Sergey Fogelson on How Data-Driven Growth Redefined a Media Giant
Sergey Fogelson
Hugo Bowne-Anderson
Duncan Gilchrist
Sergey Fogelson (VP of Data Science, Televisa Univision) joins High Signal to reveal how the world’s largest Spanish-language media company built a sophisticated data engine from the ground up. This transformation fueled a tenfold expansion of its digital streaming business by redefining how the company connects with 300 million viewers worldwide. We dig into the journey from basic data unification to building production-ready recommendation engines, how his team uses embeddings on user behavior to uncover surprising connections in content consumption, and the trade-offs between investing in internal data tools versus direct revenue-driving products.
Guest
Sergey Fogelson
Televisa Univision
Key Takeaways
Unification precedes value. Before building any advanced models or personalization engines, Televisa Univision focused on the foundational work of standardizing disparate data sources. This data unification was the necessary prerequisite for creating a single view of the audience and ultimately driving 10x growth in the digital business.
Consumption patterns are the real signal. Sergey’s team found that modeling the sequence of what users watch provides a much richer signal for recommendations than the content’s own metadata. This approach uncovers non-obvious connections between seemingly unrelated shows and sports, leading to more effective personalization.
A targeted identity graph is the foundation. Instead of attempting to model the entire US population, the team built a custom, privacy-first "household graph" focused specifically on consumers of Spanish-language media. This targeted approach creates a more accurate and powerful foundation for understanding a core audience.
Monolithic models fail niche audiences. Standard personalization approaches often break down when applied to multicultural or non-mainstream user bases. Building successful data products requires moving beyond generic models to deeply understand the specific behaviors and affinities of a target community.
Generative AI is a force multiplier, not a silver bullet. The team saw a 10-15% lift in engagement not by building a user-facing chatbot, but by using an LLM internally to generate richer metadata for their content catalog. This treats AI as a component to improve an existing system rather than as the final product itself.
Problem-solution fit determines the right tech. To manage executive expectations, the team developed a framework to assess if a business problem truly requires an AI solution. Often, the right answer is a simpler dashboard or a classic machine learning model, not the newest, most complex technology.
Mature data functions build for themselves and the business. An effective data strategy balances resources between revenue-driving products and internal infrastructure. Building tools that make the data team's own life easier is a critical, often overlooked, investment in long-term velocity and capability.
You can read the full transcript here.
Timestamps
00:00 Introduction to Televisa Univision's Digital Transformation
00:52 Meet Sergey Fogelson: VP of Data Science 01:27 The Role of Data Science in Media 05:46 Building the Data Infrastructure 10:25 Launching VIX: A New Streaming Service 15:43 Leveraging Embeddings in Data Science 24:43 Understanding the Household Graph 28:12 Filtering and Thresholding for Graph Creation 30:28 Importance of Large Screens for Engagement 31:19 Evolution of the Recommendation System 34:00 Challenges with Cold Start Recommendations 35:20 Transition to Sequential Recommendation Models 37:16 Personalization in Short Form Video Content 40:16 Generative AI for Metadata Enhancement 45:45 Challenges and Caution with LLMs 54:28 Advice for Executives on AI Investment