List Deduper And Cleaner

Turn a messy lead CSV into a clean, reconciled export with duplicates removed, risky rows flagged, and the original file preserved.

Anonymous web run

Generate and export without saving history

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Product input

Build the result

A dirty list creates fake campaign lessons because the campaign is learning from bad data.

Source CSVUpload the original lead list. The app should preserve this file and create a separate cleaned version.

Column mappingMap messy headers to standard lead fields before dedupe, normalization, and export.Dedupe key preferenceOrder matters in the UI: stronger keys should be checked before looser company-name matching.

EmailCompany domainLinkedIn URLCompany namePhoneCustom composite key

Duplicate handlingControls which record survives when two or more rows match the selected dedupe keys.Suppression listOptional CSV of emails, domains, companies, or phone numbers that should be matched and removed or flagged.

Suppression policyUse remove for send-ready exports, or flag-only when the user wants to inspect matches first.Sending-tool format targetDetermines required columns, header names, and export templates for the cleaned file.Normalization rulesThese rules should be previewed before export so users can trust what changed.

Trim whitespaceLowercase emailsNormalize domainsNormalize company namesNormalize LinkedIn URLsNormalize phone formattingRemove blank rowsSplit full name into first and last name

Risk checksFlags rows that may be unsafe, incomplete, or likely to create bad campaign learning.

Malformed emailFree email providerMissing domainMalformed domainRole-based emailMissing companyMissing required personalization fieldPossible competitor or internal domain

Required export columnsUsed to warn about missing fields before the user imports the cleaned file into a sending tool.Review modeControls whether the app exports immediately or asks the user to inspect flagged rows first.Save run to dashboardSave run metadata, reconciliation counts, and exports in the shared B2BOutreach.io dashboard.

Step 1

Upload the source CSV and preserve the untouched original file as the immutable run input.

Step 2

Parse headers, detect encoding issues, remove empty rows from the working copy, and show a preview before any export.

Step 3

Guide the user through column mapping for email, company, domain, LinkedIn URL, phone, name, title, segment, and custom fields.

Result workspace

Upload a CSV to preview headers, map columns, choose dedupe keys, and generate a clean export without overwriting the original file.

Clean CSV in the selected sending-tool format

Duplicate rows removed or separated into a review export

Invalid, incomplete, suppressed, and risky rows flagged with reason codes

Normalized company names, domains, emails, phone numbers, and LinkedIn URLs

Row reconciliation from original upload to final clean export

Dashboard access

Anonymous web runs do not save history. Dashboard runs are designed to save after purchase, login, or credit access.

Exports

Generic clean CSVSmartlead-ready CSVInstantly-ready CSVEmailBison-ready CSVApollo import CSVClay table CSVHubSpot import CSVDuplicate review CSVRisky-row review CSVSuppression-match CSVRow reconciliation report PDFJSON run summaryDashboard saved run

Audit bridge

Each result should make the next consulting/audit step obvious without overpromising meetings.

B2B2BOutreach.io

We build B2B outreach systems that turn cold prospects into real sales conversations.

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