Key Takeaways
- Dirty marketing data inflates lists with duplicates plus typos plus stale rows that waste sends.
- TRIM plus LOWER plus REGEXMATCH fix spacing plus casing plus shape before matching starts.
- Smart Deduplicate at 0.85 groups near-duplicates that exact tools miss.
- Flookup Data Wrangler runs the full pass inside Sheets before verification or CRM sync.
What Counts as Marketing Data Quality
Marketing data quality means each contact maps to one real person with a valid address and a known source. Clean marketing data produces reports that count each buyer once, while dirty marketing data inflates list size with duplicates plus typos plus stale rows.
This post is the Sheets workflow companion to the data cleaning overview . It covers the hands-on pass for campaign exports, from normalisation through fuzzy matching to the audit before sending or syncing.
Normalise Campaign Exports First
Campaign exports arrive with mixed casing plus trailing spaces plus display names packed around addresses. Wrap the email column with TRIM and LOWER in one ARRAYFORMULA step, then pull plain addresses from angle brackets with REGEXEXTRACT before any check runs.
Normalise company and country fields alongside the address so later grouping stays stable. Paste results as values with Ctrl plus Shift plus V so imports read plain text rather than formulas.
Fuzzy Match and Dedupe Contacts
Remove exact copies first with Data cleanup plus Remove duplicates, then run Smart Deduplicate in Flookup Data Wrangler on the standardised column. A threshold around 0.85 groups casing variants and plus-tagged addresses for review while distinct contacts stay apart. The same pattern powers the email list cleaning workflow for send-ready lists.
Flag domain typos in the same pass by scoring extracted domains against a short list of expected providers. Scores above 0.85 mark likely typos such as gamil for gmail, while lower scores point to rare but valid providers that deserve a manual look.
Standardise UTM Casing and Dedupe Exported Conversions
Ad exports duplicate conversions when one row writes the source as Email and another writes email, so reports credit two sources for one click. Lowercase the source plus medium plus campaign columns and trim whitespace before any pivot runs.
Then dedupe on the click or order key and keep the earliest master row with source beside it. This sheet-level pass catches export duplicates, while true attribution questions stay with the ad platform where click data lives.
Audit Checklist Before You Send or Sync
| Check | Action | Target |
|---|---|---|
| Backup | Copy tab with date in name | Original untouched |
| Format | REGEXMATCH helper column | Zero FALSE rows left |
| Domains | Fuzzy review above 0.85 | Typos fixed or logged |
| Duplicates | Exact pass then fuzzy pass | One master row per contact |
| UTMs | Lowercase plus trim | One source per click |
Clean Data Reports Honestly
Clean marketing data keeps campaign data quality high across sends and syncs. Lists stay lean, segments count each buyer once and CRM reimports stop recreating duplicate contacts. Repeat the pass on every import so the marketing database stays trustworthy. When segments come next, the customer data segmentation workflow continues from a clean master.
Frequently Asked Questions
What is marketing data cleaning?
Marketing data cleaning is the removal of duplicates plus typos plus stale rows from contact and campaign exports before sending or syncing. The pass covers normalisation with TRIM and LOWER plus format checks plus fuzzy deduplication in Google Sheets.
How do I deduplicate a marketing contact list in Google Sheets?
Remove exact copies with Data cleanup plus Remove duplicates, standardise with LOWER and TRIM and run Smart Deduplicate in Flookup Data Wrangler at 0.85. Keep one master row per contact with source and consent date for future matching.
How does bad marketing data waste budget?
Duplicates waste sends while typos bounce and stale rows skew reports, so spend flows to addresses that never convert. Cleaning first means the verifier checks a lean list and the CRM receives masters instead of fresh duplicates.
Does Flookup verify that email addresses exist?
No. Flookup prepares the sheet through standardisation plus typo flagging plus deduplication, while a dedicated mailbox verifier confirms delivery after export. Each tool covers the task it fits best.