Case Study: Thomson Reuters achieves improved AI/ML search relevance and user sentiment insights with Pendo

How Thomson Reuters used Pendo to track events in a new AI and machine learning-powered tool and understand the impact of feature engagement on user sentiment

Thomson Reuters’ tax and accounting division introduced an AI/ML-powered search and auto-suggest feature in its Checkpoint Edge platform and needed to understand how users actually engaged with it—when they stopped typing, whether they picked a suggestion, and how those behaviors affected the relevance of results and overall user sentiment.

They instrumented three Pendo track events (first input stop, dropdown selection, and final search) and correlated those events with NPS scores, giving product, editorial, and non-technical teams easy access to actionable data. The result: clearer measures of feature success, faster improvements to the AI/ML recommendations and content relevance, and democratized analytics that drive ongoing product enhancements.

Thomson Reuters

Vinay Shukla

Product Manager