Seller Report Salary Data Methodology

Every salary, OTE band, and quota benchmark on Seller Report ties back to a single source: job postings that companies publish on their own career pages and on the major job boards. This page documents exactly how the data is collected, normalized, and surfaced so that readers can judge each number on its merits.

Where the data comes from

The underlying dataset is built from a weekly crawl of B2B sales job postings. Each crawl pulls listings directly from employer career pages plus aggregated feeds. The 2026 snapshot covers 1,309 live US-based sales postings spanning SDR, BDR, Account Executive, Enterprise AE, Sales Manager, Director, and VP roles. Postings are deduplicated on a hash of company plus normalized role title plus city. Duplicate reposts from the same employer are dropped before any salary aggregation.

The dataset is refreshed on a rolling basis. The visible Seller Report numbers reflect the most recent stable build, not a streaming feed. Build dates are stamped on each article page so readers can see how fresh a given number is.

How salary numbers are computed

Each posting is parsed for an explicit salary range. 58.6% of postings disclose pay in dollar terms (the rest list "competitive," "DOE," or no range at all). The disclosed-pay subset becomes the analytical base for every median, average, and percentile cited on the site.

For each posting with a disclosed range, the low and high bounds are extracted. The "median" reported on the site is the median of the midpoints across the relevant slice. The "min average" and "max average" columns on salary tables are the mean of the disclosed low bound and the mean of the disclosed high bound respectively. This convention is consistent across every salary table on the site.

Salaries are reported in nominal US dollars without inflation adjustment. Reported figures refer to base salary unless the source posting explicitly used "OTE" or "total compensation" framing, in which case the field is labeled accordingly.

OTE, quota, and variable compensation

On-target earnings (OTE) figures are derived from the subset of postings that explicitly state OTE alongside base salary, or that publish a base salary plus a stated commission percentage. Where only base is disclosed, OTE is estimated by applying role-typical base-to-variable splits: 70/30 for SDR roles, 50/50 for mid-market AE, 60/40 for Enterprise AE, 70/30 for Sales Manager. These splits are documented per-role on each city-by-role page and reflect the observed concentration in the disclosed-pay subset.

Quota bands are taken from postings that publish quota expectations or from public earnings calls and S-1 filings of large public employers in the dataset. Quota bands quoted on the site are stated as ranges (for example, "$600K-$1.2M annual ARR" for mid-market AE) rather than point estimates because the underlying variance is real.

City and metro adjustments

City-specific salary pages combine the national role median with a metro multiplier derived from disclosed-pay postings in that metro. Multipliers are capped between 0.85x and 1.55x to avoid extreme outliers from small metro samples. Cost-of-living indexes referenced on city pages are sourced from the Bureau of Economic Analysis (BEA) Regional Price Parities series and the C2ER Cost of Living Index, indexed to the US national average of 100.

Top-employer lists for each city are cross-referenced with active job postings in the same dataset. Employers shown on city pages all maintain active sales hiring pipelines in the relevant metro at the time of the build.

Tool and methodology adoption signals

Tool adoption figures (e.g. "Salesforce appears in X postings") and methodology adoption figures (e.g. "MEDDIC appears in Y postings") are derived from full-text search of the same 1,309-posting corpus. Each tool or methodology is matched with a case-insensitive substring search anchored to word boundaries to reduce false positives. Counts are conservative; a posting that mentions "MEDDIC or similar" counts as one mention of MEDDIC, not as a vote for the framework.

What this data is good for

Salary medians and ranges from a live job-posting dataset are most useful for two questions: "What are companies posting today for this role in this city?" and "How does the disclosed compensation for one role compare to another?" The dataset captures the market clearing price for new hires, not the comp of established employees who may earn above the posted range after multi-year accelerators.

The OTE, quota, and tool-adoption figures are most useful as benchmarks for compensation negotiations, interview prep, and sales-team comp-plan design. A reader who wants to understand whether a posted offer is competitive can compare it against the median, range, and city-adjusted figures published here.

Limitations

Disclosed-pay postings are a non-random subset of the labor market. Pay-transparency laws in California, Colorado, New York, Washington, and other states materially increase disclosure rates in those metros relative to states without such laws. Cross-metro comparisons of disclosure rates should be read against that legal backdrop, not as a clean signal of employer transparency.

Equity compensation is not captured at the dollar level. Where a posting mentions equity, the page acknowledges it qualitatively, but no equity dollar figure is included in the base or OTE median calculations. RSU grants at public companies often add $20K-$80K of annual vesting value to AE roles; private-company equity grants vary widely in expected value and are excluded from the headline numbers.

Sample size matters. Where a city or role has fewer than 30 disclosed-pay postings in the build, the median should be treated as an estimate rather than a precise benchmark. The site flags small-sample cells where they appear in tables.

Update cadence and corrections

The full dataset is rebuilt on a weekly cadence. Pages that depend on the build (salary tables, city-role estimates, methodology adoption counts) refresh with each build. Editorial articles in the Insights section reference a fixed build snapshot stated in the byline. If a number on the site looks off versus a known external benchmark, the most likely cause is the build snapshot lagging a market shift; the second most likely cause is a parsing edge case in the disclosed-pay subset.

Corrections and data questions can be sent to the editor through the newsletter reply path. Documented corrections are applied at the next weekly build and noted in the build changelog.

What you can do next

Compare a specific role against the dataset on the salary index, drill into by-seniority breakdowns, or browse city-by-city pay with cost-of-living adjustments. The insights articles apply this dataset to specific career questions: SDR-to-AE moves, AE comp negotiation, remote pay premiums, methodology adoption, and more.

Frequently Asked Questions

How many job postings does Seller Report analyze?

The 2026 build covers 1,309 US-based B2B sales job postings spanning SDR through VP roles. Postings are deduplicated by company plus normalized title plus city before salary aggregation.

What does 'disclosed pay' mean on Seller Report?

Disclosed pay refers to postings that publish an explicit salary range in dollar terms. About 58.6% of the 2026 dataset discloses pay; the rest list 'competitive,' 'DOE,' or no range. All site medians and percentiles come from the disclosed-pay subset.

How is OTE estimated when only base salary is disclosed?

Where only base is disclosed, OTE is estimated by applying role-typical base-to-variable splits: 70/30 for SDR, 50/50 for mid-market AE, 60/40 for Enterprise AE, 70/30 for Sales Manager. These splits reflect the observed concentration in postings that publish both base and OTE.

What are the limits of this dataset?

Disclosed-pay postings are a non-random subset shaped by pay-transparency laws. Equity grants are not included in median or OTE figures. Small-sample cells (fewer than 30 postings) should be treated as estimates rather than precise benchmarks.

How often is the data refreshed?

The dataset is rebuilt on a weekly cadence. Salary tables and city-role estimates refresh with each build. Insights articles cite the build snapshot stated in the byline.