What Is a Good ATS Resume Score? Understand What the Number Measures

There is no universal good ATS resume score. A useful target depends on the tool, what it measures, and which resume and job description it compares. Use a score to locate issues you can investigate, then check the document's accuracy, readability, and relevance. A percentage from a resume website is not an employer's published pass mark or your probability of getting an interview.

Choose an actual description before interpreting a match number; recent roles on LandOffer can supply a starting point. LandOffer publishes this guide. The product distinctions below come from official documentation checked on October 8, 2026. We did not run a signed-in comparison or observe an employer's screening decisions; numerical examples are explicitly fictional.

Ask which question the score is answering

“ATS score” is often used loosely for several different assessments. One tool may compare resume terms with a particular posting. Another may assess structure, writing, or missing sections without that same job-specific comparison. A parsing check asks whether software can read information from the file and map it to fields. Those questions are related, but they are not the same measurement.

A job-specific match score needs both a resume and a description. Change the role and the comparison changes, even when the document stays identical. A general resume-quality score can highlight a missing section or vague writing, while telling you little about whether your strongest experience fits a particular backend opening. Always identify the input and output before deciding what a number means.

Parsing concerns the reading and mapping of information. Can text be extracted? Are dates associated with the correct employer? Did the contact details import into the intended fields? A resume could contain relevant evidence yet import incompletely. Conversely, successful text extraction does not establish that the candidate meets the role's requirements.

Employer evaluation adds another layer. An employer may review application answers, use its configured tools, and assess experience under its own criteria. A third-party website does not know all those settings. The correct question is therefore “What feedback can this report support?” rather than “What number gets me through every company's system?”

Compare documented scores before comparing percentages

The following table compares documented definitions and guidance, not measured outputs on one resume. The products do not share a calibrated scale. Their target wording applies within their own interfaces and should retain that context whenever it is quoted.

Tool or check	What the official source describes	How to interpret it
Simplify Keywords Score	Percentage comparing the selected resume with one job description; matched and missing terms	Job-specific wording feedback; its roughly 70% recommendation guides tailoring rather than blocking applications.
Teal Resume Analyzer score	Foundational resume structure and content analysis	A general document assessment, distinct from Teal's Match Score.
Teal Match Score	Job Matcher feedback associated with a selected job	A separate comparison; Teal's export guide gives different suggestions for matching and analysis.
Jobscan Match Rate	Resume-to-description comparison with skill, title, and other checks	Its 75% suggestion applies to this scanner, not a universal hiring threshold.
Greenhouse resume parsing	Detects resume information and fills candidate-profile fields	An import operation that can fail or be partial, not a candidate-facing universal percentage.

Sources: Simplify Keywords Score, Teal Resume Analyzer, Teal export guide, and Greenhouse parsing.

Teal's export guide currently suggests 60% for its Analysis report and 50% for Job Matching. Simplify's guide suggests around 70% keyword coverage. These are different vendor recommendations attached to different assessments. None becomes a general ATS standard merely because it is expressed as a percentage. This guide does not claim that reaching any of them improves your outcome by a measured amount.

If you use another scanner, read its own current definition and suggestion. Jobscan's resume-scanner page recommends at least 75% for its Match Rate and describes resume-to-description comparison. That is another product's guidance, not a conversion formula for Teal or Simplify. Do not average several scores and call the result an employer-readiness percentage. A number useful for one report can be misleading when copied into another tool's scale or quoted without its target job.

Separate candidate tools from employer-side matching

It would also be inaccurate to say that applicant tracking systems never support matching or scores. Greenhouse's Talent Matching FAQ, updated October 5, 2026, describes an employer-side feature based on configured hiring criteria and weights. It assigns match categories and says the feature assists human review rather than automatically advancing or rejecting applicants.

The hiring team's calibration explains why employer context matters. The calibration belongs to the hiring team; a resume scanner you use independently does not establish the same configuration. A list of terms from a posting may be useful to review, but it is not a complete reproduction of the employer's criteria or evaluation process.

Nor does this single product description establish what every Greenhouse customer enables, or how every other ATS behaves. Keep the claim bounded: some documented employer tools support configured matching, and the candidate's third-party number cannot be treated as their output. Avoid both the universal-pass-score myth and the equally broad claim that no employer software assesses relevance.

Read two fictional reports on the same resume

Consider a fictional teaching example, not a test of a named product. Ravi has a resume describing Python service work and relational databases. The target posting requests Python, PostgreSQL, testing, and Kubernetes. His actual experience supports the first three areas; he has not used Kubernetes. He saves the same resume text and the same posting for two hypothetical tools.

Report A shows 82 out of 100 for general document quality. It finds clear sections but asks for more quantified results. Report B shows 58% job matching and flags PostgreSQL, testing, and Kubernetes as missing. These invented outputs demonstrate interpretation; they are not Simplify, Teal, Jobscan, or LandOffer results.

Finding	Investigation	Defensible action
General quality score is relatively high	The report assesses writing and structure, not this role's full requirements	Keep the clear structure; do not interpret 82 as a hiring likelihood.
PostgreSQL is reported missing	Ravi's project notes confirm PostgreSQL, but the resume says only “database”	Name the database in the relevant contribution.
Testing is reported missing	Ravi added invalid-input tests, described vaguely as “quality work”	Explain what was tested and his contribution.
Kubernetes is missing	Ravi has no supporting experience	Leave it out; assess the actual skill gap.

After those changes, Ravi's resume communicates two existing capabilities more clearly. That is the completed useful result. We do not invent a new score or claim the employer now accepts it. The unsupported Kubernetes requirement stays visible as a gap even if omitting it prevents a perfect keyword match.

The two reports can both be internally consistent while differing numerically. Report A asks about document quality; Report B asks about alignment with a particular description. Even two tools aimed at job matching may select or weight terms differently. Disagreement is a reason to inspect the findings, not evidence that the lower number is necessarily wrong.

Diagnose a low score by issue type

Start with the underlying report rather than changing every sentence. Is the problem missing text, unclear wording, a genuine experience gap, or a product's formatting preference? Each requires a different response. Adding repeated keywords will not fix a missing employment date, and moving a heading will not create experience operating a production system.

For extraction problems, compare the uploaded file with the visible imported content. Check contact details, employers, dates, education, and reading order. Greenhouse's parsing documentation lists several formatting patterns that can produce failure or partial import. Its guidance supports checking actual fields; it does not establish that a separate scanner replicates the employer's parser.

For wording problems, connect the reported term to a real task. Ravi can replace “quality work” with his specific invalid-input tests. Someone who used a relational database can name the actual product when relevant. But a suggested term that changes responsibility or scope deserves scrutiny: “team leadership” is not automatically supported by participating in a team.

For real gaps, consider importance. A preferred skill may be one trade-off among several, while a stated mandatory qualification can change the application decision. The report's color or percentage does not decide that distinction for you. Read the employer wording and evaluate the evidence you can present without transforming absence into a claim.

Avoid chasing a high score at the expense of the resume

Repeated terms can make a document less useful to a human reader. A skills list that duplicates itself, a summary packed with unrelated tools, and bullets copied from the posting may appear aligned while saying little about actual work. Your goal is an understandable account of relevant experience, with terminology that makes that account easier to recognize.

Be especially careful with suggested metrics. A writing checker may encourage numbers, but inventing a percentage to remove an issue is not an improvement. Describe verifiable scope, the specific task, and how you checked the result when reliable outcome measurements are unavailable. The resulting sentence may earn less praise from a generic heuristic while communicating more honestly.

Preserve employment titles and dates. If a tool flags the target title as missing, a truthful target-role statement or functional explanation may clarify intent, but do not silently promote your historical position. A scanner recommendation should remain subordinate to the record you can explain in an interview or employment check.

Set a stopping condition based on findings: correct extraction problems you can observe, clarify relevant evidence, remove unsupported additions, and review the export. Rescanning unchanged inputs repeatedly provides little new information. When a remaining mismatch reflects a real gap, decide whether to apply rather than spend another hour searching for language that disguises it.

Keep comparisons reproducible and the application decision separate

If you compare tools, use the same resume version and the same complete description. Record the date, product, report type, plan if relevant, and any normalization or missing sections. Changing both the resume and job input between scans makes it difficult to know why a score moved. Save the resume version and job description with the report; a number alone cannot explain a later decision.

Compare issue lists, not just totals. If two reports flag an absent database name, investigate that shared finding. If one penalizes a layout while the other extracts all text, inspect the file yourself and follow the actual application's instructions. A tool may provide useful feedback without being the final authority on every formatting choice.

Keep a short decision note: “Existing PostgreSQL and testing experience clarified; Kubernetes absent; fields reviewed; application decision based on required versus preferred qualifications.” This explains what the report changed and what it did not establish. It also leaves a useful record when you prepare for an interview without turning the score into a promised result.

To assess your next resume, choose a suitable role from LandOffer's recent jobs or your existing shortlist, identify the report's actual measurement, and inspect its findings against your evidence. Apply with a truthful, relevant document when the role merits it. Treat the number as one piece of feedback, not permission to invent experience or a universal barrier to submitting.

Sources and Further Reading

• Simplify: Keywords Score — job-specific percentage and bounded recommendation.
• Teal: Resume Analyzer and export guide — separate analysis and match guidance.
• Jobscan resume scanner — documented resume-to-description comparison.
• Greenhouse: Resume parsing and Talent Matching FAQ — import versus configured employer-side matching.