Input Metrics
Metrics that measure the activities and actions teams directly control, such as features shipped, experiments run, or campaigns launched, representing the effort and execution that should drive output metric improvements.
Input metrics track the controllable actions and activities that teams execute. They measure effort and execution rather than outcomes: number of experiments launched per week, features shipped per quarter, outreach emails sent per day, or content pieces published per month. They are the levers teams pull to influence results.
For growth teams, input metrics provide accountability for execution and help diagnose why output metrics are or are not changing. If output metrics are flat despite strong input metric performance, the strategy may be wrong. If input metrics are below targets, execution is the problem. AI can help identify which input metrics most strongly predict output metric improvements, enabling teams to focus their execution on the highest-leverage activities. Growth engineers should track input metrics alongside output metrics to create a complete picture of team performance. Key principles include ensuring input metrics are genuinely controllable by the team, setting targets that are ambitious but achievable, and regularly analyzing the relationship between input and output metrics to validate that the right activities are being prioritized. Teams should use input metrics for weekly operational management while using output metrics for monthly and quarterly strategic evaluation.
Related Terms
Event Tracking
The practice of recording specific user interactions within a digital product, such as clicks, form submissions, page views, and feature usage, as structured data events that can be analyzed to understand user behavior.
Event Taxonomy
A structured naming convention and classification system for analytics events that ensures consistency, discoverability, and usability of tracking data across teams, platforms, and analysis tools.
Funnel Analysis
The process of tracking and measuring user progression through a defined sequence of steps toward a conversion goal, identifying where users drop off and quantifying the conversion rate between each stage.
Conversion Rate Analytics
The systematic measurement and analysis of the percentage of users who complete a desired action out of the total who had the opportunity, applied across multiple conversion points throughout the user journey.
Drop-Off Rate
The percentage of users who leave a process or sequence at a specific step without completing the next step, the inverse of step-level conversion rate, used to identify friction points in user flows.
Cohort Analysis
A technique that groups users by a shared characteristic or experience within a defined time period and tracks their behavior over subsequent periods, revealing how user behavior evolves and differs across groups.