Bing, not Google, shapes which brands ChatGPT recommends

In this case study, we went deep instead of broad. We focused on one question: why wasn’t a brand present in a single ChatGPT prompt across ~70 iterations?

We chose one prompt: “What are the best hotels in New York City?” 

We analyzed mentions, citations, fanouts, and SERPs in Google and Bing. We also planned to analyze GPT memory, but it made no discernible difference to mentions, citations, or fanouts.

What we did and what we found

We chose NYC hotels because it’s a crowded, mature market with juggernauts and up-and-comers. We also have no connection to the NYC luxury hotel space — we intentionally picked an area where we could stay objective and learn from scratch.

After running the prompt “what are the best hotels in New York City” 68 times, we identified which hotels appeared most consistently and which were nearly invisible.

We chose the Baccarat Hotel as our “client” because it appeared only once (1.5% of the time), despite strong reviews and clear alignment with the prompt’s intent. We wanted to know why — and whether it could change that.

Key findings:

• You can dominate query fanouts on Google SERPs and still underperform in ChatGPT brand mentions.
• Bing matters most. Ranking in Bing articles for fanouts aligns more directly with ChatGPT mentions — not just citations.
• In verticals dominated by third-party content, you face complex digital PR paths to increase visibility.

Note: A full methodology breakdown appears in the appendix.

Mentions of the Baccarat vs. the Fifth Avenue Hotel show just how wide the disparity in ChatGPT visibility can be

The Baccarat Hotel appeared once in 68 trials (1.5%).

Top performers were large luxury hotels like the Four Seasons Hotel New York Downtown.

ChatGPT also identified boutique hotels as a subcategory, generating a secondary list in its answers. Boutique hotels like the Baccarat are typically smaller and not part of large chains.

Within this boutique subcategory, the Baccarat still underperformed. The Fifth Avenue Hotel, the top-performing boutique property, appeared 13 times, cited 20% of the time, versus the Baccarat’s 1.5%.

Reputation can’t explain visibility disparities

We first checked whether anything in the hotel’s history or reputation could explain the gap. As the chart below shows, nothing significant did:

Overall, the Baccarat has been around longer and has more reviews. On quality, the Fifth Avenue Hotel has no edge in Google reviews and only a slight edge in Expedia reviews. The only area where the Baccarat lags is price — but that’s unlikely the issue when The Ritz-Carlton, a consistent non-boutique winner, is listed at $1,100.

Further reinforcing the Fifth Avenue’s underdog status: one of its most prominent Google results (rank 2) was a Wikipedia page for a different Fifth Avenue Hotel that closed in 1908, creating potential entity confusion similar to the two Danny Goodwins.

If the Fifth Avenue Hotel had been the one missing, it would suggest a less established brand with entity confusion. But the opposite happened — it prevailed in ChatGPT.

So what was the problem for the Baccarat Hotel?

Winning Google SERPs for query fanouts doesn’t help, but winning Bing SERPs does 

When ChatGPT performs a web search, it sends a series of queries you can extract via Chrome DevTools. In this case study, examples included:

• [Best hotels in new york city]
• [Top rated luxury hotels in new york city recommendations]
• [Best hotels in nyc top luxury and boutique hotels new york]
• [Best luxury and boutique hotels in new york city recommendations reviews]
• [Best hotels in new york city nyc top hotels]
• [Top hotels in nyc luxury boutique best places to stay new york city]

In total, we extracted 25 unique query fanouts.

What we saw in the Google SERPs

If we only looked at the articles dominating fanout SERPs in Google, we’d expect the Baccarat to narrowly outperform the Fifth Avenue in ChatGPT. That didn’t happen.

In the table below, the Baccarat “wins” three of the top 10 most frequently appearing pages, while the Fifth Avenue Hotel “wins” two. The other five feature neither. A “win” means one of the following:

• Appearing when the other does not.
• Appearing higher on the page.
• Having more positive sentiment.

The data:

What we saw in the Bing SERPs

By contrast, looking only at the articles dominating fanout SERPs in Bing, we’d expect the Fifth Avenue to outperform the Baccarat in ChatGPT — and it did.

In the table below, the Fifth Avenue “wins” five of the eight most frequently appearing URLs.

Note: The table includes two fewer URLs because Bing SERPs were slightly less diverse for these fanouts.

The data:

The connection between Bing visibility and brand mentions

Bing rank strongly predicts ChatGPT citations — 87% align with Bing’s top results, Seer Interactive found. Our case study supports this and extends it.

We examined the relationship between fanouts (Seer focused on prompts) and brand mentions.

Example mention: “For a luxury boutique feel: listings like The Fifth Avenue Hotel or Crosby Street Hotel consistently make ‘top NYC’ lists from travel editors.”

Mentions are often more valuable than citations. Most people won’t follow citations but will remember the top recommendation.

There’s ongoing debate about whether fanouts shape ChatGPT’s answers and mentions, or simply support answers generated from training data. For example, Leigh McKenzie argued on LinkedIn:

• “The citations you see at the bottom? Those are surfaced after the answer is generated, not before. It’s post-hoc rationalization. The model didn’t choose your brand because it found your URL. It generated an answer based on what it already knows, then pointed to sources that support it.”

By contrast, our data aligns with Beehiiv’s research, which suggests citations do shape mentions.

Training data doesn’t appear to be the issue for the Baccarat. Compared to the Fifth Avenue, it’s older, has more reviews, and holds similarly high ratings across major platforms. What it lacks is strong presence in Bing results for fanouts and citations, which appears to lead to fewer mentions.

A simple flow might look like this:

• Brand ranks in Bing → ChatGPT fanouts pull in Bing pages → ChatGPT synthesizes training and Bing data to generate mentions

Coda: A tale of two Forbes articles, or why the details matter

In this vertical, third parties like Forbes and Condé Nast control the space. Visibility depends on who mentions you, so you need a strong outreach strategy — not just updates to your own content.

Our data shows that “targeting Forbes” isn’t specific enough.

The top result surfaced in both Bing and ChatGPT was the same Forbes article. In Google, the most frequent fanout result was also a Forbes article — but a different one.

As we’ve seen, getting into Google’s Forbes article likely wouldn’t provide a meaningful boost. The Baccarat “won” in that piece.

Getting into Bing’s Forbes article, where the Baccarat wasn’t mentioned, could make all the difference. This requires a highly surgical approach grounded in Bing data.

Generalities won’t work; detail reigns supreme.

Appendix: Methodology

Model: We prompted GPT-5.2 Instant and manually extracted results. We didn’t use APIs within ChatGPT.

Number of iterations: We ran the same prompt 68 times.

Prompt: “What are the best hotels in New York City?”

Settings: We tested three memory states:

• Saved memories off
• Saved memories on, using unrelated real user memories
• Saved memories on, with one memory about needing gluten-free travel accommodations

For all trials, we turned off “reference chat history” to avoid interference across iterations.

We expected differences based on memory settings but found none, so we treated all trials as a single dataset.

What we extracted:

• All query fanouts.
• Full ChatGPT text output.
• Citations.
• Google SERPs for all fanouts.
• Bing SERPs for all fanouts.