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Time to First Key Action

Definition

Time to First Key Action measures the average time it takes for a new user to complete a product’s primary activation event — often referred to as the “aha moment.” It helps track how quickly users begin experiencing real value.

Description

Time to First Key Action is a key indicator of activation quality, UX clarity, and time-to-value, reflecting how quickly users take the first meaningful step in your product — such as uploading a file, sending a message, or completing setup.

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

  • In PLG SaaS, it highlights the “aha!” moment
  • In Sales-assisted models, it reflects guided success via onboarding or CSM
  • In B2C, it surfaces initial engagement and fit

A shorter time shows clear onboarding and intuitive design, while delays point to misalignment, confusion, or UX breakdowns. By segmenting by acquisition source, journey stage, or persona, you uncover ways to accelerate the path to value.

Time to First Key Action informs:

  • Strategic decisions, like activation goal-setting or onboarding redesign
  • Tactical actions, such as CTA testing, UI simplification, or tooltips
  • Operational improvements, including in-app guidance and success content
  • Cross-functional alignment, by linking product, growth, and CS around a shared definition of user success

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

  • Onboarding Design: Fast access to high-value actions improves first-use speed.
  • Product Intuitiveness: If users struggle to find or complete the key action, value is delayed.
  • Motivating Context: Users need to know why the action matters.

Improvement Tactics & Quick Wins

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

  • If this takes too long, re-sequence onboarding to drive toward the key action earlier.
  • Add visual cues, tooltips, or modals directing users to the action.
  • Run time-based emails highlighting the benefits of that first key behavior.
  • Refine default settings or data to remove setup friction.
  • Partner with PM to test variations in activation flow entry points.

  • Required Datapoints to calculate the metric

    • User signup timestamp
    • Timestamp of first key action (defined per product)
    • Only include new users in the analysis timeframe

  • Example to show how the metric is derived

    2,000 users signed up Average time to upload first file (key action): 3.2 hours Time to First Key Action = 3.2 hours

Formula

Formula

\[ \mathrm{Time\ to\ First\ Key\ Action} = \mathrm{Avg}\left(\mathrm{Time\ from\ Signup\ to\ Key\ Action\ Completed}\right) \]

Data Model Definition

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

cube('UserActions', {
  sql: `SELECT * FROM user_actions`,

  joins: {
    Users: {
      relationship: 'belongsTo',
      sql: `${CUBE}.user_id = ${Users}.id`
    }
  },

  measures: {
    averageTimeToFirstKeyAction: {
      sql: `TIMESTAMPDIFF(SECOND, ${Users}.signup_timestamp, ${CUBE}.first_key_action_timestamp)`,
      type: 'avg',
      title: 'Average Time to First Key Action',
      description: 'Measures the average time in seconds for a new user to complete the first key action after signing up.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },

    userId: {
      sql: `user_id`,
      type: 'number'
    },

    firstKeyActionTimestamp: {
      sql: `first_key_action_timestamp`,
      type: 'time',
      title: 'First Key Action Timestamp',
      description: 'The timestamp when the user completed their first key action.'
    }
  }
});

cube('Users', {
  sql: `SELECT * FROM users`,

  measures: {
    count: {
      sql: `id`,
      type: 'count',
      title: 'User Count',
      description: 'Counts the number of users.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },

    signupTimestamp: {
      sql: `signup_timestamp`,
      type: 'time',
      title: 'Signup Timestamp',
      description: 'The timestamp when the user signed up.'
    }
  }
});

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.

    • Complex Onboarding Design: A complicated onboarding process can increase the time it takes for users to reach the first key action, as users may become confused or frustrated, delaying their path to value.
    • Low Product Intuitiveness: If the product is not intuitive, users may struggle to navigate to the key action, increasing the time to first key action as they require more time to understand how to use the product.
    • Lack of Motivating Context: Without a clear understanding of why the key action is important, users may not prioritize completing it, leading to delays in reaching the first key action.
    • High Cognitive Load: If users are overwhelmed with too much information or too many options, it can slow down their ability to focus on and complete the key action.
    • Technical Barriers: Technical issues such as slow loading times or bugs can hinder users' ability to quickly reach the first key action, increasing the time required.

  • Positive influences

    Factors that push the metric in a favorable direction, supporting growth or improvement.

    • Streamlined Onboarding Design: A well-designed onboarding process that guides users directly to the key action can significantly reduce the time to first key action by providing clear and easy-to-follow steps.
    • High Product Intuitiveness: An intuitive product design allows users to naturally find and complete the key action quickly, reducing the time to first key action.
    • Clear Motivating Context: Providing users with a clear understanding of the benefits of the key action can motivate them to complete it sooner, decreasing the time to first key action.
    • Effective User Guidance: Offering helpful tips or tutorials that guide users to the key action can accelerate their journey to experiencing value, reducing the time to first key action.
    • Responsive Technical Performance: Ensuring fast loading times and a bug-free experience can facilitate quicker access to the key action, decreasing the time to first key action.

Involved Roles & Activities

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: Activation Rate measures the percentage of users reaching a defined initial milestone of meaningful engagement. A higher Activation Rate indicates that users are more effectively progressing through onboarding and reaching key value moments quickly, which directly reduces the Time to First Key Action. Monitoring Activation Rate alongside Time to First Key Action provides a robust early signal system for onboarding performance and product adoption health.
    • Onboarding Completion Rate: Onboarding Completion Rate tracks the proportion of users finishing the onboarding sequence. High completion rates tend to shorten the Time to First Key Action by removing friction and ensuring users are equipped to take core actions sooner. It contextualizes Time to First Key Action by highlighting where drop-offs or friction in onboarding may delay core engagement.
    • Short Time to Value: Short Time to Value (STTV) highlights how quickly users experience their first significant benefit. A reduction in STTV usually precedes or coincides with improvements in Time to First Key Action, as both reflect the speed at which users realize value. STTV provides an additional early warning signal for friction or inefficiencies in the user journey.
    • Time to First Value: Time to First Value measures the speed at which users perceive initial value from the product. Faster Time to First Value typically correlates with a reduced Time to First Key Action, as both are milestones in the early user journey. Tracking both helps identify granular delays and optimize early-stage experiences.
    • Product Qualified Leads: Product Qualified Leads (PQLs) are users who reach high-intent engagement thresholds. A shorter Time to First Key Action often accelerates progression to PQL status, making PQL volume and velocity a strong contextual indicator for the effectiveness of the activation experience.

  • Lagging

    These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.

    • Activation Cohort Retention Rate (Day 7/30): This metric measures what percentage of users who reached activation (and thus completed their first key action) are retained after 7 or 30 days. Analyzing retention by cohort helps recalibrate and validate whether a shorter Time to First Key Action truly drives longer-term engagement, informing improvements to the onboarding and activation process.
    • Percent of Accounts Completing Key Activation Milestones: This metric quantifies the share of accounts reaching additional activation checkpoints beyond the first key action. Reviewing this alongside Time to First Key Action helps identify whether rapid initial actions translate into sustained progression, or if users stall after initial engagement. Insights can be used to refine what is defined as the 'key action' and adjust onboarding strategies.
    • Action-to-Activation Time Lag: This measures the time between a user’s first meaningful interaction and full activation. If the Time to First Key Action is reduced but the Action-to-Activation Time Lag remains high, it may indicate that the defined 'key action' is not sufficiently predictive of true activation. This feedback can be used to recalibrate leading indicators and optimize activation pathways.
    • First Feature Usage Rate: This metric tracks how many new users engage with a core feature during early sessions. High or improving rates can confirm that reductions in Time to First Key Action are meaningful and not just procedural. If First Feature Usage Rate stalls, it may prompt a reevaluation of what constitutes a 'key action' in onboarding.
    • Multi-Session Activation Completion Rate: This measures the percentage of users who complete activation across more than one session. A long Time to First Key Action may be caused by multi-session journeys, so analyzing this lagging KPI helps refine expectations for activation velocity and informs adjustments to leading metrics and onboarding design.