In episode #2656, we discuss a paper that reveals the limitations of sophisticated ad engagement propensity scoring techniques. Emphasizing the importance of creativity and the need to add a unique spin to marketing strategies, the conversation then shifts to the increasing number of media layoffs in 2023, with major companies like BuzzFeed experiencing significant stock drops. We stress the importance of adapting marketing approaches to stay ahead in a rapidly changing landscape. Tune in for valuable insights and predictions.

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Check out more of Eric’s content (Leveling UP YT) and Neil’s videos (Neil Patel YT)

TIME-STAMPED SHOW NOTES:

(00:00) Today’s topic: Why Machine Learning Sucks for Predicting Ad Success & What Big Media Layoffs Mean for Marketing
(00:34) Discussion on the limitations of machine learning algorithms in predicting campaign success
(01:37) Graph showing the discrepancy between machine learning models and control tests in predicting campaign success
(02:58) Conclusion that creativity cannot be predicted and the importance of adding a unique spin to ads
(06:11) Conversation on the increasing prevalence of advertisements in various aspects of life
(08:57) Exploring the idea of embracing the advertising trend rather than complaining about it
(09:33) Introduction to Peter Drucker's perspective on the two main things that matter in business: marketing and innovation
(10:08) Emphasis on the need for businesses to adapt to the changing marketing landscape or risk failure
(10:24) Media layoffs in 2023: Impact on marketing
(11:12) Reasons for media layoffs: Lack of adaptation and cheaper content creation
(11:51) BuzzFeed's stock plummeted 97% in five years
(12:32) Overvalued media companies and lack of profitability
(13:09) Major news outlets announcing layoffs
(14:11) CNBC's viewership during economic downturns
(14:38) That’s it for today! Don’t forget to rate, review, and subscribe!

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Why Machine Learning Sucks for Predicting Ad Success & What Big Media Layoffs Mean for Marketing

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