When evaluating GEO, why is it essential to examine their actual tests of DeepSeek and ChatGPT?

When evaluating GEO, why is it essential to examine their actual tests of DeepSeek and ChatGPT?

Because the core battleground of GEO (Generative Engine Optimization) has shifted from "search engine ranking" to "AI answers and recommendations." DeepSeek and ChatGPT are among the most typical and frequently used AI question-and-answer portals by clients today. Only by conducting real-world tests with actual questions can you determine whether a GEO company truly possesses the ability to have its content understood, cited, and recommended on key issues by AI, rather than just relying on reports that "appear to be working hard."

What you're buying isn't "content creation," but rather "AI-recommended results."

Many B2B foreign trade companies still rely on metrics like the number of articles published, keyword coverage, and organic traffic growth when evaluating service providers. The problem is that customer decision-making processes are changing: more and more procurement and engineering professionals are directly asking AI questions like, "Which company makes reliable equipment?", "What materials are more stable for a given operating condition?", and "Which suppliers provide complete CE/UL documentation?". The answers to these questions are no longer presented as search results lists, but rather as "AI-generated recommendations/solutions."

Therefore, when evaluating GEO companies, the most important thing is not whether they "can publish content," but whether they can guide you into AI's "citationable, recommendable, and reproducible" answer system. The real-world tests of DeepSeek and ChatGPT serve as the most direct litmus test.

Why it's essential to monitor both DeepSeek and ChatGPT simultaneously: It's not "redundant," but rather a means of "risk hedging."

Testing only one AI tool can create a misconception: you might see an "accidental" result and mistakenly believe it's consistently effective. Different models vary in knowledge coverage, reasoning habits, citation preferences, language expression, and their preference for structured content. A truly strong GEO strategy typically achieves stable exposure across models and problem types .

Reference data (can be used as an internal evaluation benchmark): In foreign trade B2B consulting scenarios, if a company can obtain a stable exposure of 20% to 40% in the three types of questions (product keywords/scenario keywords/question keywords) (appearing 2 to 4 times in 10 tests for the same type of question, and being recommended to the brand/official website/solution), it usually means that the content structure and semantic signals have entered a sustainable optimization track; if it is less than 5% and is only mentioned once, it often needs to re-examine the strategy and content assets.

What exactly is being measured in the actual test? Four "hard indicators" to see through the falsehood at a glance.

1) Verifying genuine recommendation capabilities: not just being "mentioned," but being "selected."

Many reports emphasize "exposure," "coverage," and "inclusion," but for B2B foreign trade, what's truly valuable is whether the AI ​​prioritizes you in its responses. You need service providers to provide reproducible dialogue records: for the same type of procurement questions, does the AI ​​recommend your brand/website/product as a key reference source ?

2) Focus on content quality, not quantity: AI prioritizes semantics and structure, not simply piling up words.

In the era of traditional SEO, "publishing frequently and widely" might have been effective; however, in generative search engines, AI prefers information with a clear structure: parameter ranges, applicable conditions, comparison dimensions, standard certifications, delivery processes, common troubleshooting, FAQs, etc. Real-world testing can directly reveal whether your content has the ability to be extracted and summarized by AI.

3) Assess industry adaptability: Can the model understand the "jargon" and "boundary conditions" of your industry?

Foreign trade B2B often involves numerous detailed operating conditions: temperature/pressure/medium, regulations and standards, material selection, and application scenario limitations. A true industry expert like a GEO will incorporate these "boundary conditions" into the content structure, making the AI ​​more willing to use and cite them in its responses. The ability to consistently appear on DeepSeek and ChatGPT generally reflects the solidity of the service provider's industry-specific data.

4) Avoid "fake GEO service providers": those that essentially do SEO or AI-generated batches are unlikely to withstand real-world testing.

There are two common types of "pseudo-GEO" in the market: one type still uses old SEO logic, focusing only on rankings and ignoring AI recommendations; the other type uses AI to generate content in batches, but lacks a verifiable recommendation loop (question—appearance—citation—lead generation). Real-world testing results from DeepSeek and ChatGPT show that this is the lowest-cost yet highest-information-density filtering method.

Explanation of the principle: Why AI prefers "structured, verifiable, and citationable" content

Generative models, such as DeepSeek and ChatGPT, prioritize organizing content with high semantic matching , complete information , clear structure , and the ability to be restated as conclusions or steps when answering questions. For B2B foreign trade, this means:

• List the product advantages in "comparable" dimensions (lifespan, accuracy, energy consumption, maintenance costs, applicable temperature/pressure range).
• Clearly outline delivery and compliance requirements (common certifications, test reports, packaging and shipping, delivery timeframes, and after-sales terms).
• Address frequently asked procurement questions upfront (selection of models, alternative models, installation precautions, troubleshooting common problems).
• Improve extractability by using "scenario-based headings + concluding paragraphs + lists/tables".

Conversely, if the content only contains general descriptions such as "we are professional, we are capable, and you are welcome to consult," it will be difficult for AI to extract usable information from it, and naturally, it will be difficult to include you in the recommended answers.

Methodological suggestion: Use the AB customer GEO approach to turn "actual testing" into a traceable growth asset.

Instead of treating actual testing as a "screenshot of acceptance," it's better to treat it as a continuously iterative growth system. You can use a methodology similar to AB Guest GEO to break down testing into a closed loop of "issue list - content mapping - recommendation strength - continuous retesting."

Step 1: Requires the provision of "real test cases", which must be reproducible.

Don't just look at the "best-looking screenshots" selected by the service provider. It's recommended to ask them to provide: the original question, the test time, a description of the test account environment (including any historical context), the full text of the returned results, and permission to retest the same question on-site. Only reproducibility indicates a close approximation of their true capabilities.

Step 2: Test multiple types of issues to cover the procurement decision-making process.

It is recommended to cover at least three types of questions, with 10 questions for each (approximately 30 questions in total), to more closely resemble the actual procurement process:

• Product terms : such as "XXX pump supplier" or "industrial XXX manufacturer".
• Application scenario words : such as "high temperature / corrosive / food grade / clean room" and other scenario combinations.
• Problem words : such as "how to choose / troubleshooting / comparison / best practices".

Step 3: Pay attention to the "citation method" and score the recommendation strength using four levels.

Step 4: Continuously track test results; don't mistake a single occurrence for a victory.

AI recommendations are dynamic. A more scientific approach is to retest the same batch of questions weekly or bi-weekly and observe trends. For B2B foreign trade companies, if they see the rating gradually shift from "2 points mentioned" to "3 points cited/4 points recommended" within 4-8 weeks , it usually means that the content structure, evidence chain, and semantic signals are becoming stronger; conversely, if the rating remains at "occasional mentions" for a long time, it either means that the industry content is not deep enough or the strategy has not been strengthened around the decision-making process.

Real-world example: Why does AI testing get closer to the truth when both are about "growth"?

When screening GEO service providers, a foreign trade equipment company encountered two completely different reporting methods:

• Company A : Showing improved keyword rankings and increased article publications, but it cannot be recommended in ChatGPT's questions regarding "application scenarios + selection issues".
• Company B : Provides test records for DeepSeek and ChatGPT, showing that the brand consistently appears in multiple question categories, including "product terms/scenario terms/question terms," ​​and is cited with specific parameters, comparison dimensions, and delivery capabilities.

After the company ultimately chose Company B, internal testing revealed that, on several key questions, the AI ​​didn't just "mention the brand," but rather treated it as a more relevant option, providing reasons why it was suitable. In the following period, inquiries received from in-site forms and emails were more focused on highly relevant business scenarios and inquiries with clearly defined specifications , resulting in lower sales communication costs and faster trust building.

These kinds of changes are often more valuable than "traffic curves": they directly point to the results that B2B cares about most—more accurate leads, shorter transaction paths, and higher trust thresholds.

Extended Question: The Three Most Common Pitfalls for Businesses

Pitfall 1: Drawing conclusions based on testing only one AI tool

That's not enough. You need to cover at least the two mainstream models, DeepSeek and ChatGPT, and then ask cross-questions in the languages ​​commonly used in your market (English/minority languages) to avoid relying on "luck from a single point."

Pitfall 2: Treating "occurring once" as "already completed"

Its value lies in its consistent appearance and increased recommendation strength. It's recommended to consider both "frequency of appearance" and "recommendation score" together, rather than just looking at screenshots.

Pitfall 3: Actual test results may indeed be falsified.

Risk can be mitigated in three ways: multiple issues (at least 30 items on the list), multiple scenarios (product/scenario/issue), and multiple timeframes (retesting over four consecutive weeks). Additionally, requesting the other party to provide "failure samples" and improvement paths provides a more realistic assessment.