Ep 534: Google’s 90% “New Customer” Illusion: How To See What Your Ads Are Really Doing | AKNF
DTC Podcast · with Dougie · August 15, 2025 · 23 min
Summary
Google Ads frequently misreports returning customers as new, leading to inflated CAC and wasted ad spend. This episode exposes the 'new customer illusion' caused by cookie-based tracking and makes a compelling case for implementing server-side tracking, citing Elevar as a solution. Ecommerce operators will learn how to audit their Google Ads accounts to uncover these inaccuracies and feed better data to the algorithm for true customer acquisition and profitable growth.
Key takeaways
Audit your Google Ads 'new vs. returning customer' data as Google's cookie-based tracking often misattributes returning customers as new, leading to inaccurate CAC and ROAS. Your 90% new customer rate is likely an illusion.
Implement server-side tracking, potentially using solutions like Elevar, to accurately distinguish between new and returning customers. This is crucial for truly understanding incrementality and optimizing ad spend.
Re-evaluate your campaign goals to ensure they align with actual business outcomes, focusing on net-new customer acquisition rather than just conversions that may be re-acquiring existing customers.
Understand that feeding accurate data to Google's algorithm is paramount. Without it, you're training the algorithm to optimize for the wrong metrics, leading to inefficient ad spend and misleading performance reports.
Subscribe to DTC Newsletter - https://dtcnews.link/signupIf Google Ads tells you 90% of your conversions are from new customers—you’re probably being misled.In this episode, Eric sits down with Pilothouse’s Dougie, who exposes one of the biggest attribution errors in digital marketing: Google’s cookie‑based misreporting that makes you believe you’re crushing new customer acquisition when you're actually…not.This episode is a must‑listen if you're spending on Google Ads and think you're scaling. You may just be paying full price to reacquire your own customers.What we expose in this episode:Why Google thinks nearly everyone is a new customer—and why that’s falseHow cookie-based tracking is sabotaging your incrementalityWhy server‑side tracking (via Elevar) is the fix—and how to set it upThe real cost of bad data: wasted CAC, poor ROAS, and misaligned goalsHow to audit your own account to uncover the truthIf you're not feeding the right data to Google, you're training the algorithm to do the wrong thing. This is the fix—revealed.Timestamps:00:00 – Why Google's new customer data is misleading02:00 – The impact of server-side tracking on attribution04:00 – How Google misidentifies returning customers as new06:00 – Training the Google Ads algorithm with quality data08:00 – Using Elevar for accurate net new conversion tracking10:00 – Aligning campaign goals with business outcomes12:00 – Auditing new vs returning customer data14:00 – LTV, cookie policies, and the future of tracking16:00 – Why server-side tracking improves data fidelity18:00 – The rise of AI summaries and their SEO impact20:00 – Optimizing for ChatGPT and the future of AI search<br /
What does this episode say about paid acquisition?
Audit your Google Ads 'new vs. returning customer' data as Google's cookie-based tracking often misattributes returning customers as new, leading to inaccurate CAC and ROAS. Your 90% new customer rate is likely an illusion.
What does this episode say about analytics & attribution?
Implement server-side tracking, potentially using solutions like Elevar, to accurately distinguish between new and returning customers. This is crucial for truly understanding incrementality and optimizing ad spend.
What does this episode say about dtc strategy?
Re-evaluate your campaign goals to ensure they align with actual business outcomes, focusing on net-new customer acquisition rather than just conversions that may be re-acquiring existing customers.
What does this episode say about paid acquisition?
Understand that feeding accurate data to Google's algorithm is paramount. Without it, you're training the algorithm to optimize for the wrong metrics, leading to inefficient ad spend and misleading performance reports.
