Duplicates

Two duplicates of the same person show different job titles. Which one should survive the merge, and when a different title means it isn't a duplicate at all.

When two duplicates have different emails, the one the person opens and clicks is the one to keep. How to use engagement data to pick the main email in a merge.

Not every match is a duplicate. When to reject a match instead of merging it, what rejecting does, and how to keep false matches from coming back.

When two duplicates have different dates, which one should the merged record keep? A field-by-field guide to created, first-touch and last-activity dates.

When a customer rebrands, new contacts arrive under a new name and domain, and the CRM creates a second company. How to spot rebrand duplicates and merge them.

Newsletter and download forms create email-only contacts that duplicate fuller records. How to find them and merge without losing data.

A past customer fills in a form and lands in the CRM as a brand-new lead, routed to a rep who's never heard of them. Why it happens and how to catch it.

When one duplicate has a bounced email and the other a working one, the wrong merge keeps the bad address. How to make the working email win.

Exporting your CRM, cleaning it in a spreadsheet and importing it back looks simple. Why the round trip often adds duplicates, loses history and breaks links.

A typo like gmial.com creates a second contact and a bounced email. How to find duplicates hidden by misspelled email domains, and fix them.

The same phone number saved in the mobile field on one record and the office field on another hides a duplicate. How to match across phone fields.

When two duplicates have different lead statuses, the merge has to pick one. How to rank statuses so the right one wins every time.

Couples and families often share one email, so an email match can merge two real people. How to spot households and keep each person's record.

When a contact changes their last name, the old and new records stop matching. How to find them, merge them and keep the right name.

Records with only a first name are hard to match safely. Which fields to pair with the name, when to merge, and when to delete instead.

A middle initial is enough to stop two records from matching. How to find duplicates with and without middle names, without merging two people.

Two records with no email don't count as a match, and that's on purpose. Why blanks are left out, and how to find duplicates that have no email.

Records with the same name but different addresses can be two people or two locations. How to decide, and how to keep them out of your merges.

Two contacts with the same name at the same company aren't always duplicates. How to tell one person from two, and how to keep real people apart.

Matching CRM duplicates on an ERP, billing or license ID is precise, but only if the ID is clean. How to set it up and what to review first.

Can you undo a merge in a CRM? In most, no. See what each CRM allows after a merge, and how to recover from a wrong merge.

How fast do duplicates come back after a cleanup? Often within days. See what sets the pace, how to measure yours and how to keep up.

Why does your CRM miss so many duplicates? Native tools compare a few fixed fields, often exactly. See what slips through and how to find it.

When a contact exists several times, each record holds part of the history. How to pick the right one today, and how merging puts calls and notes on one.

Start with exact matches on email for contacts and domain for companies, then combine fields like name and company. Choose fields per object and source.

You don't need to standardize CRM data before deduplicating. See how matching handles formatting differences and how the merge keeps the value you trust.

Web forms create duplicate contacts when one person uses different emails, a form tool creates instead of updates, or several forms feed one CRM.

One company can turn into hundreds of records after a list import, a sync or a migration. Why it happens and how to bring it back to one record.

Subsidiaries, branches, shared inboxes and families can look like duplicates. Define why they aren't, then reject the match so it stops coming back.

"AI-powered" can mean very different things in duplicate matching. See the three common designs and the questions to ask an AI deduplication tool.

Duplicate matching compares records exactly, by similarity, by sound or across several fields. See what each match type catches and gets wrong.

Two company records with the same core domain are often duplicates. Compare clean domains, follow contacts' emails and filter out gmail.com.

Duplicates return because the forms, tools and imports behind them keep running. Find each source, test a search and merge automatically on a schedule.

Leads duplicate faster than contacts because forms, imports and prospecting tools create new ones and nobody owns them yet. Here's how to handle them.

Duplicate deals inflate your pipeline, skew forecasts and can pay a commission twice. See where they come from and when two deals aren't duplicates.

Shared inboxes, families, subsidiaries, partners who buy and customers who come back need a person to decide. See the question that settles each case.

Some duplicates only show up when several fields line up, and some matches look perfect but aren't. See how to combine fields and when to decide by hand.

Suffixes like Inc., LLC, GmbH or S.A. de C.V. make one company look like several. Match names while ignoring suffixes, and match the core domain too.

When a cleanup merges two different people, the search is usually why. The five patterns behind false matches and how to set up searches that avoid them.

B2B duplicates are people inside companies, matched on domain and company. B2C duplicates are individuals and households, matched on name, phone, address.

Entry rules prevent some duplicates; scheduled searches revisit stored records and missed matches. See how to combine them and review uncertain cases.

Close CRM merges leads one pair at a time and has no contact merge in its API. How duplicate leads and contacts form, and how to clean them.

Data decay leaves outdated records in your CRM while new ones are created next to them. How it differs from duplicates, and which to fix first.

Duplicate contacts mean the same person emailed twice, bounces from old addresses and quiet inboxes, all of which can hurt deliverability. How to fix it.

Nimble merges contacts on import by email or name, and lets you merge up to five duplicates by hand. See why duplicates form and how to merge them safely.

Google Contacts suggests duplicates in Merge & fix, but only within one Google Account. Learn why duplicates form and how to merge them safely.

Records with no email slip past email matching. Match them on name with company or phone, review near matches, and never let a blank overwrite a value.

When a customer writes to support from a new address or alias, the helpdesk can create a contact that syncs to your CRM. How to find and reduce them.

Accents, name order, phone country codes and address formats make one international customer look like several. How to match them safely.

Phone numbers find duplicates once you compare their digits. See when a phone match is strong, when shared lines mislead, and which field to pair it with.

Matching decides which records are duplicates. Merging decides what the final record keeps. Treat them as two decisions to avoid wrong merges.

Merge duplicates that carry history, associations or attribution. Delete only junk, test and spam records. A simple rule for each case.

One person with a personal email, a work email and an alias becomes several contacts. Match on first and last name plus phone or company instead.

Phone and call-tracking apps can create a new contact for each unknown caller, often with a placeholder name. How to match these contacts back.

Values like test@test.com, John Doe or 1234567890 group unrelated records as duplicates. How to spot them, leave them out and keep them from winning.

AI chat tools, no-code automations and scripts can find duplicates, with trade-offs in privacy, accuracy and upkeep. See how they compare.

Complex deduplication comes from thin overlapping data, mixed criteria and field rules. Automate once three or four strong fields match.

Duplicate records are CRM records for the same person or company. Clean the ones you have in phases, by certainty, then stop new ones from spreading.

CRM duplicates come from migrations, imports, connected tools and near matches CRMs miss. Find your sources, clean what exists, then fix what adds more.

Every outreach tool formats a prospect differently, so one target multiplies into three or four separate CRM records.

Key, big and VIP accounts deserve a person on every merge. Keep them out of bulk merges, check what's attached, pick values field by field and preview.

Field-level merge rules let you control how each value is kept or replaced during a CRM merge. More accurate merges, fewer reviews, and logic you can scale.

Duplicates dragging you down? Discover why they happen, how they hurt, and what you can do to keep your data in check!