# Analytics: where users actually get stuck

> Real intent in users' own words and which route they stalled on. ~38% of support volume is navigation-shaped.

Canonical: https://peeve.ai/product/analytics/

Peeve measures real intent in your users' own words and which route they stalled on: the product-journey corpus a chat log can't give you. Across live workspaces, ~38% of support volume is navigation-shaped. Your own number appears in the dashboard within weeks.


## More questions

### How do I measure whether AI customer support is actually working?

Measure resolution and repeat-contact, not deflection alone. A deflected ticket that bounces back is not a win. Peeve reports outcomes per route in your users' own words, shows your real cost per resolved conversation each month, and surfaces the routes people stall on, so you can tell solving apart from closing.

### What is the difference between deflection rate and resolution rate?

Deflection counts tickets avoided; resolution counts problems actually solved. Deflection can look great while customers leave unhappy. Peeve is built around resolution: it shows and does the task, measures completion across live workspaces, and reports the routes where users get stuck instead of just how many tickets were avoided.

### How do I know if the AI is solving problems or just closing tickets?

The truth signal is whether the same customer comes back. Peeve reports outcomes and stuck-points per route rather than raw closed-ticket counts, and because it resolves by taking the action, the root cause is handled, which is what keeps repeat contacts down.

### Can I audit and QA what the AI told each customer?

Yes, you should be able to review any conversation. Peeve records the route and confidence it used on every session, attaches sources to answers, and writes a signed audit entry for every backend action, so you can QA exactly what was said and done.