On this page
- What Is the Artemis GTM Research Methodology?
- How Does Artemis GTM Collect Research Data?
- How Are Research Participants Selected?
- What Statistical Methods Are Used?
- What Quality Controls Are Applied?
- What Are the Known Limitations?
- What Studies Have Been Published?
- How Can Researchers Access the Data?
- Frequently Asked Questions
Research Methodology
How Artemis GTM designs, collects, analyzes, and publishes original B2B go-to-market research.
What Is the Artemis GTM Research Methodology?
Artemis GTM conducts original research on B2B go-to-market performance. All studies use rigorous methodology to ensure actionable, trustworthy insights. Our goal is to give revenue leaders the benchmarks they need to diagnose problems, set targets, and prioritize fixes with confidence.
How Does Artemis GTM Collect Research Data?
Our benchmarks are built from anonymized data across 100+ B2B SaaS GTM engagements and Flash Audit submissions, combined with published industry research. Figures are illustrative and directional — not a controlled-study result.
- GTM Flash Audit submissions: Structured self-assessment covering speed-to-lead, pipeline health, lead quality, sales effectiveness, data quality, and tech stack integration
- CRM integrations: Read-only connections to Salesforce, HubSpot, and Pipedrive for lead response time, pipeline conversion, and deal velocity analysis
- Website analytics: Visitor identification rates, traffic-to-lead conversion, and engagement metrics from consenting participants
- Sales engagement platform data: Outbound sequence performance, reply rates, and booking rates from connected tools
Anonymization policy: All data is anonymized before analysis. Company names, employee names, customer names, domain names, and any identifying details are removed. Only aggregate statistics and anonymized benchmarks are retained for published research.
How Are Research Participants Selected?
Our research focuses on B2B companies that match the following criteria:
ARR Range: $1M - $500M
Growth-stage through upper-mid-market companies actively scaling their go-to-market operations.
Industry Verticals
SaaS, FinTech, Cybersecurity, HR Tech, and Professional Services. Other B2B verticals are included when sample sizes permit.
Minimum Company Size: 10 Employees
Ensures participants have established GTM operations with measurable processes and data.
Self-Selection Method
Companies enter the dataset by taking the free GTM Flash Audit. Participation is voluntary and confidential.
What Statistical Methods Are Used?
Descriptive Statistics
We report means, medians, and quartiles for all key metrics. Median values are preferred for benchmarks to reduce the impact of outliers.
How We Report Figures
- Directional, not definitive: figures are reported as medians and ranges, not precise population statistics
- Not a controlled study: observations blend client engagements with published industry benchmarks; results are not a guarantee
Quartile Analysis
Companies are segmented into performance quartiles (top 25%, median, bottom 25%) to identify what separates best-in-class performers from underperformers across each GTM dimension.
Correlation Analysis
We analyze correlations between specific GTM practices (such as lead response time, scoring model maturity, and tech stack integration depth) and revenue outcomes (pipeline velocity, win rates, and revenue per rep).
What Quality Controls Are Applied?
- Duplicate submission detection and removal: Submissions from the same company are de-duplicated, retaining only the most recent entry.
- Outlier analysis: Responses more than 3 standard deviations from the mean are excluded from benchmark calculations.
- Internal consistency checks: Self-reported data is validated for logical consistency (e.g., lead volume vs. team size, deal size vs. ARR range).
- Cross-validation against CRM data: Where CRM integrations are available, self-reported metrics are compared against actual CRM data to calibrate accuracy.
What Are the Known Limitations?
We believe in transparent research. The following limitations should be considered when interpreting our findings:
- Self-selection bias: Companies taking the audit may skew toward those actively experiencing GTM problems or seeking improvement. Results may not represent the full population of B2B companies.
- Self-reported data: Some metrics rely on self-reported responses, which may contain inaccuracies or optimistic estimates.
- Sample composition: The sample is weighted toward US-based B2B SaaS companies. Benchmarks may differ for companies in other regions or industries.
- Directional guidance: Results should be interpreted as directional guidance, not absolute benchmarks. Every company's GTM context is unique.
What Studies Have Been Published?
The following studies have been published using this methodology:
2026 GTM Benchmark Study
Directional benchmarks on GTM performance across B2B SaaS engagements, covering revenue leaks, pipeline health, and AI adoption.
Read StudySpeed-to-Lead Benchmark 2026
Directional benchmarks on B2B lead response, illustrating the impact of response time on conversion rates.
Read StudyWebsite Visitor De-anonymization ROI Benchmark 2026
ROI analysis of website visitor identification tools across B2B companies.
Read StudyHow Can Researchers Access the Data?
Researchers and journalists can request access to anonymized datasets for academic or editorial use.
Contact us at tom@artemisgtm.ai with your name, affiliation, and intended use. We respond to all requests within 5 business days.
Frequently Asked Questions
Participate in Future Research
We are continuously collecting data for upcoming benchmark studies. Get your free GTM diagnostic and contribute to industry benchmarks.
Run the Free DiagnosticParticipation is voluntary and confidential. Opt-out available anytime.