Quick Answer (TL;DR)
Error Rate measures percentage of requests that result in errors. The formula is Error responses / Total requests x 100. Industry benchmarks: <0.1%. Track this metric when monitoring product reliability.
What Is Error Rate?
Percentage of requests that result in errors. This is one of the core metrics in the operational metrics category and is essential for any product team serious about data-driven decision making.
Error Rate measures the health and efficiency of your product infrastructure and team operations. While not a customer-facing metric, it directly impacts user experience and your team's ability to ship improvements.
Understanding error rate in context --- alongside related metrics --- gives you a more complete picture than tracking it in isolation. Use it as part of a balanced metrics dashboard.
The Formula
Error responses / Total requests x 100
How to Calculate It
Suppose you measure error responses at 500 and total requests at 2,000 in a given period:
Error Rate = 500 / 2,000 x 100 = 25%
This tells you that one quarter of the base is converting or meeting the criteria.
Benchmarks
<0.1%
Benchmarks vary significantly by industry, company stage, business model, and customer segment. Use these ranges as starting points and calibrate to your own historical data over 2-3 quarters. Your trend matters more than any absolute number --- consistent improvement is the goal.
When to Track Error Rate
When monitoring product reliability. Specifically, prioritize this metric when:
You are building or reviewing your metrics dashboard and need operational indicators
Leadership or investors ask about operational performance
You suspect a change in product, pricing, or go-to-market strategy has affected this area
You are running experiments that could impact error rate
You need a quantitative baseline before making a strategic decision
How to Improve
Optimize the numerator. Increase the number of users or events in error responses through better UX, clearer CTAs, and reduced friction in the conversion path.
Qualify the denominator. Ensure total requests represents the right audience. Better targeting means a higher conversion rate.
Automate monitoring and alerting. Do not rely on manual checks. Set up automated alerts that trigger when this metric crosses a threshold so your team can respond immediately.
Invest in infrastructure and tooling. Operational metrics improve when you invest in better CI/CD pipelines, monitoring tools, and incident response processes.
Set clear SLAs and track compliance. Define service-level agreements for this metric and hold teams accountable. What gets measured and targeted gets improved.
Common Pitfalls
Ignoring sample size. Small sample sizes produce volatile rates that do not reflect true performance. Ensure you have statistically significant data before drawing conclusions or making changes.
Setting thresholds too tightly or loosely. Overly sensitive alerts cause alarm fatigue while loose thresholds miss real issues. Calibrate against historical baselines and adjust as the system matures.
Measuring without acting. Tracking this metric is only valuable if you have a process for reviewing it regularly and a playbook for responding when it moves outside acceptable ranges.
Related Metrics
Page Load Time --- time to fully render a page
Support Ticket Volume --- number of support tickets per period
System Uptime --- percentage of time the product is available
First Response Time --- time to first support response
Product Metrics Cheat Sheet --- complete reference of 100+ metrics