Drop-Off Rate During Onboarding¶

Definition¶

Drop-Off Rate During Onboarding measures the percentage of users who start but do not complete the onboarding process. It helps identify friction points in user activation and early product engagement.

Description¶

Drop-Off Rate During Onboarding is a key indicator of activation efficiency and early value delivery, reflecting how many new users disengage before reaching their first “aha” moment or activation milestone.

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

In B2B SaaS, it highlights friction in account setup, integrations, or team onboarding
In B2C or mobile apps, it reflects lost users before profile completion or first meaningful action
In freemium/PLG, it surfaces users who stall before crossing the paywall or reaching core value

A rising trend typically signals confusing UX, delayed value realization, or onboarding gaps, which helps teams improve guidance, shorten time-to-value, and streamline entry paths. By segmenting by cohort — such as signup source, job title, device, or plan type — you unlock insights for adjusting onboarding flows and nudging high-potential users toward activation.

Drop-Off Rate During Onboarding informs:

Strategic decisions, like investment in activation UX or onboarding toolkits
Tactical actions, such as in-app messaging experiments or onboarding email flow updates
Operational improvements, including guided tours, checklists, or live support for high-value users
Cross-functional alignment, by connecting signals across product, UX, onboarding, and growth teams, keeping everyone focused on turning signups into active users

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

Onboarding Flow Complexity and Length: Long or multi-step onboarding flows cause fatigue. Simpler journeys reduce abandonment.
Perceived Value of Each Step: If users don’t understand why a step matters, they’re more likely to bounce. Clarity = commitment.
Technical or Integration Barriers: Tasks like connecting third-party tools or inviting teammates often trigger 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 onboarding drop-off is high, shorten the number of steps or group them into logical “mini-wins.”
Add tooltips or inline guidance that explain why a step matters (“This helps personalize your dashboard”).
Run a test with optional vs. required fields at friction points like integrations or user invites.
Refine copy and visual design to reinforce momentum (“Step 2 of 3 — You’re almost there!”).
Partner with product to introduce “skip and return later” functionality without blocking progress.

Required Datapoints to calculate the metric
- Total Users Who Started Onboarding
- Users Who Did Not Complete It
- Definition of Completion (based on activation milestone)
Example to show how the metric is derived
- Users who started onboarding: 1,200
- Users who didn’t complete: 720
- Formula: 720 ÷ 1,200 = 60% Drop-Off Rate

Formula¶

Formula

\[ \mathrm{Drop\text{-}Off\ Rate\ During\ Onboarding} = \left( \frac{\mathrm{Users\ Who\ Did\ Not\ Complete\ Onboarding}}{\mathrm{Total\ Users\ Who\ Started}} \right) \times 100 \]

Data Model Definition¶

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

OnboardingMetrics

cube('OnboardingMetrics', {
  sql: `SELECT * FROM onboarding_metrics`,

  measures: {
    totalUsersStartedOnboarding: {
      sql: `total_users_started_onboarding`,
      type: 'sum',
      title: 'Total Users Who Started Onboarding',
      description: 'Total number of users who initiated the onboarding process.'
    },

    usersDidNotComplete: {
      sql: `users_did_not_complete`,
      type: 'sum',
      title: 'Users Who Did Not Complete Onboarding',
      description: 'Number of users who started but did not complete the onboarding process.'
    },

    dropOffRate: {
      sql: `100.0 * ${usersDidNotComplete} / NULLIF(${totalUsersStartedOnboarding}, 0)` ,
      type: 'number',
      title: 'Drop-Off Rate During Onboarding',
      description: 'Percentage of users who started but did not complete the onboarding process.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each onboarding record.'
    },

    completionStatus: {
      sql: `completion_status`,
      type: 'string',
      title: 'Completion Status',
      description: 'Status indicating whether the onboarding was completed.'
    },

    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the onboarding process was started.'
    }
  }
});

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.
- Onboarding Flow Complexity and Length: Long or multi-step onboarding flows increase user fatigue, leading to higher drop-off rates.
- Perceived Value of Each Step: Lack of clarity in the importance of each step results in users abandoning the process.
- Technical or Integration Barriers: Challenges in connecting third-party tools or inviting teammates cause users to drop off.
- Time to Complete Onboarding: Longer time requirements discourage users from completing the onboarding process.
- Initial User Frustration: Early frustrations due to unclear instructions or technical issues increase drop-off rates.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Simplified Onboarding Steps: Reducing the number of steps or simplifying them decreases drop-off rates.
- Clear Communication of Value: Clearly explaining the value of each step increases user commitment to complete onboarding.
- Seamless Integration Processes: Easy integration with third-party tools or inviting teammates reduces drop-off rates.
- User Guidance and Support: Providing guidance and support during onboarding helps users complete the process.
- Personalized Onboarding Experience: Tailoring the onboarding process to individual user needs enhances engagement and reduces drop-off.

Involved Roles & Activities¶

Involved Roles

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

Growth
Customer Lifecycle Management
Product Management (PM)
Product Marketing (PMM)
UX Designer / Researcher
Activities

Common initiatives or actions associated with this KPI:

Onboarding Optimization
Activation Flow Design
PLG Strategy
UX Walkthroughs

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 Drop-off Rate: 'Onboarding Drop-off Rate' directly tracks the percentage of users abandoning the onboarding flow at early steps, serving as a leading indicator for overall Drop-Off Rate During Onboarding. Spikes or patterns in this metric help anticipate increases in the target lagging KPI and pinpoint where friction or confusion arises, enabling targeted UX fixes before overall completion rates are affected.
- Activation Rate: 'Activation Rate' measures the share of users reaching key onboarding milestones. A low or declining Activation Rate usually precedes a rise in Drop-Off Rate During Onboarding, as more users fail to experience early product value. Improvements in Activation Rate are expected to reduce onboarding drop-off in subsequent periods.
- Feature Adoption Rate (Early): Early feature adoption, though listed as 'Feature Adoption Rate (Early)' in the candidates, is a leading signal for onboarding drop-off. Users who engage with a core feature early are less likely to abandon onboarding, so a drop in this metric forecasts higher overall onboarding drop-off.
- Onboarding Completion Rate: This metric is the inverse of Drop-Off Rate During Onboarding and thus acts as a powerful early signal. Declines in onboarding completion typically precede increases in drop-off, allowing teams to respond proactively.
- Percent of Users Engaging with Top Activation Features: This metric indicates how many new users interact with critical onboarding features. Lower engagement rates with these features usually result in increased onboarding drop-off, as users who don't hit value moments early tend to abandon the process.
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: Signup Abandonment Rate quantifies users dropping off during the earlier signup phase, serving as an upstream companion metric. High signup abandonment may amplify Drop-Off Rate During Onboarding, as users who do not finish signup are also likely to abandon onboarding. Together, they explain full-funnel conversion health.
- Activation Conversion Rate: This measures the percent of users who complete onboarding and reach activation. A high Drop-Off Rate During Onboarding will depress this metric, making it a downstream confirmation of onboarding friction's impact on ultimate user activation.
- Trial Engagement Rate: This tracks how actively users engage during a trial period, often following onboarding. If onboarding drop-off rises, fewer users reach the trial's value-driving moments, resulting in lower engagement. It quantifies the downstream business impact of onboarding friction.
- Conversion Rate: Overall Conversion Rate (e.g., from signup to key action or paid plan) is depressed by poor onboarding completion. High Drop-Off Rate During Onboarding is often reflected in lower overall conversion, confirming the broader business effect.
- First Session Completion Rate: This measures what percent of users complete onboarding in their first session, providing a more granular view of onboarding friction. High Drop-Off Rate During Onboarding will directly reduce this metric, validating and quantifying the effect of onboarding experience on user momentum.