『Channel Diversity: How To Think About New Channels Past Meta』のカバーアート

Channel Diversity: How To Think About New Channels Past Meta

Channel Diversity: How To Think About New Channels Past Meta

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In this episode of Measurement Talks, the hosts discuss insights from Dr. John Snow's

study on Meta's declining advertising performance, diving into the nuances of

attribution models versus deterministic analysis. A significant focus is placed on

exploring alternative advertising channels beyond Meta, emphasizing the importance of

diversifying ad spend across various platforms such as Reddit, Live Intent, and more

traditional marketing channels. The dialogue also delves into strategies for audience

segmentation, effective message delivery for different demographics, and the value of

personalization in advertising. Additionally, the conversation touches on the challenges

and opportunities presented by Meta's lack of clear communication regarding internal

tests and attribution issues.


Timestamps:

00:00 Welcome to Measurement Talks: Diving into Meta's Performance Mysteries

00:10 Unraveling the Meta Performance Puzzle: Insights and Analysis

02:56 The Humor in Marketing Metrics: A Light-Hearted Interlude

03:42 Exploring Attribution Challenges and Theories in Digital Marketing

10:58 The Quest for New Marketing Channels: Beyond Meta and TikTok

24:31 Mastering Audience Targeting: Incremental Audiences vs. Conversion

Optimization

Our promise to you: Every episode will have actions you take back to your business


Bryan was the Director of Efficacy at Kargo and directly helped answer this question for

many brands through his work there. Now, he leads marketing for one of the fastest-

growing, profitable DTC companies, Nood.


Rishabh led new business at LiveRamp, the largest provider of online digital identity, and

directly helped scale many measurement and attribution programs for the ad tech

ecosystem. He is now the CEO of Fermat Commerce, a commerce platform that

enables content-native shopping that has no attribution loss.

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