Fingerprint's ABM machine scores its entire market on real signals, not static firmographics, sorting accounts into TAM, SAM, then 4 distinct tiers. With plays sized to their potential, it's how the team moved upmarket while tripling ARR.

This session opens with a quick walkthrough of that model: how accounts get scored and tiered, and why signal beats volume. Then it zooms into where Char Di Placido owns execution, the 1:1 tier. With 300+ named accounts and only around 30 receiving personalised treatment at a time, production was the real bottleneck. Every campaign needed four teams and took one to two weeks to launch. Char rebuilt that motion around tiered personalisation instead of manual production, cutting launch time to under an hour, growing coverage 10x, and influencing $13M in pipeline. You'll leave with the full picture: how a signal-based tiering model decides who to prioritise, and how to actually execute the 1:1 personalisation that lives on top of it.

What will the you learn? :

• How Fingerprint scores and tiers its market using real signals, not static firmographics
• How to cut campaign launch time from weeks to under an hour
• How to grow 1:1 account coverage 10x without adding headcount
• How real-time engagement signals route straight to sales so they act on intent immediately 

Key delegate takeaway

Signal-based tiering tells you who to prioritise, but it's the production model behind your 1:1 tier that decides whether you can actually act on it at scale. 

Leave this session with

A copy of the full deep dive into the Fingerprint model