Percent of Retained Feature Users¶

Definition¶

Percent of Retained Feature Users measures the proportion of users who continue to use a specific feature over a defined retention window. It helps assess feature stickiness and long-term value.

Description¶

Percent of Retained Feature Users is a nuanced view of feature-level retention, reflecting how many users continue engaging with a specific product capability over time — versus just logging in or performing unrelated actions.

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

In collaboration tools, it could mean continued use of @mentions, shared files, or real-time comments
In finance apps, it may track recurring engagement with reporting, tagging, or exports
In consumer apps, it reflects habitual behavior, not just feature curiosity

A high retention rate signals a mission-critical feature, ideal for roadmap investment, messaging, and user education. A low rate may indicate feature bloat, poor UX, or a mismatch between promise and value delivered. Segment by cohort, role, or tenure to find which features support long-term stickiness — and which ones need refinement or sunsetting.

Percent of Retained Feature Users informs:

Strategic decisions, like feature prioritization, positioning updates, or packaging changes
Tactical actions, such as adding nudges, improving documentation, or onboarding walkthroughs
Operational improvements, including measuring feature discoverability and usage depth
Cross-functional alignment, by showing product and marketing teams which features truly drive value over time

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

Perceived Ongoing Value of the Feature: If users don’t see consistent ROI from a feature, they’ll stop using it — even if it was useful at first. Sustained relevance drives retention.
Ease of Access and Reusability: Features that require too many steps to re-engage or don’t fit naturally into daily workflows are at risk of abandonment. Friction kills repeat usage.
Feature-Level Education and Reinforcement: Users may forget how or why to use a feature if it isn’t re-surfaced. Without periodic nudges or reminders, even valuable features get lost.

Improvement Tactics & Quick Wins¶

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

If feature usage drops after onboarding, set up in-product reminders or lifecycle campaigns that surface use cases tied to real outcomes.
Add quick re-entry points (e.g., dashboard widgets, recent activity, “pick up where you left off”) to keep the feature top of mind and accessible.
Run a test comparing usage before and after simplifying feature workflows, such as reducing clicks or automating setup.
Refine the UI to visually emphasize key features during return visits, especially if they’re buried or under-discovered.
Partner with lifecycle marketing to run “Did you know?” style campaigns, reminding users how and when to use high-value features.

Required Datapoints to calculate the metric
- Feature Users (Day 0): Users who engaged with a feature at time of first use.
- Feature Users Retained (e.g., Day 7/30/60): Users still using the same feature at later time points.
- Retention Timeframe: Specify the window (e.g., 7, 14, 30 days).
Example to show how the metric is derived
- Feature Users on Day 0: 1,000
- Still Using by Day 30: 250
- Formula: (250 ÷ 1,000) × 100 = 25%

Formula¶

Formula

\[ \mathrm{Percent\ of\ Retained\ Feature\ Users} = \left( \frac{\mathrm{Feature\ Users\ Retained}}{\mathrm{Feature\ Users\ on\ Day\ 0}} \right) \times 100 \]

Data Model Definition¶

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

FeatureUsers

cube('FeatureUsers', {
  sql: `SELECT * FROM feature_users`,
  measures: {
    featureUsersDay0: {
      sql: `feature_users_day_0`,
      type: 'count',
      title: 'Feature Users (Day 0)',
      description: 'Users who engaged with a feature at time of first use.'
    },
    featureUsersRetained: {
      sql: `feature_users_retained`,
      type: 'count',
      title: 'Feature Users Retained',
      description: 'Users still using the same feature at later time points.'
    },
    percentRetained: {
      sql: `100.0 * feature_users_retained / NULLIF(feature_users_day_0, 0)`,
      type: 'number',
      title: 'Percent of Retained Feature Users',
      description: 'Proportion of users who continue to use a specific feature over a defined retention window.'
    }
  },
  dimensions: {
    userId: {
      sql: `user_id`,
      type: 'string',
      primaryKey: true,
      title: 'User ID',
      description: 'Unique identifier for each user.'
    },
    featureId: {
      sql: `feature_id`,
      type: 'string',
      title: 'Feature ID',
      description: 'Identifier for the feature being used.'
    },
    retentionTimeframe: {
      sql: `retention_timeframe`,
      type: 'number',
      title: 'Retention Timeframe',
      description: 'The window of time (e.g., 7, 14, 30 days) for retention measurement.'
    },
    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.
- Lack of Perceived Ongoing Value: If users do not see continuous value in a feature, they are likely to stop using it, decreasing the Percent of Retained Feature Users.
- High Friction in Access: Features that are difficult to access or require multiple steps to use can deter users, negatively impacting retention.
- Insufficient Feature-Level Education: Without adequate education and reminders, users may forget about a feature or how to use it, leading to decreased retention.
- Competing Priorities: When users have other priorities or features that demand their attention, they may abandon less critical features, reducing retention.
- Technical Issues or Bugs: Persistent technical problems or bugs can frustrate users and lead to abandonment of a feature, negatively affecting retention.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Perceived Ongoing Value of the Feature: When users perceive continuous value from a feature, they are more likely to keep using it, thereby increasing the Percent of Retained Feature Users.
- Ease of Access and Reusability: Features that are easy to access and integrate seamlessly into users' workflows encourage repeated use, positively impacting the Percent of Retained Feature Users.
- Feature-Level Education and Reinforcement: Regular reminders and educational content about a feature can help users remember its benefits and how to use it, leading to higher retention rates.
- User Engagement Initiatives: Proactive engagement strategies, such as personalized messages or incentives, can enhance user interaction with a feature, boosting retention.
- User Feedback and Iterative Improvements: Incorporating user feedback to refine and improve a feature can increase its relevance and usability, thus positively affecting retention.

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:

Feature Roadmap Prioritization
Retention Campaigns
UX Improvement
Sticky Feature Analysis

Funnel Stage & Type¶

AAARRR Funnel Stage

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

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.
- Activation Rate: A higher Activation Rate indicates more users are reaching initial value milestones, which is a strong early predictor of whether users will become long-term retained feature users. Improvements in Activation Rate often lead to future increases in Percent of Retained Feature Users.
- Feature Adoption / Usage: Early adoption and frequent usage of key features are strong signals that users will continue engaging with those features over time, boosting the percent of retained feature users.
- Stickiness Ratio: A high Stickiness Ratio (i.e., frequent usage compared to the active user base) signals habit formation and deeper product engagement, which directly forecasts future feature retention rates.
- Customer Loyalty: Strong customer loyalty, measured by likelihood of repeat engagement and advocacy, is an early indicator that users will continue using a specific feature and contribute to higher retention rates.
- Monthly Active Users: A growing or stable base of Monthly Active Users provides a larger pool for feature retention and reflects overall product health, which typically precedes improvements in retained feature user percentages.
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 quantifies the percent of users who return after reaching activation, directly amplifying and confirming trends in retained feature users over similar time windows.
- Feature Adoption Rate (Ongoing): Measures how many users regularly use a feature over time, offering a like-for-like quantitative confirmation of the percent of retained feature users and surfacing deeper insights on stickiness.
- Customer Churn Rate: A high churn rate often explains declines in retained feature users, as loss of customers directly reduces the population available to be retained on any feature.
- Breadth of Use: Customers using a wider range of features are more likely to be retained as feature users. This metric contextualizes and explains increases or decreases in retained feature user percentages.
- Customer Feedback Retention Score: This metric reveals whether users who provide feedback are more or less likely to be retained as feature users, offering explanatory power and highlighting the effectiveness of feedback loops on long-term retention.