Clean Apollo and ZoomInfo Lead Exports for Cold Email Campaigns
When multiple SDRs on your team search Apollo.io or ZoomInfo targeting the same companies but different job titles, the exported CSVs inevitably overlap. SDR #1 pulls VP-level contacts at Acme Corp while SDR #2 targets Directors at the same accounts — both exports contain [email protected] because that person matches both search criteria. Merging these files without deduplication causes internal collisions: two SDRs email the same prospect from different sending domains within 48 hours, triggering spam complaints that damage your entire sending infrastructure's reputation. Beyond duplicates, Apollo exports frequently return First Name values in inconsistent casing — john, MARY, De instead of De — because the platform aggregates data from multiple providers without normalizing capitalization. This workflow deduplicates by Email address while preserving the most recent Added to Sequences timestamp, then applies intelligent title casing to First Name and Last Name fields, handling edge cases like hyphenated names (Jean-Pierre).
Why this matters?
A 12-person SDR team at a Series B SaaS company merged their individual Apollo exports monthly to build a unified outreach database. Without deduplication, 2,340 email addresses appeared 2-3 times across the combined list. When uploaded to Instantly, the platform's round-robin sending infrastructure emailed the same prospects from different mailboxes, causing a 7.2% spam complaint rate that got three of their eight sending domains blacklisted by Spamhaus. Recovery took 6 weeks and $4,200 in dedicated IP warming costs. Separately, their reply rate dropped from 4.1% to 1.8% after switching to Apollo data because lowercase first names like sarah made every email look like a mass blast. Apollo's native export interface offers no cross-search deduplication.
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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Related Workflows
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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.
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.