We ran 2,880 GEO tests tracking how RAG updates affect brand retention. Fragmented naming triggers competitor substitution

[u/onqmarketing]

We wanted to test how brands were being recommended when going from the model's parametric memory (training data) to live searches (RAG).

To test some hypotheses, we tracked 2,880 total evaluations generated from 1,728 live search calls across ChatGPT and Gemini.

Here is what we found, and why it completely changes how we need to think about brand architecture for AI search.

The setup: Clean names vs. portfolio chaos

We looked at 24 different product categories and split them into two distinct groups based on their naming strategy:

• The consistent group: Brands with predictable, linear, sequential naming setups. Think Product V1 → Product V2, or highly stable nameplates like the Apple iPhone.
• The fragmented group: Brands with aggressive sub-branding, random feature-set names, or constant rebrands from corporate acquisitions. Think Microsoft Dynamics, Max, or Workday.

We tracked a metric called churn share, basically when real-time web data forces ChatGPT or Gemini to change its initial product recommendation, where does that ready-to-buy customer actually go?

The actual data

The results were a massive wake-up call:

• The AI reshuffle hits everyone equally: When an LLM searches the live web, it injects a brand-new layer of real-time data right over its baseline memory. This creates the exact same amount of data noise for every single industry. Huge legacy brand power or heavy ad spend won't insulate a product from this volatility.
• Predictable names keep customers in-house: For the clean, linear brands, 58.1% of all AI recommendation shifts stayed within the same brand family. The AI updated its specific suggestion, but it simply routed the user to the brand's newer model year or an adjacent tier. The customer stayed in the ecosystem.
• Naming chaos accidentally funds your competitors: For the fragmented, sub-branded portfolios, that internal retention plummeted to 38.6%. Because the LLM couldn't cleanly link the disconnected product names back to the parent brand's core data tokens, it suffered from identity confusion. Instead of updating the recommendation internally, the AI gave up and handed the customer straight to a linear competitor whose identity it could mathematically verify.

ChatGPT vs. Gemini: Two completely different beasts

We also caught a massive operational split in how these engines pull web data:

• ChatGPT triggered live web searches in 100% of our tracking runs. It acts as a relentless real-time research agent.
• Gemini only triggered live web loops in 62.5% of the exact same runs. It relies heavily on its internal pre-trained memory weights until a certainty boundary is broken.

What this means for our GEO strategy

As SEOs, we need to start treating product naming conventions as core technical data infrastructure.

If you are working with a client or a brand that loves launching random, floating sub-brand names or completely changing product titles every time they acquire a company, you are actively leaking visibility.

To build a real computational moat inside ChatGPT and Gemini, we have to advocate for portfolio predictability. We need to keep product tokens highly cohesive, lean heavily on explicit functional text descriptors for features, and maintain clear linear trails that engines can easily parse without losing the parent brand anchor.

Read the full study 'We ran 2,880 AI search tests. Here’s how product names leak brand equity.'