TL;DR
- Standardizing CRM data first is optional when matching can read through formatting differences.
- Phone formats, capitalization, accents, company suffixes and website prefixes all hide duplicates.
- The merge decides which value survives, so a well-formatted value can be the one you keep.
- Standardizing a file before an import keeps new formatting problems out of the CRM.
- Dedupely matches duplicates through formatting differences and lets you choose which value wins.
Many teams plan their CRM cleanup in two steps. First they fix every phone number, company name and website into one format. Then they look for duplicates.
The first step can take weeks, and it often stalls before the second one starts. Meanwhile, the duplicates stay where they are, and new ones keep arriving.
In this post, you'll see why you don't need to standardize your data before you deduplicate. You'll see which formatting differences hide duplicates, how Dedupely matches through them, and how the merge keeps the value you trust. You'll also see three free tools for cleaning a file before it reaches your CRM.
The short answer: no
Standardizing and deduplicating solve different problems. Standardizing makes values look the same. Deduplicating finds records that describe the same person or company and merges them into one.
The two only meet at matching. If a search compares values character by character, "(555) 123-4567" and "555.123.4567" look like two different numbers. If the search compares what the values mean, the format stops mattering.
That's why the order can flip. You can deduplicate first, with matching that reads through formats, and let the merge keep the best-formatted value. The cleanup then leaves you with fewer records to standardize afterward.
Formatting differences that hide duplicates
These differences come up in almost every CRM. Each one splits a single person or company into records that an exact comparison treats as different.
| Field | How it varies | Example |
|---|---|---|
| Phone | Spaces, hyphens, parentheses, country codes | +1 (555) 123-4567 and 555-123-4567 |
| Names | Capitalization, accents, nicknames | José Müller and jose muller; Robert and Bob |
| Company name | Legal suffixes, word order | Acme Inc and Acme LLC |
| Website or domain | www, https, country domains | https://www.acme.com and acme.co.uk |
| Capitalization | Ana.Ruiz@Acme.com and ana.ruiz@acme.com |
Most of these come from how records enter the CRM. Forms, imports, enrichment tools and reps typing by hand each use their own format. Our post on how international data creates hidden duplicates covers the ones that come from other countries and languages.
How Dedupely matches through formatting differences
In Dedupely, each field in a duplicate search gets its own match option. Several of them are built to read past formatting, so the values in your CRM can stay as they are while you search.
Phone numbers
For phone fields, the match option for small differences strips formatting, hyphens, parentheses and country codes. It then compares the last seven digits. "+1 (555) 123-4567" and "555.123.4567" become the same number.
Seven digits can also be shared by two different numbers in different area codes. Pair the phone with a name or company, so a match needs both to agree. Our post on matching duplicates by phone number goes deeper.
Names, accents and capitalization
Exact matching in Dedupely ignores capitalization, and matching treats accented and plain letters as the same. José and Jose match, and so do Müller and Muller.
For spelling differences, one match option catches small typos and missing letters, and another catches names that sound alike, like Sean and Shawn. Matching on shared words can also pair nicknames, like Robert and Bob.
Company names and suffixes
Matching on shared words compares the words in a name, in any order or by the first or last words. You can also add up to 50 terms to ignore, like Inc., LLC or Ltd., and each team adds the ones its data needs.
With Inc. and LLC on the list, "Acme Inc" and "Acme LLC" both compare as "Acme". Our guide on cleaning a CSV before a CRM import shows why spreadsheet tools miss these.
Domains and websites
Matching on the root of a domain compares the core domain of an email or URL. It ignores everything before and after the domain itself, like https, www, paths and country endings, so "https://www.acme.com/contact" and "acme.co.uk" share the root "acme."
It also matches sales@acme.com with support@acme.com. That's useful for companies and risky for people, so use it on company records or pair it with a name.
Let the merge decide which format survives
Matching finds the duplicates. The merge decides what the record looks like afterward, and that's where formatting can be fixed for free.
When three records for one person merge, one phone number, one name spelling and one company name survive. If you choose those values on purpose, the cleanup leaves you with a better-formatted record than any of the originals.
In Dedupely, you set rules for which record survives and which value wins in each field. The surviving record can be the oldest, the newest, the most recently updated or the most complete. Field-by-field rules sit on top.
A field rule can also pick a value by what it contains. For example, set the phone rule to keep the number that contains "+", so the version with the country code wins.
Blank fields never overwrite filled ones, so a value only disappears when you choose another.
Preview a few merges before running a whole search. The preview shows the winning values in bold, so you can see which format the merged record will keep.
Where standardizing still helps
Skipping standardization before deduplication doesn't mean skipping it altogether. Consistent formats still matter for reports, segments, workflows and the tools that read your CRM.
- After the merge: fewer records means less to fix, and many fields already carry the value you chose.
- At the source: a form field that asks for a country code, or a picklist for country names, stops new variations.
- Before an import: a file is the easiest place to fix formats, since nothing in the CRM depends on it yet.
The first two belong to your regular routine. The third has some free help.
Three free tools to clean a file before an import
Dedupely's free tools run in the browser, with no account needed. Three of them fix the formats that cause the most hidden duplicates in a list.
Phone number formatter
Paste a column of phone numbers and get them back in E.164, the international format that starts with a plus sign and country code. The tool points out the numbers it can't format, so you can fix or drop them before the import. Phone number formatter
Company normalizer
Company names arrive with suffixes, capitals and punctuation in every combination. The normalizer turns "Acme Inc" and "ACME Incorporated" into one company name, so the list reaches your CRM with one spelling. Company normalizer
Email normalizer
Email is the field many CRMs use to recognize a contact on import. The normalizer lowercases every address and catches typos, so a mistyped address doesn't become a second contact. Email normalizer
Once the clean file is in your CRM, run your duplicate searches there. A clean file can still hold people who already exist in the CRM, and matching catches those.
How Dedupely finds duplicates in data that isn't standardized
Dedupely connects to your CRM and matches each field with its own option, reading past phone formats, capitalization, accents, company suffixes and website prefixes. You set rules for which value wins and preview each merge, so the record that survives carries the value you trust.
Deduplicate first, then tidy what's left
Standardizing a whole CRM before deduplicating is a project of its own, and it delays the cleanup that matters most. Matching that reads through formats finds the duplicates in the data you have today.
The merge then becomes your standardization step for every record it touches. What's left to tidy afterward is smaller, and a clean import keeps new variations out.
Frequently asked questions
Should I standardize CRM data before deduplicating?
You don't have to, if your matching reads through formatting differences like phone formats, accents and company suffixes. Deduplicating first leaves fewer records to standardize, and the merge can keep the best-formatted value. Standardizing still helps afterward for reports and workflows, and before any new import.
Can duplicates be found if phone numbers are formatted differently?
Yes, if the matching strips spaces, hyphens, parentheses and country codes before comparing. Pair the phone with a name or company, since matching on the last digits alone can pair two different numbers from different area codes. Test the search on a sample first.
Do company suffixes like Inc. and LLC stop duplicates from matching?
They do with exact matching, because "Acme Inc" and "Acme LLC" are different text. Matching that ignores a list of suffixes compares the core name instead, so both read as "Acme". Build the list from your own data, since suffixes differ by country, like GmbH or Pty Ltd.
What's the difference between data standardization and deduplication?
Standardization changes values so they follow one format, like E.164 phone numbers or one spelling per country. Deduplication finds records for the same person or company and merges them into one. They meet at matching: inconsistent formats hide duplicates from exact comparisons, which flexible matching can read past.
Should I clean a CSV file before importing it into my CRM?
Yes, a file is the easiest place to fix formats, since nothing depends on its rows yet. Format phone numbers, lowercase emails and normalize company names, then remove duplicates inside the file. After the import, check for records that match people already in your CRM.
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