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Time Between Logins (Post-Activation)

Definition

Time Between Logins (Post-Activation) measures the average time elapsed between logins for users who have already completed activation. It helps track engagement frequency and detect signs of drop-off or stickiness in the user experience.

Description

Time Between Logins (Post-Activation) is a key indicator of habit formation, user retention, and product value realization, reflecting how frequently users return after their initial activation milestone.

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

  • In B2B SaaS, it highlights whether users are integrating the product into their workflow or drifting into churn-risk territory
  • In Freemium or PLG models, it reflects how sticky and essential your product is on a daily/weekly basis
  • In Consumer Apps, it surfaces the cadence of usage and when to prompt re-engagement

A shortening interval signals high engagement and strong product value, while a lengthening gap is often an early warning of churn or disengagement. This helps you trigger re-engagement campaigns, refine onboarding, and improve in-product nudges. By segmenting by persona, industry, or feature usage, you unlock insights for optimizing activation flows, tailoring lifecycle messaging, and retaining key segments.

Time Between Logins (Post-Activation) informs:

  • Strategic decisions, like feature prioritization or pricing for active vs. dormant users
  • Tactical actions, such as timing of email nudges, push notifications, or win-back flows
  • Operational improvements, including behavior-based onboarding and engagement scoring
  • Cross-functional alignment, by connecting usage signals across marketing, success, and product, keeping everyone focused on sustained engagement and retention

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

  • User Role and Workflow Fit: Some users may only need the product weekly — others, daily.
  • In-Product Utility and ROI Visibility: The clearer the ongoing benefit, the more frequent the return.
  • Lifecycle Engagement Nudges: Email reminders and prompts help reduce login gaps.

Improvement Tactics & Quick Wins

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

  • If login gaps are widening, trigger in-app nudges and emails after inactivity periods.
  • Add in-app “recent activity” or suggestions to re-engage dormant users.
  • Run cohort analysis by role or plan type to identify typical login cadences.
  • Refine feature adoption campaigns to create regular usage patterns.
  • Partner with growth PM to build a “log in again” automation based on drop-off thresholds.

  • Required Datapoints to calculate the metric

    • Activated Users
    • Timestamps of Each Login Event (per user)
    • Average Time Elapsed Between Logins (e.g., days)

  • Example to show how the metric is derived

    User A logs in every 2 days User B logs in every 4 days User C logs in once a week Average: (2 + 4 + 7) ÷ 3 = 4.3 days between logins

Formula

Formula

\[ \mathrm{Time\ Between\ Logins} = \mathrm{Avg}\left(\mathrm{Time\ Between\ Login\ Events\ for\ Activated\ Users}\right) \]

Data Model Definition

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

cube('UserLogins', {
  sql: `SELECT * FROM user_logins`,

  joins: {
    Users: {
      relationship: 'belongsTo',
      sql: `${CUBE}.user_id = ${Users}.id`
    }
  },

  measures: {
    averageTimeBetweenLogins: {
      sql: `TIMESTAMPDIFF(DAY, LAG(login_timestamp) OVER (PARTITION BY user_id ORDER BY login_timestamp), login_timestamp)`,
      type: 'avg',
      title: 'Average Time Between Logins',
      description: 'Average time elapsed between logins for activated users.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },

    userId: {
      sql: `user_id`,
      type: 'number'
    },

    loginTimestamp: {
      sql: `login_timestamp`,
      type: 'time'
    }
  }
})

cube('Users', {
  sql: `SELECT * FROM users WHERE activated = TRUE`,

  measures: {
    count: {
      sql: `id`,
      type: 'count',
      title: 'Activated Users Count',
      description: 'Count of users who have completed activation.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },

    activated: {
      sql: `activated`,
      type: 'boolean'
    }
  }
})

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.

    • User Role and Workflow Fit: Users whose roles require less frequent interaction with the product tend to have longer times between logins, as their workflow does not necessitate daily engagement.
    • In-Product Utility and ROI Visibility: If users do not perceive clear ongoing benefits or utility from the product, they are less likely to log in frequently, increasing the time between logins.
    • Lifecycle Engagement Nudges: Lack of effective email reminders and prompts can lead to longer gaps between logins, as users may forget or deprioritize using the product.
    • Product Complexity: High complexity or difficulty in using the product can discourage frequent logins, leading to increased time between logins.
    • Technical Issues: Frequent technical issues or downtime can frustrate users, causing them to log in less often.

  • Positive influences

    Factors that push the metric in a favorable direction, supporting growth or improvement.

    • User Role and Workflow Fit: Users whose roles require daily interaction with the product tend to have shorter times between logins, as their workflow necessitates frequent engagement.
    • In-Product Utility and ROI Visibility: Clear and visible ongoing benefits and utility from the product encourage users to log in more frequently, reducing the time between logins.
    • Lifecycle Engagement Nudges: Effective email reminders and prompts can shorten the time between logins by keeping the product top-of-mind for users.
    • User Satisfaction: High levels of user satisfaction and positive user experience can lead to more frequent logins.
    • Feature Updates: Regular updates and new features can entice users to log in more often to explore and utilize new functionalities.

Involved Roles & Activities

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.

    • Stickiness Ratio: A lower Stickiness Ratio (DAU/MAU) often leads to longer Time Between Logins (Post-Activation), while a higher ratio signals that users return more frequently. Changes in stickiness often precede changes in login intervals, making it a strong leading signal for engagement drop-off or improvement.
    • Drop-Off Rate: An increase in Drop-Off Rate during key user journeys is a strong early sign that users will become less engaged, directly resulting in increased Time Between Logins (Post-Activation). Monitoring drop-off allows you to identify friction points before lagging engagement metrics worsen.
    • Weekly Active Users: Declines in Weekly Active Users (WAU) tend to appear before increases in Time Between Logins (Post-Activation), as fewer users returning each week signals lower engagement frequency and potential retention problems.
    • Activation Rate: A drop in Activation Rate among new users often predicts that, post-activation, those who do activate will show lower engagement and longer gaps between logins. This makes Activation Rate a useful early signal for future engagement health.
    • Session Frequency: Session Frequency directly forecasts Time Between Logins (Post-Activation). A decrease in frequency is usually observed before an increase in the lagging metric, highlighting early disengagement and forming a critical leading indicator.

  • 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): Retention rates for activated users at Day 7 and Day 30 quantify how many users continue returning post-activation. Lower retention at these milestones typically confirms and amplifies increases in Time Between Logins (Post-Activation), showing a lagging impact on long-term engagement.
    • Customer Churn Rate: Customer Churn Rate often rises after sustained increases in Time Between Logins (Post-Activation), confirming that disengaged users are ultimately cancelling or leaving. It quantifies the downstream business impact of reduced engagement frequency.
    • Customer Engagement Score: A decline in overall Customer Engagement Score follows increases in Time Between Logins (Post-Activation), validating that infrequent logins are part of broader disengagement trends and helping to quantify the scope of the issue.
    • Breadth of Use: Decreased Breadth of Use (fewer features or modules used per account) often follows longer login intervals, as disengaged users interact with less of the product. This lagging metric helps explain the depth of disengagement after the fact.
    • Activated-to-Follow-Up Engagement Rate: A lower rate of activated users returning soon after activation usually confirms increased Time Between Logins (Post-Activation), providing additional context about early engagement drop-off and reinforcing the lagging pattern.