territory-potential score.
- [./references/capacity-and-ramp-math.md](./references/capacity-and-ramp-math.md): RRE math, over-assignment sources, ramp schedules.
- [./references/fair-share-allocation.md](./references/fair-share-allocation.md): allocation formulas, worked example, fairness trade-off.
- [./references/validation-checks.md](./references/validation-checks.md): attainment benchmarks, re-baselining thresholds, sanity ratios.