Mailchimp Audience Export Sample CSV (Dirty + Clean)
A realistic Mailchimp subscriber export (12,000 rows) covering data inconsistencies that break ESP migrations and cohort analytics. The Mailchimp quirk: OPTIN_TIME is frequently blank for API-imported contacts (bypassing native double opt-in). This causes pandas to_datetime() and SQL date-parsers to throw NaT or NULL errors. The 'dirty' version includes: 4,800 blank OPTIN_TIME values, mixed date formats (MM/DD/YYYY vs YYYY-MM-DD), and emails with trailing spaces that ruin engagement calculations. Ideal for ESP migration dry runs (to Klaviyo/ActiveCampaign) and deliverability audits. All processing happens locally — your subscriber list stays private.
This Mailchimp dataset contains realistic, fully anonymized records. We purposely engineered it to include common messy data patterns—such as unmapped columns, dirty formatting, and duplicate IDs—so you can safely test your processing pipelines without exposing real PII.
💡 Pro Tip: Pandas just threw a NaT error because Mailchimp mixed MM/DD/YYYY and YYYY-MM-DD. Run this file through Format Cleaner to standardize dates and handle null opt-ins.
⚡ Next Step: Moving to Klaviyo? Prep this audience with Klaviyo Prep — normalize email casing and format dates to Klaviyo's strict ISO 8601 standard before migration.
Data Schema Definition
| Column Name | Data Type | Description |
|---|---|---|
| Email Address | string | Subscriber email (often has trailing spaces) |
| First Name | string | FNAME merge tag |
| Last Name | string | LNAME merge tag |
| MEMBER_RATING | integer | Mailchimp's 1-5 star engagement score |
| OPTIN_TIME | timestamp | Often blank/null for API imports; causes parsing errors |
| OPTIN_IP | string | IP address at time of opt-in |
| CONFIRM_TIME | timestamp | Mixes MM/DD/YYYY and YYYY-MM-DD formats |
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