Sanitize Email Lists Before Klaviyo Import
Importing a dirty contact list into Klaviyo is the fastest way to torch your sending domain's reputation. Exported suppression lists from legacy ESPs are riddled with invisible zero-width spaces, malformed addresses like user@@domain.com, and role-based emails (info@, admin@) that trigger spam traps. If your hard bounce rate exceeds 2% on a single campaign, Klaviyo will throttle your account and your transactional emails start landing in Gmail's Promotions tab. This workflow cross-references your prospect list against historical bounce logs, strips non-printable Unicode characters, and validates RFC 5322 email formatting entirely in your browser.
Why this matters?
A DTC skincare brand recently imported 340,000 contacts from a legacy Mailchimp export into Klaviyo without scrubbing. The legacy list contained 18,000 hard-bounced addresses with invisible zero-width joiner characters that bypassed basic regex filters. Within 48 hours, their hard bounce rate hit 5.3%, Klaviyo automatically suspended their sending profile, and their Shopify order confirmation emails started routing to customer spam folders — causing a 12% drop in post-purchase support ticket volume and a wave of 'where is my order' chargebacks. Most email validation SaaS tools charge 0.008 per verification (totaling $2,720 for this list) and require uploading your entire customer database to their servers.
The 3-Step Solution
Follow this streamlined workflow to transform your raw data export into a clean, analysis-ready dataset. Each step leverages our browser-based tools to ensure your sensitive data never leaves your device.
By following these three steps, you eliminate manual data wrangling, reduce human error, and maintain full GDPR compliance throughout the process.
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Sample Datasets
Shopify Standard Order Export CSV Sample
A realistic Shopify order export with 200 rows including multi-line items, refunded orders, and discount code fields. Includes both a 'dirty' version (raw export with duplicates and nested line items) and a 'clean' version showing the expected output after normalization. Use this to test the Shopify Normalizer and CSV Merger tools.
Stripe Payout Export Sample
A Stripe payout CSV with 80 transactions including charges, refunds, disputes, and fee breakdowns across USD and EUR. Timestamps are in raw UTC format. Use this to test the Stripe Payout Formatter and validate QuickBooks-compatible output.
Apollo.io B2B Leads Export Sample
An Apollo.io lead list export with 300 contacts including duplicate emails, role-based addresses (info@, admin@), invalid domains, and inconsistent company name casing. Perfect for testing the Apollo Leads Cleaner and CSV Deduplicator on a realistic dirty dataset.
HubSpot Contacts Export Sample Dataset
This dataset contains 8,500 synthetic HubSpot contact records with exact production export columns: First Name, Last Name, Email, Lifecycle Stage, Associated Company, Lead Status, and HubSpot Score. It represents a mid-market B2B SaaS database with contacts spanning multiple lifecycle stages from subscriber to customer. The HubSpot-specific quirk is that multi-checkbox custom properties and system fields like Lead Status are exported as semicolon-separated strings (e.g., New;Qualified;SQL) rather than comma-separated values. Data engineers unfamiliar with this behavior often split these fields incorrectly during ETL, destroying the multi-select taxonomy. The dirty data inventory includes: semicolon-delimited strings in 2,100 Lead Status cells, trailing whitespace in 890 Email addresses that would cause duplicate contact creation on re-import, and 420 rows where Lifecycle Stage is completely blank due to API-synced contacts bypassing the form submission flow. After deduplication and whitespace trimming, this yields 8,340 unique, import-ready contacts. Ideal for: CRM migration testing, HubSpot import validation, data warehouse schema design, and reverse-ETL dry runs. Run this dataset through the csv-deduplicator tool to identify and merge the whitespace-polluted email duplicates.