Sellm

How Reliable Are Real-Time Brand-Monitoring Features in Geo-Platforms?

…and why Sellm fires 10 simultaneous queries before showing you a ranking on generative engines

With the rise of AI search, Google Search Console alone is no longer enough to understand search brand positioning. Clicks are dropping to zero and citations are no longer sufficient. We need a way to understand what is being recommended by LLMs such as Gemini or ChatGPT. For this reason, many brand-monitoring geo-platforms have risen.

But how trustworthy are these systems, really? Generative engines sample from probability distributions; temperature, system state and context inject randomness, so the same query can yield different brand lists. As a result, they do not provide deterministic answers as in the SEO era. So, can we trust generative engine monitoring systems at all?

In this post, we conduct an experiment analysing thousands of responses coming from ChatGPT, for the exact same query, and apply statistical methods to understand the variance in the response. By doing so, we pinpoint exactly how many repeats are needed before a GEO‑platform’s brand‑monitoring numbers are reliable.

Executive Snapshot

This is the summary of our study:

  • 4,000 identical live searches for "top-rated generative engine optimisation tool" were run from multiple devices and IPs.
  • The searches produced 12,000 tool mentions across 17 distinct products; the market leader appeared 24 % of the time, the next two tools 15 % each, and the long-tail of 14 tools shared the remaining 46 %.
  • Using a binomial–geometric probabilistic sampling model, we show that platforms that only trigger a query once can mis-state the leader-recommended brand by ± 43 percentage points (ppt), meaning you can barely trust those results. Ten queries cut that fluctuate to ± 14 ppt, and one hundred queries shrink it to ± 4 ppt.

1. Testing the Reliability of Brand-Monitoring Features

To mimic a real user journey we fired the same question "What is the top-rated generative engine-optimisation tool?" 4,000 times. Each response listed three tools. Counting every appearance gave us 12 000 data points and a clear popularity curve: one clear leader (~24 %), two solid contenders (~15 % each) and a long-tail of 14 brands that each landed below 8 %.

That empirical curve from our data is the benchmark. If the dashboard is working, each new reading should stay close to those percentages instead of jumping all over the place.

To quantify exactly how much any single snapshot can fluctuate around that curve, we apply the Binomial–Geometric probability model. This framework turns raw counts into confidence intervals, telling us the grade of precision we can expect from any query budget.

2. The Binomial–Geometric Model in Plain English

Think of every query as a coin-flip for each tool:

  • Heads: the tool appears in the shortlist.
  • Tails: it doesn't.

If a tool's true share is p (p = 0.24 for the leader) and you fire n queries in one burst, the natural sampling error—statisticians call it one sigma (± 1 σ)—is:

σ = √[ p × (1 – p) ⁄ n ]

This single formula powers everything that follows: double your queries and the error shrinks roughly by 30 %. Keep it in your pocket; it works for any brand, any share.

(σ formula above uses p = 0.24; swap in any share to size your own risk.)

3. Reliability of Brand-Monitoring Features by Query Budget

Real-time brand-monitoring reliability varies dramatically with how many queries a geo-platform fires under the hood. Here's what that means in everyday business language:

  • Single-query features (1 query): error ± 43 ppt. A brand leader's percentage of mentions that should read 24 % might show up at 0 % or spike to 100 % on the very next refresh. Nothing to be trusted there.
  • Platforms that use 3 queries: error ± 25 ppt. Good enough to get a feeling of the market share but still risky: your "winner" could really be anywhere between 0 % and 49 %.
  • Standard marketing clouds (5 queries): error ± 19 ppt. A brand being mentioned 24 % of the time could actually be between ≈ 5 % and 43 %. Fine for "direction-of-travel" calls, e.g., if you use the platform to understand your regular improvement with different campaigns, you have enough accuracy to distinguish a campaign that's clearly rising, but not for precise ranking or spend allocation.
  • Sellm's 10-query engine: error ± 14 ppt. Sharp enough to separate winners from runners-up in real time, yet still lightning-fast. Perfect for tactical moves during live campaigns.
  • Enterprise or investor-grade platforms (100 queries): error ± 4 ppt. Single-digit fluctuation suitable for quarterly reporting, budget splits, and board-level KPIs—but at the cost of extra compute.

Remember, these numbers are for a 24 % leader. Rarer tools fluctuate more, dominant tools fluctuate less, but the pattern holds: more queries → less noise.

4. What Marketers Should Do Next

  • Stop trusting single snapshots. One lonely data-point can lie by forty points.
  • Match query budget to the decision. Need real-time agility? Ten queries hit the sweet spot. Need audit-proof numbers? Go twenty-plus or let Sellm accumulate over time.
  • Check the fine print. Ask your geo-platform how many calls it batches per refresh. If it's fewer than ten, factor that error into every spend.

5. Sellm: Focusing on Accuracy of Results

Sellm focuses on tracking brand mentions with accuracy across the major AI models, ChatGPT, Gemini, Claude, Perplexity, and others, then translates that raw data into clear, percentage-based visibility scores. With multi-language support and topic-level tracking, it helps teams see not just if they're mentioned, but how often and in what context. Plus, a suite of free tools lets anyone get started measuring their brand's AI positioning before they commit to a paid plan.

6. Bottom Line

Real-time brand-monitoring is only as good as its sample size. Sellm's default ten-query burst turns eye-candy into decision-grade data—no extra clicks, no lag, just numbers you can trust.