Results.
Over 1 million cold emails sent across B2B SaaS and tech client builds. Same operator, same copy framework, same infrastructure. Reply rates differed by audience.
Six campaigns sorted by reply rate. All audiences in the B2B SaaS, tech, or automation orbit. Same playbook on each.
The pattern.
The two campaigns at the top were sourced from real signals: a directory of self-identified buyers, an automation tool's user base filtered for buying-readiness. Send volume under 300. Reply rate 19-28%.
The four below were list-led: filtered by job title and firmographics, then sent at scale. Send volume 2,300 to 11,500. Reply rate 2-6%.
Same operator. Same copy. Same infrastructure. The variable was who got the email.
Lists tell you who. Signals tell you when.
linkedin outbound
From engagement to connection.
Scrape comments on category-leader and competitor posts. Enrich the commenters with full work context. Send personalised connection requests referencing what they wrote. Reply once accepted.
Same lesson as the email work. Real engagement on a specific post is the highest-quality signal LinkedIn gives you.
case study
Oneshot.ai. 0 to $1M ARR in 2 years.
Joined as the outbound operator. Built the sourcing pipelines, wrote the copy framework, ran the infrastructure, owned the campaigns end to end. Stayed 2 years, helped take the company from 0 to $1M ARR before it wound down.
What worked: signal-led sourcing over list buying. The 7-agent pipeline that became Probe. Deliverability-first infrastructure. Weekly copy iteration tied to reply-rate diagnostics.
What I'd do differently next time: tighter ICP from Day 1, stronger founder-buyer interviews before scaling, less vendor-stack experimentation.
The methodology, the IP, and the engines built there are now ravirevops.com's stack.