Hightouch Launches Adaptive Identity Resolution
The Adaptive Identity Resolution tool is part of Hightouch’s warehouse-native CDP and introduces “multi-zone” matching, letting businesses switch between deterministic and probabilistic identity resolution in a single project setup.
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As brands struggle to unify sprawling customer data across devices, platforms, and identities, Hightouch has launched Adaptive Identity Resolution—a tool that turns messy customer data into usable profiles that adapt to different use cases.
Part of Hightouch’s warehouse-native Customer Data Platform (CDP), this new capability introduces “multi-zone” identity resolution, enabling businesses to toggle between high-confidence deterministic matching and higher-reach probabilistic matching within a single project setup.
Modern customers engage across multiple devices, emails, and channels, making clean, accurate customer data a persistent challenge. Deterministic identity resolution, which relies on exact matches (e.g., identical email addresses or phone numbers), struggles when data is inconsistent or incomplete.
Probabilistic identity resolution uses AI to fill the gap, using machine learning models to infer connections between similar, but not identical, records, such as linking a nickname and personal email to a business identity.
“Most identity resolution tools are either rigid or opaque,” said Tejas Manohar, Co-Founder and Co-CEO of Hightouch.
“We’ve built something entirely new, a multi-zone identity engine that lets customers toggle between high-confidence deterministic graphs and higher-reach probabilistic graphs, without sacrificing transparency or control. The ways you use customer identities are situational; Adaptive Identity Resolution adjusts to fit each situation.”
Most businesses struggle with fragmented customer records that limit the impact of their marketing and personalisation efforts. While some platforms support probabilistic matching, Hightouch is offering a fully configurable, transparent, and warehouse-native approach that combines probabilistic and deterministic methods.
This gives teams direct control over how identity resolution is handled, allowing them to tailor the process to specific downstream needs while keeping data ownership within their systems. Rather than relying on a fixed algorithm, it provides a flexible framework designed to adapt over time.
By bringing AI directly into the data warehouse, identity resolution becomes a manageable, strategic process rather than just a maintenance task.
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