# Cross-field Matching – Definition, FAQs & How Dedupely Helps > Cross-field matching explained: how to find duplicates when the same email, phone or name sits in different fields, and how to keep false matches low. - URL: https://dedupe.ly/glossary/cross-field-matching - Section: glossary - Site index: https://dedupe.ly/llms.txt - Any page: append `.md` to its URL for this plain-text form --- [Glossary](https://dedupe.ly/glossary) Cross-field Matching ## Cross-field Matching Cross-field matching is a record-matching method that compares a value in one field with the values in other fields, so two records can pair even when the same email address, phone number or name was saved in a different field in each. It's built for duplicates hidden by misplaced values: a work email in the secondary email field, a mobile number in the phone field, a first and last name swapped, or a company name typed into the website field. Values end up in different fields for ordinary reasons. Web forms, imports, integrations and manual entry each map fields their own way, and many records carry several fields for the same kind of value, such as two or three emails or phone numbers. A comparison that only lines up email with email, or phone with phone, misses those pairs. Cross-field matching widens the comparison to the other fields in the match, which finds more duplicates and, if the fields are chosen loosely, more false matches too. See how Dedupely finds misplaced values in your CRM [Try for free](https://v2.app.dedupe.ly/signup) [Talk to sales](https://meetings.dedupe.ly/meetings/gaby-salinas/talk-to-sales) Overview Cross-field matching finds duplicate records when the same value sits in different fields, such as a work email saved as one record's main email and another record's secondary email. It compares each value with other fields in the match, so misplaced values still pair. On this page - What Is Cross-field Matching? - How Cross-field Matching Works - Why Cross-field Matching Matters for CRM Teams - Getting Started With Cross-field Matching - Key Takeaways: Cross-field Matching - Frequently Asked Questions About Cross-field Matching - Related Terms and Concepts See how Dedupely finds misplaced values in your CRM [Try for free](https://v2.app.dedupe.ly/signup) [Talk to sales](https://meetings.dedupe.ly/meetings/gaby-salinas/talk-to-sales) ### What Is Cross-field Matching? Cross-field matching, also called matching across fields or related fields matching, is the part of record matching that looks for the same value in different places. Standard matching compares each field with the same field in the other record. Cross-field matching also compares it with the other fields in the match. Take two contacts. One reads "First name: Priya, Last name: Shah, Email: priya.shah@acme.com, Phone: 555-010-4477." The other reads "First name: Shah, Last name: Priya, Email: priya@gmail.com, Secondary email: priya.shah@acme.com, Mobile: 555-010-4477." Compared field by field, nothing lines up. Compared across fields, the work email, the phone number and the swapped names all match. Misplaced values appear on every object that stores more than one field of the same kind: contacts in HubSpot, Nimble and Google Contacts, people in Pipedrive, leads in Salesforce and Close, and companies or accounts where a name lands in the website field. Tools take two common approaches. Paired-field matching compares only the fields you name as related, such as email with secondary email, or phone with mobile. Its strength is control: nothing pairs through a field you didn't choose. Open cross-field matching compares the values across all the fields chosen for a match. Its strength is coverage: it catches misplacements nobody planned for, such as a name swap, without a list of pairs to maintain. Some teams also move misplaced values back to the right field, which keeps reports and field-based workflows accurate after the duplicates are merged. Resources: - [Match Options](https://dedupe.ly/knowledge-base/match-options) (KB) - [What is a duplicate?](https://dedupe.ly/blog/what-is-a-duplicate) ### How Cross-field Matching Works Cross-field matching adds a wider comparison to the matching stage of deduplication, so a rule can match across multiple fields instead of pairing them one to one. The rest of the process, from grouping duplicate sets to merging, stays the same. - **Choose the fields in scope.** Cross-field matching only looks at the fields included in the match. A search on name, email and phone compares those fields with each other, and leaves the rest of the record out. - **Compare each value with the other fields.** Each value is checked against its own field in the other record, and also against the related fields, or against every field in scope, depending on the approach. An email in one record's secondary email field can then match the main email of another. - **Apply the comparison method.** A misplaced value still has to meet a method, such as an exact comparison for emails or a looser one that ignores phone formatting. How strictly each cross-field comparison is made depends on the tool. - **Count the match.** A value found in another field stands in for the match the rule was looking for, so the records can meet the rule even though their fields don't line up. - **Group the matches.** Records that match are grouped into duplicate sets, ready to review and merge. The wider comparison has a cost. More comparisons mean more chances for an unrelated value to match, such as a shared office phone saved as one contact's phone and another contact's mobile, or a generic info@ address. Keeping the fields in scope to the ones that hold the same kind of value keeps those false matches low. Resources: - [Finding Duplicates](https://dedupe.ly/knowledge-base/find-duplicates) (KB) ### Why Cross-field Matching Matters for CRM Teams Duplicates that appear when the same data is in different fields can be hard to see. The records often look different at a glance, with a different main email, a different phone field filled in or the names in the wrong order, so field-by-field searches keep missing them. They stay in the CRM, inflating contact counts and, in CRMs that bill by contact or record count, adding records the company pays for twice. The effects reach past the record count. Activity history splits between the two records, so a rep working one sees only part of the conversation. Two owners can be assigned to the same customer, and a contact who unsubscribed on one record can keep receiving email on the other. Reports that count customers, leads or accounts come out higher than the real number. Misplaced values also keep creating new duplicates. A form, import or integration that checks only the main email field can add a new record for someone whose email already sits in a secondary field. Each new record repeats the problem until the misplaced value is found. The wider comparison has to be set up with care, though. Shared values, such as a company's main phone line or a generic inbox, can sit in different fields across many unrelated records. Limiting cross-field matching to fields that hold the same kind of value is what keeps those records apart. Resources: - [Why your CRM database is turning into a mess (and how to fix it)](https://dedupe.ly/blog/why-your-crm-database-is-turning-into-a-mess) ### Getting Started With Cross-field Matching Start with the duplicates your standard searches already find, and merge the clear ones first. What's left is easier to read, and the pairs that differ only because a value sits in another field stand out. Then look at where misplaced values come from. Fields that hold the same kind of value, such as main and secondary email, phone and mobile, or first and last name, are the natural scope for cross-field matching. Keep fields that hold different kinds of values out of the same match, because a short or common value can show up in an unrelated field by chance. Combining a cross-field comparison with a strong second field, such as company or last name, keeps one shared value from pairing different people. Run the wider search separately from your strict ones, and check a sample of what it finds before merging anything. Watch for shared values, such as a main office line or a generic inbox, that pair records across a company. Once the duplicates are merged, look at the form, import or integration that put the values in the wrong field, so new records arrive in the right place. In Dedupely, Cross-field matching is one of a Duplicate Search's Enhancements: it finds misplaced values in other fields used in the match, and it applies to the whole match rather than to a single field. Because it's turned on per search, you can clone a search, turn it on in the clone and compare the results without touching the active one. Resources: - [Duplicate Searches](https://dedupe.ly/knowledge-base/search-pads) (KB) - [How to Approach CRM Deduplication](https://dedupe.ly/knowledge-base/how-to-approach-crm-deduplication) (KB) ### Key Takeaways: Cross-field Matching Cross-field matching finds duplicates when the same value sits in different fields, such as an email in the secondary email field or a first and last name swapped. It compares each value with other fields in the match, not only the same field, so it finds duplicates that field-by-field searches miss. Keeping its scope to fields that hold the same kind of value keeps false matches low. In Dedupely, Cross-field matching is one of a Duplicate Search's Enhancements, applying to the whole match and comparing across the fields used in the match, and Typo allowance is a separate Enhancement. Each field keeps its own Match Option, Rejected Matches keeps false pairs out of future results, and Auto Match and Auto Merge keep trusted searches running. Resources: - [Dedupely product overview](https://dedupe.ly/product) ### Frequently Asked Questions About Cross-field Matching #### How do you find duplicates when an email is in the secondary email field? Compare the main email field and the secondary email field with each other, not only with themselves. A contact whose work email sits in the secondary email field won't pair with a record that holds the same address as its main email, because a field-by-field search only lines up email with email. Matching across both fields closes that gap, and it's the direct fix for email vs secondary email duplicates. It helps to keep the scope to email fields and to add a second field, such as last name or company, so a shared inbox like info@ or sales@ doesn't pair unrelated people. The same logic applies to phone and mobile fields. In Dedupely, Cross-field matching is one of a Duplicate Search's Enhancements and finds misplaced values in other fields used in the match. With both email fields in the search, it compares across them, along with the other fields used in the match. You can clone the search first to test it without touching the active one. #### How is cross-field matching different from multi-field matching? Multi-field matching decides how many fields have to agree, and cross-field matching decides where a value can be found. A multi-field rule on first name + last name + email compares each field with the same field in the other record, and the records pair when the fields meet their criteria. Cross-field matching widens each of those comparisons to the other fields in the match, so an email in the secondary email field or a swapped first and last name can still count. The two work together: multi-field matching keeps a match precise, and cross-field matching keeps it from missing duplicates when values are out of place. In Dedupely, the KB recommends combining 2 to 4 key fields in a Duplicate Search, and if a search compares three fields, all three must meet their criteria. Cross-field matching is a separate Enhancement that applies to the whole match and compares across all the fields used in the match. #### Which fields should you include when using cross-field matching? Include fields that hold the same kind of value, and keep the rest out. Main and secondary email, phone and mobile, and first and last name are natural groups, because a value from one field often lands in the other. Mixing fields that hold different kinds of values widens the comparison without finding real duplicates, and it raises the chance that a short or common value matches by accident. A strong field outside the cross-field comparison, such as company or a customer ID, helps confirm the match. Leave out fields full of placeholder values, such as "N/A" or a shared inbox, because they can pair many unrelated records. In Dedupely, Cross-field matching compares across all the fields used in the match, so the fields you add to the Duplicate Search set its scope. The None Match Option leaves an unreliable field out of the match, and Safety Settings, on by default, hold back matches built on poor or example data. #### How do you catch duplicates when first and last names are swapped? Compare the first name field with the last name field, as well as each with itself. A record saved as "Shah, Priya" and another saved as "Priya Shah" share both names, but a field-by-field search sees two different first names and two different last names. Cross-field matching across the two name fields pairs them. Because names are shared by many people, a name match alone isn't enough to merge on: add a stronger field, such as email, phone or company, so two different people with the same names don't pair. Swaps often come from imports or forms that map name fields in a different order, so fixing that mapping keeps new swaps out. In Dedupely, Cross-field matching finds misplaced values in other fields used in the match, so a Duplicate Search with both name fields compares across them. Rejected Matches removes a set that isn't a real duplicate from current and future Duplicate Searches. #### Should you move misplaced values back to the right field before or after merging? Either works, but merging first is often simpler, because the merge leaves one record to fix instead of two. Moving values first suits teams that rely on the field itself, such as a phone field used for calls or a main email used for sends, because standard matching then finds the duplicates without a wider comparison. Whichever order you choose, check which values the merged record keeps in each field, so the correct email or phone number isn't replaced by a misplaced one. Then fix the form, import or integration that put the values in the wrong field. In Dedupely, Merge Rules decide what each merge keeps, field by field, and two previews show the result before merging. Merges can't be undone, so check the rules first. Merge History records every merge, and it can be exported as a CSV with the original values of each merged record. ### Related Terms and Concepts #### [Multi-field matching](https://dedupe.ly/glossary/multi-field-matching) Multi-field matching sets how many fields have to agree for records to pair. Cross-field matching works inside it, letting each of those fields match a value saved somewhere else. #### [Visible fields](https://dedupe.ly/glossary/visible-fields) Visible fields are the fields a search shows and compares. With cross-field matching, they also set its scope, because values are compared only across those fields. #### [Domain root matching](https://dedupe.ly/glossary/domain-root-matching) Domain root matching compares the core domain of an email or website. It handles differences inside a domain value, such as www or a country ending, while cross-field matching handles values that sit in a different field. #### [False matches](https://dedupe.ly/glossary/false-matches) False matches are records paired as duplicates that aren't. Cross-field matching raises the risk when its scope includes fields with shared or placeholder values, and lowers it when the scope stays narrow. #### [CRM data hygiene](https://dedupe.ly/glossary/crm-data-hygiene) CRM data hygiene keeps values in the fields where they belong. It reduces the misplaced values cross-field matching has to find, and keeps new ones from arriving through forms and imports. #### [AI duplicate matching](https://dedupe.ly/glossary/ai-duplicate-matching) AI duplicate matching compares many field combinations at once. It can find duplicates where values sit in unexpected fields, alongside rule-based searches that use cross-field matching. Skip to content