Yali Sassoon

At Autotrader, our consumer data is increasingly being used to personalise the on-site experience, and to provide insight to our retailers. Four years ago, we built an identity stitching solution, allowing us to join together user activity across mobile web, desktop and app, but nearly turned it off because of the complexity and low proportion of users who were being stitched. Then we launched Buying Signals, a product which tells retailers what the consumers enquiring on their adverts are actually in the market for. Suddenly, knowing that two sessions belonged to the same person in real-time became important and we needed to rethink our approach to identity stitching. We decided to run an experiment: how would our in-house Spark Streaming solution compare to the Snowplow Identities product? We will share this journey, from the complexity of implementing real-time graph traversal in Spark, to demonstrating the business value of stitching with consumer segmentation. Learn: - How we ran a build-versus-buy test on live production traffic and our findings and conclusions - Why real-time graph traversal is complex, and the challenges of doing it with Spark streaming - How we discovered that stitching matters most for consumers who are close to transaction

Customer-facing agents are replacing digital interfaces at companies like Booking.com, Amazon and Canva, breaking customer data infrastructure at both ends: On collection: Pages are no longer the schema, but user intent in natural language is richer than anything before. On activation: Agents need context that is compressed, information-dense, real-time and relevant to in-session behavior. This session covers how Snowplow is re-imagining the customer context layer for the agentic era: agent-side tracking, bot detection and Snowplow Signals — behavioral intelligence that fuels both customer-facing agents and Snowflake CoWork, built on Snowflake.

The web is on the verge of a major shift. Agentic applications will redefine how customers engage with digital experiences—delivering highly personalized, relevant interactions. In this talk, Snowplow CTO Yali Sassoon explores how Snowplow Signals enables agents to perceive users through short- and long-term memory, natively on the Databricks Data Intelligence Platform.

The web is on the verge of a major shift. Agentic applications will redefine how customers engage with digital experiences — delivering highly personalized, relevant interactions. In this talk, Snowplow CTO Yali Sassoon explores how Snowplow Signals enables agents to perceive users through short- and long-term memory, and how Snowflake Cortex Agents can help leverage this long-term memory using Snowflake's connected data platform.