Businesses rely on identity resolution to unify customer data, eliminate duplicates, and track engagement across multiple devices and channels. But not all identity resolution methods are the same.
There are two core approaches: deterministic matching and probabilistic matching. One prioritizes accuracy, the other scalability—and knowing which one to use can make or break your data strategy.
Let’s break down how each method works, where they’re used, and which one is right for your business.
Deterministic identity resolution matches identities using hard, verifiable data points like email addresses, phone numbers, customer IDs, or login credentials.
If two records have the exact same email address or user ID, deterministic matching confidently merges them into a single profile.
✔ A user logs into a website with their email address → The system recognizes them as an existing customer.
✔ A CRM contains multiple entries with the same phone number → The records are merged into a single customer profile.
✔ A user signs up for a loyalty program with the same email they used for a past purchase → The system links the data automatically.
✅ High accuracy – There’s no guesswork when matching exact identifiers.
✅ Reliable across multiple platforms – Works well for known customers and logged-in users.
✅ Great for personalized marketing – Ensures the right messages reach the right people.
🚩 Limited reach – If a user switches devices, clears cookies, or uses different emails, deterministic matching won’t connect them.
🚩 Requires known data – Can’t track anonymous visitors or first-time users without an identifier.
✔ CRM data management
✔ Loyalty programs & customer logins
✔ Financial & fraud prevention systems
Probabilistic identity resolution uses AI and machine learning to analyze patterns and behaviors across multiple data points—without needing an exact match.
Instead of requiring the same email or phone number, probabilistic matching identifies likely connections based on factors like:
✔ IP address and device ID
✔ Behavioral similarities (same browsing patterns, location, or purchase history)
✔ Machine learning predictions based on past interactions
If someone browses a website on their phone in the morning, then returns on a laptop later in the day, probabilistic matching can recognize them as the same person—even without login data.
✅ Catches hidden connections – Links fragmented identities even when data is incomplete.
✅ Works for anonymous visitors – Helps track users who haven’t provided an email or phone number.
✅ Scalable – Can analyze large amounts of data to detect patterns across millions of users.
🚩 Not 100% accurate – Since it relies on probabilities, false matches can occur.
🚩 More complex to implement – Requires AI-powered algorithms and constant model refinement.
✔ Digital advertising & retargeting
✔ Cross-device tracking
✔ Identity resolution for anonymous users
| Factor | Deterministic Matching | Probabilistic Matching |
|---|---|---|
| Matching Method | Exact identifiers (email, phone, user ID) | AI-based pattern recognition |
| Accuracy | High (no guesswork) | Medium (uses probability models) |
| Data Requirement | Requires first-party, known data | Works with anonymous & indirect data |
| Scalability | Limited (depends on logged-in users) | High (can analyze large-scale behavior patterns) |
| Best For | CRM, known customer interactions | Digital advertising, cross-device tracking |
✔ If you need high-accuracy customer profiles for CRM, sales, and fraud prevention → Use deterministic identity resolution.
✔ If you want to track anonymous users, ad engagement, or cross-device behavior → Use probabilistic identity resolution.
✔ If you want the best of both worlds → Combine both for a hybrid approach.
Many companies use a combination of both methods—deterministic for accuracy where possible and probabilistic for scaling insights where exact matches aren’t available.
AI-driven hybrid models will combine deterministic and probabilistic matching for smarter identity resolution.
Privacy regulations will push businesses to rely more on first-party deterministic data, with AI filling in the gaps probabilistically.
Real-time identity resolution will become the new standard, ensuring customer profiles update dynamically.
If you’re struggling to track customer identities across multiple touchpoints, identity resolution can help.
View the Identity Resolution Slide Deck to see how it works. If you’re ready to start using identity resolution the right way, you can purchase it here.
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