Replacing SDR Research with Automated Lead Scoring Increases Pipeline Coverage 160%
The "SDR is Dead" debate is lazy. The role didn't die, it split. I’ve been auditing the "48-Hour Void" in Series A pipelines the window where manual lead scraping causes signal to decay by 90%. My thesis: We are trying to solve a Latency Problem (Data) with a Bandwidth Solution (Headcount). I recently ran an experiment replacing the "SDR Research" layer with a Python-based "Signal Refinery" (n8n + Clay + Custom Logic Gates). The Architecture:
- 1.
Ingest: APIs listen for intent (Job posts, Tech install, Dark Social).
- 2.
Refine: Python logic scores the lead (0-100) instantly.
- 3.
Route: Only "Whales" (Score >80) trigger a Slack alert to a human.
The Result: 160% increase in pipeline coverage with zero manual data entry. I recorded a 57-second build log of the actual workflow running live (no pitch, just the code/logic). Question for other GTM Engineers: Are you keeping the "Research" function with the SDRs, or have you successfully decoupled it to an automated layer yet? (https://www.loom.com/share/ec261842b5474ecc9262b9ae28764ba4)
