Usage Depth¶

Definition¶

Usage Depth measures the extent to which users engage with the features, functionalities, or content of your product. It reflects how comprehensively users utilize available features, providing insight into their engagement and the product’s perceived value.

Description¶

Usage Depth is a key indicator of product adoption, user satisfaction, and engagement breadth, reflecting how thoroughly users explore and utilize the available features in your product or platform.

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

In SaaS, it highlights how deeply users interact with key workflows and advanced features
In Mobile apps, it reflects multifeature engagement across core modules
In B2B tools, it surfaces account maturity and upsell opportunities

A high usage depth suggests strong value recognition and user commitment, while low depth may reveal unmet needs, onboarding gaps, or under-discovered features. By segmenting by industry, account size, or use case, you unlock insights to tailor onboarding, expand usage, and uncover upgrade potential.

Usage Depth informs:

Strategic decisions, like feature investment, roadmap prioritization, and tier packaging
Tactical actions, such as in-product education or engagement campaigns
Operational improvements, including tooltips, user guides, and adoption nudges
Cross-functional alignment, empowering product, CS, and lifecycle teams to drive deeper, stickier product engagement

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 and Education: Feature discovery leads to deeper usage.
Role-Specific Relevance: Different personas need different features — align flows accordingly.
Feature Visibility and Cross-Prompts: Hidden features = unused features.

Improvement Tactics & Quick Wins¶

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

If depth is low, use feature heatmaps to surface most/least used areas by segment.
Add cross-feature nudges (“Try this next”) after a feature is used successfully.
Run onboarding tracks that introduce one key feature at a time — no overwhelm.
Refine “power user” content and checklists to showcase advanced use cases.
Partner with product to track usage depth vs. retention correlation — then double down.

Required Datapoints to calculate the metric
- Number of Features Used: The number of features or functionalities used by a customer.
- Total Available Features: The total number of features available within the product.
- User Segments: Data categorized by user type (e.g., free vs. paid users, new vs. returning users).
Example to show how the metric is derived

A project management tool tracks usage depth for its paid users:
- Total Features Available: 20
- Average Features Used Per User: 10
- Usage Depth = (10 / 20) × 100 = 50%

Formula¶

Formula

\[ \mathrm{Usage\ Depth} = \left( \frac{\mathrm{Number\ of\ Features\ Used}}{\mathrm{Total\ Available\ Features}} \right) \times 100 \]

Data Model Definition¶

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

UserEngagement

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

  measures: {
    numberOfFeaturesUsed: {
      sql: `number_of_features_used`,
      type: 'sum',
      title: 'Number of Features Used',
      description: 'Total number of features used by a customer.'
    },
    totalAvailableFeatures: {
      sql: `total_available_features`,
      type: 'sum',
      title: 'Total Available Features',
      description: 'Total number of features available within the product.'
    },
    usageDepth: {
      sql: `number_of_features_used / total_available_features`,
      type: 'number',
      title: 'Usage Depth',
      description: 'Ratio of features used to total available features, indicating engagement depth.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each user engagement record.'
    },
    userSegment: {
      sql: `user_segment`,
      type: 'string',
      title: 'User Segment',
      description: 'Category of user type, such as free vs. paid users.'
    },
    usageDate: {
      sql: `usage_date`,
      type: 'time',
      title: 'Usage Date',
      description: 'Date when the features were used.'
    }
  }
});

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.
- Complexity of Features: Overly complex features can overwhelm users, leading to reduced engagement and lower usage depth.
- Lack of Personalization: A lack of personalized experiences can make the product feel less relevant to users, decreasing their engagement and usage depth.
- Poor User Interface Design: A confusing or unintuitive user interface can hinder users from fully exploring the product, reducing usage depth.
- Inadequate Support Resources: Insufficient support resources can leave users unable to fully utilize features, negatively impacting usage depth.
- Feature Overload: Too many features can overwhelm users, leading to disengagement and reduced usage depth.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Onboarding and Education: Effective onboarding and education increase feature discovery, leading to deeper usage as users become more aware of and comfortable with the product's functionalities.
- Role-Specific Relevance: Aligning product features with the specific needs of different user personas ensures that users find the product more relevant, increasing their engagement and usage depth.
- Feature Visibility and Cross-Prompts: Making features more visible and using cross-prompts encourages users to explore and utilize more functionalities, enhancing usage depth.
- User Feedback and Iteration: Incorporating user feedback into product development helps refine features to better meet user needs, increasing engagement and usage depth.
- Product Updates and Enhancements: Regular updates and enhancements keep the product fresh and relevant, encouraging users to explore new features and deepen their usage.

Involved Roles & Activities¶

Involved Roles

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

Data & Analytics
Customer Lifecycle Management
Product Management (PM)
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Product Adoption and Use
Feature Engagement Tracking
Usage Heatmap Analysis

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.
- Monthly Active Users: High MAU signals a large, engaged user base likely to explore product features, thus driving deeper usage patterns and increasing Usage Depth.
- Feature Adoption / Usage: As more users adopt and consistently use specific features, it indicates broader and deeper engagement with the product, directly raising Usage Depth.
- Stickiness Ratio: A higher DAU/MAU ratio suggests users engage frequently and habitually, providing early indication of increased depth in feature exploration and usage.
- Product Qualified Leads: PQLs represent users who have engaged meaningfully with the product; their behaviors often precede and forecast higher Usage Depth across the user base.
- Engagement Rate: Higher engagement rates reflect that users interact more frequently and meaningfully with the product, setting the stage for deeper feature utilization and Usage Depth.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Breadth of Use: Breadth of Use captures the number of distinct features utilized, which can contextualize and recalibrate what constitutes Usage Depth, helping refine segmentation and product strategy.
- Activation Cohort Retention Rate (Day 7/30): Retention among activated users after 7 or 30 days demonstrates whether early deep usage (Usage Depth) translates into sustained engagement, informing how Usage Depth relates to actual long-term value.
- Percent of Accounts Completing Key Activation Milestones: Completion of key activation milestones post-engagement quantifies if initial deep usage (Usage Depth) leads to successful onboarding and long-term engagement, enabling refinement of leading metrics.
- Active Feature Usage Rate: This metric confirms how many active users are engaging with specific features over time, validating whether Usage Depth as a leading indicator translates to consistent, valuable product interaction.
- Customer Engagement Score: Aggregated engagement scores post-interaction help determine if increased Usage Depth correlates with overall customer engagement and satisfaction, guiding adjustments to leading strategies.