Task Success Rate¶

Definition¶

Task Success Rate measures the percentage of users who successfully complete a specific task or goal on a website, app, or product interface. It indicates how effectively the design and functionality support user needs.

Description¶

Task Success Rate is a key indicator of product usability and goal completion, reflecting how often users successfully complete key actions — like submitting a form, booking a meeting, or finishing onboarding.

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

In SaaS, it shows completion of flows like adding teammates, building reports, or exporting data
In eComm, it captures checkout, cart actions, or filter usage
In mobile apps, it includes profile setup, content uploads, or feature activations

A high success rate means your experience is intuitive and goal-oriented. A low rate suggests UX gaps, unclear CTAs, or broken flows. By segmenting by task type, device, or audience, you gain clarity on where friction lives and how to fix it.

Task Success Rate informs:

Strategic decisions, like redesigning critical workflows or simplifying onboarding
Tactical actions, such as tooltip improvements, form logic edits, or flow sequencing
Operational improvements, including UX testing priorities and analytics tagging
Cross-functional alignment, connecting UX, product, and CS on making actions more successful

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

UI Design and Microcopy: If users don’t understand what to do, they’ll abandon mid-task.
Guidance and Support Access: Tooltips, walkthroughs, or help docs reduce friction and confusion.
Task Complexity and Workflow Fit: Some tasks may not make sense to all personas or at early stages.

Improvement Tactics & Quick Wins¶

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

If task success is low, review session replays to find drop-off moments and pain points.
Add “Did this work?” micro-feedback next to key actions.
Run guided walkthroughs with progress checkmarks for core tasks.
Refine task UI — break up multi-step tasks into simpler actions.
Partner with product design to A/B test microcopy and CTA placement.

Required Datapoints to calculate the metric
- Number of Users Attempting the Task: Total users who begin the task.
- Number of Users Completing the Task: Total users who successfully achieve the task goal.
- Task Definition: Clear criteria for what constitutes task success or failure.
Example to show how the metric is derived

An e-commerce platform evaluates the Task Success Rate for checkout:
- Number of Attempts: 1,000
- Successful Completions: 900
- Task Success Rate = (900 / 1,000) × 100 = 90%

Formula¶

Formula

\[ \mathrm{Task\ Success\ Rate} = \left( \frac{\mathrm{Number\ of\ Successful\ Completions}}{\mathrm{Number\ of\ Task\ Attempts}} \right) \times 100 \]

Data Model Definition¶

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

TaskMetrics

cube('TaskMetrics', {
  sql: `SELECT * FROM task_metrics`,

  measures: {
    numberOfUsersAttemptingTask: {
      sql: `number_of_users_attempting_task`,
      type: 'sum',
      title: 'Number of Users Attempting the Task',
      description: 'Total users who begin the task.'
    },

    numberOfUsersCompletingTask: {
      sql: `number_of_users_completing_task`,
      type: 'sum',
      title: 'Number of Users Completing the Task',
      description: 'Total users who successfully achieve the task goal.'
    },

    taskSuccessRate: {
      sql: `100.0 * ${numberOfUsersCompletingTask} / NULLIF(${numberOfUsersAttemptingTask}, 0)`,
      type: 'number',
      title: 'Task Success Rate',
      description: 'Percentage of users who successfully complete a specific task or goal.'
    }
  },

  dimensions: {
    taskId: {
      sql: `task_id`,
      type: 'string',
      primaryKey: true,
      title: 'Task ID',
      description: 'Unique identifier for each task.'
    },

    taskDefinition: {
      sql: `task_definition`,
      type: 'string',
      title: 'Task Definition',
      description: 'Criteria for what constitutes task success or failure.'
    },

    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the task was created.'
    }
  }
});

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.
- UI Design and Microcopy: Poorly designed interfaces and unclear microcopy can lead to user confusion, resulting in task abandonment and a lower Task Success Rate.
- Task Complexity and Workflow Fit: Tasks that are overly complex or do not align with user workflows can discourage users, reducing the Task Success Rate.
- Loading Times: Slow loading times can frustrate users, causing them to abandon tasks before completion, negatively impacting the Task Success Rate.
- Error Rates: High error rates in task execution can lead to user frustration and task failure, decreasing the Task Success Rate.
- Navigation Issues: Difficult or unintuitive navigation can prevent users from completing tasks, lowering the Task Success Rate.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Guidance and Support Access: Effective tooltips, walkthroughs, and help documentation can assist users in completing tasks, increasing the Task Success Rate.
- User Feedback Mechanisms: Incorporating user feedback can lead to improvements in task design, enhancing the Task Success Rate.
- Personalization: Tailoring tasks to user preferences and behaviors can make tasks easier to complete, boosting the Task Success Rate.
- Responsive Design: A responsive design that adapts to different devices can facilitate task completion, improving the Task Success Rate.
- Clear Call-to-Action: Well-defined calls-to-action can guide users effectively through tasks, increasing the Task Success Rate.

Involved Roles & Activities¶

Involved Roles

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

Product Management (PM)
Insights Manager
UX Designer / Researcher
Activities

Common initiatives or actions associated with this KPI:

Product Adoption and Use
UX Testing
Workflow Simplification

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Activation
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.
- Activation Rate: A higher Activation Rate suggests users are reaching key milestones early on, which directly correlates with their ability to successfully complete core tasks. Monitoring Activation Rate alongside Task Success Rate provides a multi-signal early warning of UX or onboarding issues.
- Drop-Off Rate: Drop-Off Rate identifies where users abandon processes before task completion, often highlighting friction in the user journey. It acts as a contextual signal for diagnosing dips in Task Success Rate and can help proactively address bottlenecks.
- Onboarding Completion Rate: This metric reflects how effectively users are prepared to engage with the product. High Onboarding Completion Rates typically lead to higher Task Success Rates, making this a key input for early detection of user experience barriers.
- Error Rate: A rising Error Rate during key user flows often foreshadows drops in Task Success Rate. Tracking Error Rate enables rapid root cause analysis and correction before Task Success Rate is significantly impacted.
- Customer Effort Score: Customer Effort Score measures perceived ease of task completion. Low effort scores are highly predictive of high Task Success Rate, and declines here can serve as an early indicator of potential issues affecting task success.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Conversion Rate: Conversion Rate validates whether improvements in Task Success Rate are translating into higher goal completions (e.g., purchases, sign-ups). It allows recalibration of Task Success Rate as a leading indicator for business outcomes.
- Churn Risk Score: Elevated Churn Risk Scores among users with low Task Success Rates can inform refinement of leading indicators and highlight where improving task success could boost retention.
- Customer Feedback Retention Score: Retention among users providing feedback post-task completion can inform whether Task Success Rate improvements are yielding long-term loyalty, allowing recalibration of early warning signals.
- Activation Cohort Retention Rate (Day 7/30): This metric shows if users who complete tasks successfully during onboarding continue engaging over time. It provides a feedback loop for optimizing Task Success Rate as a predictor of longer-term engagement.
- Net Promoter Score: NPS captures customers' overall satisfaction and likelihood to recommend, often post-task completion. Correlating NPS with Task Success Rate helps refine the latter as a leading indicator for customer advocacy.