Marketers are increasingly dealing with fragmented digital channels, receiving more different signals from their customers. To effectively communicate with customers, organisations must have a clear approach to identity resolution. This involves finding ways to match devices, digital accounts and other identifiers with one person. There are two main ways to do this: deterministic and probabilistic matching. Deterministic matching means merging profiles where the match is certain, usually when one common identifier is found in multiple profiles. Probabilistic matching, on the other hand, uses AI to combine behavioural data with other signals to predict the likelihood that separate customer interactions are all from the same customer.
Businesses should also establish a single source of truth for customer data. All use cases need to be taken into consideration when deciding where data should be consolidated to avoid redundancies, competing sources of truth and edge cases which might undermine assumptions made when merging data from multiple profiles.
Finally, when merging customer profiles and matching identifiers, businesses should rank their confidence on a sliding scale depending on the use cases. This calculation is important because it helps determine the chance of getting one of the data points wrong. Keeping use cases in mind will help teams make the most confident decisions in matching identifiers, merging profiles, and delivering the best experiences to customers.
Originally reported by Martech: https://martech.org/3-approaches-to-merging-profiles-when-resolving-identities/
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