# The complete guide to duplicate records in your CRM | Dedupely

> 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.

- URL: https://dedupe.ly/blog/duplicate-records-in-crm-guide
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- Published: 2026-10-15
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Duplicates

## The complete guide to duplicate records in your CRM

By
[Gaby Salinas](https://www.linkedin.com/in/gbyslns)
Updated October 15, 2026
#
min read
TL;DR
- Duplicate records are two or more CRM records for the same person, company or deal.
- Duplicates come from imports, forms, connected tools and migrations, in a different mix for each team.
- Clean in phases: agree on what a duplicate is, then merge the surest matches first.
- No team can prevent every duplicate, so merge new ones as they appear.
- Dedupely finds near matches, bulk merges sure ones, holds back risky ones and merges new ones automatically.
Most CRMs with a few years of history hold duplicate records. They reach it from imports, forms, connected tools, migrations and people, and every team's mix is different. What they do to a team looks much the same everywhere: reports drift, reps step on each other, and people stop trusting the CRM. The good news is that a cleanup follows a pattern, even when the causes don't. In this post, you'll learn the types of duplicates, what they cost, how to clean them in phases, and what realistic prevention looks like.

### What are duplicate records?

**Duplicate records are two or more records in a CRM that describe the same person, company or deal.** Deduplication is finding those records and merging each set into one, so every customer has a single record with its full history.
They show up in HubSpot Contacts, Salesforce Accounts, Pipedrive People and any object that gets records from more than one place.

### Types of duplicates

Duplicates differ in how sure you can be that two records belong together. That certainty decides how you handle each type later, so it's worth sorting them early.
The table below lists the most common types, from the surest matches to the least sure.
| Type | Example | How sure you can be |
| --- | --- | --- |
| Exact matches | Two contacts with the same name, email and company | Very sure |
| Near matches | "Bob Lee" and "Robert Lee" at the same company; "Northwind Inc." and "Northwind" | Sure once two or three other fields agree |
| Values in the wrong field | A mobile number saved as the office phone; a personal email in the work email field | Sure when you compare across fields |
| Shared identifiers | Two branches of one company with the same name; two people who use the same info@ address | Often separate records that look alike, so a person decides |

### Where duplicates come from

Duplicates have many causes, and they're specific to each team. Two companies on the same CRM and the same plan can have very different problems.
The sources I see most often in calls are migrations and acquisitions, imports, web forms and other connected tools, and sales and prospecting tools. A CRM's built-in deduplication also misses near matches. There are many more, and your list will be your own.
It helps to separate one-off waves, such as a migration, from steady sources, such as a form that runs every day. A wave is cleaned once, and a steady source keeps adding duplicates until something changes.

### What duplicates cost

The causes vary from team to team. The consequences look much the same in every CRM I've seen.
Reports count one customer two or three times, so contact totals, pipeline and attribution are off. History splits across records, so the calls sit on one and the deal on another.
Sales feels it next. Two reps call the same customer, or a rep pitches someone who already buys from you. When two reps own the same account on two records, commissions and territories turn into disputes.
Marketing sends the same email twice, or a person lands in the wrong segment. And when your CRM or a connected tool bills by contacts or records, every duplicate counts toward that limit.
Around 20% of teams have said duplicates caused commercial problems: a higher CRM bill, rep territory disputes or a blocked software rollout.
Source: Dedupely call research
**The main cost is trust.** Once people stop trusting the CRM, they keep their own spreadsheets and check numbers by hand. The CRM then holds less of what the team knows, and the cleanup gets harder to justify.

### How to approach deduplication, in phases

In most cases, I'd clean the existing duplicates first and then fix the sources. A clean CRM makes new duplicates easy to spot, and each new one points back to what created it.
The exception is a sudden spike, such as a misconfigured integration creating hundreds of records a day. Fix that integration as early as you can, alongside the cleanup.

#### Phase 1: Agree on what counts as a duplicate

Before anyone merges, look at the data with the people who use it. Pull a sample of likely duplicates from each object and ask the team which ones they consider the same.
The answers shape your criteria. Some teams treat each branch office as its own company. Others want one record per company, whatever the location.
Decide at this stage which value wins when records merge: the newest email, the oldest owner, or the value from a trusted source. A rule has to give the same answer across thousands of merges, because nobody can decide them one by one.

#### Phase 2: Merge the matches you're most sure of

Start with exact matches and strong multi-field matches, such as the same first name, last name, email and company. Preview a sample, check the result in the CRM, then merge the rest in bulk.
Keep a record of every merge with the original values. In many CRMs, a merge can't be fully undone, so that record is how you answer questions later or recreate a record.

#### Phase 3: Work the likely matches, save edge cases for manual review

Near matches and values in the wrong field come next. They need matching that allows for typos, nicknames and suffixes like "Inc." Field rules then protect valid data, such as a second phone number.
Shared identifiers and anything high-stakes go to manual review. A wrong merge on a key account, a billing record or a synced record costs more than the time a review takes.
Reject the sets that turn out to be separate records, so they don't come back in the next search.

#### Phase 4: Fix the sources and keep checking

With the backlog gone, new duplicates stand out. Trace them to the form, tool or import that created them, and fix what you can with each tool's owner.
Then decide how often to check again. Teams with steady sources check continuously or weekly. Teams whose duplicates came from one migration may only need a review every few months.
Pro tip
The more certain a match, the more you can automate it. Three or four strong fields matching, such as name, email and company, are far safer than a shared first and last name.

### Prevention: what's realistic

From what I've seen, you can't prevent every duplicate. CRMs, tools and integrations do things natively that you can't change, and busy teams skip steps when they're in a hurry.
What you can do is stop most duplicates at the door and keep the rest from spreading. Agree on who imports and how, check spreadsheets before they go in, and use your CRM's native deduplication for what it catches.
For the rest, merging new duplicates automatically catches them soon after they arrive.

### Connected tools, migrations and synced CRMs

You can't deduplicate inside every tool. An enrichment tool, for example, usually can't be cleaned on its side, and its data goes into the CRM anyway.
Where you can, clean data before it enters: check spreadsheets before import and clean a source system before a migration. Then clean again in the CRM for everything you couldn't catch earlier.
Two CRMs synced both ways need extra care, because a duplicate exists on both sides and the merge has to happen in both.

### When deduplication gets complex

Size alone rarely makes a cleanup hard. With clear criteria, 10,000 duplicates can be easier to merge than a small, messy database.
Complexity comes from thin overlapping data, groups that need different criteria, detailed rules for which value wins, and unfamiliar languages. The biggest factor our customer success team sees is confidence: being sure enough to merge thousands of records at once.
Reaching that point usually means narrowing the criteria with the team until everyone agrees the matches are safe.

### How Dedupely helps you clean duplicates in phases

Dedupely finds duplicates with matching that allows for typos, nicknames and company suffixes. You set rules for which value wins, preview each merge, bulk merge the matches you're sure of and send unclear sets to review, and every merge is saved with its original values. Our guide on
[how to plan a CRM deduplication project in 3 phases](https://dedupe.ly/knowledge-base/how-to-approach-crm-deduplication)
shows how to set it up.
[See how many duplicates your CRM holds today](https://v2.app.dedupe.ly/signup)

### Start with the duplicates you're surest of

Every team's duplicates come from its own mix of sources, yet the cleanup follows the same pattern. It starts with an agreement on what a duplicate is and which values win, then moves from the certain matches to the edge cases that need a person's review.
A record of every merge keeps that work safe to revisit. Once the backlog is gone, new duplicates point back to the sources that created them, and regular checks keep them from spreading again.

### Frequently asked questions

#### What causes duplicate records in a CRM?

Duplicate records come from many sources, and the mix is different for every team. Common ones are migrations, acquisitions, spreadsheet imports, web forms, prospecting and enrichment tools, and built-in deduplication that misses near matches. Because many of these processes run every day or week, new duplicates keep arriving after a cleanup.

#### How do you clean up duplicate records in a CRM?

Work in phases, starting with an agreement on what counts as a duplicate and which values win. Then merge the matches you're most sure of in bulk, after checking a sample. Handle near matches with field rules, send edge cases to manual review, and finally fix the sources and keep checking.

#### Can you prevent all duplicate records?

No, because CRMs and connected tools create records in ways you can't fully control, and busy teams skip steps. You can stop most duplicates at entry with import rules, spreadsheet checks and native deduplication. Frequent scans and merges then catch the rest before they spread into reports.

#### Is it safe to merge CRM duplicates automatically?

It is safe for matches you're very sure of, such as records sharing name, email and company. Three or four strong matching fields give much more confidence than a shared name. Review a sample by hand first, automate only the patterns that hold up, and send edge cases to manual review.

#### Can you undo a merge in a CRM?

Usually not in full. Many CRMs don't let you un-merge records, and some keep merged-away records in a recycle bin only for a short time. Before a large merge, keep a record of every merge with its original values, so you can recreate a record if you need to.
Customer stories

### How other teams cleaned up their duplicates

[All case studies](https://dedupe.ly/case-studies)
-
[140,000+ duplicate records merged How a Healthcare Software Company Stopped Cleaning the Same Duplicates Twice in HubSpot and Salesforce with Dedupely Read the story](https://dedupe.ly/case-studies/healthcare-software-hubspot-salesforce-duplicates)
-
[2,158 company matches merged in under 7 weeks How a Fintech Gave Its Sales Team a Pipeline It Can Trust by Clearing 4,000+ Duplicate Matches with Dedupely Read the story](https://dedupe.ly/case-studies/fintech-hubspot-duplicate-deals)

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[CRM Organization What is a duplicate? How to define what a duplicate is from your CRM’s fields, usage and logic, before you merge anything. Gaby Salinas September 29, 2025](https://dedupe.ly/blog/what-is-a-duplicate)
[Dedupely Insights How to correctly identify all duplicates in your CRM This guide explains how to identify CRM duplicates by using reliable fields, applying match options like exact or fuzzy, testing logic to avoid errors, and. Gaby Salinas September 17, 2025](https://dedupe.ly/blog/merge-with-confidence)
[CRM Organization Automate CRM Deduplication Without Losing Data Sort matches by confidence, decide which values win and schedule each data source on its own. Gaby Salinas September 30, 2026](https://dedupe.ly/blog/automate-crm-deduplication-without-losing-data)

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