Error Rate¶

Definition¶

Error Rate measures the percentage of errors or failures occurring during a specific process, interaction, or system operation. It reflects the quality and reliability of a product, service, or workflow.

Description¶

Error Rate is a key indicator of system reliability, user experience quality, and operational accuracy, reflecting how frequently failures occur in digital or service interactions — from bugs to broken workflows.

The relevance and interpretation of this metric shift depending on the model or product:

In SaaS, it highlights system crashes, API failures, or UX bugs
In customer support, it reflects incorrect resolutions or ticket mishandling
In manufacturing or logistics, it flags defects, delays, or process gaps

A rising trend signals friction, frustration, or risk, while a declining trend indicates improved reliability and trust. By segmenting by platform, device, or flow type, you unlock insights for targeted fixes, QA prioritization, and experience stability.

Error Rate informs:

Strategic decisions, like infrastructure investments or support automation
Tactical actions, such as hotfix deployment or UI/UX redesigns
Operational improvements, including QA workflows, dev cycles, or CS training
Cross-functional alignment, by connecting signals across product, engineering, QA, and CS, ensuring a consistent and reliable experience

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Product Stability and QA Coverage: Gaps in testing lead to bugs in production. More test coverage = fewer surprises.
Release Velocity and Rollback Process: Frequent releases without rollback guardrails increase risk. One broken update can tank user trust.
Monitoring and Alerting Systems: Without real-time error tracking, small issues can compound before being noticed.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If error rate is rising, audit recent releases and roll back any regressions tied to spike points.
Add real-time monitoring tools (e.g., Sentry, Datadog) to catch user-facing errors fast.
Run error cohort analysis: which flows or user types hit issues most often?
Refine QA process with regression tests for high-impact features pre-release.
Partner with product and eng to prioritize bug fixes for flows tied to churn or activation.

Required Datapoints to calculate the metric
- Total Instances: The total number of occurrences during the measurement period (e.g., transactions, interactions, or operations).
- Error Instances: The number of instances where errors or failures occurred during the same period.
Example to show how the metric is derived

An e-commerce platform tracks shipping errors over a month:
- Total Orders: 10,000
- Shipping Errors: 200
- Error Rate = (200 / 10,000) × 100 = 2%

Formula¶

Formula

\[ \mathrm{Error\ Rate} = \left( \frac{\mathrm{Error\ Instances}}{\mathrm{Total\ Instances}} \right) \times 100 \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

ErrorMetrics

cube('ErrorMetrics', {
  sql: `SELECT * FROM error_metrics`,

  measures: {
    totalInstances: {
      sql: `total_instances`,
      type: 'sum',
      title: 'Total Instances',
      description: 'The total number of occurrences during the measurement period.'
    },
    errorInstances: {
      sql: `error_instances`,
      type: 'sum',
      title: 'Error Instances',
      description: 'The number of instances where errors or failures occurred during the measurement period.'
    },
    errorRate: {
      sql: `100.0 * error_instances / NULLIF(total_instances, 0)`,
      type: 'number',
      title: 'Error Rate',
      description: 'The percentage of errors or failures occurring during a specific process, interaction, or system operation.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each record.'
    },
    processName: {
      sql: `process_name`,
      type: 'string',
      title: 'Process Name',
      description: 'The name of the process or interaction being measured.'
    },
    eventTime: {
      sql: `event_time`,
      type: 'time',
      title: 'Event Time',
      description: 'The time when the event occurred.'
    }
  }
});

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Product Stability and QA Coverage: Insufficient testing and QA coverage can lead to undetected bugs making it to production, increasing the Error Rate.
- Release Velocity and Rollback Process: High release velocity without proper rollback mechanisms can introduce errors that are difficult to correct quickly, raising the Error Rate.
- Monitoring and Alerting Systems: Lack of effective monitoring and alerting can result in delayed detection of errors, allowing them to escalate and increase the Error Rate.
- Code Complexity: Increased code complexity can lead to more errors during development and maintenance, thus increasing the Error Rate.
- Team Experience and Skill Level: A less experienced team may introduce more errors during development, leading to a higher Error Rate.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Product Stability and QA Coverage: Comprehensive testing and QA processes can catch potential errors before they reach production, reducing the Error Rate.
- Release Velocity and Rollback Process: Controlled release processes with effective rollback options can quickly mitigate errors, lowering the Error Rate.
- Monitoring and Alerting Systems: Robust monitoring and alerting systems enable quick detection and resolution of errors, reducing the Error Rate.
- Automated Testing: Implementing automated testing can efficiently identify errors early in the development cycle, decreasing the Error Rate.
- Continuous Integration/Continuous Deployment (CI/CD): A well-implemented CI/CD pipeline can ensure consistent quality checks, reducing the likelihood of errors and thus the Error Rate.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Software Development
Product Management (PM)
QA Manager
Activities

Common initiatives or actions associated with this KPI:

Product Adoption and Use
Customer Support
QA Processes
Monitoring Tools

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Activation
Retention
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Drop-Off Rate: Drop-Off Rate identifies friction points and user disengagement in flows, which are often direct precursors to increased Error Rate. By monitoring Drop-Off Rate, teams can proactively address usability or technical barriers before errors spike.
- Ticket Volume: Ticket Volume reflects the number of issues or problems reported by users. A rising Ticket Volume often signals underlying system or process issues, which can lead to or indicate an increasing Error Rate.
- Activation Rate: Activation Rate measures successful onboarding and early product adoption. A low Activation Rate may signal confusion or obstacles that, if unresolved, can manifest as increased errors during user interactions.
- First Contact Resolution: First Contact Resolution tracks the percentage of issues resolved on the first attempt. Low rates may mean lingering or repeated problems, often correlating with recurring errors and a higher Error Rate.
- Onboarding Drop-off Rate: Onboarding Drop-off Rate highlights where users abandon onboarding due to confusion, friction, or bugs. High rates in this metric can foreshadow errors, as users who struggle early are more prone to experience failures later.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Cost of Poor Quality: Cost of Poor Quality quantifies the financial impact of errors and defects on the business. Tracking this lagging KPI provides feedback on how Error Rate is affecting overall operational costs and informs which types of errors are most costly.
- Complaints Received: Complaints Received capture customer-reported issues, often stemming directly from a high Error Rate. Analyzing complaint trends can help recalibrate Error Rate thresholds and prioritize error reduction efforts.
- Customer Churn Rate: Customer Churn Rate rises as a downstream effect of recurring or unresolved errors. Reviewing churn in context with Error Rate helps refine leading indicators and validates the long-term impact of error-related experiences.
- Cost per Resolution: Cost per Resolution increases when Error Rate is high, as more resources are required to fix issues. Examining this KPI after an Error Rate spike provides insight into the efficiency and downstream cost burden of errors.
- Customer Feedback Retention Score: Customer Feedback Retention Score measures whether customers who provide feedback (often about errors) stay with the product. A low retention score after high Error Rates can inform adjustments to Error Rate monitoring and prevention strategies.