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Support Analytics

Support analytics turns raw ticket data into operational intelligence. The goal is not to generate reports - it is to change behavior. Whether measuring how satisfied customers are after an interaction, how quickly issues are resolved, or how often customers find answers without contacting support, every metric should connect to a decision. This skill covers the full analytics lifecycle: what to measure, how to measure it, and how to act on what you find.

When to use this skill

Trigger this skill when the user:

• Wants to set up or improve a CSAT or NPS measurement program
• Needs to track, report on, or reduce resolution time or first-contact resolution
• Asks about deflection rate or self-service effectiveness
• Wants to analyze support ticket trends, topic clusters, or volume forecasting
• Needs to build a support dashboard for an executive, team lead, or agent
• Is creating a support metrics framework or KPI hierarchy
• Asks about survey design, response rate improvement, or score interpretation
• Needs to segment support data by channel, tier, topic, or agent

Do NOT trigger this skill for:

• Product analytics or funnel metrics (use analytics-engineering instead)
• Infrastructure monitoring, SLOs, or error rate tracking (use backend-engineering instead)

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.

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

Measure and improve CSAT - survey design and analysis

Survey design checklist:

• Send within 1 hour of ticket close - response rate drops sharply after 24 hours
• Keep to 1-2 questions: the rating plus one optional free-text follow-up
• Use a consistent scale - do not mix 5-star with thumbs up/down across touchpoints
• Personalize the subject line with the agent's name and ticket topic

Calculation:

CSAT = (4-star + 5-star responses) / total responses * 100

Analysis steps:

1. Segment by channel, agent, ticket category, and customer tier
2. Tag all 1-2 star responses within 24 hours - look for patterns in verbatim feedback
3. Build a weekly trend chart with 4-week moving average to smooth noise
4. Create a detractor recovery workflow: manager outreach within 24 hours for any 1-star

Improving response rate:

• Subje