SDRlist-building · intermediate
Building a 200-account ABM list with Clay + Common Room signals
Last reviewed: 2026-05-23 · saves ~1 day/run
Our take
Most SDRs build lists from Apollo filters and call it a day. The 2-3x outbound performance comes from layering live signals — funding events, exec hires, product launches, community activity — on top of firmographic fit. Clay + Common Room together do this in 30 minutes.
Tool stack
Steps
- Start with Apollo or ZoomInfo: pull 1000 accounts matching firmographic ICP.
- In Clay: enrich each row with funding events (last 90 days), exec hires (last 60 days), tech stack changes.
- Common Room layer: add community/product signals if you have a PLG motion or relevant community presence.
- Filter to 200 accounts with ≥2 signals AND firmographic fit. These are your weekly outbound targets.
- Run research-brief playbook against the 200; allocate top 30 to highest-tier reps.
Prompts
Score account fit + signal density · GPT-4o-mini
Given an account record with these fields: - Firmographic data (industry, size, location, funding stage) - Signals (funding events, exec hires, product launches, community activity, tech stack changes — each with date) Score the account 0-100 with this weighting: - Firmographic ICP match: 40% - Signal density (count of signals in last 90 days): 30% - Signal recency (most recent signal): 20% - Signal type fit (some signals matter more for our product): 10% Output: JSON with score + top-3-signals reasoning. If signal count = 0, max score is 50.
Pitfalls
- Don't trust any single signal alone. ≥2 signals is the bar.
- Funding events older than 90 days are noise.
- If you don't have community signals, don't fake them — skip Common Room and weight other signals more.