DTC Podcast Ep 534 Summary

Episode Title: Google’s 90% “New Customer” Illusion: How To See What Your Ads Are Really Doing | AKNF
Date: August 15, 2025
Host: Eric Dick (DTC Newsletter/Podcast)
Guest: Douglas (Pilothouse, Google Lead)

Overview

This episode dives into the critical issue of Google Ads’ inaccurate reporting on “new customer” acquisition and how direct-to-consumer (DTC) brands can get a true picture of campaign incrementality. The team discusses pitfalls in native Google tracking, the benefits of server-side tools like Elevar, and actionable strategies for improving acquisition reporting and campaign effectiveness. The tone is practical, candid, and packed with tactical insight for DTC marketers.

Key Discussion Points & Insights

The Problem with Google’s “Net New” Customer Data

• Google’s Built-In Segmentation is Flawed

  • Google Ads often claims 90%+ of conversions are from "net new" customers.
    • Douglas: "Google generally, in our experience, has about a 90% rate on just about every customer being identified as net new, which we just know to be false." (02:38)
  • This figure is inconsistent with data from reliable CRM sources (Shopify, Klaviyo).
  • Google’s approach relies on third-party cookies, which are unreliable and short-lived.

• Why It Matters

  • Treating returning customers the same as new customers leads to inefficient ad spend.
  • DTC brands should optimize and budget differently for acquisition vs. retention.

Why Google Mislabels Customers

• Technical Limitations

  • Cookie durations are shrinking (varies by browser and region).
  • Imported conversion goals from GA4 inherit these attribution issues.

• Result:

  • Google’s model can’t distinguish true net new from returning buyers unless it’s fed more accurate, server-side, first-party data.

• Quote:

  • Douglas: "Using a platform, a server side tool like Elevar allows us to get a bit more accuracy on okay, who are truly the net new versus the returning." (02:00)

Feeding Better Data: The Elevar Solution

• What is Elevar?
  • Server-side tracking platform that improves new/returning customer identification by using first-party data.
• Implementation:
  • Pull conversion goals from Elevar into Google Ads to focus on verified net new customers.
  • Requires sufficient conversion volume to be effective.
• Impact:
  • More accurate attribution leads to better algorithmic targeting and reduced Customer Acquisition Cost (CAC).
• Results:
  • Douglas: "We're starting to see an uptick in new customer purchases overall coming through Google, as well as a decrease in customer acquisition costs." (09:09)

Quality of Data Input Drives Output

• Eric: “Bad inputs lead to bad outputs. Right. Bad prompts probably don't lead to the optimal result ... when using AI or what have you. And all this algorithmic bidding is automated. It is AI.” (06:35)
• Drawing analogy to AI prompts, feeding Google low-quality or inaccurate data skews campaign results.
• Focus conversion reporting on qualified, net new acquisitions rather than all purchases.

How to Audit Your "New Customer" Data

• Steps:
  1. Compare Google’s new vs. returning reporting to trusted CRM sources (e.g., Shopify, Klaviyo).
  2. Strongly consider implementing server-side tracking (e.g., via Elevar) to cross-check and correct this data.
  3. Use accurate data segmentation for optimizing campaigns toward genuine business goals.
• Douglas: "Don't rely on just the platform itself." (15:54)

Balancing Value of Repeat vs. Net New

• Consider your repeat rate and LTV (Lifetime Value).
• Analyze what returning buyers purchase and spend compared to first-time customers.
• Explicitly define and optimize for what your brand needs: growth (net new) vs. retention.

Implications for Campaign Setup and Strategy

• Douglas: “...the general philosophy of aligning your conversion action to the ultimate goal is what I'm trying to get across here. And most of the time for DTC brands, we're trying to get net new acquisition through Google...” (09:29)
• Use distinct conversion goals for new vs. returning customers.
• Avoid assuming high ROAS driven mainly by retention or repeat engagement is the same as true incremental growth.

The Importance of Server-Side Tracking at Scale

• Data Privacy Laws:
  • Regulations in California, UK, etc. make cookie-based tracking less reliable.
  • Server-side methods less susceptible to browser/cookie restrictions.
• Scale Matters:
  • Benefits (5-10%+ improvement) become significant with higher sales volume.
  • Douglas: “If you're of a high enough scale and if you have enough data flowing in, you should absolutely be adopting server side tracking because the benefit, even if it is 5%, is pretty significant at scale." (18:13)

Evolving Google Ad Features and Industry Trends

• Deprecation of Lookalike Audiences:
  • Google has reduced support for lookalike audiences except on some platforms (e.g., YouTube Demand Gen).
• AI Search and Content Aggregation:
  • Eric references reports of Google’s search revenue potentially being reduced by AI summaries, but Douglas notes this impacts content aggregators more than product-focused DTC brands. (20:28)

Notable Quotes & Memorable Moments

• Eric Dick:

  • “Almost every marketer would look at that if they saw a 90% new customer rate from a platform like Google. They probably would be smart to doubt that.” (03:56)

• Douglas:

  • “We applied this to a multitude of brands and found that … Google's still aligning these users as being net new when for a fact we know that they are not.” (02:56)
  • “Optimizing to a net new conversion goal ... allows the algorithm to focus in on that as the performance target, as opposed to targeting any purchase.” (05:37)
  • “If you give [AI] a bad prompt, you're probably not going to—bad inputs lead to bad outputs.” (06:35)
  • “Get Elevar ... try to get server side tracking set up. It isn't that expensive to get sorted." (15:48)

• Eric Dick:

  • “It’s one of those changes that has ongoing incremental benefits because you're actually putting good data in versus bad data…” (06:15)

Timestamps for Key Segments

| Time | Segment | |-------|-------------------------------------------------------------------------------| | 00:00 | Opening: Problems with Google’s “new customer” metric | | 02:00 | Why cookie-based tracking fails; why server-side is better | | 03:56 | Marketer skepticism and data validation | | 05:37 | How to optimize Google campaigns for net new acquisition | | 06:35 | AI analogy: Bad data in, bad results out | | 08:15 | Q&A: Elevar vs. other solutions | | 09:04 | Post-Elevar results: More new purchases, lower CAC | | 10:20 | How to weigh repeat vs. new acquisition in marketing goals | | 15:38 | Step-by-step: How to audit your “new customer” data | | 17:18 | Evolving benefits of server-side tracking, especially at scale | | 20:28 | Impact of AI search summaries and Google’s revenue |

Conclusion

This episode emphasizes the critical importance of challenging “out-of-the-box” Google Ads reporting, especially regarding new customer acquisition. Douglas and Eric urge marketers to implement robust, server-side tracking (such as Elevar), audit their data rigorously, and clearly differentiate between net new and repeat purchasers in both reporting and campaign strategy. The message is clear: better data means better results, and tactical upgrades in attribution can drive real incremental growth for DTC brands.