What's the latest comp benchmark from Pavilion / Bridge Group in 2027?

What it is and why it matters

There is a persistent confusion worth clearing up before any number gets quoted: Pavilion and The Bridge Group are separate, independent companies. Pavilion is a paid membership community for go-to-market executives that surveys its own members and their organizations. The Bridge Group is an independent sales consultancy that has run the same SaaS inside-sales and AE metrics study for decades. Neither acquired the other, neither publishes the other's data, and their samples barely overlap. When a recruiter, a board deck, or a comp consultant says "the Pavilion/Bridge benchmark," they are almost always splicing two different studies with two different methodologies into one sentence — and that splice is where most comp arguments go wrong.

What each one actually measures also differs. Pavilion's compensation work leans toward the executive and full-GTM-org view: it reports base, variable, equity, and total compensation across roles from SDR through CRO, plus organizational context like headcount, funding stage, and pay mix. Its respondent base is dominated by venture-backed SaaS companies whose leaders pay for a community membership, which means it skews toward funded, U.S.-heavy, growth-stage orgs. The Bridge Group's AE report is narrower and deeper on the individual-contributor sales motion: OTE, pay mix, quota, quota-to-OTE ratio, ramp time, tenure, average contract value, sales cycle, and attainment distribution. Because The Bridge Group has run the same questions for many consecutive years, its value is less the absolute number in any single edition and more the trend line across editions.

Why any of this matters to a RevOps or finance leader is straightforward. Comp is usually the single largest controllable line in the sales P&L, and it is the one line that changes rep behavior directly. If you set quota-to-OTE at 4x when the benchmark cohort is running 5x, you are structurally overpaying per dollar of ARR. If you set it at 7x, you have built a plan the median rep cannot hit, and you will pay for it in attrition instead of commission. The benchmark is not a target — it is a sanity check that tells you whether your plan sits inside the range where comparable companies have found equilibrium, or whether you have wandered somewhere that will produce a predictable failure mode.

The second reason these reports matter is negotiation leverage in both directions. Reps use them to argue that an offer is below market. Hiring managers use them to argue that a candidate's ask is above it. Boards use them to challenge a CRO's comp-cost-of-revenue. Because all three parties are quoting the same handful of documents, knowing exactly what the document does and does not measure — which roles, which segment, which geography, which year the data was collected — is the difference between winning that conversation and getting caught quoting a number that does not apply to your situation.

Finally, these benchmarks are the input to a lot of downstream RevOps modeling. Capacity plans multiply headcount by quota. Hiring plans multiply ramp time by time-to-first-deal. Board models multiply attainment assumptions by pipeline coverage. Every one of those models inherits whatever bias sits in the benchmark you fed it. A ramp assumption that is two months optimistic compounds across twenty hires into a full quarter of missing capacity, which is exactly the kind of miss that shows up as a surprise in month nine rather than in the plan.

The step-by-step process

Using a comp benchmark properly is a sequence, not a lookup. The failure mode is opening the PDF, finding the row that matches your job title, and pasting the number into an offer. Here is the sequence that actually holds up when a CFO or a candidate pushes back.

First, define the comparison cohort before you look at a single number. Write down your segment (SMB, mid-market, enterprise by ACV, not by aspiration), your ARR band, your funding stage, your primary geography, and your motion (inbound-led, outbound-led, partner, product-led with sales assist). Most benchmark reports let you cut by at least two of these. If your cohort definition does not match the cut you are reading, the number is decorative.

Second, pull both sources rather than one. If Pavilion and The Bridge Group broadly agree on median AE OTE for your segment, you have a defensible range. If they disagree materially, that gap is itself the finding — it usually means one sample is skewing toward better-funded companies or a different segment mix, and you should widen your range rather than pick a winner.

Third, normalize on pay mix, not just total. A stated OTE means nothing until you know the base/variable split. A 50/50 plan and a 70/30 plan at the same OTE are wildly different offers in terms of risk to the rep and fixed cost to the company. Convert every comparison point to base dollars and target variable dollars separately before you compare.

Fourth, convert OTE into quota-to-OTE ratio. Divide median quota by median OTE for the cohort. That single ratio is more portable across companies than either input, because it tells you how much revenue the market expects a rep to produce per dollar of target pay. If your ratio is far off the cohort's, either your quota or your pay is misplaced — and the ratio tells you which conversation to have.

Fifth, adjust for attainment reality. Benchmarks report OTE, which is what a rep earns at 100% of quota. They separately report attainment distribution, which is what fraction of reps actually get there. Multiply, do not assume. If the cohort's median attainment is well below 100%, the expected earnings for a median rep are meaningfully below the headline OTE, and both your comp expense forecast and your recruiting pitch need to reflect that.

Sixth, layer in ramp and tenure to get cost per productive rep-year. A rep who ramps in five months and stays eighteen months delivers roughly thirteen productive months against a full period of fully-burdened cost. That is the number your capacity model should use, not headcount.

Seventh, date-stamp everything. Survey data is fielded months before it is published and describes plans that were designed months before that. Treat the report as a description of last year's plans, not this quarter's market, and widen your bands accordingly when the hiring market has moved.

Costs, timelines, and typical ranges

The practical ranges below are the shape practitioners consistently see in published SaaS comp research. Treat them as bands to test against your own cohort, not as authoritative point estimates — the specific figures move every edition, and the current numbers should come from the reports themselves.

On pay mix, individual-contributor AE plans cluster around a 50/50 base-to-variable split, with some SMB and transactional roles running more variable-heavy and some technical or expansion-focused roles running more base-heavy. Sales managers typically sit in the 70/30 to 75/25 range because a manager's output is a team aggregate that they influence rather than close. VP and CRO plans are usually more base-heavy still, often 70/30 to 80/20, sometimes with a portion of variable tied to company-level rather than individual targets.

On quota-to-OTE, the durable rule of thumb in SaaS is that a healthy plan lands somewhere in the four-to-six times range: a rep is expected to book roughly four to six dollars of new ARR for every dollar of target total compensation. Below four, the plan is expensive relative to what it produces and the CFO will find it. Above six, the plan tends to be structurally hard to hit, and you should expect attainment to fall and attrition to rise. Enterprise roles with long cycles typically sit at the lower end of the ratio; velocity and SMB roles sit at the higher end because volume compensates for smaller deals.

On ramp, the reported medians for SaaS AEs have long sat in the three-to-six-month range, but that median assumes a functioning playbook, working enablement, and inbound pipeline on day one. At small companies without those assets, real ramp routinely runs eight to eleven months, and a nontrivial share of hires leave before they ever reach full productivity. When you build a hiring plan, use your own historical time-to-first-closed-won, not the benchmark, and use the benchmark only to check whether your number is anomalous.

On tenure, median AE tenure in SaaS has been trending down for years and now commonly reads under two years. Pair that with ramp: a rep who takes five months to ramp and leaves at twenty months delivered fifteen productive months. That is the denominator for your cost-per-productive-rep-year math. Loading fully-burdened comp, recruiting fees, onboarding cost, manager time, and the opportunity cost of an unworked territory typically pushes the true cost of a productive rep-year well above the headline OTE — often by a third or more.

On accelerators and cliffs, the common structure pays a base commission rate up to quota, then steps up above it — frequently 1.5x to 2x in the band just above 100%, and higher still in the top band. Many plans also impose a floor or cliff below which no commission is paid, and an increasing share include clawback provisions if a customer churns inside a defined window. Each of these is a transfer of risk from the company to the rep, and each one has a measurable price in recruiting difficulty and attrition.

On timelines for the reports themselves: both studies field their surveys and publish annually, meaning the data you are reading was collected weeks-to-months before publication and describes plans designed in the prior planning cycle. Budget for a lag of roughly three quarters between the plan year the data describes and the plan year you are designing.

On the cost of benchmarking itself, Pavilion's compensation data is a membership benefit, so access is bundled into a paid community membership rather than sold as a standalone report. The Bridge Group has historically made its metrics reports available publicly with registration. Formal comp consulting engagements from the large advisory firms are a different category of spend entirely — five figures and up — and are usually only worth it when you are redesigning plans across a large organization rather than checking a single offer.

Where teams get it wrong

The most common error is treating OTE as earnings. OTE is a plan parameter, not a paycheck. In a year where median attainment lands well under 100%, the median rep's W-2 is base plus a fraction of target variable. A company that recruits on a headline OTE it has historically paid to only a minority of its reps is running a credibility problem on a delay — new hires figure out the real distribution within a quarter or two, usually by talking to each other, and the resulting trust damage shows up as early attrition.

The second error is ignoring survivorship bias. Compensation surveys are answered by people who still have the job. Reps who washed out at month fourteen are not in the sample, and neither are their zero-attainment years. This biases reported attainment and reported earnings upward. It also means the reports understate how brutal the bottom of the distribution is. When you model comp expense, model the full hired cohort including the people who will not make it, not just the survivors.

The third error is cohort mismatch dressed up as ambition. A bootstrapped twelve-million-ARR company benchmarking itself against venture-backed growth-stage SaaS is not aiming high; it is importing a cost structure its unit economics cannot carry. The correct move is to benchmark against your actual cohort and then decide explicitly whether to pay a premium above it for a specific reason — a hard-to-hire skill, a brutal geography, a strategic hire — rather than accidentally paying a premium because you read the wrong row.

The fourth error is averaging away the distribution. Medians hide everything interesting. Two orgs can share a median AE OTE while one has a tight cluster of consistent performers and the other has three stars carrying twenty laggards. The second org has a much worse business, and the median will never tell you. Always pull the quartiles or the attainment bands, not just the midpoint.

The fifth error is changing multiple plan variables at once. A team that raises quota, steepens accelerators, and introduces a cliff in the same plan year has made its results uninterpretable. If attainment falls, you cannot tell which lever caused it. Change one structural variable per plan cycle where you can, and instrument the change so you can attribute the result.

The sixth error is benchmarking pay without benchmarking pipeline. Comp plans do not create pipeline. If your marketing-sourced pipeline supports two and a half times coverage and the benchmark cohort runs at four times, no accelerator structure will close the gap. Raising the top-end payout while pipeline is thin is a signal of desperation that experienced reps read immediately, and it selects for gamblers over builders.

The seventh error is treating the benchmark as a mandate in negotiation. Median means half the market pays less. Quoting the median as a floor in a candidate conversation concedes the entire range above it. Know the quartiles, know which quartile your role genuinely sits in given your segment and equity package, and negotiate from that position rather than from a single number.

The eighth error, and the one RevOps specifically owns, is failing to reconcile the benchmark back to the company's own data. You have the ground truth: your actual attainment distribution, your actual ramp, your actual tenure, your actual quota-to-OTE. The benchmark's job is to tell you where you are unusual. If you never do that reconciliation, you are buying a report to confirm a plan you already wrote.

Decision framework: when to choose what

Which benchmark to lean on depends on the decision in front of you, and the two reports are genuinely better at different jobs.

If you are designing or auditing an AE plan — quota, ramp expectations, attainment targets, quota-to-OTE — The Bridge Group's AE metrics work is the sharper instrument, because it measures the operating metrics around the plan rather than just the pay level, and its multi-year consistency lets you read trend rather than a single snapshot. Use it to answer "is my quota reasonable" and "is my ramp assumption fantasy."

If you are building a full GTM org comp structure across levels — SDR through CRO, including equity and pay mix by seniority — Pavilion's compensation data covers more of the org chart and more of the executive layer, which the AE-focused study does not attempt. Use it to answer "what does the ladder look like" and "is my VP band credible."

If you are making a single offer to a single candidate, neither report is sufficient on its own. Combine a published median for the role and segment with real-time signal: what candidates are actually declining, what your last three offers closed at, and what your recruiters are hearing. Published data lags; your own funnel does not.

If you are defending comp expense to a board, lead with ratios rather than levels. Comp-cost-of-revenue, quota-to-OTE, and cost per productive rep-year travel across companies far better than absolute dollars, and they are harder to argue with because they normalize away company size and geography.

If your attainment is collapsing, resist the reflex to redesign comp. Diagnose first: is it pipeline coverage, is it win rate, is it ramp, is it a segment that stopped working, or is it genuinely a quota-setting error? Comp is the last lever to pull, not the first, because it is the most expensive to change and the hardest to change back.

And if you cannot get a clean cohort match at all — you are in an unusual vertical, an unusual geography, or a genuinely new motion — build the plan bottom-up from your own unit economics instead. Take your gross margin, your target CAC payback, and your realistic capacity per rep, and derive the quota and the pay envelope that hold those constraints. Then use the published benchmark only as a reasonableness check on the answer, not as its source.

Related questions

How often should we re-benchmark comp?

Once per annual planning cycle for plan design, plus a light mid-year check if your hiring market moves sharply. Published surveys only refresh annually, so more frequent formal benchmarking adds no new information — but your own offer-acceptance data should be monitored continuously.

Is Pavilion data available without a membership?

Pavilion's compensation work is primarily a benefit of its paid membership community, so access is generally bundled with membership rather than sold as a standalone public report. The Bridge Group has historically published its metrics reports publicly with registration.

What single ratio matters most?

Quota-to-OTE. It normalizes across company size, geography, and pay mix, and it tells you directly how much revenue the market expects per dollar of target pay. Most healthy SaaS AE plans land in the four-to-six times range.

Should we pay above the benchmark median?

Only deliberately, and only for a stated reason: a scarce skill, a brutal hiring market, or a strategic hire. Paying above median by accident — because you read the wrong cohort's row — is how comp-cost-of-revenue drifts without anyone deciding to let it.

Does equity belong in the comparison?

Yes, but separately. Compare cash OTE to cash OTE first, then compare equity as its own line with its own risk discount. Blending illiquid equity into a total-comp number makes offers look comparable when the actual risk profiles are not.

FAQ

Are Pavilion and The Bridge Group the same organization?

No. They are separate, independent companies with different business models and different data. Pavilion is a paid membership community for go-to-market executives that surveys its members and their organizations. The Bridge Group is an independent sales consultancy that has published its own SaaS sales metrics research for many years. Neither owns nor publishes the other's work, and their respondent samples are largely distinct — which is exactly why comparing the two is useful rather than redundant.

What does each report actually cover?

Pavilion's compensation research spans the go-to-market org chart, reporting base, variable, equity, and pay mix across roles from SDR through executive leadership, with organizational context like stage and headcount. The Bridge Group's AE metrics report focuses on the individual-contributor sales role and its operating metrics: OTE and pay mix, quota, quota-to-OTE ratio, ramp time, tenure, average contract value, sales cycle length, and the distribution of quota attainment.

Why does the reported OTE differ from what reps actually take home?

OTE is what a rep earns at exactly 100% of quota. Actual earnings depend on where the rep lands in the attainment distribution. When median attainment across a cohort sits well below 100%, the median rep's actual W-2 is base pay plus a partial variable payout, which lands materially under the headline OTE. Any comp expense forecast or recruiting pitch built on OTE alone will overstate what the typical rep earns.

How stale is the data by the time I read it?

Meaningfully. Surveys are fielded weeks to months before publication, and respondents are describing plans designed in the prior planning cycle. In practice you should assume roughly three quarters of lag between the plan year the data describes and the plan year you are designing. In a fast-moving hiring market that lag matters, so widen your bands and cross-check against live offer and decline data from your own funnel.

What biases should I discount for?

Three main ones. Opt-in sampling means companies with the budget and inclination to benchmark are overrepresented, which skews toward better-funded organizations. Survivorship bias means reps who washed out are not in the sample, which pushes reported attainment and earnings upward. And geographic and segment concentration means the median may reflect a cohort quite different from yours. None of these invalidate the data — they just mean you should read the quartiles and cohort cuts, not the headline median.

What should RevOps do with the benchmark once it has it?

Reconcile it against your own ground truth. You already have actual attainment distribution, actual ramp, actual tenure, and actual quota-to-OTE from your CRM and comp system. The benchmark's job is to flag where you are unusual, and then to force the question of whether that difference is intentional. Ending the exercise with a documented list of deliberate deviations is a better outcome than ending it with a number.

Sources

• https://www.bridgegroupinc.com/
• https://www.joinpavilion.com/
• https://www.saastr.com/
• https://openviewpartners.com/blog/
• https://www.gartner.com/en/sales
• https://www.alexandergroup.com/
• https://www.xactlycorp.com/
• https://www.repvue.com/
• https://www.salesforce.com/resources/research-reports/state-of-sales/

Related on PULSE

• What's the right cadence for benchmarking your sales metrics against industry peers?
• What's the latest median CAC payback for Series B SaaS?
• How do you bridge the operational gap between founder-led sales and a new fractional CRO?
• How do you design an SDR compensation plan that does not reward activity theater?
• How should quota be set when historical attainment data is thin?