Surround-Sound ABM

Cognassist's best-fit accounts don't convert on spray-and-pray demand gen — they have to be worked one-to-one. But real ABM is operationally heavy: picking the right accounts, mapping a multi-role buying committee at each, researching verified contacts and a live reason to reach out now, arming AEs with the right message per persona, and coordinating paid air cover against the exact same list. By hand that's a full-time job per wave — and every wave starts from scratch.

Designed a repeatable 100-account wave — Select → Map → Build → Execute → Learn — where the scoring, the messaging framework and the tooling are the reusable template, so the motion compounds instead of being rebuilt. Selection runs deterministically off CRM data (exclude current and former customers, rank by learner volume, split across provider types, allocate fairly across AEs) with guards for the traps that bite ABM: a live customer slipping into the list, or one institution sitting under two CRM records. Behind it, custom AI skills do the heavy lifting — buying-committee research with verified-contact write-back (dry-run → approved → apply → verified), a LinkedIn matched-audience export, and per-wave campaign blueprints plus per-account one-page dossiers.

Every wave, an AE opens a single dossier and knows exactly who to reach across the buying committee, why now — the account's live trigger — and what to say for each persona and org type, while paid display and LinkedIn ads run against the identical account list in the same window. By the time the AE's message lands, the account has already seen Cognassist in the wild: paid and outbound act as one coordinated “surround”, not two separate efforts.