03 · 2024 · Data pipeline
Fieldwork
A conference prospecting pipeline: three disconnected data sources in, one CRM-ready lead import out.
Live — in production
Solo build
Per-conference run
Overview
Conference prospecting used to be hours of bridging three tools by hand. Now it's a single command.
Fieldwork merges LinkedIn, ZoomInfo, and Apollo against a named-account territory file, classifies each lead by verified email domain, and scores it against the conference portfolio — producing thousands of import-ready rows per run.
01 — The problem
Three tools that don't talk.
Conference prospecting runs on three sources that don't connect — LinkedIn, ZoomInfo, and Apollo — plus a named-account territory file in its own spreadsheet. Every cycle meant hours of manual work: matching names, guessing domains, and assigning territory by hand. It was tedious, error-prone, and different every time.
02 — The approach
Three deterministic stages.
First, merge LinkedIn and ZoomInfo on a cleaned Name + Org key. Second, classify territory by verified email domain instead of guesswork. Third, score every lead against the conference portfolio so the best-fit accounts rise to the top. Each stage is auditable and can be re-run on its own.
03 — The build
One CLI, a subcommand per stage.
A single Python CLI runs the whole pipeline, one subcommand at a time. The domain-to-territory map is stored and grows with every run, so classification gets sharper over time. An outbound skill feeds the scored output straight into sequenced outreach via Dripify — no reformatting or copy-paste.
04 — The outcome
Thousands of clean leads per run.
Every run produces thousands of import-ready leads — conference-assigned, hierarchy-tagged, and Sales-Comments filled in. What used to be an afternoon of spreadsheet work is now a command I start and walk away from.
3 → 1
sources reconciled into one import
1000s
import-ready leads per run
Want more detail? Get in touch →