Import Tradeshow Leads into Salesforce from Google Sheets
Fuzzy-match badge scans against your full Salesforce org, auto-resolve 80%+ of rows, review the exceptions in a real spreadsheet, and commit Leads plus Campaign Members in one pass — all from a Google Sheets sidebar.
No more manual reconciliation after every conference. FoundryOps replaces the badge-scan-to-Salesforce fire drill with fuzzy matching, structured review, and one-click commit.
Data Import Wizard vs FoundryOps Tradeshow Import
The gap is structural. Salesforce has no built-in fuzzy matching for tradeshow files.
| Capability | Data Import Wizard | FoundryOps |
|---|---|---|
| Matching | Exact email only | Name, company, domain, geo, email — fuzzy + exact |
| Duplicate handling | Blocks insert, no resolution | Ranks candidates with evidence, suggests best match |
| Review surface | Error log CSV | Structured Sheets tab with dropdowns and filters |
| AI assistance | None | Gemini-powered exception review + autopilot |
| Batch size | Manual chunking | Up to 5,000 rows, async for large batches |
| Campaign membership | Separate step | Included in commit |
| Sequence enrollment | Not supported | Optional, same pass |
| Time for 500 rows | 2-4 hours (manual reconciliation) | ~15 minutes (mostly auto-resolved) |
The problem every conference creates
You come back with 500-3,000 badge scans in a CSV. You need those in Salesforce — as campaign members, with the right Lead or Contact record — before the follow-up window closes. Here is where it breaks.
Data Import Wizard only matches on exact email
Badge scans frequently capture a different email than what is in Salesforce. The attendee used a personal Gmail, or the scanner captured a nickname variant. Data Import Wizard sees a non-matching email and either creates a duplicate or silently fails.
Duplicate rules block the insert with no resolution path
Salesforce duplicate rules catch the mismatch and block the record. But they do not tell you which existing record to link to. You get an error log with 40+ blocked rows and no actionable next step.
No fuzzy match option exists in Salesforce
Salesforce has no built-in way to say "this badge scan is probably the same person as this existing Lead, based on name + company + geo, even though the email is different." You either match on exact key or you do not match at all.
The fallback is manual reconciliation
Ops teams export the blocked rows, open two browser tabs, and manually search Salesforce for each person. For a 500-row file, that is 2-4 hours of copy-paste. For a 3,000-row conference, it does not get done — the leads go cold.
What Tradeshow Import does differently
The feature runs inside the Google Sheets sidebar. It takes the badge scan file, fuzzy-matches every row against your full Salesforce database, and presents a structured review before anything touches CRM.
Fuzzy matching, not just exact email
The matcher uses name similarity, company name, company family relationships, business domain, email domain, and geographic signals — weighted and combined. "Sue Harvey at American At Home Health" matches "Susan Harvey" at "AAH" even when the emails are completely different.
Smart candidate ranking with evidence
Each row gets a ranked list of Salesforce candidates with evidence chips showing why the match was suggested: NAME90 (strong name match), DOM (domain match), FAM (company family match), GEO (geographic match), exact_email. The operator sees the reasoning, not just a score.
Automatic bucketing — humans only review the hard ones
Every row is classified into auto-safe, likely match, exception, or no match. For a typical 500-row file, 80%+ of rows land in auto-safe or likely-match. The operator only needs to look at the exception bucket.
AI review for the exceptions
For ambiguous rows, Gemini-powered AI evaluates each exception against its top 3 Salesforce candidates and recommends an action. The AI never writes to Salesforce directly — it fills in the review sheet, and the operator confirms before commit.
Review happens in a real spreadsheet
Match results are written to a structured review tab with native Sheets filtering, sorting, and data validation. Action cells have dropdowns. Hidden columns carry stable row IDs so the commit layer executes structured decisions, not scraped text.
One-click commit with campaign membership
Once the review sheet is clean, the commit step executes all decisions: update existing Leads, add Contacts to the campaign, create new Leads where needed. Campaign membership and optional sequence enrollment are handled in the same pass.
Automatic bucketing — humans only review the hard ones
Every row is classified into one of four buckets. For a typical 500-row file, 80%+ of rows land in auto-safe or likely-match.
Auto-safe
Exact email + high score + clear margin over next candidate. Pre-approved.
Likely match
Strong name + real company/domain support. Defaulted to "use match" but visible for spot-checking.
Exception
Family-only evidence, competing candidates, or ambiguity. These need review or AI assist.
No match
No credible Salesforce candidates. Defaulted to "create new Lead."
The workflow in 5 steps
Pick the tab
Select the sheet tab containing your badge scan data.
Match
The system uploads the file, fetches your Salesforce Leads and Contacts, and fuzzy-matches every row. Results appear in a review tab.
Review
Check the exception rows. Optionally run AI review to fill in recommendations. Most rows are already resolved.
Commit
Choose a campaign, set the member status, and commit. Unresolved rows can be auto-resolved by AI at commit time.
Done
Updated Leads, new Leads, campaign memberships, and optional sequence enrollment — all in one pass.
Who this is for
Event marketers
Run 4-10 conferences per year and need leads in Salesforce the same week.
SDR managers
Want badge scans in active sequences before the follow-up window closes.
Marketing ops
Tired of the post-conference fire drill of duplicate resolution and manual cleanup.
Revenue ops
Need campaign attribution to be accurate, not "we created duplicates and hoped for the best."
Technical details
Related
Tradeshow Lead Cleanup
The full workflow: match, filter ICP, enrich only what matters, import, and activate.
See use caseTradeshow Leads into Salesforce
The step-by-step answer page for getting tradeshow files into Salesforce cleanly.
Read guideTradeshow Playbook
The execution proof with CLI prompts, logs, and results from a real tradeshow sprint.
See playbookFAQ
How is this different from Salesforce Data Import Wizard?
Data Import Wizard only matches on exact email. If the badge scan email does not match what is in Salesforce, the import either creates a duplicate or blocks with no resolution. FoundryOps fuzzy-matches on name, company, domain, and geography — then presents ranked candidates with evidence so you can make an informed decision.
How many rows can it handle?
Up to 5,000 rows per batch. Files under 1,000 rows run synchronously in seconds. Larger files run as background jobs with progress updates in the sidebar.
Does the AI write directly to Salesforce?
No. The AI reviews exception rows and recommends an action in the review sheet. The operator confirms every recommendation before the commit step executes any Salesforce writes.
What matching signals does it use?
Name similarity, company name, company family relationships, business domain, email domain, geographic signals, and exact email — all weighted and combined. Evidence chips show exactly why each match was suggested.
Can I add leads to a Salesforce Campaign at the same time?
Yes. The commit step creates or updates Leads and adds Campaign Members in one pass. You choose the campaign and member status before committing.
Does it work with HubSpot?
The Tradeshow Import feature currently targets Salesforce. For HubSpot tradeshow workflows, see the HubSpot tradeshow guide.
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