How do I find duplicate customers and locations in ServiceTitan?
Match on normalized phone number and normalized service address rather than on name. Names vary by spelling, spouse, business entity and nickname; the phone and the address are what actually repeat. Normalize aggressively first, strip formatting from phone numbers and standardize street suffixes and unit designators, then review candidate pairs by hand before merging anything.
Why duplicates matter beyond tidiness
Duplicate customers break the three things owners care about most: lifetime value, repeat rate and acquisition attribution. A returning customer recorded as new inflates your new-customer count, understates retention, and hands acquisition credit to whichever channel they used the second time.
Duplicate locations are worse in a different way, because equipment history and membership entitlements split across the copies and technicians arrive without knowing what is installed.
Normalize before you match
After normalization most duplicate pairs are exact matches on at least one field. The residual fuzzy cases are where careful review is required and where automated merging causes damage.
- Phone. Strip everything but digits, drop the leading country code, compare the last ten. Store all known numbers per customer, not one.
- Address. Standardize suffixes, directionals and unit prefixes to a single form. Apartment, unit and suite are the same concept spelled four ways.
- Email. Lowercase, trim, and treat plus-addressing as the same mailbox.
- Name. Useful only as a tiebreaker. Never as the primary key.
The cases that are not duplicates
Several patterns look like duplicates and are not. A property manager with dozens of service addresses is one customer and many locations. A landlord and a tenant at the same address are two customers and one location. A commercial account with multiple sites should never be collapsed. And two households at the same multi-unit address are distinct unless the unit number says otherwise, which is exactly why unit normalization matters.
Merging any of these creates a mess that is far harder to undo than the duplicates were to live with. Merge conservatively and log every merge.
Prevention beats cleanup
Duplicates are created at the moment of booking, usually because the person taking the call could not find the existing record fast enough. Improving search at the point of entry and matching inbound calls to existing customers before the record is created removes most of the ongoing supply.
We build that matching into the layer between the phone system and the field service system, so a known caller is recognized on the way in. The same identity resolution underpins the repeat-rate and lifetime-value work in customer intelligence.
Topics: data quality · duplicates · customers · matching
Have a version of this question about your own business?
The useful answer usually depends on which systems you run and how they're connected. That's a conversation, not a blog post.