Exact Matching
Exact matching is a record-matching method that treats two records as duplicates only when the values in a chosen field are the same. It's the strictest way to compare a field, and it works best on values that point to one person, company or deal, such as an email address, a customer ID or a tax number.
Because it allows no variation in the value itself, exact matching rarely pairs records that aren't duplicates. The same strictness means it misses records that differ by a typo, a nickname or a phone number in another format. Tools also differ on what counts as the same value: some ignore capitalization or punctuation before they compare, and others compare values character by character. That's why exact matching is often one part of a search, combined with other fields or with looser methods like similar or fuzzy matching.
See how Dedupely finds exact duplicates in your CRM
What Is Exact Matching?
Exact matching is the strictest record-matching method: a field counts as a match only when both records hold the same value. A typical example: "Jon Smith, jon.smith@acme.com" and "Jonathan Smith, JON.SMITH@acme.com." An exact match on email pairs them in a tool that ignores capitalization, and misses them in one that compares every character. An exact match on full name misses them either way, because "Jon" and "Jonathan" are different values.
What counts as "the same" depends on the tool, and there are two common approaches. Strict exact matching compares values character by character, so "Acme" and "ACME" are different. Its strength is that nothing is assumed or changed before the comparison. Normalized exact matching first evens out differences that don't change the meaning, such as capitalization, punctuation or formatting, and then compares. Its strength is that it catches the same value typed in different styles, while still requiring the same value. Which differences a tool evens out varies from one tool to the next.
Exact matching applies to every object a CRM stores: email addresses on contacts in HubSpot, Nimble and Google Contacts; company names on accounts in Salesforce and organizations in Pipedrive; lead names in Close; and product codes on products in Pipedrive. It's most reliable on fields where each value belongs to only one record.
How Exact Matching Works
Exact matching is the comparison step inside the matching stage of deduplication. It decides which records are paired, and merging comes after.
- Choose the fields to compare. Unique identifiers, such as email addresses, customer IDs or website domains, give the most reliable results. Fields that many records share, such as a first name or a city, flag records that only happen to agree on that one value.
- Prepare the values, or not. Depending on the tool, values are compared as stored, or after differences like capitalization or formatting are evened out. A phone number saved as "(555) 010-2000" in one record and "555.010.2000" in another is a different value to exact matching unless the tool strips that formatting first.
- Compare the records. Records whose values match are paired. A blank field often isn't treated as a value, so two empty fields don't make a match.
- Combine fields. When a rule uses several fields, such as first name + last name + email, each field has to match for the records to pair. Some fields can use exact matching while others use a looser method in the same rule.
- Group the matches. Records that match are grouped into duplicate sets, ready to review and merge.
The result is a short list of high-confidence matches. What it leaves out are the near-duplicates: typos, nicknames, legal suffixes on company names and values entered in different formats. Those need similar, fuzzy or domain root matching, often run as a second pass after the exact matches are merged.
Why Exact Matching Matters for CRM Teams
Exact matching produces the duplicates that are safest to act on. When two records share the same email address or customer ID, they very likely describe the same person or company, so those sets need little review and are the strongest candidates for bulk or scheduled merging. Clearing them first makes the remaining duplicates easier to work through, because they're no longer mixed in with the obvious ones.
Relying on exact matching alone has a cost, though. The duplicates it misses stay in the CRM: a contact with a typo in their email, a company entered as "Acme Inc" and "Acme, Inc.", a phone number saved in two formats. Those records keep inflating contact counts, splitting activity history and putting the same customer in front of two reps. In CRMs that bill by contact or record count, each one is also a record the company pays for twice.
Exact matching on the wrong field causes the opposite problem. A match on first name + last name alone pairs different people who share a name, and placeholder values such as "test@test.com" or "N/A" can tie dozens of unrelated records into one set. Choosing the right fields matters as much as choosing the method, because both decide which records end up merged together.
Getting Started With Exact Matching
Start with exact matching on your strongest fields, such as email addresses or customer IDs, and combine a few key fields so one shared value doesn't pair different records. Check what each search finds before merging anything, and watch for placeholder values that match across unrelated records.
Merge the clear exact matches first, then loosen the matching for the duplicates that are left. Switching one field to a looser method while keeping another on exact matching catches typos and formatting differences without opening the door to false matches. Then check a sample of the new sets before merging them.
Before relying on an exact rule, check how the field is stored. Values copied in from other systems can carry hidden formatting, so two records that look the same on screen may not match exactly.
Keep exact matching on the fields that suit it best once looser methods are added. An exact field, such as email, anchors a rule and keeps false matches low while another field is compared more loosely.
In Dedupely, Exact is one of the Match Options in a Duplicate Search. It matches identical values while ignoring capitalization, and you can switch a field to a looser option once the exact matches are merged.
Key Takeaways: Exact Matching
Exact matching pairs records only when a field holds the same value in each, which makes it the most precise way to find duplicates. It works best on unique identifiers like email addresses and IDs, and poorly on names or phone numbers saved in mixed formats. Tools differ on whether they ignore capitalization or formatting first. Because it misses near-duplicates, it's often combined with other fields and followed by looser methods. In Dedupely, Exact is one of the Match Options in a Duplicate Search, and it ignores capitalization. Merge Rules decide what each merge keeps, Bulk Merge merges many duplicate sets at once, and Auto Match and Auto Merge keep trusted searches running.
Frequently Asked Questions About Exact Matching
Which CRM fields should you match exactly, and which need a looser method?
Match exactly on fields where each value belongs to one record: email addresses, customer or account IDs, tax or government IDs, and website domains. Two records with the same value in one of those fields very likely describe the same person or company. Use a looser method on fields where the same thing can be written several ways. Names vary with nicknames, initials and typos, phone numbers are saved with and without country codes or punctuation, and company names come with and without legal suffixes. Exact matching on those fields misses real duplicates, and on common names it can pair different people. In Dedupely, the Match Options guide recommends Exact for high-stakes fields like email addresses, government IDs or primary names. For the rest, Similar catches typos and strips formatting from phone numbers, Fuzzy catches names that sound alike, and Domain Root compares the core domain of an email or website.
Why does exact matching miss duplicates that look identical?
Because the stored values aren't identical, even when they look it on screen. A hidden character copied in from another system, a different dash in a phone number or a period at the end of a company name all make two values different to a strict comparison. Some tools also treat capitalization as a difference, so "acme.com" and "ACME.com" don't match. Whether these differences count depends on how a tool prepares values before comparing them. To find the cause, open both records and compare the raw values, or switch the field to a looser method and see if the pair appears. In Dedupely, Exact ignores capitalization. If two records that look the same still don't pair, the Match Options guide suggests checking the CRM for hidden formatting, such as trailing spaces, or using Similar, which strips formatting from phone numbers before it compares them.
When should you use exact vs fuzzy matching to find CRM duplicates?
Use exact matching first, and switch to fuzzy or similar matching once the exact matches are merged and duplicates you can see are still left. Exact matching finds the clearest duplicates first, so the remaining ones are the records that differ in small ways, and that's the job of looser methods. Similar matching catches typos and values in different formats, fuzzy matching catches names that sound alike, such as "Sean" and "Shawn," and domain root matching pairs email addresses or websites from the same company. Looser methods find more duplicates but also more false matches, so it helps to keep at least one strong field on exact matching while loosening another. In Dedupely, a search that combines several Exact fields and finds nothing is a sign to switch one of them to Similar or Fuzzy. You can clone a Duplicate Search to try new criteria without changing the active one, then click "Scan" to see the results.
How does exact matching work when a rule combines several fields?
In a combined rule, each field has to match for two records to pair. A rule on first name + last name + email only pairs records where all three agree, which makes it more precise than any one of those fields alone, and also stricter. A combined rule also tolerates a weaker field better: last name alone pairs many unrelated people, but last name + email rarely does. Fields can also mix methods: email on exact matching and company name on a looser method, for example. Combining a few strong fields often gives the best balance between finding duplicates and avoiding false matches. In Dedupely, each field in a Duplicate Search gets its own Match Option, and if a search compares three fields, all three must meet their criteria to flag a duplicate. One Duplicate Search can hold several match groups, to combine different criteria.
Is it safe to merge exact matches automatically?
Exact matches on strong identifiers are the safest candidates for automatic merging, but they still need a check first. Run the rule by hand, look at what it finds, and watch for placeholder values such as "test@test.com" or "none," which can match exactly across many unrelated records. Make sure the merge settings keep the right value in each field, because a merge is hard or impossible to reverse in most systems. In Dedupely, merges can't be undone. Safety Settings are on by default and hold back matches with more than 20 records or built on placeholder data, and Merge Rules decide what each merge keeps. Once a Duplicate Search has proven reliable, turn on Auto Match and Auto Merge: Auto Match notifies you when new matches reach a count you set, and Auto Merge merges new duplicates on the schedule you choose.
