Percent of Users Engaging with Top Activation Features

Definition

Percent of Users Engaging with Top Activation Features measures how many new users interact with the highest-impact features tied to activation. It helps assess onboarding effectiveness and early value delivery.

Business Context

Percent of Users Engaging with Top Activation Features is a key indicator of onboarding success and early product value realization, reflecting how new users interact with your most impactful, “aha” features during their first experiences with the product.

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

In B2B SaaS (like a CRM), it highlights initial utility by tracking critical actions such as contact imports or pipeline creation
In collaboration tools, it reflects early teamwork behavior — e.g., assigning tasks or inviting teammates
In consumer apps, it surfaces value clarity and product intuitiveness, such as adding content or completing personalization steps

A rising trend indicates effective onboarding, feature discoverability, and product alignment with user expectations. A low or declining trend may signal confusing UX, poor messaging, or underwhelming early experiences — blocking activation and long-term retention.

By segmenting by acquisition source, persona, or behavior, you can tailor in-app education, optimize onboarding flows, and design nudges that surface these features earlier in the journey.

Percent of Users Engaging with Top Activation Features informs:

Strategic decisions, like validating activation definitions and evolving onboarding strategies
Tactical actions, such as prioritizing early feature discovery in walkthroughs and tooltips
Operational improvements, including design tweaks to UX flows or improved contextual guidance
Cross-functional alignment, by helping product, marketing, and CS teams focus on guiding users to value fast and friction-free

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 Discoverability During Onboarding: If users don’t see or recognize your key features early, they’re unlikely to explore them later. You need to actively introduce and contextualize these features in the onboarding experience.
Clarity of Feature Value in Context: Users must understand not just what the feature does, but why it matters in the moment they’re using it. If the perceived value isn’t clear, even exposed features will be ignored.
In-App Nudges and Guidance: Without timely prompts (tooltips, nudges, checklists), many users won’t complete the setup or steps needed to engage top features. This results in silent drop-off before activation.

Improvement Tactics & Quick Wins

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

If users aren’t reaching key features in the first session, update onboarding to trigger feature-specific tooltips or “get started” prompts once core setup is complete.
Add a “power user path” in onboarding that leads directly to high-value features, bypassing basic steps for more experienced users. Highlight these as the “fastest way to get value.”
Run a test adding embedded success stories or GIF demos next to top features (e.g., “See how Sarah used this to 10x her workflow”). Help users imagine success.
Refine in-product copy to highlight outcomes, not actions. Instead of “Use Filters,” try “Narrow down results to spot quick wins.”
Partner with growth or lifecycle marketing to send Day 2–4 nudges highlighting unused high-value features with contextual CTAs (e.g., “You haven’t tried [X] yet — here’s why it’s a game changer”).

Activities commonly tied to improving or operationalizing this KPI.

Activity	Description
Onboarding Optimization	Onboarding Optimization is a strategic process focused on refining and streamlining the experience new customers have when adopting a product or service. It improves performance by removing friction, testing changes, and scaling what works. Relevant KPIs include Action-to-Activation Time Lag and Activation Cohort Retention Rate (Day 7/30).
Activation Campaigns	Activation Campaigns is a targeted initiative designed to motivate new or existing users to experience key value moments within a product or service. It coordinates execution across touchpoints so teams can move users or accounts toward the target outcome. Relevant KPIs include Multi-Session Activation Completion Rate and Percent of Users Engaging with Top Activation Features.
Product-Led Growth	Product-Led Growth focuses on positioning the product as the primary driver of customer acquisition, conversion, expansion, and retention. It helps teams translate strategy into repeatable execution. Relevant KPIs include Action-to-Activation Time Lag and Customer Feedback Score (Post-activation).
Feature Discovery Nudges	Feature Discovery Nudges are targeted messages or cues delivered within a product or sales workflow to proactively introduce users or buyers to new, relevant, or underutilized product features. It coordinates execution across touchpoints so teams can move users or accounts toward the target outcome. Relevant KPIs include Percent of Users Engaging with Top Activation Features.

Required Datapoints

New Users: Defined cohort (e.g., all signups in the past week).
Users Engaging with Top Features: Users who interact with 1+ predefined high-impact feature.
Top Activation Feature List: Curated by Product + PMM teams.

Example

In one week:

New Users: 2,000
Used Top Activation Features: 1,400
Formula: (1,400 ÷ 2,000) × 100 = 70%

Formula

\mathrm{Percent\ of\ Users\ Engaging\ with\ Top\ Activation\ Features} = \left( \frac{\mathrm{Users\ Using\ Key\ Features}}{\mathrm{Total\ New\ Users}} \right) \times 100

Positive & Negative Influences

Negative Influences

Complexity of Feature Interface: A complex or unintuitive interface can deter users from engaging with top features, reducing the percentage of users interacting with them.
Lack of Onboarding Customization: A one-size-fits-all onboarding process may fail to highlight relevant features for different user segments, decreasing engagement with top activation features.
Delayed Feature Introduction: Introducing key features too late in the user journey can result in lower engagement, as users may have already formed habits around other features.
Insufficient User Feedback Mechanisms: Without mechanisms to gather user feedback, it is difficult to identify and address barriers to feature engagement, leading to lower interaction rates.
Overwhelming Number of Features: Presenting too many features at once can overwhelm users, causing them to overlook or ignore top activation features, thus reducing engagement.

Positive Influences

Feature Discoverability During Onboarding: Improved discoverability during onboarding increases the likelihood that users will engage with top activation features, as they are more aware of these features from the start.
Clarity of Feature Value in Context: When users understand the value of a feature in their current context, they are more likely to engage with it, leading to higher engagement with top activation features.
In-App Nudges and Guidance: Effective in-app nudges and guidance can prompt users to engage with top features, increasing the percentage of users interacting with these features.
User Education and Training: Providing users with educational resources and training can enhance their understanding and usage of top features, boosting engagement rates.
Personalized User Experience: Tailoring the user experience to individual needs and preferences can increase engagement with top activation features by making them more relevant to each user.

Funnel Stage & Type

AAARRR Funnel Stage

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

Activation

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.

Ownership

Primary Owner

This role is directly accountable for the KPI and is expected to drive progress and decisions around it.

Product Management (PM)

Secondary Owners

These roles contribute directly to performance and typically partner on execution, reporting, or optimization.

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: Activation Rate measures the percentage of users reaching meaningful initial engagement and is a direct early indicator of how many users are likely to interact with top activation features, setting the stage for the lagging KPI.
Product Qualified Accounts: The number of Product Qualified Accounts reflects accounts demonstrating strong product engagement early on, often via activation feature use, thus forecasting downstream increases in the percent of users engaging with top activation features.
Onboarding Completion Rate: A high Onboarding Completion Rate signals that more users are successfully introduced to the product and exposed to top activation features, leading to greater engagement rates downstream.
First Feature Usage Rate: The rate at which new users use a core feature early on is a strong predictor of future engagement with top activation features, directly influencing the target KPI.
Feature Adoption / Usage: Early feature adoption and usage patterns act as a bellwether for how many users will ultimately engage with top activation features, anticipating future performance on the lagging metric.

Lagging

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

Percent Completing Key Activation Tasks: The percent of users or accounts completing key activation tasks quantifies the number of users progressing through activation, helping to explain and confirm trends in engagement with top activation features.
Feature Adoption Rate (Early): Early feature adoption rates offer a granular view of which features drive activation and help explain variations in the overall percent of users engaging with top activation features.
Activation Cohort Retention Rate (Day 7/30): This metric confirms whether users engaging with top activation features remain active, amplifying the business impact and stickiness associated with strong activation feature engagement.
Percent of Accounts Completing Key Activation Milestones: The share of accounts completing important activation steps provides a broader context for understanding how engagement with key features translates to overall activation success.
Activation Conversion Rate: The conversion rate from onboarding to activation helps explain the effectiveness of the activation process and correlates closely with the percent of users engaging with top activation features.

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_engagement`,

  measures: {
    newUsers: {
      sql: `new_user_id`,
      type: 'countDistinct',
      title: 'New Users',
      description: 'Count of new users who signed up in the defined cohort period.'
    },
    usersEngagingWithTopFeatures: {
      sql: `user_id`,
      type: 'countDistinct',
      title: 'Users Engaging with Top Features',
      description: 'Count of users who interacted with at least one top activation feature.'
    },
    percentEngaging: {
      sql: `100.0 * ${usersEngagingWithTopFeatures} / NULLIF(${newUsers}, 0)` ,
      type: 'number',
      title: 'Percent of Users Engaging with Top Activation Features',
      description: 'Percentage of new users engaging with top activation features.'
    }
  },

  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 engaged with by the user.'
    },
    engagementDate: {
      sql: `engagement_date`,
      type: 'time',
      title: 'Engagement Date',
      description: 'Date when the user engaged with the feature.'
    }
  }
});

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