Identity resolution is the process of linking multiple identifiers that belong to the same real-world entity into a single, unified profile. In analytics, this is most commonly applied to users, customers, or accounts.

For example, a single customer might appear as:

• A website visitor cookie

• A mobile app user ID

• An email address

• A CRM contact

• A billing account

Identity resolution connects these identifiers so analytics systems treat them as one customer rather than multiple disconnected records.

From a BI perspective, identity resolution enables:

• Accurate customer counts

• Cross-channel attribution

• Cohort analysis

• Customer lifetime value modeling

• Personalization and targeting

Technically, identity resolution can be:

• Deterministic: exact matches (email, user ID)

• Probabilistic: statistical matching based on behavior, device, or patterns

Deterministic matching is more accurate but limited by available identifiers. Probabilistic matching increases coverage but introduces uncertainty.

Identity resolution requires careful governance. Incorrect matches can distort analytics, violate privacy policies, or lead to poor business decisions.

Modern analytics platforms often build identity graphs that evolve as new identifiers appear. These graphs must handle merging, splitting, and versioning over time.

Identity resolution is especially critical as users interact across devices and platforms. Without it, analytics becomes fragmented and misleading.