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By Ben Argeband, Founder & CEO of Swordfish.AI<\/strong><\/p>\n Treat CSV as a pipeline: stress clean identity fields, consistent columns, and CRM import readiness; include pitfalls that break enrichment.<\/em><\/p>\n RevOps and recruiting ops exporting LinkedIn contacts\/leads and preparing files for csv enrichment<\/strong> and crm import<\/strong>. If you own data hygiene<\/strong>, dedupe<\/strong>, contact enrichment<\/strong>, CRM import<\/strong>, and field mapping<\/strong>, this is the workflow.<\/p>\n LinkedIn natively exports connections; other lead lists may require a controlled internal CSV built from your source list.<\/p>\n Compliance & Safety<\/strong><\/p>\n This method is for legitimate business outreach only. Always respect Do Not Call (DNC) registries and opt-out requests.<\/p>\n<\/blockquote>\n Most teams treat a CSV export like a file transfer. Operators treat it like a pipeline with failure points. This revops workflow reduces rework by preventing duplicates and mis-matched enrichment before anything hits the CRM.<\/p>\n Identity<\/strong> is what lets you match a row to a real person and avoid duplicates. Context<\/strong> is what makes the record usable in routing and segmentation. Enrich<\/strong> is where you append missing fields to the same rows. Verify<\/strong> is where you sanity-check match quality before you import.<\/p>\n The trade-off is speed vs. reliability: you can import fast and clean up later, or standardize now and avoid downstream cleanup.<\/p>\n Decision Heuristic<\/strong><\/p>\n If this CSV created 500 new CRM records tomorrow, would you trust it not to create duplicates or attach the wrong enrichment to the wrong person?<\/p>\n<\/blockquote>\n LinkedIn provides a native connections export (a CSV download). In LinkedIn, use the Settings & Privacy menu to request\/download from LinkedIn’s data export your Connections data export (CSV). A common path is Settings & Privacy → Data privacy → Get a copy of your data → Connections (CSV); labels can vary by account and UI updates.<\/p>\n What you get in the export can vary by account and what data is available, so plan to normalize and fill gaps before you run enrichment or crm import.<\/p>\n<\/li>\n Your goal is one row = one person, with stable identifiers. Use this header order for a working file so enrichment and CRM import<\/strong> mapping stay consistent:<\/p>\n first_name,last_name,linkedin_url,email,company,title,location,source<\/strong><\/p>\n Create\/normalize these columns (even if some are blank):<\/p>\n If company or title are missing, leave them blank now and fill them during enrichment or manual review. Keep columns consistent for enrichment.<\/p>\n<\/li>\n Enrichment is only as good as your matching keys. Dedupe first so you don’t enrich the same person multiple times.<\/p>\n Example (collision):<\/strong> Two rows for “Alex Kim” at different companies will merge incorrectly if you dedupe on name alone. Add company and linkedin_url before you merge anything.<\/p>\n<\/li>\n Use a contact enrichment workflow that appends results to the same rows, not a separate file you have to reconcile later. Use Swordfish File Upload to enrich the exported CSV with mobile numbers.<\/p>\n Once you have your LinkedIn CSV, drop it here to add mobile numbers automatically.<\/a><\/p>\n This requires manual verification, especially when the company name is ambiguous or the LinkedIn URL is missing.<\/p>\n<\/li>\n Example (column drift):<\/strong> A company name containing a comma can shift columns if the CSV isn’t properly quoted. That’s how “title” ends up in “company” and your CRM import becomes cleanup work.<\/p>\n<\/li>\n Do a test import of 20–50 rows into a sandbox or a test list. Confirm:<\/p>\n Example (field mapping):<\/strong> Map linkedin_url<\/em> to a dedicated CRM field (often “LinkedIn Profile URL”) and map source<\/em> to your lead\/contact source property so you can attribute pipeline back to the export sales navigator leads<\/strong> list or connections export. For recruiting ops, map linkedin_url<\/em> to an ATS “Profile URL” field and map source<\/em> to your campaign\/source field.<\/p>\n<\/li>\n<\/ol>\n Most failures aren’t “LinkedIn export problems.” They’re pipeline problems: weak identifiers, inconsistent columns, and rushed imports that create duplicates you can’t unwind.<\/p>\n Most teams don’t need more data. They need fewer broken rows.<\/p>\n If you’re doing export linkedin contacts<\/strong> weekly, this standardization reduces rework because your csv enrichment and crm import csv steps stop changing every time the file changes.<\/p>\n If source<\/strong> isn’t mapped at import, you can’t attribute pipeline or hiring outcomes back to the list that produced it.<\/p>\n For a deeper walkthrough on enrichment from a spreadsheet, see csv contact enrichment<\/a>.<\/p>\n<\/span>Who this is for<\/span><\/h2>\n
<\/span>Quick Answer<\/span><\/h2>\n
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<\/span>Framework: The “Clean CSV” Standard: Identity → Context → Enrich → Verify<\/span><\/h2>\n
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<\/span>Step-by-step method<\/span><\/h2>\n
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<\/span>Checklist: Weighted Checklist<\/span><\/h2>\n
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<\/span>Diagnostic: Why this fails<\/span><\/h2>\n
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<\/span>Decision Tree: Conditional Decision Tree<\/span><\/h2>\n
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<\/span>How to improve results<\/span><\/h2>\n
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<\/span>Troubleshooting Table: Diagnostic Table<\/span><\/h2>\n
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