Marketing and analytics teams are expected to deliver increasingly relevant recommendations. However, many approaches struggle because customer behavior is inherently relational. When interactions between customers and products are flattened into tables and aggregates, important signals about similarity and shared behavior are lost.

Join this webinar to see how Neo4j Graph Analytics for Snowflake helps overcome this limitation by running graph algorithms—such as node similarity—directly on your existing Snowflake tables. Without ETL or graph modeling, you can uncover meaningful behavioral patterns that traditional relational approaches miss, all using SQL and elastic, serverless compute inside the Snowflake AI Data Cloud.

We’ll walk through how graph analytics reveals customer similarities based on shared interactions, enabling more relevant and explainable recommendations—while keeping data governance, security, and workflows entirely within Snowflake.

You’ll learn:

• How node similarity uncovers behavioral patterns directly from relational data

• How to run graph algorithms in Snowflake with no ETL and no infrastructure overhead

• Practical patterns for improving recommendations using graph analytics results stored directly in Snowflake tables

Join us to see how graph-powered insights can help you build better recommendations—without leaving Snowflake.