How to analyze support tickets and find repeat customer problems

The decisions that matter

• Identify tickets, messages, status events, customers, products, and linked incidents.
• Create a reviewed taxonomy that can coexist with operational routing tags.
• State business hours, waiting states, first response, resolution, and reopen treatment.
• Use volume, customer count, recurrence, severity, effort, and product evidence.

Choose the approach that matches the evidence and repetition

Where the difficulty begins

One issue may create several tickets and many messages, while merged or reopened tickets alter duration. Raw tags drift between agents and often describe routing rather than the customer's underlying problem.

Volume and customer impact are not the same

A small number of highly persistent issues can generate many contacts, while one incident can generate many customers. Viewing ticket, customer, and incident grains together helps support and product teams choose the right owner and action.

Where this usually goes wrong

Ticket text can contain credentials, health, payment, or personal information. Minimize fields, restrict raw text, and remove secrets before assisted analysis or sharing.

From the field · Support operations lead

Find which support problems deserve a product fix

Customers contact chat and email about login, billing, export, and setup. A platform outage creates hundreds of linked tickets, and some users open both chat and email for one issue.

Columns used in the example

ticket_id · customer_id · channel · created_at · status_event · linked_incident · agent_tag · message_text · resolved_at · reopened_flag

Follow it with your own file

1. Thread merged and cross-channel contacts where governed links support it.
2. Separate the outage incident from recurring individual product issues.
3. Code issue families and calculate eligible response, resolution, and reopen measures.
4. Rank problems by affected customers, recurrence, severity, and support effort.

Check: Ticket and customer counts must reconcile to accepted grains, incident-linked volume must remain visible, and time calculations must reproduce from status events and waiting-state rules.

Build the result step by step

1. Separate grains: Identify tickets, messages, status events, customers, products, and linked incidents.
2. Build issue families: Create a reviewed taxonomy that can coexist with operational routing tags.
3. Define time measures: State business hours, waiting states, first response, resolution, and reopen treatment.
4. Prioritize impact: Use volume, customer count, recurrence, severity, effort, and product evidence.

What may be distorting the answer

• Message rows are counted as ticket volume.
• Merged tickets appear as separate customer problems.
• Resolution time includes hours when the team was waiting for the customer without disclosure.

Check the source before you publish

• Ticket/message grains separated
• Merged contacts handled
• Issue taxonomy reviewed
• Time-state rules defined
• Sensitive text protected

Doing these steps in Prepyr

Prepyr accepts CSV files only. Export the worksheet or table you need as a UTF-8 CSV first; workbook formulas, formatting, macros, charts, and other sheets will not be included. Describe the result you need: Analyze customer support ticket data and recurring issue themes. Check the columns selected, preview any proposed changes, and compare the output with the source before applying or exporting it.

Prepyr steps used here: Text analysis · Charts · Reports · Sensitive Column protection

Questions readers usually ask next

Should reopened tickets have a new resolution time?

Define whether to measure initial resolution, final resolution, and reopen duration separately. One blended duration can hide repeat failure.

Can support ticket text be sent to an AI tool?

Only under approved privacy and security controls after minimizing or protecting sensitive content and following organizational policy.