Common challenges with B2B customer data include account records spread across different CRM and ERP systems, duplicate records, and disconnected data sets. These challenges prevent B2B companies from gaining a holistic, 360-degree view of their customer accounts which, in turn, can lead to disjointed customer experiences, limited growth opportunities, and increased risk exposure.

Tamr uses AI and machine learning to go beyond basic matching—using patented, probabilistic models and third-party data enrichment to resolve customer records against a vast corpus of global firmographic data. Once customer records are matched, Tamr validates them on a given attribute level, such as location, to ensure they represent real, valid companies.

By resolving fragmented entities and surfacing relationships, Tamr’s B2B Customers data product enables teams to see and map the relationships and connections with and between other company and contact records. With this context, teams can better understand parent-subsidiary relationships, employer-employee relationships, and how different organizations relate to one another.

Enriching B2B customer data with reliable, third-party sources improves data quality and completeness, enabling teams to access holistic, comprehensive 360-degree views of their accounts that drive better, more personalized customer experiences.

Tamr continuously improves data quality by providing real-time APIs and webhooks to instantly propagate change events to downstream systems and ensure data quality across systems. Using machine learning and agentic data curation, Tamr surfaces and resolves data issues such as duplicate or incomplete records. Tamr also matches and enriches data at the point of creation to prevent new duplicates from entering the system in the first place, ensuring operational workflows are utilizing the most accurate and up-to-date version of the data.