Ticket Analyses

Service Hub Tips, Tricks & Best Practices
1

Hello,

We have been using Hubspot to manage our customer service tickets for year. We have Services Pro but I didn’t really configure it so it’s a bit messy.

I need to analysis our ticket data to answer questions like:

1. What types of tickets are being raised?

2. What were the solutions to those tickets?

This is so we can make improvements to our customer service proposition going forward, and also so that we can resource plan the types of people to hire. I also need to know if we should create any particular types of knowledge pages (using AI) and stuff like that.

My instinctive approach is to export it all to Excel and analyse it myself or with an AI agent helping me. However I cannot figure out how to export the email exchanges. I can only export 1 record per ticket which does contains limited useful information.

I do not know if Hubspots embedded AI can do this for me. If yes, that would be even better.

Does anyone have any advice on how to proceed, please?

2

Hi @nigeld27

I would recommend leveraging Breeze Assistant, which can review ticket details—including associated email activities—and generate a summarized overview of the scenario and resolution. You can learn more about Breeze here: https://www.hubspot.com/products/artificial-intelligence

Hope this helps!


1214×1266 276 KB

Tzuriel Lopez

Solutions EngineerPre-Sales | HubSpot
tmontano@hubspot.com
LinkedIn

If this helped, mark it as a solution for others in the community.
3

Hi Tzuriel. I will look into it although it seems a bit outrageous that I need to pay for Breeze in order to explore my own data. If there is no other native Hubspot functionality, does that mean I need to export my data and if so, can someone tell me how, please?

4

Hey @nigeld27

The Breeze Assistant do not uses HubSpot Credits, which means that you can use it without paying extra, this is included in all plans.

Currently, exporting contact engagements (notes, tasks, emails, meetings, calls) isn’t supported.
There are a few potential workarounds. Some users have leveraged third-party tools such as Ultimate Data Export for data migration.

Alternatively, a custom solution could be built using the HubSpot Engagements API.

There’s also an existing thread in the HubSpot Ideas section addressing this limitation — I’d recommend upvoting it and sharing your specific use case to help drive visibility.

Hope this helps!


1214×1266 276 KB

Tzuriel Lopez

Solutions EngineerPre-Sales | HubSpot

tmontano@hubspot.com

LinkedIn

If this helped, mark it as a solution for others in the community.
5

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:

  1. Pull your ticket records along with key properties like ticket type, pipeline stage, owner, timestamps, etc.

  2. Pull the associated email engagements tied to those tickets.

  3. Combine those with contact data if needed (customer type, company, etc.).

  4. 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.