support-analytics

Files (6)

• README.md
• SKILL.md
• evals.json
• benchmarks.md · references/
• metric-formulas.md · references/
• metrics-benchmarks.md · references/

SKILL.md (first lines)

## Key principles

1. **Measure what matters, not what's easy** - Ticket volume is easy to count but
   rarely actionable on its own. Focus on metrics that reveal customer experience
   and operational efficiency: CSAT, resolution time, and deflection rate expose
   the health of your support operation far more than raw volume does.

2. **Benchmarks are starting points, not goals** - Industry benchmarks give you
   a calibration point, not a finish line. A CSAT of 85% may be excellent for a
   complex enterprise product and unacceptable for a consumer app. Compare to
   your own historical trend first; compare to benchmarks second.

3. **Trends matter more than snapshots** - A single week's CSAT score means
   almost nothing. A 12-week trend that is declining 1 point per week means
   something is systematically wrong. Always show time-series data alongside
   point-in-time figures. Week-over-week and month-over-month comparisons prevent
   overreaction to normal variance.

4. **Segment by channel, tier, and topic** - Aggregate scores hide the story.
   A CSAT of 82% overall might mask a chat score of 91% and an email score of
   68%. Segmenting by channel, customer tier, product area, and ticket topic
   reveals where to invest and what is working.

5. **Close the loop - insights to action** - An analytics program that produces
   dashboards no one acts on is a cost center. Every metric should own a DRI
   (directly responsible individual), a target, and a process for escalating when
   the target is missed. The cadence is: measure, review, decide, act, re-measure.

---

## Core concepts

### Satisfaction metrics

**CSAT (Customer Satisfaction Score)** - A post-interaction rating, typically
1-5 stars or a thumbs up/down, sent immediately after a ticket closes. Measures
satisfaction with a specific support interaction, not the product overall. The
score is the percentage of positive responses out of total responses received.

**NPS (Net Promoter Score)** - A relationship-level survey asking "How likely
are you to recommend us to a colleague?" on a 0-10 scale. Promoters (9-10) minus
Detractors (0-6) equals the NPS. Transactional NPS (tNPS) is sent after support
interactions to capture loyalty impact from a specific resolution.

**CES (Customer Effort Score)** - Measures how easy it was to get help: "How
much effort did you personally have to put forth to handle your request?" Low
effort correlates with reduced churn more reliably than high satisfaction does.

### Operational metrics

**First Contact Resolution (FCR)** - The percentage of tickets resolved on the
first reply without the customer needing to follow up. High FCR is the single
strongest predictor of high CSAT. Improving FCR reduces cost and improves
satisfaction simultaneously.

**Resolution Time** - The elapsed time from ticket creation to resolution. Report
as median (p50) and p90 to capture both typical experience and worst-case outliers.
Segment by ticket priority, channel, and topic - a blanket average hides whether
P1 bugs are being prioritized over billing questions.

**Handle Time** - Agent-active time spent on a ticket (not elapsed clock time).
Useful for capacity planning and identifying where agents need tooling or training
improvements.

**Reopen Rate** - Percentage of resolved tickets reopened by the customer. A high
reopen rate indicates resolutions are incomplete or unclear, or that the underlying
issue is recurring.

### Self-service metrics

**Deflection Rate** - The percentage of potential support contacts handled by
self-service (docs, chatbot, FAQ) without reaching a human. Calculated as
`deflections / (deflections + human contacts)`. Hard to measure precisely -
proxy methods include doc views before ticket submission and chatbot resolution
rates.

**Article Effectiveness** - For knowledge bases: the percentage of doc views
that end without a support ticket being submitted. Track alongside
search-with-no-results counts to identify content gaps.

**Containment Rate** - For chatbots and IVR: the percentage of sessions that
reach a resolution without escalating to a human. A session can be contained
but still leave the customer unsatisfied - always pair with a satisfaction signal.

### Quality metrics

**QA Score** - Internal quality assurance review of ticket handling: tone,
accuracy, policy adherence, completeness. Typically sampled (5-10% of tickets)
and scored on a rubric. Correlates with CSAT but catches issues that surveys miss
such as correct but cold responses.

**Agent CSAT** - CSAT segmented by individual agent. Useful for coaching, not
for ranking. Agents on complex ticket queues will have lower scores than agents
on simple billing questions - normalize by ticket type before comparing agents.

---

## Common tasks

### Set up a metrics framework - KPI hierarchy

Build a three-tier hierarchy: strategic, operational, and diagnostic.

| Tier | Audience | Cadence | Examples |
|---|---|---|---|
| Strategic | Leadership | Monthly / Quarterly | NPS, CSAT trend, cost-per-ticket, deflection rate |
| Operational | Support managers | Weekly | FCR, median resolution time, reopen rate, volume by channel |
| Diagnostic | Team leads, agents | Daily | Queue depth, SLA breach rate, handle time, QA score |

Start by identifying who reads each metric and what decision it drives. If no
one owns the decision triggered by a metric, do not track it yet.

Steps:
1. List current pain points from support team retrospectives
2. Map each pain point to a metric category (satisfaction, operational, quality)
3. Define the measurement method and data source for each metric
4. Assign a DRI and a target for each metric
5. Build the minimal dashboard needed to surface all three tiers

From mkurman/zorai, MIT.

More from mkurman/zorai