Onboarding Drop-off Rate¶

Definition¶

Onboarding Drop-Off Rate measures the percentage of users who begin the onboarding process but fail to complete it. It highlights where users lose interest or encounter obstacles during onboarding.

Description¶

Onboarding Drop-Off Rate reveals where users disengage or abandon during their first experience—whether that’s after signing up or mid-flow through product setup.

It’s especially critical for:

Self-serve SaaS, where activation = retention
Mobile apps, where drop-offs often occur within minutes
Trials, where the first few steps determine if users come back

A high drop-off rate indicates confusion, friction, or perceived effort, while a low rate signals a smooth, motivating early path. By tracking drop-offs by step, persona, or device, you can optimize UX and sequencing to keep users progressing.

Onboarding Drop-Off Rate informs:

Strategic decisions, like where to invest in education or design
Tactical actions, such as tooltips, videos, or live chat triggers
Operational improvements, including error handling or save-state UX
Cross-functional alignment, helping product, onboarding, and CS spot risk before it leads to churn

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

First-Time UX Friction: Clunky, buggy, or overwhelming interfaces drive exits fast.
Unclear Value Messaging: If users don’t understand why they’re being asked to do something, they bail.
Distractions or Incomplete Setup Context: Switching apps, getting lost in integrations, or needing more info = drop-off.

Improvement Tactics & Quick Wins¶

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

If drop-off is high, heatmap and session-record onboarding steps to locate choke points.
Add tooltips or “why this matters” copy at friction-heavy points.
Run a test removing or deferring complex integrations to a post-activation phase.
Refine success screens and small wins to keep motivation high across steps.
Partner with support to auto-trigger proactive help for stalled users.

Required Datapoints to calculate the metric
- Total Users: Number of users who start the onboarding process.
- Drop-Off Users: Number of users who fail to complete the onboarding process.
- Stage Completion Data: Completion rates for each step in the onboarding flow.
Example to show how the metric is derived

A fintech app tracks 10,000 users starting onboarding in a month, with 3,000 failing to complete. The drop-off rate is:
- Onboarding Drop-Off Rate = (3,000 / 10,000) × 100 = 30%

Formula¶

Formula

\[ \mathrm{Onboarding\ Drop\text{-}off\ Rate} = \left( \frac{\mathrm{Drop\text{-}Off\ Users}}{\mathrm{Total\ Users}} \right) \times 100 \]

Data Model Definition¶

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

OnboardingProcess

cube('OnboardingProcess', {
  sql: `SELECT * FROM onboarding_process`,

  measures: {
    totalUsers: {
      sql: `total_users`,
      type: 'sum',
      title: 'Total Users',
      description: 'Number of users who start the onboarding process.'
    },
    dropOffUsers: {
      sql: `drop_off_users`,
      type: 'sum',
      title: 'Drop-Off Users',
      description: 'Number of users who fail to complete the onboarding process.'
    },
    dropOffRate: {
      sql: `100.0 * ${CUBE.dropOffUsers} / NULLIF(${CUBE.totalUsers}, 0)` ,
      type: 'number',
      title: 'Onboarding Drop-Off Rate',
      description: 'Percentage of users who begin the onboarding process but fail to complete it.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    stageCompletionData: {
      sql: `stage_completion_data`,
      type: 'string',
      title: 'Stage Completion Data',
      description: 'Completion rates for each step in the onboarding flow.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the onboarding process was initiated.'
    }
  }
});

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.
- First-Time UX Friction: High levels of friction in the user interface during the first-time experience can lead to increased drop-off rates as users become frustrated or confused.
- Unclear Value Messaging: When users do not understand the purpose or benefit of the onboarding steps, they are more likely to abandon the process.
- Distractions or Incomplete Setup Context: Users who encounter distractions or lack sufficient context for setup are more prone to dropping off as they may switch to other tasks or applications.
- Lengthy Onboarding Process: A long and tedious onboarding process can cause users to lose interest and drop off before completion.
- Technical Issues: Bugs or technical problems during onboarding can prevent users from progressing, leading to higher drop-off rates.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Clear Value Proposition: Clearly communicating the benefits and value of completing the onboarding process can encourage users to continue.
- Streamlined User Interface: A smooth and intuitive user interface can reduce friction and help users complete onboarding more easily.
- Guided Onboarding Experience: Providing a guided experience with clear instructions and support can help users navigate the onboarding process successfully.
- Personalized Onboarding: Tailoring the onboarding process to individual user needs and preferences can increase engagement and reduce drop-off rates.
- Incentives for Completion: Offering rewards or incentives for completing onboarding can motivate users to finish the process.

Involved Roles & Activities¶

Involved Roles

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

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

Common initiatives or actions associated with this KPI:

Product Adoption and Use
UX Testing
Friction Point Resolution

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.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Onboarding Completion Rate: A direct inverse of Onboarding Drop-off Rate, this metric measures the percentage of users who complete onboarding. Higher completion rates signal successful onboarding flows and lower drop-off, providing a primary contextual signal for diagnosing and forecasting drop-off changes.
- Drop-Off Rate: Measures abandonment in any process, not just onboarding. Spikes in general drop-off rates may forecast higher onboarding drop-off, signaling friction or UX issues affecting early user journeys.
- First-time User Conversion Rate: Indicates how many new users complete a desired action during their first interaction. Low conversion rates can forecast higher onboarding drop-off, highlighting issues with initial value demonstration or onboarding clarity.
- First Feature Usage Rate: The rate at which new users engage with a core feature during early sessions. If this rate is low, it can predict higher onboarding drop-off, as users may not experience early value or get lost in the process.
- Activation Rate: Measures the percentage of users reaching a meaningful activation milestone. A declining activation rate often preempts or coincides with rising onboarding drop-off, serving as an additional early-warning signal for onboarding effectiveness.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Signup Abandonment Rate: Directly quantifies the volume of users abandoning the signup process, which often overlaps with onboarding drop-off. High abandonment rates provide feedback for recalibrating onboarding drop-off expectations and identifying critical friction points.
- Trial Engagement Rate: Measures engagement during the trial period, after initial onboarding. Poor engagement can validate that onboarding drop-off is leading to downstream inactivity, informing improvements to early onboarding touchpoints.
- Percent of Accounts Completing Key Activation Milestones: Quantifies how many accounts reach critical onboarding checkpoints. Low percentages validate and amplify onboarding drop-off trends, helping recalibrate leading indicators and prioritize onboarding improvements.
- Activation Conversion Rate: Measures the conversion of users through onboarding to activation. If this is low, it confirms that onboarding drop-off is impacting the pool of activated users, justifying further investigation into onboarding steps.
- Cohort Retention Analysis: Tracks user retention over time after onboarding. Low retention in early cohorts can confirm that onboarding drop-off correlates with poor long-term engagement, informing recalibration of onboarding success metrics.