The symptom is specific: aI analysis counts duplicate customers as separate people. Start with the affected item and identify the decision or input that could produce this behavior.
Find the likely cause
Source systems use inconsistent identifiers.
Treat this as an explanation to verify against the actual work. Look at the input, the relevant decision, and the final result together. If the evidence does not support this diagnosis, investigate the mismatch before applying a convenient but unrelated fix.
Make the targeted correction
Define a deduplication rule using appropriate reliable keys.
Clean inputs make analysis and personalization more dependable by reducing ambiguity before generation begins.
Check that the repair worked
Compare record counts before and after resolving duplicates.
Repeat the check on the final version that the reader or customer will encounter. An approved draft, a preview, and a published result can differ; the acceptance decision should concern the version people actually use.
Prevent the next related failure
A separate issue to watch for is this: dates are interpreted in the wrong format. Convert dates to an unambiguous documented format.
Monitor the useful outcome
Track invalid records, unresolved fields, and downstream errors attributable to input quality.
Keep a short record of the original symptom, the evidence behind the diagnosis, and the result of the acceptance check. That record makes the solution reusable when the same condition appears again, without assuming that every superficially similar problem has the same cause.