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Key Feature Exploration Rate

Definition

Key Feature Exploration Rate measures the percentage of users who engage with a high-value feature for the first time—regardless of whether they complete or repeat use. It helps evaluate feature discoverability and user curiosity.

Description

Key Feature Exploration Rate is a key indicator of product curiosity and discoverability, reflecting how many users engage with high-value or strategic features—regardless of depth or frequency.

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

  • In SaaS, it may include clicks into advanced dashboards, integrations, or automation tools
  • In mobile apps, it could reflect taps on filters, previewing premium content, or opening new modules
  • In self-serve PLG, it reveals early indicators of upsell or expansion readiness

A rising exploration rate typically signals better feature visibility and intuitive UI/UX, while a drop may indicate feature clutter, poor in-app messaging, or onboarding gaps. By segmenting by user persona, lifecycle stage, or acquisition channel, you unlock insights to optimize product education, update UI placement, and prioritize what to spotlight during onboarding.

Key Feature Exploration Rate informs:

  • Strategic decisions, like feature prioritization, roadmap visibility, or adoption campaigns
  • Tactical actions, such as triggering nudges or guided tours for unexplored features
  • Operational improvements, including UX testing and onboarding refinements
  • Cross-functional alignment, by helping product, marketing, lifecycle, and design teams align on which features users notice first, even before they use them deeply

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

  • In-App Discoverability and Prompts: If the feature isn’t visible or lacks context, users won’t explore it.
  • Onboarding Journey Design: Early journeys that guide users toward high-impact features increase exploration.
  • Role-Based Relevance: If the feature doesn’t feel directly useful to the user’s job-to-be-done, they’ll skip it.

Improvement Tactics & Quick Wins

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

  • If exploration is low, surface the feature in context — e.g., when users hit a related task or workflow.
  • Add a modal or spotlight tip during onboarding that previews the feature’s benefits.
  • Run a test using checklists or progress bars that reward exploring “power” features.
  • Refine feature names and microcopy to make functionality and value obvious.
  • Partner with product marketing to embed feature walkthroughs in post-signup nurture flows.

  • Required Datapoints to calculate the metric

    • Target Feature(s)
    • Eligible Active Users
    • First-Time Feature Engagement Logs

  • Example to show how the metric is derived

    • 4,000 eligible users
    • 1,120 explored advanced filters
    • Formula: 1,120 ÷ 4,000 = 28%

Formula

Formula

\[ \mathrm{Key\ Feature\ Exploration\ Rate} = \left( \frac{\mathrm{Users\ Who\ Interacted\ with\ Feature}}{\mathrm{Total\ Eligible\ Users}} \right) \times 100 \]

Data Model Definition

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

cube('UserEngagement', {
  sql: `SELECT * FROM user_engagements`,

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

  measures: {
    eligibleActiveUsers: {
      sql: `user_id`,
      type: 'countDistinct',
      title: 'Eligible Active Users',
      description: 'Count of unique active users eligible for feature engagement.'
    },

    firstTimeFeatureEngagements: {
      sql: `user_id`,
      type: 'countDistinct',
      title: 'First-Time Feature Engagements',
      description: 'Count of unique users engaging with a feature for the first time.'
    },

    keyFeatureExplorationRate: {
      sql: `100.0 * ${firstTimeFeatureEngagements} / NULLIF(${eligibleActiveUsers}, 0)` ,
      type: 'number',
      title: 'Key Feature Exploration Rate',
      description: 'Percentage of users engaging with a high-value feature for the first time.'
    }
  },

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

    userId: {
      sql: `user_id`,
      type: 'number',
      title: 'User ID',
      description: 'Unique identifier for the user.'
    },

    featureId: {
      sql: `feature_id`,
      type: 'number',
      title: 'Feature ID',
      description: 'Unique identifier for the feature.'
    },

    engagementTime: {
      sql: `engagement_time`,
      type: 'time',
      title: 'Engagement Time',
      description: 'Timestamp of the feature engagement.'
    }
  }
});

cube('FeatureLogs', {
  sql: `SELECT * FROM feature_logs`,

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

    featureName: {
      sql: `feature_name`,
      type: 'string',
      title: 'Feature Name',
      description: 'Name of the feature.'
    }
  }
});

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

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

    userName: {
      sql: `user_name`,
      type: 'string',
      title: 'User Name',
      description: 'Name of the user.'
    }
  }
});

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.

    • Lack of In-App Discoverability: If features are not easily visible or lack context, users are less likely to explore them, reducing the Key Feature Exploration Rate.
    • Poor Onboarding Journey Design: Ineffective onboarding that fails to guide users to high-impact features results in lower exploration rates.
    • Role-Based Irrelevance: Features that do not align with the user's specific needs or job-to-be-done are often ignored, decreasing exploration rates.
    • Complexity of Feature: Features perceived as too complex or difficult to use deter users from exploring them.
    • Lack of User Motivation: If users do not see immediate value or benefit from exploring a feature, they are less likely to engage with it.

  • Positive influences

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

    • In-App Discoverability and Prompts: Enhanced visibility and contextual prompts increase user engagement with high-value features, leading to a higher Key Feature Exploration Rate.
    • Onboarding Journey Design: Well-designed onboarding processes that highlight high-impact features encourage users to explore them, boosting the Key Feature Exploration Rate.
    • User Curiosity: Features that pique user curiosity tend to have higher exploration rates as users are more inclined to engage with them.
    • Feature Accessibility: Easier access to features, such as through intuitive navigation, increases the likelihood of users exploring them.
    • User Education and Support: Providing educational resources and support helps users understand the value of features, encouraging exploration.

Involved Roles & Activities

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.

    • Product Qualified Leads: A surge in Product Qualified Leads (PQLs)—users demonstrating high-value engagement—typically forecasts an increase in Key Feature Exploration Rate, as these users are more likely to explore high-value features as part of their qualification journey.
    • Monthly Active Users: Growth in Monthly Active Users (MAU) signals a larger active user base, increasing the pool of users who can discover and engage with key features for the first time, thereby driving up the Key Feature Exploration Rate.
    • Activation Rate: A higher Activation Rate means more users reach meaningful engagement milestones early, making them more likely to explore additional key features, which in turn elevates the Key Feature Exploration Rate.
    • Feature Adoption / Usage: Increases in general feature adoption/usage indicate higher overall product engagement, which serves as a leading signal for first-time exploration of high-value features.
    • Trial-to-Paid Conversion Rate: When Trial-to-Paid Conversion Rate rises, it usually reflects that users are discovering value in the product (often by engaging with key features), thus acting as a precursor to spikes in Key Feature Exploration Rate.

  • Lagging

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

    • Percent of Accounts Completing Key Activation Milestones: This quantifies how many accounts fully progress through activation, confirming whether initial explorations (as measured by Key Feature Exploration Rate) translate into broader adoption and deeper engagement.
    • Activation Cohort Retention Rate (Day 7/30): High retention rates in activation cohorts (users who continue engaging days after activation) help validate that initial feature exploration leads to sustained product usage.
    • Percent of Retained Feature Users: This measures the portion of users who continue using a feature after first exploration, amplifying the business impact of Key Feature Exploration Rate by showing conversion from curiosity to habit.
    • First Critical Feature Reuse Rate: Tracks the percentage of users who not only explore a key feature but return to use it again, confirming that high exploration rates are translating into deeper, repeat engagement.
    • Expansion Feature Usage Frequency: If users frequently use expansion or upsell-eligible features after initial exploration, it demonstrates how Key Feature Exploration Rate influences downstream revenue opportunities and overall product stickiness.