hispanic

Expanding Spanish Language Audiences

Expanding Spanish Language Audiences

Sergey Fogelson – Head of Data Science, TelevisaUnivision

Edouardo Vitale – Data Scientist, TelevisaUnivision

Sergey Fogelson and Edouardo Vitale, both from TelevisaUnivision, outlined their motivations for developing a custom lookalike model (LAM) to expand Spanish-language audiences, which were under-represented:
  • Misidentification: 4 in 10 Hispanics are excluded from 3p datasets.
  • Waste: 70% of impressions targeted at Hispanics are wasted.
  • Scale: The true scale of the Hispanic population within a given brand’s 1p dataset is hard to identify without extensive validation.
In order to address this audience underrepresentation, data sources were leveraged to create a household graph incorporating 1P (first-party) viewership, 3P (third-party) viewership and demographics on age, gender, income and education from TelevisaUnivision’s partner. The combination of these sources created a robust household level representation of approximately 17MM Hispanic households in the U.S. Embeddings were used to create a latent space that allowed for the comparison of user similarities. Mathematically, very similar users have very similar embeddings. Similarities between individuals may be based on what content they viewed, where the content was viewed (zip code) or demographics of the viewer. Additional details of the embedding process as well as the autoencoder architecture steps and validation process were presented.

Key Takeaways

  • Developing a Lookalike Model (LAM) to expand Spanish-language audiences, corrected for the underrepresentation of this consumer target.
  • Expanding an audience with LAM identifies individuals who look and act just like a given target audience. These look-alike models are used to build larger audiences from smaller segments in order to create reach for marketers and advertisers and enable them to transact on an expanded audience.
  • Use of LAM can overcome the challenges of misidentification, waste and scale. LAM plus the household graph achieves significant increases in overall audience scale.

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