AI should suggest possible duplicate hotel guest profiles before merging uncertain matches. Automatic merging is appropriate only under explicit, tested identity rules with an audit trail and a recovery process. A shared surname, phone number, or email address does not always establish that two records belong to the same person.
Why duplicate detection is not identity proof
Families share contact details. An assistant or travel coordinator may book for several people. OTA records can use masked contact information, and phone numbers can be reassigned. Treat each matching signal according to its meaning rather than counting similar fields as certainty.
A mistaken merge can expose stay history, confuse preferences, misattribute spending, or send a message to the wrong person. The cost is larger than an untidy database.
Use a review table
| Signal | Useful evidence | Reason for caution |
|---|---|---|
| Same name | Candidate for review | Common names and family members |
| Same phone | Possible shared contact | Family, organizer or reassigned number |
| Same email | Possible identity link | Corporate assistants and shared inboxes |
| Verified guest correction | Stronger evidence under the hotel's process | Requester still needs appropriate verification |
| Conflicting stays or identity fields | Reason to pause | May represent different people |
Inspect what a merge actually changes
Ask whether reservations, preferences, messages, consent, company links, and spending history move together. Determine which value survives when fields disagree. Keep the original identifiers and the reason for the decision so an operator can reconstruct it.
Mews' guest-management page describes a Match and Merge capability. That is evidence that this is an established product area, not proof that every suggested match should be accepted or that another platform behaves identically.
Do not merge marketing permission by convenience
Two profiles may have different communication preferences or consent records. Combining them should not silently turn an opt-out into an opt-in. Preserve the scope, origin, and time of the relevant permission and apply the hotel's approved suppression rules.
Likewise, a household relationship is not the same as a single identity. Linking a family for a group stay can be useful without collapsing every person's history into one profile.
Run a synthetic identity test set
Create cases for two people with the same name, a family sharing a phone, an assistant booking for an executive, a changed email address, and a genuine duplicate created by two channels. Ask the system to show why it recommends each merge.
Test an incorrect merge in a sandbox and restore the records. If reversal is not supported, understand the limitation before enabling automation. Recovery should include the dependent records, not merely splitting the display name.
Measure false merges separately
Reducing the number of profiles is not the goal. Track correctly resolved duplicates, disputed merges, wrong-recipient messages, and review time. An aggressive matcher may look productive while introducing more serious identity errors.
The NIST framework provides general evaluation context. For identity work, the hotel needs concrete error categories and a conservative approval boundary.
Review Hotelary's CRM overview and the operations CRM guide. Pair profile cleanup with property isolation and data-processing questions. Clean guest data should make recognition more reliable without making assumptions about who a guest is.
Sources and further reading
Sources reviewed on September 14, 2026. Check current vendor terms and policies before implementation. Examples and checklists are editorial guidance unless explicitly identified as reported research.
- Mews' guest-management page — mews.com
- NIST framework — nist.gov
