The podcast discusses the resurgence of Media Mix Modeling in advertising, focusing on Google's Meridian. It also explores the rise of shoppable TV, T-commerce, and the challenges faced by platforms. The chapters cover topics like the impact of walled gardens, solutions for video content production, and innovations in T-commerce and shoppable TV experiences.
MMM gains popularity due to campaign optimization effectiveness despite precision challenges.
Companies leverage machine learning in MMM for improved campaign optimization and attribution.
Deep dives
The Shift to Media Mix Modeling (MMM) in Digital Marketing
The industry is moving towards Media Mix Modeling (MMM) as a preferred method for campaign attribution. Platforms like Google, Facebook, and Meta are adopting MMM due to its effectiveness in capturing and optimizing campaign performance over time, despite signal loss in precise measurement. MMM with new machine learning capabilities has gained importance, as highlighted by Google's Meridian MMM tool.
Challenges of Precision in Measurement with MMM
MMM challenges precision in measurement compared to Multi-Touch Attribution (MTA). The deterministic nature of MTA, especially evident in platforms like Facebook, allowed for user-level confirmations of ad campaign success, enabling quick optimization and attribution. However, the shift to MMM leads to a slower, more aggregated approach, impacting campaign optimization timelines and requiring a different strategic focus.
Adoption of Machine Learning in MMM Solutions
Companies are leveraging machine learning capabilities in MMM solutions to streamline campaign optimization and attribution. While platforms like Google's Meridian and Facebook's Robin offer advanced MMM tools, they rely on algorithmic and API-based approaches rather than full-fledged machine learning. This adoption aims to enhance data integration and optimization, catering to a broader range of advertisers.
Differentiation in MMM Products and Open Source Initiatives
Google's Meridian distinguishes itself by using Python for MMM, making it more accessible to marketers. This open-source approach allows for customization and transparency, empowering developers to enhance the model for specific brand needs. In contrast, proprietary models like Amazon's offer limited visibility and customization, restricting adaptability. Open source initiatives aim to address concerns of bias and lack of neutrality by providing transparency and flexibility for further development.
One side effect of signal loss? Media mix modeling is coming back in fashion. Google’s Meridian is the latest entrant in the MMM space, which is being embraced by large ad platforms and startups alike. Plus: Differentiating between shoppable TV and T-commerce.
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode