Activation Rate by Source¶

Definition¶

Activation Rate by Source measures the percentage of users from each acquisition channel who reach activation. It helps assess the quality of acquisition sources and their ability to drive users to value.

Description¶

Activation Rate by Source is a cross-functional diagnostic metric that ties acquisition strategy to product success, showing how many users from each acquisition channel reach the activation milestone — where they first experience core product value.

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

In PLG businesses, it might reveal that community referrals drive higher activation than paid search
In SaaS, it reflects whether demo signups or partner referrals actually convert to engaged users
In freemium funnels, it helps identify which sources bring real users, not just clicks

A high activation rate from a source = strong alignment with product value and onboarding. A low rate means you may be attracting the wrong audience or creating onboarding friction post-click. Segment by channel, campaign, or persona to diagnose what’s working — and what needs rethinking.

Activation Rate by Source informs:

Strategic decisions, like budget reallocation and channel prioritization
Tactical actions, such as custom onboarding or landing pages by source
Operational improvements, including targeting adjustments or acquisition messaging
Cross-functional alignment, by syncing growth, product, and marketing around source-to-value fit, not just cost per lead

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

Intent Match Between Source and Product: If users come from awareness-stage content but land in a trial experience, activation drops. The more aligned the source and landing experience, the better the outcome.
Landing Page Quality and CTA Clarity: Activation starts before signup — poor messaging or weak CTAs lead to unqualified users entering the funnel.
Post-Signup Onboarding Personalization: Different sources often represent different personas or use cases. Treating them the same post-signup can reduce activation rates.

Improvement Tactics & Quick Wins¶

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

If activation rate is low for a specific source, audit messaging and expectations at entry points — are you over-promising?
Add source-specific onboarding flows or quick-start templates, aligning onboarding with the user’s likely intent (e.g., “Coming from G2? Start your first integration in 2 mins”).
Run a test customizing activation nudges by source, comparing behavior and follow-through across channels.
Refine attribution tracking to ensure you're segmenting activation by the true source, not just last touch.
Partner with growth marketing to double down on high-activation sources, reallocating spend based on performance.

Required Datapoints to calculate the metric
- New Users by Source: Users grouped by UTM or channel attribution.
- Activated Users by Source: Number of users from each source who completed the activation milestone.
- Source Definitions: Consistent taxonomy for channels (e.g., organic, paid, direct, referral).
Example to show how the metric is derived

For March:
- Paid Social Users: 500 → Activated: 75 → 15%
- Organic Search Users: 400 → Activated: 140 → 35%

Formula¶

Formula

\[ \mathrm{Activation\ Rate\ by\ Source} = \left( \frac{\mathrm{Activated\ Users\ from\ Source}}{\mathrm{Total\ Users\ from\ Source}} \right) \times 100 \]

Data Model Definition¶

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

NewUsersActivatedUsersActivationRate

cube(`NewUsers`, {
  sql: `SELECT * FROM new_users`,
  measures: {
    newUsersCount: {
      sql: `user_id`,
      type: 'count',
      title: 'New Users Count',
      description: 'Count of new users by source.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    source: {
      sql: `source`,
      type: 'string',
      title: 'Source',
      description: 'Acquisition source of the user.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Time when the user was created.'
    }
  }
})

cube(`ActivatedUsers`, {
  sql: `SELECT * FROM activated_users`,
  measures: {
    activatedUsersCount: {
      sql: `user_id`,
      type: 'count',
      title: 'Activated Users Count',
      description: 'Count of activated users by source.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    source: {
      sql: `source`,
      type: 'string',
      title: 'Source',
      description: 'Acquisition source of the activated user.'
    },
    activatedAt: {
      sql: `activated_at`,
      type: 'time',
      title: 'Activated At',
      description: 'Time when the user was activated.'
    }
  }
})

cube(`ActivationRate`, {
  sql: `SELECT * FROM (
    SELECT
      new_users.source AS source,
      COUNT(DISTINCT activated_users.user_id) * 1.0 / COUNT(DISTINCT new_users.user_id) AS activation_rate
    FROM
      new_users
    LEFT JOIN
      activated_users ON new_users.user_id = activated_users.user_id
    GROUP BY
      new_users.source
  )`,
  measures: {
    activationRate: {
      sql: `activation_rate`,
      type: 'number',
      title: 'Activation Rate',
      description: 'Percentage of users from each source who reach activation.'
    }
  },
  dimensions: {
    source: {
      sql: `source`,
      type: 'string',
      title: 'Source',
      description: 'Acquisition source of the user.'
    }
  }
})

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.
- Misalignment Between Source and Product: If the acquisition source does not match the product experience, users may become disinterested, leading to lower activation rates.
- Poor Landing Page Experience: Landing pages that are confusing or lack clear messaging can result in users not understanding the value, thus reducing activation rates.
- Generic Onboarding Process: A one-size-fits-all onboarding approach can fail to address the specific needs of users from different sources, negatively impacting activation.
- High Friction Signup Process: Complex or lengthy signup processes can deter users from completing activation.
- Lack of Source-Specific Communication: Failing to communicate in a way that resonates with users from specific sources can lead to decreased activation rates.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Intent Match Between Source and Product: When the acquisition source aligns well with the product experience, users are more likely to activate as their expectations are met, leading to higher activation rates.
- Landing Page Quality and CTA Clarity: High-quality landing pages with clear calls-to-action ensure that users understand the value proposition, resulting in a higher likelihood of activation.
- Post-Signup Onboarding Personalization: Tailoring the onboarding process to match the specific needs and expectations of users from different sources increases the chances of activation.
- Source-Specific Incentives: Offering incentives that are tailored to the acquisition source can enhance user motivation to activate.
- User Education and Support: Providing adequate educational resources and support for users from different sources can positively impact activation rates.

Involved Roles & Activities¶

Involved Roles

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

Demand Generation
Growth
Marketing Operations
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Campaign Analysis
CAC Optimization
Onboarding Customization
Source Attribution

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Acquisition
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.

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 Leads: The volume and quality of Product Qualified Leads (PQLs) are strong early signals of users likely to reach activation. High PQL counts from specific sources often forecast higher future Activation Rate by Source as these users have already demonstrated high engagement and intent.
- Activation Rate: Overall Activation Rate is a direct precursor to Activation Rate by Source, as trends in aggregate activation often manifest first at the source level. Increases or declines in general activation provide an early indicator of how specific sources will perform downstream.
- Onboarding Completion Rate: A high Onboarding Completion Rate from a source typically precedes improved Activation Rate by Source, as successful onboarding is a critical step toward reaching activation. Tracking this metric highlights where drop-off occurs before users reach value.
- Trial-to-Paid Conversion Rate: Higher trial-to-paid conversions from a channel often correlate with higher Activation Rate by Source, since users who activate are more likely to convert. This metric signals the effectiveness of acquisition sources in driving not just trials but meaningful product engagement.
- Lead Quality Score: Lead Quality Score by source identifies which acquisition channels bring in users most likely to activate, serving as a predictive indicator for Activation Rate by Source. High lead quality typically results in higher subsequent activation rates.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Percent of Accounts Completing Key Activation Milestones: This measures the share of accounts progressing through essential activation steps, providing additional granularity and confirming patterns seen in Activation Rate by Source. It helps diagnose where sources succeed or fail in facilitating deeper activation.
- Signup Source Quality Rate: This quantifies the quality of signups per source, validating whether high Activation Rate by Source is driven by genuinely high-value users or just volume. It complements activation data by adding a quality filter.
- Multi-Session Activation Completion Rate: This shows which sources produce users who persist through multiple sessions to complete activation, confirming the sustainability and robustness of Activation Rate by Source.
- Activation Cohort Retention Rate (Day 7/30): This validates whether users who activate from a source continue to return and engage, quantifying the stickiness and long-term value of activated users from each source.
- Activation Conversion Rate: This measures how efficiently users progress from onboarding or trial to activation, providing a process-based confirmation and deeper explanation for variations in Activation Rate by Source.