Use case: support tickets
Support Ticket Analysis: Find What Drives Your Ticket Volume
Categorize support tickets automatically with AI. Group ticket subjects and descriptions into labeled themes, find top contact drivers and cut repeat tickets.
By Apercu team · Last updated
Support ticket analysis groups your tickets into recurring contact drivers, so you can see which problems create the most work and fix them at the source. Apercu reads ticket subjects or descriptions, groups similar tickets into AI-labeled themes and shows how many tickets each one accounts for.
Why ticket tags are not enough
Most helpdesks have tags or categories, but they rarely give a reliable picture:
- Agents tag under time pressure. The same issue ends up under three tags, or under "Other".
- Tags only cover known problems. A new bug after a release has no tag yet, so it stays invisible until it is large.
- Taxonomies drift. Tags added over years overlap and stop meaning the same thing to different teams.
Analyzing the ticket text directly gives you a consistent view, including problems nobody has tagged yet.
How to analyze support tickets with Apercu
- Export your tickets. Pick a period, such as the last 90 days, and export to CSV with the ticket ID, date, subject and first customer message.
- Clean the text. Remove email signatures, auto-replies and internal notes if they are included. Mask personal data you do not need.
- Choose the text column. Subjects give a fast overview. First messages give more detail when subjects are vague.
- Upload and describe the data. For example: "Customer support tickets for a B2B SaaS product, subject lines, last 90 days."
- Review the contact drivers. Each theme shows its ticket count, its share of volume and example tickets. Rename or merge themes and move misgrouped tickets.
- Act and re-run. Fix the top drivers, then analyze the next period to see whether volume moved.
Example: top contact drivers
An illustrative example for 1,800 tickets from a SaaS product:
| Contact driver | Tickets | Share | Typical fix |
|---|---|---|---|
| Password reset emails not arriving | 342 | 19% | Email deliverability and a self-serve reset fallback |
| SSO and OAuth callback errors | 270 | 15% | Clearer setup docs and error messages |
| Invoice and billing mismatches | 216 | 12% | Billing page that explains proration |
| API authentication and rate limits | 180 | 10% | Rate limit headers and docs |
| Data export functionality issues | 144 | 8% | Bug fix and status page note |
| Mobile app crashes and bugs | 126 | 7% | Prioritized crash fixes |
| Other | 522 | 29% | Review and re-run with more themes |
In this example, the top two drivers are a third of all tickets and both have product fixes. That is a stronger case for engineering time than "support is busy".
What to do with the results
- Product: attach the example tickets to bug reports and roadmap items for the top drivers
- Support: write or update help articles for the top how-to themes, and add macros for the rest
- Leadership: report contact drivers as a share of volume each month, so fixes can be measured
- Onboarding: turn setup-related themes into onboarding steps
Related pages
Frequently asked questions
How do I categorize support tickets automatically?
Export your tickets with the subject or description in one column, upload the file to an AI thematic analysis tool such as Apercu, and it groups similar tickets into named categories with counts and example tickets. You can rename categories and move tickets that landed in the wrong group.
Should I analyze ticket subjects or descriptions?
Start with subjects for a fast overview, since they are short and focused. Use descriptions, or the customer's first message, when subjects are vague, for example when many tickets are titled 'Help' or 'Question'.
Which helpdesks can I use with Apercu?
Any helpdesk that exports tickets to CSV or Excel, such as Zendesk, Freshdesk, Intercom, Help Scout or Jira Service Management. Apercu works on file uploads rather than direct integrations.
How is this different from my helpdesk's ticket tags?
Agent tags depend on each agent choosing correctly under time pressure, and they only cover categories someone thought of in advance. Thematic analysis reads the text itself, applies the same method to every ticket and can surface new problems that no tag exists for yet.
How many tickets can I analyze at once?
Up to 2,500 per analysis on the free plan, 50,000 on the Analyst plan and 100,000 on the Pro plan.
Should I remove personal data before uploading tickets?
It is good practice. Remove or mask names, emails, phone numbers and account IDs you do not need for the analysis. Apercu does not use your content to train AI models.