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Episode 25: How Data-Driven Growth Redefined a Media Giant

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Manage episode 509843771 series 3615441
Content provided by Delphina. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Delphina or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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. At the heart of this success is a proprietary household graph that creates a single, privacy-first view of a massive and culturally diverse audience.

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. The conversation also explores a pragmatic framework for AI adoption, showing how foundational machine learning often outperforms chasing the latest trends and where LLMs can deliver real, measurable value.

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25 episodes

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iconShare
 
Manage episode 509843771 series 3615441
Content provided by Delphina. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Delphina or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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. At the heart of this success is a proprietary household graph that creates a single, privacy-first view of a massive and culturally diverse audience.

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. The conversation also explores a pragmatic framework for AI adoption, showing how foundational machine learning often outperforms chasing the latest trends and where LLMs can deliver real, measurable value.

LINKS

  continue reading

25 episodes

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