Turning anonymous self-serve signups into product-qualified pipeline

The problem

The play

1. 1
   Usage and spend signals into the CRMHubSpot

   Piped Snowflake spend and usage data into HubSpot so behavioral reality, spend run rate, usage pattern, activity trend, sat on the record where scoring and routing could actually use it.

2. 2
   A fit × behavioral PQL model

   Built a sixth-iteration PQL model combining firmographic fit tiers with behavioral tiers, banding every contact into a fit × behavior grid rather than collapsing everything into a single score.

3. 3
   Controlled routing into sales

   Gated AE-versus-SDR routing on the model and rolled net-new product-qualified leads to AEs on a delay with notifications throttled, an explicitly staged release designed around the earlier alert flood rather than pretending it had not happened.

4. 4
   Enrichment to unmask the base

   Ran reverse-email and funding enrichment across the contact base to turn anonymous personal-email signups into identified companies and fill missing firmographic data, then proposed a criteria-based intake filter to stop paying enrichment credits on records that would never qualify.

5. 5
   Contracting and deal hygienePandaDoc

   Built PandaDoc contract templates with CRM sync, mapped qualification-framework fields into Gong, backfilled historical contracts, and opened a CPQ workstream off an analysis of executed agreements and 90 days of recorded pricing calls.

What it moved

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