You’re running into a common limitation in HubSpot exports. The standard ticket export usually only gives you the ticket-level properties, not the full thread of associated email conversations, which is why the dataset feels incomplete for the type of analysis you’re trying to do.
Your instinct to analyze it in Excel/Sheets with AI is actually a good one, but the trick is getting all the related data (tickets + emails + contacts) together first.
A practical way to approach it:
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Pull your ticket records along with key properties like ticket type, pipeline stage, owner, timestamps, etc.
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Pull the associated email engagements tied to those tickets.
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Combine those with contact data if needed (customer type, company, etc.).
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Once everything is together, you can start analyzing things like:
- Common ticket themes
- Frequent customer questions
- Resolution patterns
- Knowledge base gaps
- Volume by category for hiring/resource planning
HubSpot’s built-in AI is helpful for individual records, but it’s not really designed to synthesize insights across thousands of tickets and email threads.
One option that works well is using Coefficient from HubSpot’s marketplace (I work there). It can pull your tickets, associated emails, and contact data into Google Sheets, keep it refreshed automatically, and then you can use Coefficient’s AI Sheets Assistant to analyze things like common themes, categorize tickets, summarize resolutions, and even generate reports.
That way you can quickly answer questions like:
- “What types of issues come up most often?”
- “What solutions are agents using?”
- “What knowledge base articles should we create?”
- “What skills do we need to hire for?”
Once the data is live in a spreadsheet, it becomes much easier to run AI analysis or build dashboards around it.