Decoding Your AEO Grader Results: Understanding the Data Behind the Insights

HubSpot's AEO (Answer Engine Optimization) Grader tool can be a powerful resource for understanding your online presence and how well you're answering customer questions across various platforms. But like any analytical tool, understanding the 'how' and 'when' behind the data is crucial for accurate interpretation. Recently, a user in the HubSpot Community raised some excellent questions about the AEO Grader, prompting a helpful discussion that sheds light on the tool's inner workings.

Understanding the AEO Grader's Data Collection

The initial question focused on the timeframe for data collection. Is it the past 30 days? Six months? A year? The answer, as pointed out by a community member, is that the timeframe is actually LLM (Large Language Model) specific. The AEO Grader pulls data from different LLMs, each with its own knowledge cutoff date.

• OpenAI's GPT-4o: Has a knowledge cutoff date of September 30, 2023 (subject to time zone variations).
• Perplexity: Uniquely, Perplexity doesn't have a knowledge cutoff date because it uses internet search to gather real-time information.
• Gemini 2.0 Flash Thinking: Knowledge cutoff date of August 2024.

This means the data you see reflects information available up to those respective cutoff dates. A crucial point to remember when analyzing trends! The original poster followed up, seeking clarification on when the data was pulled, specifically if the tool tracked queries about their company on ChatGPT, and over what period those queries occurred. Unfortunately, that level of detail isn't currently available. The community member confirmed that the tool doesn't specify recency beyond the LLM cutoff dates; those 600 queries, for example, could have happened anytime between January 1, 2023, and September 30, 2023.

Location and Language Considerations

Another interesting question was whether altering the location during the grading process would change the results. While not explicitly stated, it's highly likely that location does influence the outcome. The community member suggested that the prompt sent to the LLM likely includes location data, which would impact the response. This makes sense; search results and AI responses are often localized. This is especially relevant if you're targeting customers in a specific geographic area.

The original poster also noted that some results appeared in Spanish, even though the rest were in English. While the exact reason wasn't definitively determined, one suggestion was that people might be searching for information about the company in Spanish. This highlights the importance of considering multilingual SEO and content strategies if you have a global audience.

LLM Popularity and Target Audiences

Finally, the original poster inquired about data on the most popular LLMs for their target audiences. While the AEO Grader doesn't explicitly provide this information, you can infer some insights from the overall results. Pay attention to which LLMs are returning the most information about your company and industry. This can give you a sense of where your target audience is likely seeking information.

ESHOPMAN Team Comment

The HubSpot AEO Grader is a great starting point for understanding your brand's presence across AI platforms. However, this community discussion highlights the need for careful interpretation of the data. For e-commerce businesses, understanding how your products and brand are discussed on these platforms can inform your content strategy and help optimize your product descriptions and online store inventory management. Don't forget to consider location and language to cater to your specific customer base.