Michael
e1f364f010
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>
37 lines
2.1 KiB
Markdown
37 lines
2.1 KiB
Markdown
# RevOps · Day 3 — The dedupe rule that catches LinkedIn drift
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**Subject:** The dedupe rule that catches LinkedIn drift
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**Send:** Day 3
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**Goal:** deepen feature understanding around the cross-source dedupe
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---
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Hi {{first_name}},
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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.
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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:
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**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.
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**Step 2 — Within each block, fuzzy-match on `first_name + last_name + title`.** Token-set ratio at 0.85 default. Catches:
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- "Sarah O'Brien, VP Marketing" = "sarah obrien, vp of marketing"
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- "Mike Chen, Head of Sales" = "Michael Chen, Sales Lead" (this one needs a 0.78 threshold; configurable)
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- "J. Smith, Director" = "Jane Smith, Director" (only with a strong company-name match)
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**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.
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**Step 4 — Field-precedence on merge.** When records merge, you choose which source wins per field. Default precedence (configurable):
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- `title`, `company`, `linkedin_url` → LinkedIn wins (more recent)
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- `email`, `phone` → Webform wins (verified)
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- `lifecycle_stage`, `owner` → HubSpot wins (your CRM is canonical)
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**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.
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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.
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— Michael
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{{support_email}}
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