Turning public data sources and social monitoring into a scored outbound engine

The problem

The play

1. 1
   Build the account universe from public sources before buying a list

   Two of the seven projects pull the population directly from public sources, a public industry registry and a public procurement dataset, rather than starting from a purchased list. When the buyer population is publicly listed somewhere, that listing is a better universe than any commercial database.

2. 2
   Fix the wrong-buyer filter

   The ICP was the downstream supplier, not the top-level counterparty. The first pass off the procurement data returned the top-level counterparty on each record rather than the downstream supplier, which was the wrong buyer entirely; the client caught it on review of the sample list.

3. 3
   Watch the registry for change instead of re-pulling it

   A change-detection workflow monitors the registry for newly listed companies and pushes alerts, rather than re-running a full extract on a schedule. It was built on a self-hosted open-source automation tool deployed on the client's own server so the client owns the runtime and the credentials from day one, the handover was designed in, not bolted on.

4. 4
   Monitor social for intent language, then dedupe at the person level

   A monitoring project captures companies whose staff post publicly about the topic the vendor sells around. The failure mode surfaced fast: people who post frequently get captured repeatedly, so the same individual arrived multiple times in the sheet distributed to SDRs.

5. 5
   Score signals and carry the evidence with the score

   Each signal contributes one point to a rolling score field on the record, with a companion summary field that carries the actual evidence, the URL of the post or the job listing that triggered it. A score with no evidence behind it gets ignored by reps within a week; a score with a clickable reason gets used.

6. 6
   Route to reps where the reps already work

   Round-robin assignment runs in the enrichment platform and writes to a live-syncing shared sheet. This was chosen over channel-based alerting specifically because it only required rep names to stand up, where the alternative needed per-rep identifiers, the cheapest routing that actually gets used beats the better one that stalls on setup.

7. 7
   Control enrichment credits at the decision points

   Automatic enrichment on the social-monitoring path was deliberately paused so SDRs could manually qualify a company before any enrichment spend landed on it. Separately, the client challenged whether a per-company AI research check should be running across thousands of rows at all, and the gating logic was tightened in response.

What it moved

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