PLG motions, sales-led motions, mid-market and enterprise — a system that respects the product signals you're already capturing and pairs them with outbound that doesn't undo your brand work.
Your product analytics know who's about to convert. Your outbound team doesn't see it. The wrong account gets the cold email; the right account never hears from you.
Marketing spent two years building a brand. Outbound's templates undo it in three months. The friction makes founders ban outbound entirely.
Self-serve doesn't fit; enterprise sales is too expensive. Most teams have no clear motion for the $20–100k ACV band.
Marketing wants credit, sales wants credit, the data team is exhausted. Every QBR is the same fight.
Combine first-party usage data with public intent signals. Score accounts on both 'fits the ICP' and 'showing buying behavior.'
Drafts in your team's voice, not a template library's. Every send goes through your brand-voice classifier first.
Account-specific creative variants for your target list, served only to people inside those accounts. Compounds with outbound touches.
Surface accounts where multiple seats, expanded usage, or specific feature adoption signal a sales-led upgrade conversation.
Multi-touch attribution that splits credit fairly between marketing and sales — built into the platform so the QBR fight stops.
Lead alerts, sequence approvals, and pipeline updates in Slack. Issues and feedback in Linear.
Numbers above are representative ranges from the last twelve months of customer outcomes in this vertical. Your results will differ; we'll model the expected range with your data before you sign anything.
The company was great at PLG. Self-serve revenue was healthy, NPS was high, churn was low. The problem: every analysis showed there was a $20–100k ACV band of customers that needed a sales-led motion the company didn't have. They'd tried hiring two SDRs the previous year and didn't get traction; the SDRs hated the company's brand-strict marketing team and the marketing team hated the SDRs' messaging.
We started by mapping their PLG signal stream — which workspaces had 10+ seats, which had API usage above a threshold, which had hit specific feature combinations. We layered our intent signals on top and produced a daily ranked list of 30–60 accounts.
Then we trained FollowUp AI on the founder's actual email style — he'd been doing the early sales motion himself and his emails worked. The result was a sequence library the marketing team approved and the (one, senior) SDR could send confidently. Sixty days later, they had a working motion, an attribution model that ended the QBR fights, and the mid-market ACV growth they'd been trying to manufacture for two years.