Entity resolution software links records that describe the same real-world entity. For customer data, the hard part is deciding which records belong to one person without combining different people or leaving useful history disconnected.
Evaluate the software against your entity definition, your source records and the decisions the output must support. A matching method, a large cluster count or a high match rate cannot establish fitness on its own.
Key Takeaways
Define whether the entity is a person, household, account or another business object before evaluating tools.
Distinguish deterministic or probabilistic methods from the quality of their actual results.
Review incorrect merges and missed matches separately using representative records and difficult cases.
Require source lineage and a process for investigating changes to customer groupings.
Include deployment, monitoring and downstream integration in the buying decision.
Define which entity the software must resolve
A shared postal address may connect a household without proving that two customer accounts belong to one person. A company account may include several contacts. Decide which relationship the first use case needs.
Write down how reviewers should treat shared contact details, changed names, recycled phone numbers and conflicting source identifiers. Include an uncertain outcome when the evidence is insufficient.
In customer marketing, identity resolution often applies entity-resolution techniques to customer records and identifiers. Keep the evaluation tied to that job rather than assuming every entity-resolution product serves the same workflows.
Separate matching methods from measured quality
Deterministic approaches apply defined matching rules. Probabilistic approaches evaluate combinations of evidence to assess relationships. Implementations may combine methods, and the same method can perform differently on different data.
Our guide to identity resolution techniques explains the distinction. Ask vendors to show results on your records rather than treating an algorithm label as a quality guarantee.
Include records that lack a preferred identifier. An email-only demonstration may miss the difficult customer relationships that created the need for a new tool.
Test a representative sample and hard cases
Use a representative sample to understand typical records and a separate challenge set to probe known failure modes. Preserve their different purposes when reporting results.
Review false merges, where distinct customers are grouped, and missed matches, where one customer's records remain separated. Record the evaluation unit, labeling method and unresolved cases.
If you report precision or recall, explain how the reviewed matches and nonmatches were selected. A deliberately difficult sample is useful for diagnosis, but its error rate should not be presented as the population's error rate.
Inspect clusters, lineage and change behavior
Pairwise decisions do not tell the whole story of a customer grouping. Inspect complete clusters and trace important fields to their source records.
Ask what happens when a source changes an identifier, a new dataset arrives or a matching policy is revised. Determine how the team investigates a suspected incorrect relationship and reviews downstream effects.
Amperity's Stitch review documentation describes diagnostic checks and human review for potentially incorrect groupings. A diagnostic warning directs investigation; it is not itself confirmation of an identity error.
Evaluate operation and deployment alongside accuracy
Document the hosting and processing model, data access requirements, configuration process, monitoring and recovery responsibilities. Confirm who needs specialist skills and who handles routine changes.
Compare normal operation with the proof-of-concept workload. A tool may perform well on a static sample while requiring substantial effort to onboard new sources or review exceptions.
Our build-versus-buy framework helps distinguish a purchased capability from the operating responsibilities you retain. Model the cost of that work alongside software and infrastructure charges.
Connect the evaluation to a usable customer profile
A resolved entity is an input to another decision. Follow representative records into a profile, a report or an audience and verify the resulting business meaning.
Check whether source IDs remain available, transaction totals reconcile and eligibility logic uses the intended records. Separate identity errors from profile-calculation errors and destination matching limits.
The cost of poor customer data quality is one reason to keep those checks in normal operation. A one-time evaluation doesn't replace a review process when the data changes.
Choose with a documented acceptance decision
State which requirements passed, failed or remain unresolved. Include reviewed examples, known limitations, operational ownership and the circumstances that would require reevaluation.
Explore our identity resolution capabilities using a test designed around your customer records. The useful output is an explainable decision about fit, backed by evidence your team can revisit.
