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>
2.0 KiB
Shopify-pet · Day 14 — Two-minute trick: hidden-character cleanup
Subject: Two-minute trick: hidden-character cleanup Send: Day 14 Goal: surface the text cleaner — non-obvious, high-value
Hi {{first_name}},
The tool inside DataTools that buyers find last is the text cleaner — and on Shopify customer exports it's usually the one with the most "wait, that was a problem?" moments.
What it catches: invisible characters that got into your customer data when customers typed on their phones. The most common offenders:
- Zero-width space (
U+200B) inside emails — Klaviyo treatssarah@acme.com(with hidden char) andsarah@acme.com(without) as different addresses - Non-breaking space (
U+00A0) inside addresses — Shopify accepts it, Klaviyo accepts it, but USPS address validation fails on it - BOM marker (
U+FEFF) at the start of CSV cells — usually from a customer pasting from Word or a PDF - Right-to-left mark (
U+200F) — rare, but appears in customer names from Hebrew/Arabic locales
The 2-minute workflow:
- After the format standardizer pass, run the text cleaner.
- It produces an additional sidecar file:
<filename>.hidden-chars.csv— every cell where it found a hidden char, with a "what was hidden where" annotation. - Skim it. Most are fine to silently strip (zero-width spaces, BOMs). For rare ones (right-to-left marks in a name), confirm before stripping — sometimes they're load-bearing.
- Click "Apply cleanup". The text cleaner replaces the hidden chars in the cleaned CSV.
The reason this matters: dedupe runs after text-clean. Two emails with a hidden char difference look identical in the GUI but get treated as two separate customers — and your dedupe pass won't catch them unless the text cleaner ran first.
The pipeline order baked into the GUI is: analyzer → format → text-clean → dedupe → gate. Stick to it; per-tool runs out of order are the most common source of "wait, why didn't dedupe catch this?".
— Michael {{support_email}}