This ARF Pharma Council event followed up on the Council’s podcast episode on “Inclusive Futures of Humancare,” focusing on the importance of inclusiveness in pharma research and marketing with respect to both demographic characteristics and health conditions. Four speakers delivered brief presentations, followed by a discussion moderated by Pharma Council Co-Chair Marjorie Reedy of Merck.
This ARF Pharma Council event followed up on the Council’s podcast episode on “Inclusive Futures of Humancare,” focusing on the importance of inclusiveness in pharma research and marketing with respect to both demographic characteristics and health conditions. Four speakers delivered brief presentations, followed by a discussion moderated by Pharma Council Co-Chair Marjorie Reedy of Merck.
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.
This ARF Pharma Council event followed up on the Council’s podcast episode on “Inclusive Futures of Humancare,” focusing on the importance of inclusiveness in pharma research and marketing with respect to both demographic characteristics and health conditions. Four speakers delivered brief presentations, followed by a discussion moderated by Pharma Council Co-Chair Marjorie Reedy of Merck.
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.
On October 25, 2022, media and measurement experts shared the latest research-based insights about today’s media landscape at OTT 2022 held on the Warner Bros. Discovery Studio Lot in Burbank, CA. Attendees learned more about what viewers really want when it comes to FAST, AVOD and SVOD, and whether there are viable solutions for cross-platform measurement.
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