| Feature | Venn | Spreadsheets |
|---|---|---|
| Setup time | ✓ 60 seconds | 2–4 hours per partner |
| Pricing | ✓ $0–$149/mo | Free (your time costs more) |
| matchAccuracy | ✓ AI-assisted fuzzy matching | Exact match only (VLOOKUP) |
| enrichment | ✓ AI signals + outreach copy | None |
| partnerCollaboration | ✓ Magic link — no back-and-forth | Manual file exchange |
| scalability | ✓ Unlimited partners | Breaks at 5+ partners |
Spreadsheet-based account mapping is the DIY approach: export your account list from your CRM, ask your partner to do the same, and run a VLOOKUP (or INDEX/MATCH) to find rows that appear in both lists. Teams also use Google Sheets shared with partners for ongoing tracking, or build custom formulas to score overlap. It's zero cost and gets the job done for a first experiment. The problems appear at scale: fuzzy company names don't match ("Acme Corp" vs "Acme Corporation"), keeping sheets in sync requires constant manual updates, and the output is raw match data — no context on which accounts to prioritize or what to say.
Venn replaces the entire spreadsheet workflow. Upload your account list (CSV from any source), send your partner a magic link — they upload their list through it, no account required — and you get a match report in 60 seconds. Venn's matching engine handles fuzzy company names, domain matching, and subsidiary resolution that VLOOKUP misses. More importantly, Venn doesn't stop at "here's your overlap list." Every matched account gets AI enrichment: tech stack signals, buying intent data, and a personalized account intelligence card with warm intro copy tailored to that specific account. That's the difference between knowing you have overlap and knowing what to do about it.
Use spreadsheets if you have one partner relationship to explore and want to test the concept before committing to tooling. Use Venn if you have more than one partner, need results in minutes instead of hours, want fuzzy matching that catches what VLOOKUP misses, or want AI enrichment to turn overlap data into actual pipeline. Venn's free tier gives you everything you need to move past the spreadsheet stage permanently.
Spreadsheets are free — but your time isn't. A typical manual mapping session (export, normalize, match, share back) takes 2–4 hours per partner, every time the list needs refreshing. At even modest hourly rates, that's hundreds of dollars per refresh cycle across multiple partners. Venn is free to start. Paid plans are $49/mo (Growth) and $149/mo (Team). Refresh takes 2 minutes.
VLOOKUP can find exact matches between two lists. Venn goes further: fuzzy matching (catches "Acme Corp" vs "Acme Corporation"), domain-based matching, and AI enrichment that turns match data into prioritized outreach opportunities. VLOOKUP produces a list; Venn produces revenue intelligence.
Three reasons: (1) Company names rarely match exactly across datasets — VLOOKUP misses "Acme Inc" vs "Acme Incorporated." (2) Manual file exchanges with multiple partners become unmanageable. (3) You get raw overlap data but no intelligence on which accounts to pursue first or how.
Yes. If you've been doing manual mapping in spreadsheets, upload your existing account CSV to Venn — no reformatting required. Venn accepts any CSV with a company name column, and optionally domain, industry, and employee count columns to improve match confidence.
Venn's matching engine normalizes company names (removes Inc, Corp, LLC suffixes), resolves common abbreviations, matches on domain when available, and uses confidence scoring to flag uncertain matches for review. You see exactly why each match was made and can override low-confidence results.
Probably. Even with two partners, the time savings on each refresh cycle add up fast. The bigger win is the AI enrichment: Venn turns your overlap list into personalized outreach copy for each shared account, which spreadsheets can never do. Free tier covers two partners comfortably.
Upload your accounts. Send a magic link. See overlap in 60 seconds. No CRM, no setup calls, no $25K contracts.
Try Venn Free →