1. Trim and lowercase

Hidden spaces and mixed case create fake duplicates and failed matches.

=LOWER(TRIM(A2))

2. Flag broken syntax

Catches missing @, spaces inside the address, and missing domain endings.

=IF(REGEXMATCH(B2,"^[a-z0-9._%+\-]+@[a-z0-9.\-]+\.[a-z]{2,}$"),"ok","bad syntax")

3. Catch common domain typos

gmial.com, gmail.co, hotmial.com, and yahooo.com show up in every scraped list.

=IF(REGEXMATCH(B2,"@(gmial|gmai|gnail|hotmial|yahooo)\.|@gmail\.co$"),"typo","")

Fix the obvious ones by hand; delete anything you can't be sure of.

4. Separate role addresses

info@, sales@, support@, and admin@ rarely reach a decision-maker and often trigger spam complaints.

=IF(REGEXMATCH(B2,"^(info|sales|support|admin|contact|hello|office|team|noreply|no-reply)@"),"role","")

5. Remove duplicates

=IF(COUNTIF(B$2:B2,B2)>1,"duplicate","")

Keep the first row, filter on "duplicate", and delete the rest.

6. Keep personal and work addresses apart

For B2B outreach, tag free-mail domains so you can decide whether to keep them:

=IF(REGEXMATCH(B2,"@(gmail|yahoo|hotmail|outlook|aol|icloud)\."),"free mail","work")

Then verify a sample

Syntax checks can't tell you whether a mailbox exists. Run a small sample through your verifier of choice, and if more than a few percent fail, verify the whole list before sending.

Free first pass: drop your CSV into the CSV Preflight checker to see blocking problems before you import.

Rather hand off the cleanup? Send up to 200 rows to the $25 Quick Clean (https://buy.stripe.com/cNi7sK3JS8Z50915Me73G0d?utm_source=hashnode&utm_medium=post&utm_campaign=quick-clean&utm_content=coldemail) and get back a cleaned CSV (same-email duplicates merged, possible duplicates flagged) plus a change log within 24 hours, no CRM login needed. Bigger list? Intro $99 covers up to 500 rows (https://buy.stripe.com/9B600idksb7d2h9eiK73G03?utm_source=hashnode&utm_medium=post&utm_campaign=quick-clean&utm_content=coldemail); Growth $299 (up to 2,000) and Bulk $499 (up to 5,000) are at https://pureservices.store/?utm_source=hashnode&utm_medium=post&utm_campaign=quick-clean&utm_content=coldemail

Originally published on DEV.to.

This post is a practical cleanup checklist you can run on a HubSpot contacts export (CSV) before the next campaign — plus a before/after from a demo pass on fictitious sample rows.

What “clean enough” means

For outbound and CRM hygiene, clean enough usually means:

1. One row per person (dedupe on email first, then soft-match name + company / phone).
2. Emails lowercased and trimmed with invalid syntax flagged, not silently deleted if you still need review.
3. Names and titles normalized (Title Case; ceo → CEO).
4. Phones in one format (or cleared and flagged when unusable).
5. A changelog so RevOps can audit what changed.

If you cannot explain the deltas to a client or manager, the cleanup is not done.

Export from HubSpot

1. Contacts → Export (include email, first/last or full name, phone, company, job title, lifecycle if you use it).
2. Prefer CSV UTF-8.
3. Do not enrich or invent missing emails in this pass — fix structure first.

A concrete before → after (demo)

We ran a messy B2B sample (fictitious demo rows) through the same rules we use for Sales List Rescue packs:

On a larger demo (40 → 32), eight duplicate emails were merged, names title-cased, and phones normalized — with missing company fields flagged rather than guessed.

Public before/after sample: Free Sample — Before & After CRM List Cleanup.

Checklist you can run today

• Deduplicate on email (exact).
• Soft-merge obvious duplicates (same person, different casing / whitespace).
• Lowercase + trim emails; flag invalid syntax into a qa_notes / status column.
• Title-case names; normalize titles without inventing seniority.
• Normalize phones to one pattern; clear garbage and flag.
• Keep a machine-readable changelog (row in → row out, fields touched).
• Mark rows ready vs needs_review so SDRs do not touch junk.

What not to promise

• Guaranteed inbox placement or bounce rates (that needs live verification and client-funded credits).
• Scraped or invented emails.
• “We fixed your whole CRM in HubSpot UI” without an export/import workflow.

Structure first. Enrichment second, and only when the buyer funds it.

Fast path if you do not want to DIY

1. Free CSV teaser — see what a cleanup pass flags on your own export: https://pureservices.store/tools/csv-teaser?utm_source=hashnode&utm_medium=organic&utm_campaign=hashnode-medium-devto-mirror
2. Quick Clean ($25): send up to 200 rows and get back a cleaned CSV (same-email duplicates merged, possible duplicates flagged) plus a change log within 24 hours, no CRM login: https://buy.stripe.com/cNi7sK3JS8Z50915Me73G0d?utm_source=hashnode&utm_medium=post&utm_campaign=quick-clean&utm_content=hubspot (Bigger list? Intro $99, up to 500 rows: https://buy.stripe.com/9B600idksb7d2h9eiK73G03?utm_source=hashnode&utm_medium=post&utm_campaign=quick-clean&utm_content=hubspot)
3. Full cleanup packs (HubSpot / Salesforce / CSV): https://pureservices.store/?utm_source=hashnode&utm_medium=organic&utm_campaign=hashnode-medium-devto-mirror
4. Referral program — know a team drowning in duplicates? https://pureservices.store/referral?utm_source=hashnode&utm_medium=organic&utm_campaign=hashnode-medium-devto-mirror
5. Agencies reselling cleanup (white-label): https://pureservices.store/for-agencies?utm_source=hashnode&utm_medium=organic&utm_campaign=hashnode-medium-devto-mirror

Before/after sample: https://sapphire-passive-3ea.notion.site/40273a511f5281a0aa77f7c8ed99ac09

Export-based cleanup only. No lead sourcing, no invented emails, no bounce guarantees.