Salary Data

Salary Data Methodology

How we collect, clean, and present ABM compensation data.

Data Collection

The ABM Pulse tracks 254 ABM-related job postings. Of those, 178 (70.1%) include disclosed salary ranges. We source data from:

Inclusion Criteria

A role is included if it meets any of these criteria:

Seniority Classification

Seniority is determined by title keywords and years-of-experience requirements:

Metro Classification

Roles are assigned to metro areas based on the listed location. "Remote" roles are classified separately. Roles listing a state but not a specific city are grouped under the nearest major metro when possible, or excluded from metro analysis.

Statistical Methods

Limitations

How the Data Gets Collected

Our salary dataset comes from public job postings that disclose compensation, primarily from US-based B2B SaaS, enterprise, and ad-tech employers. We pull from a mix of company career sites, LinkedIn job posts, and aggregator feeds, then dedupe by company plus role plus posting date. As of the current snapshot, we have 342 total ABM-related records and 225 with disclosed pay, which is a 66% disclosure rate. That rate has been climbing since the California, New York, and Washington pay-transparency laws took effect, but it's still not uniform. Companies headquartered in states without disclosure mandates often omit pay even when they're hiring into states that require it, which is technically non-compliant but widely tolerated. Refresh cadence runs weekly. New postings are ingested, expired postings drop out of the active dataset after 60 days, and median calculations re-run on the current active set. We don't backfill historical pay into the current median, because role definitions drift and a 2023 'ABM Manager' job is often a different job than a 2026 one. Quality control: we manually review roles flagged with implausible bands (the dataset shows a Radio One 'Integrated Marketing Specialist' posting with a $60K-$864K range, which is clearly an OFCCP-compliance-driven artifact, not a real offer band, so we exclude it from median calculations even though it's technically disclosed pay). Honest limits: sample sizes for individual metros are small (Austin and Chicago both have 3 records, Boston has 3), and disclosed pay underrepresents private compensation, which is often higher than the public posting suggests. ABM as a job title is also ambiguous in ways that matter. Some employers use 'ABM' to mean named-account marketing, others use it to mean any B2B campaign that targets a list. We try to filter to the former, but the latter still leaks in.

How to Read the Percentile Bands

When you read a percentile band, the median is the 50th percentile, which means half the disclosed roles pay below it and half pay above. That's not the same as 'what most people make,' especially when the distribution is skewed, which it almost always is at the top end of the seniority ladder. The 'avg range' columns show the average minimum base and average maximum base across postings at that segment. The average minimum is roughly where a recruiter starts the negotiation. The average maximum is roughly the top of the disclosed band, but the actual top of a company's internal band is often 10-20% higher than the posted max. Disclosed pay is also distinct from offer-letter pay. Disclosed bands are usually the company's full range for the role, including future merit increases. The offer to a specific candidate usually lands in the lower-to-middle third of the disclosed band, unless the candidate is being recruited against a competing offer. 'Median' in our data means the median of disclosed maxes (or mins, depending on the column), not the median of actual offer-letter pay across hires. The two are correlated but not identical.

Frequently Asked Questions

Where does the salary data come from?

We collect compensation data from public job postings on major job boards and company career pages. We focus on roles that explicitly list ABM, demand generation, or field marketing in the title or description.

How often is the data updated?

The dataset is refreshed weekly. Historical trends are tracked month-over-month.

Why do some roles show very high or very low salaries?

Outliers exist. Some postings include commission or OTE in the range. We report the numbers as listed and flag known outliers in the top-paying roles section.

Do you include equity and bonus?

Base salary only. Equity, bonus, and OTE are tracked separately when disclosed but not included in the headline numbers.

Why don't your medians match what I see on Glassdoor or Levels.fyi?

Different sources use different definitions. Glassdoor self-reports actual pay (often base plus bonus, sometimes inflated). Levels.fyi focuses on tech employers with self-reported total comp. We use disclosed public job-posting bands, which are different but more standardized. The three sources usually agree on direction but can differ by 10-15% on absolute numbers.

How often is the data updated?

Weekly. The current snapshot was generated on 2026-04-04 and reflects the active job-posting set as of that date. Medians get recalculated each refresh on the active dataset only.

Why is the entry-level sample size so high?

Because entry-level pay is disclosed more often (it's lower-risk for the employer to disclose) and because 'ABM Coordinator' and 'Integrated Marketing Specialist' postings churn faster as employers backfill these roles more frequently than senior ones.