Onboarding Completion Rate¶

Definition¶

Onboarding Completion Rate measures the percentage of users who successfully complete the onboarding process, transitioning from new sign-ups to fully onboarded users. It reflects how effectively your onboarding flow prepares users to engage with your product or service.

Description¶

Onboarding Completion Rate is a key indicator of initial product experience and value realization, tracking how many users complete key steps required to activate or begin meaningful use.

Its scope varies:

In SaaS, it may mean completing a checklist, data import, or team invite
In apps, it could involve profile setup or walkthrough completion

A high completion rate signals an intuitive, motivating experience, while a low rate suggests confusing flows, unclear value, or time-to-value delays. By segmenting by persona, acquisition channel, or device, you can tailor onboarding to different needs and uncover UX blockers.

Onboarding Completion Rate informs:

Strategic decisions, like activation goals or lifecycle investment
Tactical actions, such as editing copy, progress prompts, or product tours
Operational improvements, including self-serve success and in-app support
Cross-functional alignment, aligning product, growth, and CS around first value

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

Flow Length and Complexity: If onboarding is too long or technical, users drop before finishing.
Value Visibility at Each Step: Users must feel progress — not just ticking boxes.
Incentives and Success Signals: Feedback loops like checkmarks, success messages, or free trials increase motivation.

Improvement Tactics & Quick Wins¶

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

If completion is lagging, shorten the flow or split into progressive steps across sessions.
Add progress indicators and motivational CTAs (“Just one more step to unlock X”).
Run a test offering rewards (credits, badges) upon onboarding completion.
Refine step copy to focus on outcome, not setup (“Set up integrations to save 3 hours/week”).
Partner with product to auto-save partially completed flows and offer easy return prompts.

Required Datapoints to calculate the metric
- Total New Users: The number of users who enter the onboarding flow.
- Onboarded Users: The number of users who complete the onboarding process.
- Onboarding Milestones: Defined steps or actions that signal completion (e.g., profile setup, first transaction).
Example to show how the metric is derived

A SaaS company tracks 10,000 new users in a month, with 7,000 completing onboarding. The completion rate is:
- Onboarding Completion Rate = (7,000 / 10,000) × 100 = 70%

Formula¶

Formula

\[ \mathrm{Onboarding\ Completion\ Rate} = \left( \frac{\mathrm{Onboarded\ Users}}{\mathrm{Total\ New\ Users}} \right) \times 100 \]

Data Model Definition¶

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

UserOnboarding

cube('UserOnboarding', {
  sql: `SELECT * FROM user_onboarding`,

  measures: {
    totalNewUsers: {
      sql: `total_new_users`,
      type: 'sum',
      title: 'Total New Users',
      description: 'The number of users who enter the onboarding flow.'
    },
    onboardedUsers: {
      sql: `onboarded_users`,
      type: 'sum',
      title: 'Onboarded Users',
      description: 'The number of users who complete the onboarding process.'
    },
    onboardingCompletionRate: {
      sql: `100.0 * ${onboardedUsers} / NULLIF(${totalNewUsers}, 0)` ,
      type: 'number',
      title: 'Onboarding Completion Rate',
      description: 'Percentage of users who successfully complete the onboarding process.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    userId: {
      sql: `user_id`,
      type: 'number',
      title: 'User ID',
      description: 'Unique identifier for each user.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the user entered the onboarding flow.'
    }
  }
});

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.
- Flow Length and Complexity: A longer or more complex onboarding process can overwhelm users, leading to higher drop-off rates before completion.
- Technical Jargon Usage: Excessive use of technical language can confuse users, reducing their likelihood of completing the onboarding process.
- Lack of Immediate Value: If users do not perceive immediate value or benefits from the onboarding steps, they are more likely to abandon the process.
- Poor User Interface Design: A cluttered or unintuitive interface can frustrate users, causing them to exit the onboarding prematurely.
- Absence of Guidance: Without clear instructions or guidance, users may feel lost and give up on completing the onboarding.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Value Visibility at Each Step: Highlighting the benefits and progress at each step encourages users to continue through the onboarding process.
- Incentives and Success Signals: Providing rewards or positive feedback, such as success messages, motivates users to complete onboarding.
- Personalization of Onboarding: Tailoring the onboarding experience to individual user needs increases engagement and completion rates.
- Interactive Elements: Incorporating interactive elements, like quizzes or tutorials, can make the onboarding process more engaging and effective.
- Clear Progress Indicators: Showing users how far they have come and what remains can help maintain their motivation to complete onboarding.

Involved Roles & Activities¶

Involved Roles

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

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

Common initiatives or actions associated with this KPI:

Product Adoption and Use
Onboarding Flow Design
Time-to-Value Analysis

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.
- Activation Rate: Activation Rate measures how many users reach a key milestone early in their journey. High Activation Rate typically precedes and drives Onboarding Completion Rate, as users who experience value are more likely to complete onboarding.
- Drop-Off Rate: Drop-Off Rate pinpoints where users abandon processes, including onboarding. Monitoring drop-off trends provides early warning of bottlenecks that will directly impact Onboarding Completion Rate.
- Onboarding Drop-off Rate: Onboarding Drop-off Rate is the inverse early indicator of Onboarding Completion Rate. High drop-off during onboarding signals future decreases in completion rate, helping teams act preemptively.
- Product Qualified Leads: Product Qualified Leads (PQLs) indicate users who engage deeply early on, often following a successful onboarding. A rise in PQLs suggests onboarding is effective at setting users up for meaningful engagement, forecasting higher completion rates.
- Immediate Time to Value: Immediate Time to Value measures how quickly users experience product value. Faster realization of value correlates with higher onboarding completion, as early positive experiences encourage users to finish onboarding.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Drop-Off Rate During Onboarding: This metric quantifies how many users fail during onboarding. Analyzing drop-off patterns recalibrates leading indicators by revealing specific friction points, informing improvements to forecast and strategy.
- Onboarding Satisfaction Score (OSS): OSS reflects user sentiment after completing onboarding. Insights from OSS help refine leading indicators by highlighting which onboarding improvements most impact satisfaction and completion.
- Signup Completion Rate: Signup Completion Rate shows how many users finish the initial sign-up process. Trends in this metric reveal upstream issues affecting onboarding, allowing leading indicators to be tuned for accuracy.
- First Feature Usage Rate: This metric measures if users engage with core features post-onboarding. Low rates may indicate onboarding isn't preparing users for success, prompting adjustments in leading indicators and onboarding strategy.
- Activation Progression Score: This score details how far users progress through onboarding milestones. Analysis of progression bottlenecks fine-tunes leading indicators, enabling more precise forecasting of onboarding outcomes.