crm-data-hygienelisted

# Lead and CRM Data Hygiene Dirty data has two costs. It renders into emails in front of prospects, and it makes every segment count wrong, so decisions get made on numbers that don't mean what they say. Both are cheap to prevent at import and expensive to fix once the data is spread across campaigns and reports. ## Normalise on the way in Do this before anything is stored, not as a periodic cleanup. **Email** ```bash # Lowercase, trim, strip plus-addressing for dedupe (keep the original for sending) tr '[:upper:]' '[:lower:]' < emails.txt | sed -E 's/^[[:space:]]+|[[:space:]]+$//g' ``` Store lowercased. Trim whitespace, which arrives constantly from spreadsheet exports. Keep the original form for sending but dedupe on the normalised one, treating `sarah+news@` and `sarah@` as the same mailbox. **Names** The field most likely to embarrass you, because it renders into the first word of the email. | Input | Output | |---|---| | `SARAH JONES` | `Sarah Jones` | | `sarah jones` | `Sarah Jones` | | `Dr. Sarah Jones-Smith, PhD` | `Sarah` for `{{first_name}}` | | `Sarah (Sales)` | `Sarah` | | `sarah.jones` | `Sarah Jones` | | `Sales Team` | flag — not a person | | `-`, `n/a`, `test` | flag — junk | Strip honorifics and post-nominals. Preserve internal capitals in names like `McDonald`, `O'Brien` and `van der Berg` — naive title-casing produces `Mcdonald` and `Van Der Berg`, which is worse than leaving it alone. Don't force-capitalise names you can't parse; use the fallback