This play monitors updates to deals in Pipedrive (such as stage changes or closed-won status), uses the associated company as a signal of fit, and leverages Lusha to identify similar companies. Those lookalikes are then automatically created as new Pipedrive organizations, ready for targeting and outreach.
The result: your CRM continuously generates new, high-fit target accounts based on real deal data—without manual list building.
Your deal data already shows what “good fit” looks like. When a deal progresses or closes, it’s a strong signal you can reuse.
This play operationalizes that insight by:
Instead of guessing who to target next, you scale what’s already working.
Ensure active connections for:
Choose which deal updates should trigger the flow:
Best practice: Start with Closed Won or late-stage deals for the highest-quality lookalikes.
Use attributes from the deal’s organization, such as:
These inputs help Lusha return higher-quality recommendations.
Map recommended company fields:
Tip: Tag these orgs clearly so reps know they’re lookalike targets.
Update a test deal and confirm:
No duplicate orgs are introduced
The goal: Every deal tells a story about who’s a good fit. This play turns those insights into a self-reinforcing growth loop, continuously expanding your target account list—directly inside Pipedrive.
One data connection. Works in Claude, ChatGPT, your CRM, or any agent you build.
