You want an optimized performance marketing stack, we have the expert insight. In season 14 of Step-By-Step, we’ve partnered with Triple Whale and Meta to help you maximize RoAS with higher-quality data.

In this episode, you’ll learn why first-party data is critical in a privacy-conscious eCommerce ecosystem, how domain-specific AI can revolutionize eCommerce analytics, and what the shift from self-hosted IT organizations to cloud-based solutions means for your business.

Garbage In, Garbage Out

Key takeaways:

Triple Whale evolved from summary pages to cutting-edge tools like their Sonar integration with Meta Conversions API.
Your data quality is key. Understanding where your marketing dollars are most effectively spent will unlock better RoAS.
Feeding Meta better data through server-side connections transforms ad spend into ad gains.
AI’s Promise and Paradox: Can we teach machines context? We debate whether AI can replicate the intuitive decision-making of a savvy e-commerce operator.
Simplifying complex workflows might mean less visibility, but greater efficiency and creative freedom.
[00:09:17] "Garbage in, garbage out. Clean data is the alpha and omega of decision-making." – Brian
[00:19:39] "The internet used to be the Wild West—now it’s a privacy-conscious frontier." – Kellet
[00:31:42] "If AI were a junior resource, it’s the one you’d micromanage." – Phillip

Associated Links:

Learn more about Sonar, Triple Whale’s Meta Integration
Check out Future Commerce on YouTube
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Subscribe to Insiders and The Senses to read more about what we are witnessing in the commerce world
Listen to our other episodes of Future Commerce

Have any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners!

[STEP BY STEP] The Secret to Smarter Meta Ad Spend: Guess Less With A Winning Mega Data Platform

Future Commerce

[STEP BY STEP] The Secret to Smarter Meta Ad Spend: Guess Less With A Winning Mega Data Platform