Breadth of Use¶

Definition¶

Breadth of Use measures the number of distinct features, modules, or product areas used by a single customer or account. It helps assess product adoption depth and customer stickiness.

Description¶

Breadth of Use measures how many distinct product features or modules a customer actively uses, offering a clear signal of depth, stickiness, and cross-functional value within an account.

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

In modular SaaS, breadth means adoption across tools like reporting, integrations, dashboards
In platform businesses, it shows multi-team or cross-department usage
In enterprise CS, it supports expansion forecasting and engagement strategy

Greater breadth usually correlates with higher retention, upsell potential, and lower churn risk. Narrow use may indicate low feature discoverability, onboarding gaps, or siloed adoption. Segment by account type, usage role, or industry to identify where to prioritize enablement, feature nudges, or success outreach.

Breadth of Use informs:

Strategic decisions, like roadmap planning or packaging design
Tactical actions, such as targeting accounts for feature adoption campaigns
Operational improvements, including custom onboarding based on usage depth
Cross-functional alignment, by connecting product, CS, and marketing teams around account health and expansion opportunity

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 Exposure to Multiple Use Cases: If users are only exposed to one feature at a time, they may never discover the rest. Breadth increases with structured onboarding flows.
In-Product Surfacing of Complementary Features: Features that are contextually introduced (not buried) drive wider usage. Relevance drives exploration.
Team-Based Collaboration: When multiple roles use the product, breadth tends to expand — each persona taps into different value areas.

Improvement Tactics & Quick Wins¶

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

If users are stuck in one feature, create post-onboarding journeys that introduce “power features” in the second or third week.
Add smart prompts recommending related features, based on current usage (“Try automating this workflow with [X]”).
Run a test adding interactive guides or success checklists tied to multi-feature engagement milestones.
Refine in-app nav and menus to highlight underused but high-value features, especially for new users.
Partner with CS to include feature exploration in onboarding calls or QBRs, tailored to the user’s role.

Required Datapoints to calculate the metric
- Feature Set List: Modules or key feature groups.
- Usage Logs: Events by user or account.
- Account or User-Level Activity Data: Who used what, and how often.
- Measurement Period: Monthly or quarterly typical.
Example to show how the metric is derived

For a customer:
- Total Key Features: 8
- Features Used Regularly: 6
- Formula: 6 ÷ 8 = 75%

Formula¶

Formula

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

Data Model Definition¶

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

FeatureUsageAccounts

cube('FeatureUsage', {
  sql: `SELECT * FROM feature_usage`,

  joins: {
    Accounts: {
      relationship: 'belongsTo',
      sql: `${CUBE}.account_id = ${Accounts}.id`
    }
  },

  measures: {
    distinctFeaturesUsed: {
      sql: 'feature_id',
      type: 'countDistinct',
      title: 'Distinct Features Used',
      description: 'Number of distinct features used by an account within the measurement period.'
    }
  },

  dimensions: {
    id: {
      sql: 'id',
      type: 'string',
      primaryKey: true
    },

    accountId: {
      sql: 'account_id',
      type: 'string',
      title: 'Account ID',
      description: 'Unique identifier for the account.'
    },

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

    usageDate: {
      sql: 'usage_date',
      type: 'time',
      title: 'Usage Date',
      description: 'Date when the feature was used.'
    }
  }
})

cube('Accounts', {
  sql: `SELECT * FROM accounts`,

  measures: {
    count: {
      sql: 'id',
      type: 'count',
      title: 'Accounts Count',
      description: 'Total number of accounts.'
    }
  },

  dimensions: {
    id: {
      sql: 'id',
      type: 'string',
      primaryKey: true
    },

    name: {
      sql: 'name',
      type: 'string',
      title: 'Account Name',
      description: 'Name of the account.'
    },

    createdAt: {
      sql: 'created_at',
      type: 'time',
      title: 'Account Created At',
      description: 'Date when the account 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.
- Complexity of User Interface: A complex or unintuitive user interface can discourage users from exploring additional features, thereby reducing Breadth of Use.
- Lack of Feature Awareness: If users are unaware of available features, they are less likely to use them, which negatively impacts Breadth of Use.
- Limited User Training: Insufficient training can lead to users not fully understanding how to utilize all available features, thus decreasing Breadth of Use.
- Siloed Product Experience: If the product experience is siloed or disjointed, users may not naturally discover or use additional features, reducing Breadth of Use.
- Inadequate Customer Feedback Mechanisms: Without effective feedback mechanisms, product improvements may not align with user needs, leading to underutilization of features and a decrease in Breadth of Use.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Onboarding Exposure to Multiple Use Cases: Structured onboarding that exposes users to multiple features increases the Breadth of Use by encouraging exploration and discovery of additional product areas.
- In-Product Surfacing of Complementary Features: Contextually introducing complementary features within the product encourages users to explore and utilize a wider range of functionalities, thereby increasing Breadth of Use.
- Team-Based Collaboration: When a product is used by multiple roles within a team, each role may utilize different features, leading to an increase in Breadth of Use as the product's value is realized across various functions.
- User Training and Support: Providing comprehensive training and support can empower users to explore and adopt more features, thus expanding the Breadth of Use.
- Feature Updates and Announcements: Regular updates and announcements about new or improved features can stimulate interest and encourage users to try out more features, enhancing Breadth of Use.

Involved Roles & Activities¶

Involved Roles

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

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

Common initiatives or actions associated with this KPI:

Onboarding
Feature Adoption
Expansion Plays
Usage Heatmaps

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Activation
Retention
Revenue
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 Accounts: Product Qualified Accounts (PQA) measures account-level engagement and readiness for conversion. High PQA counts indicate early and broad adoption across an account, often preceding a growth in Breadth of Use as more users and teams begin leveraging multiple features.
- Activation Rate: Activation Rate measures the percentage of users reaching the first value milestone. A high Activation Rate is a precursor to increased Breadth of Use, as activated users are more likely to explore and adopt additional features over time.
- Monthly Active Users: Monthly Active Users (MAU) tracks unique user engagement. Growth in MAUs typically leads to higher Breadth of Use, as a larger and more active user base increases the chances of wider feature adoption within accounts.
- Stickiness Ratio: The Stickiness Ratio (DAU/MAU) reflects habitual usage. Higher stickiness suggests users are returning frequently, which often correlates with deeper and broader product exploration—driving increases in Breadth of Use.
- Feature Adoption / Usage: Feature Adoption measures the proportion of users engaging with specific features. Early high adoption rates for key features usually signal upcoming expansion in Breadth of Use as users branch out to additional modules.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Customer Engagement Score: Customer Engagement Score quantifies overall account interaction depth and frequency. An increase in Breadth of Use often results in a higher Engagement Score, confirming the impact of broader feature adoption on sustained engagement.
- Percent of Accounts with Multi-Role Engagement: This metric tracks accounts where multiple user roles are active. Growth in Breadth of Use frequently coincides with more roles engaging, highlighting organizational adoption and explaining broader business value.
- Expansion Revenue Growth Rate: Expansion Revenue Growth Rate reflects increased monetization from existing customers. Broader feature adoption (higher Breadth of Use) often precedes or accompanies revenue expansion, quantifying its financial impact.
- Net Revenue Retention: Net Revenue Retention (NRR) tracks retained and expanded revenue. Higher Breadth of Use helps reduce churn and increase upsell/cross-sell, which is confirmed and amplified in NRR trends.
- Percent of Retained Feature Users: This metric indicates how many users continuously use specific features. Sustained Breadth of Use is validated by high retention of feature users, confirming that broad adoption leads to long-term, multi-feature engagement.