Expansion Feature Usage Frequency¶

Definition¶

Expansion Feature Usage Frequency measures how often a specific upsell-eligible feature is used by existing accounts. It helps assess product stickiness, value realization, and readiness for expansion.

Description¶

Expansion Feature Usage Frequency is a key indicator of product stickiness and upsell readiness, reflecting how often users engage with premium features or value-adding modules post-purchase.

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

In SaaS, it highlights tools like automation, analytics, and collaboration add-ons
In eCommerce, it reflects engagement with loyalty tools or exclusive product categories
In multi-product platforms, it shows usage of high-tier features (e.g., AI, billing, advanced workflows)

A rising trend signals habit formation and increased value extraction, while a drop can point to UX complexity or feature misalignment. By segmenting by account type, industry, or lifecycle stage, you unlock insights for expansion forecasting, feature education, and upgrade sequencing.

Expansion Feature Usage Frequency informs:

Strategic decisions, like which features to gate, bundle, or surface in pricing
Tactical actions, such as triggering CS outreach or surfacing in-app upgrade prompts
Operational improvements, including tooltips, walkthroughs, and behavioral nudges
Cross-functional alignment, across product, CS, and RevOps, to support scalable, value-led expansion

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

Feature Usability and Integration in Workflow: If the feature fits seamlessly into daily work, usage climbs.
Initial Exposure and First-Time Success: Poor first impressions tank future usage. Value must land early.
Ongoing Prompts and Nudges: Without reminders or use case inspiration, even good features gather dust.

Improvement Tactics & Quick Wins¶

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

If usage is low, analyze first-session drop-off points and add walkthroughs or quick wins for that feature.
Add in-app suggestions (“Use [X] to save time here”) based on previous behavior.
Run a test with milestone unlocks or usage streak rewards tied to key features.
Refine documentation and video content to showcase advanced workflows and outcomes.
Partner with lifecycle to embed feature nudges in post-activation emails or in-product alerts.

Required Datapoints to calculate the metric
- Target Feature(s) to Track
- Total Number of Accounts or Users
- Usage Frequency Over Time (e.g., daily/weekly/monthly)
Example to show how the metric is derived
- 500 accounts use advanced reporting
- 3,000 total sessions in 30 days
- Formula: 3,000 ÷ 500 = 6 sessions/account/month

Formula¶

Formula

\[ \mathrm{Expansion\ Feature\ Usage\ Frequency} = \frac{\mathrm{Total\ Uses}}{\mathrm{Total\ Accounts}} \]

Data Model Definition¶

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

FeatureUsage

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

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

  measures: {
    usageFrequency: {
      sql: `usage_count`,
      type: 'sum',
      title: 'Usage Frequency',
      description: 'Total usage count of the target feature by accounts.'
    },
    totalAccounts: {
      sql: `account_id`,
      type: 'countDistinct',
      title: 'Total Number of Accounts',
      description: 'Total number of unique accounts using the feature.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    featureName: {
      sql: `feature_name`,
      type: 'string',
      title: 'Feature Name',
      description: 'Name of the target feature being tracked.'
    },
    usageDate: {
      sql: `usage_date`,
      type: 'time',
      title: 'Usage Date',
      description: 'Date when the feature was 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 and Learning Curve: If the feature is perceived as complex or difficult to learn, users may avoid it, reducing usage frequency.
- Lack of Integration with Existing Tools: If the feature does not integrate well with other tools or systems users rely on, it may be used less frequently.
- Poor Initial Exposure and First-Time Experience: A negative first experience can deter users from using the feature again, decreasing its usage frequency.
- Infrequent Updates and Improvements: If the feature is not regularly updated or improved, users may lose interest, leading to decreased usage frequency.
- Lack of Awareness or Visibility: If users are not aware of the feature or its benefits, they are less likely to use it frequently.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Feature Usability and Integration in Workflow: When the expansion feature is seamlessly integrated into the user's daily workflow, it becomes a natural part of their routine, leading to increased usage frequency.
- Initial Exposure and First-Time Success: A positive first experience with the feature encourages users to continue using it, thereby increasing its usage frequency.
- Ongoing Prompts and Nudges: Regular reminders and prompts about the feature keep it top-of-mind for users, encouraging more frequent use.
- User Training and Support: Effective training and support increase user confidence and competence, leading to higher usage frequency of the feature.
- Perceived Value and Benefits: When users clearly understand the benefits and value of the feature, they are more likely to use it frequently.

Involved Roles & Activities¶

Involved Roles

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

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

Common initiatives or actions associated with this KPI:

Expansion Enablement
Product Analytics
Post-Sale Strategy
Usage Coaching

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Revenue
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 Accounts: Product Qualified Accounts (PQAs) represent accounts with multiple users demonstrating high engagement and readiness for upgrade. High PQA counts typically precede and drive increases in expansion feature usage, as these accounts are more likely to explore and adopt upsell-eligible features, forecasting future growth in Expansion Feature Usage Frequency.
- Activation Rate: Activation Rate measures how many users successfully reach meaningful engagement milestones. High activation rates indicate a larger pool of users primed for deeper feature adoption, which is a crucial precursor to increased usage of expansion features among existing accounts.
- Customer Loyalty: Customer Loyalty signals the likelihood of repeated engagement and positive brand sentiment. Loyal customers are more inclined to explore and repeatedly use advanced or upsell features, leading to higher Expansion Feature Usage Frequency over time.
- Stickiness Ratio: Stickiness Ratio (DAU/MAU) quantifies how habit-forming the product is for users. A higher stickiness ratio suggests frequent usage and comfort with core features, which typically leads to greater willingness to try and consistently use expansion features, forecasting future increases in Expansion Feature Usage Frequency.
- Upsell Conversion Rates: Upsell Conversion Rates reflect how effectively existing customers are moved to higher tiers or add-ons. A rise in this metric often precedes an increase in expansion feature usage, as it indicates that customers are actively engaging with and adopting premium features, directly influencing Expansion Feature Usage Frequency.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Expansion Activation Rate: Expansion Activation Rate measures the percentage of existing accounts that begin using new upsell features. Increases here confirm and amplify trends seen in Expansion Feature Usage Frequency, providing additional evidence of expansion momentum and helping to explain the broader impact on account growth.
- Expansion Revenue Growth Rate: Expansion Revenue Growth Rate quantifies the monetary results of upsell and cross-sell efforts. Surges in Expansion Feature Usage Frequency often precede or coincide with revenue growth from expansions, so this metric helps confirm the financial impact and amplifies the business value of increased feature usage.
- Expansion Readiness Index: The Expansion Readiness Index scores accounts based on behavioral and fit data, indicating which are most likely to expand. High readiness scores often explain why Expansion Feature Usage Frequency is rising and help attribute increases to targeted expansion efforts.
- Percent of Accounts Completing Key Activation Milestones: This metric measures how many accounts reach important adoption benchmarks. High completion rates often correlate with subsequent increases in Expansion Feature Usage Frequency, and can help confirm that observed usage increases are part of a broader trend in product maturity and readiness for upsell.
- Net Revenue Retention: Net Revenue Retention (NRR) incorporates expansion, contraction, and churn effects on recurring revenue. Sustained increases in Expansion Feature Usage Frequency typically contribute to higher NRR, so this metric quantifies the long-term business impact and confirms the strategic value of driving expansion feature adoption.