Complete formula reference for all support analytics metrics, including statistical considerations for reliable measurement. Use weighted CSAT when comparing ac
Metric Formulas provides a comprehensive reference for calculating key support analytics metrics with precision and statistical rigor. It covers formulas for customer satisfaction (CSAT), Net Promoter Score (NPS), resolution times, deflection rates, and trend analysis, alongside guidance on sample sizes and bias correction. This resource helps marketers and analysts interpret support data reliably by applying proper statistical methods and avoiding common pitfalls such as skewed averages or biased response sets.
The skill also addresses business hours adjustments for resolution times and outlines how to measure SLA compliance by priority tiers. By translating raw data into actionable metrics with clear error margins and significance testing, it supports informed decision-making and performance optimization in customer support and service experience measurement.
This skill is designed for performance marketers and growth leads who need to track and improve customer support outcomes as part of broader retention or loyalty strategies. SEO and PPC operators can use it to align customer experience metrics with campaign performance and reduce churn risk. Agency strategists advising clients on customer satisfaction measurement and support optimization will find it essential for validating data quality and interpreting results with statistical confidence.
It suits those managing support teams or analyzing ticket data who require reliable benchmarks and want to avoid misleading conclusions from small sample sizes or response biases.
Practitioners start by calculating baseline metrics such as CSAT and NPS using the provided formulas, ensuring sample sizes meet minimum thresholds for reliability. They then evaluate response rates to check for selection bias, targeting a 15-25% rate and applying stratified sampling if needed. Next, resolution times are analyzed using percentile reporting instead of averages, with adjustments for business hours to reflect realistic customer experience.
Teams track SLA compliance by priority tiers and measure deflection rates by comparing self-service interactions with ticket volumes to assess the effectiveness of help content. Finally, trend analysis workflows use smoothing techniques and seasonality decomposition to detect significant changes and avoid false alarms, enabling data-driven prioritization of support improvements.
What sample size is needed for reliable NPS? Approximately 400 responses are required for a ±5 point margin of error at 95% confidence.
How should resolution times be reported? Use percentile values (p50, p75, p90, p95) rather than averages to avoid distortion by outliers.
How do I correct for response bias in CSAT surveys? Aim for a response rate between 15-25%, compare respondent demographics to the full ticket population, and apply stratified sampling for underrepresented segments.
Attach the Metric Formulas skill to any Metaflow agent task analyzing support data to access standardized formulas and sampling guidelines directly within your workflow. Expect detailed metric calculations, confidence intervals, and bias correction suggestions to be integrated into your reports. This enables you to validate data quality and measure customer support performance accurately at scale. For more information on applying these formulas across different support channels and timeframes, refer to...
For broader context, see our roundup of claude skills for marketing, and read Claude Code workflows for marketing agencies for related setup guidance.