User Alias
A method for linking multiple identifiers to the same person, such as connecting an anonymous cookie ID with an email address, or merging two separate accounts that belong to the same individual.
Also known as: identity alias, user merging, identity stitching
Why It Matters
Real users do not neatly fit into single identifiers. They browse anonymously before signing up, use different email addresses for work and personal accounts, and interact across multiple devices. Without aliasing, one person can appear as multiple separate users in your analytics, fragmenting their data and inflating your user counts.
User aliasing solves this by merging multiple identifiers into a single unified profile. When done correctly, it gives you an accurate count of unique users, complete behavioral histories, and proper attribution across the full customer lifecycle.
Poor aliasing leads to data quality problems that compound over time. You might overcount users, undercount conversions, send duplicate marketing messages, or misattribute revenue. Clean identity resolution is the invisible foundation that makes every other analytics capability trustworthy.
Industry Applications
A fashion marketplace aliases guest checkout email addresses with registered account IDs. This reveals that 30% of "new" customers are actually returning guests, dramatically changing their new-vs-returning customer ratio and acquisition cost calculations.
A product analytics tool aliases pre-signup anonymous activity with post-signup user IDs. This lets them see the complete journey from first documentation visit to paid conversion, revealing that the average enterprise customer visits 11 times before signing up.
How to Track in KISSmetrics
KISSmetrics provides a built-in alias method that links two identifiers together. Call alias when you learn that two identifiers belong to the same person - typically at the moment of login, signup, or account linking. KISSmetrics automatically merges the behavioral histories associated with both identifiers into a single profile.
Common Mistakes
- -Aliasing identifiers that do not actually belong to the same person, which merges unrelated user profiles
- -Not aliasing anonymous IDs to identified IDs, leaving pre-signup behavior disconnected
- -Calling alias with a shared device identifier (like a household computer cookie) which can incorrectly merge family members
- -Forgetting to alias across platforms when the same user interacts via web, mobile app, and email
Pro Tips
- +Only alias when you have high confidence that two identifiers belong to the same person - a login event, not just a matching name
- +Test your aliasing logic in a development environment before deploying to production to avoid irreversible bad merges
- +Use KISSmetrics People Search to audit merged profiles and verify that aliases are resolving correctly
- +Implement server-side aliasing for critical identification moments to ensure reliability
Related Terms
Anonymous User
A website or product visitor whose identity is unknown, typically tracked via a cookie or device identifier until they provide identifying information like an email address.
Identified User
A user whose identity is known through a unique identifier such as an email address, user ID, or account number, allowing their behavior to be tracked across sessions and devices.
Identity Graph
A database that maps and connects all known identifiers for a single person - such as email addresses, device IDs, cookie IDs, and phone numbers - into a unified profile that represents one real human.
People Tracking
An analytics approach that ties every event and interaction to an individual person rather than to anonymous sessions or pageviews, enabling full lifecycle analysis and person-level insights.
Probabilistic Matching
An identity resolution technique that uses statistical methods to link identifiers that likely belong to the same person based on signals like IP address, device type, browser fingerprint, and behavioral patterns, rather than exact deterministic matches.
See User Alias in action
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