We publish original research on the topics our customers wrestle with. Each study includes its methodology, source disclosure, and caveats about generalizability.
Studies
What Wins in 2026: Analysis of 1.2M LinkedIn Cold Messages
Reply rates broken down by length, send time, day, channel order. Patterns from 1.2M LinkedIn cold messages observed across our customer base in Q1 2026.
LinkedIn Restriction Trigger Patterns: 12 Months of Observed Data
What patterns precede LinkedIn account restrictions in 2026. Distribution of trigger types, recovery rates, and the 7-day warning window most operators miss.
AI vs Human Cold Messages: 5,000-Prospect Deep A/B Test
We ran 5,000 prospects through three personalization tiers — mail merge, hand-written by a top SDR, AI synthesis. The reply-rate spread is bigger than the marketing copy suggests.
LinkedIn Algorithm Shifts in 2025-2026: What Changed and Why
Reverse-engineered analysis of LinkedIn algorithm changes from late 2024 through 2026. Reach impact by post type, timing, and format. Operator implications.
Cost Per Meeting Across the B2B Sales Stack
Real per-channel cost per booked meeting in 2026. LinkedIn vs cold email vs cold call vs paid ads. Founder-led vs SDR vs automated. With sources.
Methodology principles
- Disclosure first. Every study leads with what data we have and where it comes from.
- Caveats explicit. Where the data has biases or doesn't generalize, we say so.
- Operator-readable. Charts and takeaways before tables. The takeaway is what matters.
- Citable. All public claims sourced where possible. Internal data labeled as such.