Data That Drives Discoverability

In this video, Melissa Todisco from Yext discusses how brands must evolve their listings management strategy to maintain visibility in the age of AI search, focusing specifically on structured data requirements for AI discoverability.

We explore why traditional listings management approaches no longer suffice as AI doesn't rank brands on presence alone but answers questions based on context. Melissa explains the evolution of listings management from the early 2000s to today's structured data era, highlighting how AI requires complete, consistent, and always up-to-date information across all digital platforms.

Key takeaways:
-Listings and local pages are top-cited sources in AI answers, requiring brands to maintain consistency across all platforms
-Data aggregators create inconsistencies that send distrust signals to AI, requiring direct relationships with publishers
-Building a knowledge graph as a centralized source of truth helps manage brand data consistency
-Mapping relationships between brand facts (locations, professionals, services) helps AI answer intent-based questions
-Yext's Scout tool measures brand visibility in AI and compares performance against competitors

Questions answered:
-Why isn't traditional listings management enough for AI visibility?
-How does AI use structured data differently than Google?
-What is a knowledge graph and why is it important?
-How can brands measure their AI visibility?
-What types of data should brands prioritize for AI discoverability?

Melissa illustrates the importance of structured data with a real-life example about searching for pet medication while on vacation, showing how AI delivers vague answers when data is inconsistent versus specific, helpful information when structured data is available. The presentation concludes by offering attendees a free personalized visibility report with recommendations for improving their AI visibility.

Видео Data That Drives Discoverability канала Yext