Expansion Activation Rate¶

Definition¶

Expansion Activation Rate measures the percentage of existing accounts that adopt a new product, feature, or service that can lead to upsell or cross-sell. It helps track momentum in expansion readiness and usage.

Description¶

Expansion Activation Rate is a key indicator of post-sale product value realization and growth readiness, reflecting how quickly and frequently customers adopt new features, modules, or higher-tier plans after their initial purchase.

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

In SaaS, it highlights activations of premium features, additional seats, or integrations
In commerce, it reflects upsells to bundles, subscriptions, or loyalty tiers
In platform models, it surfaces cross-module activations (e.g., automation, analytics, payments)

A rising trend signals strong adoption momentum and expansion opportunity, while a declining trend may indicate activation friction or value messaging gaps. By segmenting by cohort, use case, or plan tier, you unlock insights for targeted nurture campaigns, CS touchpoint design, and product nudges that drive upgrades.

Expansion Activation Rate informs:

Strategic decisions, like resource prioritization for self-serve vs. assisted growth paths
Tactical actions, such as time-based prompts, contextual upsell banners, or lifecycle email triggers
Operational improvements, including success milestone tracking and in-app onboarding
Cross-functional alignment, across product, growth, lifecycle, and CS, to unlock land-and-expand efficiency

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

In-App Visibility of Expansion Features: Hidden features don’t get tried. Clear, contextual prompts drive discovery.
Feature Value Communication: Customers need to understand why this feature matters — not just that it exists.
Onboarding or Education for Add-Ons: Without enablement, customers won’t feel confident exploring expansion features.

Improvement Tactics & Quick Wins¶

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

If activation is low, surface expansion features inside current workflows (e.g., “You could automate this with [X]”).
Add tooltips or “feature unlocked” banners when users hit usage thresholds.
Run a test offering temporary access to paid features with guided tours.
Refine your upgrade messaging to focus on outcomes and ROI, not just feature lists.
Partner with CS to include expansion walkthroughs in onboarding and QBRs.

Required Datapoints to calculate the metric
- Total Number of Eligible Accounts
- Number of Accounts Who Activated Expansion Offerings
- Timeframe and Expansion Feature Tracked
Example to show how the metric is derived
- 800 accounts eligible for analytics module
- 212 enabled it in Q1
- Formula: 212 ÷ 800 = 26.5% Expansion Activation Rate

Formula¶

Formula

\[ \mathrm{Expansion\ Activation\ Rate} = \left( \frac{\mathrm{Accounts\ Who\ Activated\ Expansion}}{\mathrm{Total\ Eligible\ Accounts}} \right) \times 100 \]

Data Model Definition¶

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

ExpansionActivation

cube(`ExpansionActivation`, {
  sql: `SELECT * FROM expansion_activation`,

  measures: {
    totalEligibleAccounts: {
      sql: `total_eligible_accounts`,
      type: `sum`,
      title: `Total Number of Eligible Accounts`,
      description: `Total number of accounts eligible for expansion offerings.`
    },
    activatedExpansionOfferings: {
      sql: `activated_expansion_offerings`,
      type: `sum`,
      title: `Number of Accounts Who Activated Expansion Offerings`,
      description: `Number of accounts that have activated the expansion offerings.`
    },
    expansionActivationRate: {
      sql: `100.0 * ${activatedExpansionOfferings} / NULLIF(${totalEligibleAccounts}, 0)`,
      type: `number`,
      title: `Expansion Activation Rate`,
      description: `Percentage of eligible accounts that have activated expansion offerings.`
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primaryKey: true,
      title: `ID`,
      description: `Unique identifier for each record.`
    },
    expansionFeature: {
      sql: `expansion_feature`,
      type: `string`,
      title: `Expansion Feature`,
      description: `The specific expansion feature being tracked.`
    },
    activationDate: {
      sql: `activation_date`,
      type: `time`,
      title: `Activation Date`,
      description: `The date when the expansion offering was activated.`
    }
  }
})

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 New Features: High complexity can deter customers from trying new features, negatively affecting the Expansion Activation Rate.
- Lack of Customer Support: Insufficient support can lead to customer frustration and reduced feature adoption, lowering the Expansion Activation Rate.
- Poor User Experience: A subpar user experience can discourage feature exploration, negatively impacting the Expansion Activation Rate.
- Inadequate Marketing of Features: Without proper marketing, customers may remain unaware of new features, reducing the Expansion Activation Rate.
- Delayed Feature Rollouts: Delays in feature availability can lead to lost interest and lower the Expansion Activation Rate.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- In-App Visibility of Expansion Features: Increased visibility leads to higher discovery and usage of new features, directly boosting the Expansion Activation Rate.
- Feature Value Communication: Effective communication of feature benefits enhances customer understanding and interest, positively impacting the Expansion Activation Rate.
- Onboarding or Education for Add-Ons: Comprehensive onboarding increases customer confidence and willingness to explore new features, improving the Expansion Activation Rate.
- Customer Engagement: Higher engagement levels correlate with increased likelihood of adopting new features, thus raising the Expansion Activation Rate.
- User Feedback Loops: Incorporating user feedback into feature development can lead to more relevant features, increasing adoption and the Expansion Activation Rate.

Involved Roles & Activities¶

Involved Roles

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

Customer Success
Customer Lifecycle Management
Customer Onboarding
Product Management (PM)
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Expansion Campaigns
Post-Onboarding
Feature Launches
Account Maturity Assessment

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) signal accounts with strong product engagement and readiness for upsell/cross-sell, making them a top early predictor for future increases in Expansion Activation Rate.
- Activation Rate: Higher Activation Rate indicates more accounts are reaching meaningful engagement milestones, which is a prerequisite for expansion and thus strongly forecasts increased Expansion Activation Rate.
- Upsell Conversion Rates: High Upsell Conversion Rates directly increase the likelihood that existing accounts will activate expansions, acting as a leading indicator for future Expansion Activation Rate.
- Monthly Active Users: Growth in Monthly Active Users among existing accounts increases the base of users that could be targeted for expansion, thus serving as an early signal for expansion readiness.
- Customer Loyalty: High Customer Loyalty predicts willingness to adopt new features or products, making it a strong precursor to expansion activation in the existing customer base.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Expansion Readiness Index: This composite score quantifies how prepared accounts are for expansion based on behavioral and fit data. A high index closely correlates with and can amplify observed Expansion Activation Rate.
- Activation-to-Expansion Rate: This measures the proportion of activated accounts that go on to expand, directly quantifying the efficiency of activation in producing expansions and explaining variance in Expansion Activation Rate.
- Expansion Revenue Growth Rate: Growth in expansion revenue directly follows increases in Expansion Activation Rate, confirming and quantifying the business impact of activating expansions.
- Expansion Opportunity Score: This score identifies accounts most likely to expand, which helps explain the source of changes seen in Expansion Activation Rate and prioritizes future expansion efforts.
- Breadth of Use: Accounts with high feature adoption across multiple modules are more likely to expand; thus, Breadth of Use confirms and amplifies increases in Expansion Activation Rate by demonstrating deeper product adoption.