Michael
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Pick up and finish yesterday's cut-off Tier B pass. - build/: PyInstaller scaffold (datatools.spec + launcher.py + hook-streamlit.py + README) — folder-mode bundle, locked 127.0.0.1, per-OS recipe - marketing/COPY.md: single source of truth for every customer-facing string — landing H1/sub/CTAs, demo CTAs, email subjects, Gumroad listing, banned phrases - marketing/community-posts/: 9 drafts (3 posts × 3 niches: bookkeeper, revops, shopify-pet) — story / tip / soft-offer - marketing/emails/: 18 drafts (Gumroad delivery + 5-touch onboarding × 3 niches), per-niche segmentation guidance - docs/NEXT-STEPS.md: flip 2.2 / 2.4 / 3.1 / 3.4 to done with pointers to the new assets; add Phase 0 inventory rows - .gitignore: narrow `build/` ignore so PyInstaller spec + launcher + hooks get tracked, only generated artifacts (build/build/, build/__pycache__/, build/dist/) stay ignored Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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RevOps · Day 3 — The dedupe rule that catches LinkedIn drift
Subject: The dedupe rule that catches LinkedIn drift Send: Day 3 Goal: deepen feature understanding around the cross-source dedupe
Hi {{first_name}},
The thing native HubSpot / Salesforce dedupe can't do, and the thing DataTools is actually best at: cross-source matching, where the same person shows up via LinkedIn, a webform, and a trade-show import — with no shared key.
The rule that does the work is in the dedupe tool's "Block by domain, fuzzy on name+title" mode. Here's what it does:
Step 1 — Block. Group rows by email domain. (LinkedIn rows with no email get bucketed by domain(linkedin_url) — usually their company website if they listed it.) This avoids the O(n²) explosion and rules out cross-company false positives.
Step 2 — Within each block, fuzzy-match on first_name + last_name + title. Token-set ratio at 0.85 default. Catches:
- "Sarah O'Brien, VP Marketing" = "sarah obrien, vp of marketing"
- "Mike Chen, Head of Sales" = "Michael Chen, Sales Lead" (this one needs a 0.78 threshold; configurable)
- "J. Smith, Director" = "Jane Smith, Director" (only with a strong company-name match)
Step 3 — Confidence-tier the merge. ≥0.95 auto-merges. 0.85-0.95 goes to <filename>.review.csv for you to eyeball. <0.85 stays unmerged.
Step 4 — Field-precedence on merge. When records merge, you choose which source wins per field. Default precedence (configurable):
title,company,linkedin_url→ LinkedIn wins (more recent)email,phone→ Webform wins (verified)lifecycle_stage,owner→ HubSpot wins (your CRM is canonical)
One trap to avoid: don't run dedupe before format standardization. If phone formats are inconsistent across sources, the dedupe tool sees "+14155550143" and "(415) 555-0143" as different keys. Always run format → analyzer → dedupe → gate in that order. The pipeline UI enforces this; the per-tool runs don't.
Reply if you want me to walk through the precedence config on a screen-share — happy to do this for any buyer in the first 30 days.
— Michael {{support_email}}