# Find the top ticket drivers and how to reduce them

A contact-driver analysis that shows why customers write in and which tickets to prevent, deflect, or automate.

- Topic: Customer Support
- Framework: RTF
- Level: Standard
- Tags: Customer Support, Analyze, Fill-in Template
- Source: https://www.thinkwithprompt.com/prompts/find-the-top-ticket-drivers-and-how-to-reduce-them

## Prompt

```text
Help me analyze why customers contact support and which contacts we can eliminate.

**Ticket data:** [PASTE an export or summary: category/tag, count, handle time, channel - or a sample of ticket subjects]
**Time period:** [DATES]
**Total volume and team size:** [NUMBERS]
**Self-service we already have:** [help center, chatbot, in-app guides, status page]
**Known tagging problems:** [e.g. "30% tagged 'other'"]

Do this:
1. Group tickets into contact drivers: the underlying reason the customer needed help, not just the tag. Re-categorize "other" or vague tags from subjects if I provided them.
2. For each driver, show volume, share of total, and estimated agent hours (volume x handle time).
3. Classify each driver as:
   - Prevent: a product, billing, or process fix would remove the need to contact us.
   - Deflect: a better help article, in-app message, or proactive notice would answer it.
   - Automate: a bot, form, or self-serve action could handle it end to end.
   - Keep: it genuinely needs a human.
4. Estimate the hours saved for the top 5 opportunities, with assumptions stated.

Return a table (Driver | Volume | % | Agent hours | Action type | Specific fix | Owner team), then the top 3 recommendations with a one-paragraph case for each. Flag where the data is too thin to trust.
```

## What to fill in

- DATES
- NUMBERS
- help center, chatbot, in-app guides, status page
- e.g. "30% tagged 'other'"

Free to copy and use. From ThinkWithPrompt (https://www.thinkwithprompt.com/), a library of 2,663 curated AI prompts.