Technology

Identity Resolution

Process of matching different identifiers (email, device ID, cookie) to recognize they belong to the same person.

Identity Resolution connects fragmented customer data across devices and channels to build unified profiles. Challenge: Same person appears as: anonymous website visitor (cookie), mobile app user (device ID), email subscriber (email address), and in-store customer (loyalty card). Identity resolution matches these to one person. Methods: Deterministic matching (same email = same person), probabilistic matching (statistical likelihood based on behavior patterns), and device graphs (mapping devices to households). Benefits: Accurate cross-device attribution, better personalization, de-duplicated customer counts, and unified customer journey view. Critical for: Multi-touch attribution (track full journey across devices), audience building (don't retarget same person 5 times), and customer lifetime value calculation. Typically handled by CDPs or specialized identity resolution platforms.

Related Terms

Frequently Asked Questions

What is Identity Resolution?

Identity Resolution is the process of matching various consumer identifiers, such as email addresses, device IDs, cookies, and loyalty program numbers, to recognize that they all belong to a single individual. Its primary goal is to connect fragmented customer data across different devices and channels to construct a unified, 360-degree customer profile. This is critical for modern marketing as it allows businesses to accurately track the complete customer journey, de-duplicate customer counts, and ensure consistent, personalized experiences across all touchpoints. The two main methods used are deterministic matching, which relies on exact identifiers like login credentials, and probabilistic matching, which uses statistical likelihood based on behavioral patterns.

How can marketers use Identity Resolution to improve multi-touch attribution?

Marketers use Identity Resolution to overcome the challenge of fragmented customer journeys, which is essential for accurate multi-touch attribution (MTA). When a customer interacts with a brand on multiple devices—for example, clicking an ad on a mobile phone, browsing on a tablet, and purchasing on a desktop—Identity Resolution links these separate touchpoints back to the same person. Without it, MTA models would incorrectly attribute the conversion to only the final device, ignoring the earlier, influential interactions. By creating a unified view, Identity Resolution ensures that credit is correctly distributed across all contributing channels and devices, leading to more reliable performance metrics and better-informed budget allocation decisions.

What is the difference between Deterministic and Probabilistic Identity Resolution?

The difference lies in the method and certainty of the match. Deterministic matching is the most accurate method, relying on non-ambiguous, personally identifiable information (PII) like a logged-in email address or a unique user ID to link devices and touchpoints. If a user logs in on their phone and desktop, the system deterministically knows it is the same person. Probabilistic matching, on the other hand, uses statistical algorithms and machine learning to analyze non-PII data, such as IP addresses, device types, and behavioral patterns, to infer that two or more identifiers likely belong to the same person. While less certain than deterministic matching, probabilistic methods are useful for matching anonymous users who have not logged in, helping to fill gaps in the customer journey.

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