01
Domain warmup state
Domains with fewer than 21 days of warmup averaged 36% open rate in the verified proof vs 54% for warmed domains. Run the warmup check separately before scheduling.
Free tool · no email gate · N=1,247
Calibrated against the FORKOFF outreach proof. 1,247 first-touch sends. 7-week collection window. Fill 5 inputs and get a predicted open-rate band in seconds.
Dataset source: /stats/cold-email-open-rates-2026 — 52% average open rate across 1,247 first-touch sends, 2026.
Five segment deltas from the same 1,247-send dataset that powers the /stats page. Composite estimate = 52% baseline + per-segment adjustments.
Predictor uses segment deltas from /stats/cold-email-open-rates-2026 (N=1,247 first-touch sends, 2026-03-15 to 2026-05-01). Real rate variance from list quality, sender reputation, and copy quality is NOT modeled. Anchor expectations against the +/-6pp confidence band.
The predictor is a campaign-composition estimator, not a deliverability audit. These five variables affect real open rates but fall outside the 5-input scope.
Domains with fewer than 21 days of warmup averaged 36% open rate in the verified proof vs 54% for warmed domains. Run the warmup check separately before scheduling.
Role-address senders (info@, hello@) averaged 41% vs 53% for named operators. The predictor assumes a named sender with verified LinkedIn presence.
Stale, scraped, or unverified lists depress open rates independent of every segment variable. Run email validation before the send.
Personalization-token leaks averaged 31% open rate in the verified proof. Subject specificity (event reference, specific post) averaged 59%. Copy quality is not a dropdown.
Image-heavy HTML with multiple CTAs averaged 39% open rate vs 55% for plain-text single-CTA sends. Email format is not captured in this predictor.
For a full deliverability and copy audit paired with this predictor, read the cold-outreach-cadence playbook or use the cold email open rates 2026 dataset directly to calibrate your segment expectations.
6-dimension AI-native page audit. Schema, snippet, LLM citation surface, linking, answers, freshness.
5-LLM AEO scorecard. Measure brand citation share across ChatGPT, Claude, Perplexity, Gemini, and Grok.
The dataset this predictor anchors against. 52% average open rate. 1,247 first-touch sends. Segment tables by industry, role, and company size.
Full FORKOFF tool suite: AI visibility, cost and ROI, schema generation, and internal helpers.
The predictor estimates the ceiling. The FORKOFF founder-funnel engagement runs the system that actually hits it: warmed domains, named operator identity, signal-driven subject lines, and a 7-step send cadence.
Authorship
Kshitij JK
Founder, FORKOFF
Last reviewed:
Published:
Methodology
Predictor weights are calibrated against FORKOFF's founder-funnel proof (50K+ B2B sends across SaaS, AI, Web3 verticals). Domain warming + identity signals weighted per industry benchmarks from Mailgun, SendGrid, and Apollo's deliverability research.
Sources cited
Have a question about the cold-email open-rate predictor methodology, or need help calibrating against your campaign data? Book a 30-min strategist call
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