Most companies assume their identity resolution system is working—until they start seeing duplicate records, mismatched profiles, and wasted marketing spend.
If your identity resolution strategy isn’t delivering accurate, actionable data, it’s costing you money. The key to fixing it? Measuring accuracy and effectiveness.
Let’s break down the right metrics, benchmarks, and tests to ensure your identity resolution is actually doing its job.
Match rate measures how often your system can successfully connect multiple data points to a single identity.
(Number of resolved identities / Total identities in the dataset) x 100
✔ B2C companies: 80-95% match rate (higher availability of personal identifiers)
✔ B2B companies: 60-80% match rate (multiple contacts per account make matching more complex)
🚩 Low match rate = Too many fragmented customer records, leading to duplicate targeting and wasted budget.
🚩 High match rate but poor data quality = False matches, merging unrelated records together.
A high match rate doesn’t mean your system is accurate. False match rate tells you how often your system incorrectly merges two different identities.
(Number of incorrect merges / Total merges) x 100
✔ False match rate under 5% for deterministic matching
✔ False match rate under 15% for probabilistic matching
🚩 Merging two different customers into one profile can wreck personalization and attribution.
🚩 Your AI model might be too aggressive in linking identities, creating bad data instead of clean data.
Resolution latency measures how quickly your system updates and links new customer interactions.
✔ Real-time or near real-time identity resolution (seconds to minutes) for high-engagement businesses
✔ Batch processing within 24 hours for companies with slower customer cycles
🚩 If it takes days or weeks to resolve identities, marketing and sales teams are working with outdated data.
🚩 Customers who just interacted with your brand might still appear as anonymous users, leading to disconnected experiences.
The more verified data points your identity resolution system uses, the higher the accuracy.
✔ Using a mix of deterministic (exact) and probabilistic (behavior-based) matching
✔ Pulling from first-party data sources (CRM, website behavior, transaction history)
✔ Ensuring data is updated in real-time or at least daily
🚩 Your system is missing key identifiers, leading to disconnected records.
🚩 Marketing, sales, and support teams don’t have the full customer journey.
If your identity resolution strategy is working, you should see clear improvements in marketing and sales performance.
✔ Higher conversion rates – Fewer duplicate leads and better targeting = more deals closed
✔ Lower cost per acquisition (CPA) – No wasted spend on duplicate or misidentified prospects
✔ Higher customer lifetime value (LTV) – Better data = better retention and upsell opportunities
🚩 Identity resolution isn’t fixing data problems—it’s making them worse.
🚩 Sales and marketing aren’t getting the right insights, leading to missed opportunities.
✔ Audit your data sources – Remove outdated or unreliable third-party data.
✔ Use a mix of deterministic and probabilistic matching – Avoid over-reliance on either one.
✔ Optimize AI models regularly – Identity resolution isn’t a set-it-and-forget-it system.
✔ Monitor key accuracy metrics continuously – Don’t assume it’s working—prove it with data.
If your match rates are low, your false matches are high, or your marketing and sales teams are working with bad data, it’s time to fix your identity resolution strategy.
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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