AI Summary
GEO's performance can be accurately understood only through large-scale iterative experiments, rather than a few queries. Chainshift analyzes in which contexts AI chooses a brand by utilizing over 2 million queries based on actual user environments. Ultimately, the core of GEO depends on the scale and realism of the data to understand AI's 'preferences.'
“Anyone can throw queries. But almost no one has thrown 2 million queries.”
The landscape of content marketing is changing. We now live in an era where people search AI chatbots before Google. Consumers are increasingly asking conversational AI questions like, “What's the hottest tone-up cream these days?” or “What's a good BB cream for combination skin?” and choosing products based on the answers they receive.
In this trend, the concept of GEO (Generate Engine Optimization) has emerged—a strategy to optimize content so that your brand is more visible in AI searches. But there’s a catch here too. What criteria do you use to measure “performance”?
🎯 If it doesn't show up after 100 tries, is it a failure?…It's not that simple.
The typical way to check GEO performance is as follows: “Let's throw in about 50 brand queries and see how many appear in the answers.” That's the level we're at. Some companies judge performance based on as few as 100 queries.
However, ChainShift directly refutes this approach.
“Concluding that ‘our brand isn’t visible’ after running 100 queries is, in a word, based on too small a sample size.”
— ChainShift CTO JY KIM
💥 ChainShift tested over 2 million times
ChainShift actually input over 2 million queries into AI for GEO analysis. It wasn’t just a matter of calling the API. They entered the queries into an AI search bar as people would, and collected the responses as they were.
Why go to such lengths?
Because only by extracting data in an environment identical to the actual user experience can we determine which brands AI truly selects.
“The API method yields different responses from AI. The prompt tokenization method is different, and there are system messages as well. Therefore, the browser environment is more accurate.”
— ChainShift CTO JY KIM
💄 Real-world example: Domestic beauty brands A and B
For example, ChainShift's recent analysis of the domestic beauty market GEO research yielded interesting results.
When asked, “Recommend a natural-toning cream,” competitors reported that Company A’s product was mentioned most frequently when 100 queries were entered. However, ChainShift discovered that when 2 million queries were entered under the same conditions, Company B’s product was mentioned 3.6 times more frequently.
Why was there such a difference?
“AI recommends completely different brands depending on the way the question is phrased or the context. Even a slight change in phrasing can alter the rankings. Therefore, you need to input a large number of diverse queries to truly understand AI's ‘preferences.’”
— ChainShift CTO JY KIM
📊 GEO is a ‘data battle’... those who have tried it know
What matters in GEO is ultimately how realistic the data used in experiments is. ChainShift invested the most time and resources in this area. Rather than simply finding ‘well-exposed keywords,’ it tracks which brands AI selects in which contexts.
Designing at least 100,000 natural language prompts
Automatically extracting whether brands are explicitly mentioned in AI responses
Analyzing brand-specific response exposure, context, and positioning
All these processes are implemented in a single analysis engine, enabling marketers to immediately receive their brand’s GEO performance report.
🧠 It’s time for marketers to adopt ‘AI’s perspective’
In the SEO era, Google was the standard. In the GEO era, AI is the standard. However, to understand what criteria AI uses to generate responses, one must think like AI, ask AI directly, and analyze its responses.
ChainShift asserts that marketers should no longer rely solely on metrics like ‘link count’ or 'search volume.' It's time to consider what brands AI prefers, what product descriptions are more favorable for recommendations, and what sentence structures are more likely to be exposed.
✨ In conclusion
ChainShift has proven with data that the statement “We asked AI 2 million times” is not just an exaggeration.
Do you want to see if your brand is mentioned in AI's responses?
Then, you have no choice but to ask AI yourself.
Ask a lot, ask accurately, and ask properly.
And there's a place that's doing just that right now. That place is ChainShift.
Company Introduction
ChainShift is a content marketing specialist company that operates the first large-scale query analysis platform in Korea based on actual user environments in the field of AI search optimization (GEO). Through GEO reports, brand exposure tracking, and query optimization strategy development, we help marketers establish content strategies tailored to the AI environment.
Chainshift Amy
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