Support Analytics

---
name: support-analytics
description: Measure support with metrics that reflect customer outcomes rather than agent activity, and use them to reduce ticket volume. Use when support is measured by throughput and quality is drifting.
---

# Support analytics

Support metrics drive behaviour hard, which makes the wrong metric
actively harmful: measuring tickets closed per hour produces fast, bad
answers and repeat contacts. The useful metrics describe whether the
customer's problem went away.

## Method

1. **Measure resolution, not closure.** A ticket closed that generates a
   follow-up two days later was not resolved, and repeat contact rate
   exposes it.
2. **Track time to first meaningful response.** An automated
   acknowledgement is not a response, and measuring it flatters the
   number while the customer waits.
3. **Watch the distribution, not the average.** A good average with a
   long tail means some customers wait days, and they are the ones who
   leave.
4. **Attribute volume to cause.** Which product areas generate tickets
   is the number that reduces future volume, rather than describing
   current load (see customer-feedback-loop).
5. **Measure deflection honestly.** Self-serve success means the
   customer solved it, not that they gave up and did not write in.
6. **Interpret satisfaction scores cautiously.** They measure the
   interaction more than the outcome, and a polite refusal can score
   well while the customer churns.
7. **Review the metrics for perverse incentives quarterly.** Any metric
   agents are judged on will be optimised, including in ways nobody
   intended.

## Boundaries

Metrics describe support; they cannot fix a product that generates the
tickets. Satisfaction surveys have severe response bias. Agent-level
metrics need care, since they affect people's livelihoods and reward
gaming (see agent-people-ops-desk).