Activation Onboarding

Automated Onboarding Optimization

Core Principles

1. Onboarding is Value Delivery Not Feature Tour: Every guidance step must let the user feel value, not just know where features are
2. Segmentation is Separate Paths: Different user segments need different Onboarding paths; one path cannot serve everyone
3. Aha Moment is the Destination: The sole goal of Onboarding is to get users to the Aha Moment; everything else is a means

Interaction Mode

🤖→👤 AI Suggests, Human Approves

Input

| Input Item | Type | Required | Source | Description | |--------|------|------|------|------| | Onboarding data | object | Yes | User provided | Completion rate, drop-off rate, user feedback | | Aha Moment data | object | Yes | output/pm-growth/activation-aha/aha_moment.json | Aha Moment data | | User segment data | object | ○ | User provided | User characteristics, behavioral characteristics |

Onboarding Stage Definitions

The standard Onboarding flow includes the following stages:

Welcome Page → Value Demonstration → Account Setup → Feature Guidance → Aha Moment → Activation Complete

Stage 1: Welcome Page

• Brand presentation
• Value proposition communication
• Guidance start

Stage 2: Value Demonstration

• Core feature demo
• User case showcase
• Value promise

Stage 3: Account Setup

• Basic information entry
• Preference settings
• Personalization configuration

Stage 4: Feature Guidance

• Core feature introduction
• Operation demonstration
• Hands-on practice

Stage 5: Aha Moment

• Guide completion of core value behavior
• Ensure user experiences product value

Stage 6: Activation Complete

• Celebrate activation success
• Show subsequent value path
• Provide help resources

Execution Steps

Step 1: Current Onboarding Effectiveness Analysis [Core]

Overall Effectiveness Assessment

• Onboarding completion rate
• Stage-by-stage conversion rates
• Completion time distribution
• User satisfaction

Drop-off Analysis

• Largest drop-off node identification
• Drop-off cause inference
• Drop-off user characteristic analysis

Effectiveness Comparison

• Onboarding differences across channel users
• Onboarding differences across user segments
• Comparison with industry benchmarks

Step 2: Segment Onboarding Strategy Generation [Core]

Based on user segments, design differentiated Onboarding strategies:

Segmentation Dimensions

• Technical background (Technical/Non-technical)
• Use case (B2B/B2C)
• Industry type
• Registration source
• User scale

Strategy Design Principles

| User Type | Guidance Style | Guidance Content | |---------|---------|---------| | Technical | Concise and direct | Quick start, provide advanced features | | Business | Detailed and friendly | Step-by-step guidance, emphasize value | | Enterprise | Professional and comprehensive | Complete training, emphasize collaboration | | Individual | Lightweight and fast | Minimum steps, immediate experience |

Step 3: Personalized Guidance Content Generation [Core]

Based on segment strategies, generate personalized guidance content:

Content Types

1. Progressive Guidance - Step-by-step guide users through key operations
2. Contextual Tips - Show help when users need it
3. Video Demos - Demonstrate core feature operations
4. Interactive Tutorials - Guide users to learn by doing
5. Reward Incentives - Earn rewards for completing guidance

Content Generation Principles

• Concise and clear, understood at a glance
• Action-oriented, emphasize the next step
• Value-oriented, emphasize benefits
• Progress awareness, let users know how much is left

Step 4: A/B Test Design [Core]

Design A/B tests for Onboarding optimization:

Test Types

1. Overall Onboarding Redesign - Compare new vs. old Onboarding plans
2. Single-point Optimization Test - Optimize a specific guidance step
3. Segment Differentiation Test - Different guidance plans for different user groups

Core Metrics

• Primary Metrics: Onboarding completion rate, activation rate
• Secondary Metrics: Onboarding duration, user satisfaction
• Guardrail Metrics: Subsequent retention rate, payment conversion rate

Output Depth Tiering

| Depth Level | Output Scope | Description | |----------|----------|------| | quick | Onboarding flow and activation strategy | Core conclusions + minimum viable output | | standard | Full output (current default) | Complete output including all Step outputs | | deep | Full strategy + Activation funnel deep analysis + Personalized Onboarding design + A/B test plan | Full output + extended analysis + deep inference |

Output

Storage Path: output/pm-growth/activation-onboarding/

Output Files: onboarding_plan.json

Output Schema:

{
  "type": "object",
  "required": ["current_effectiveness", "segment_strategies"],
  "properties": {
    "current_effectiveness": {"type": "object", "description": "Current Onboarding effectiveness assessment, including completion rate, drop-off points, and average completion time"},
    "segment_strategies": {"type": "array", "description": "Segment Onboarding strategy list, including segment characteristics and expected improvement"},
    "personalized_content": {"type": "array", "description": "Personalized guidance content list, including content type and trigger conditions"},
    "ab_tests": {"type": "array", "description": "A/B test design plan list"}
  }
}

onboarding_optimization

{
  "current_effectiveness": {
    "overall_completion_rate": 0.45,
    "stage_completion_rates": {
      "welcome": 0.85,
      "profile_setup": 0.65,
      "first_action": 0.55,
      "aha_moment": 0.35
    },
    "drop_off_points": [
      {"stage": "profile_setup", "drop_off_rate": 0.24}
    ],
    "avg_time_to_complete": 12.5
  },
  "segment_strategies": [
    {
      "segment": "New User - Technical Background",
      "size": 5000,
      "characteristics": ["Has technical background", "Prefers self-service exploration"],
      "strategy": "Simplify guidance, provide advanced feature entry",
      "expected_improvement": "+20% activation rate"
    }
  ],
  "personalized_content": [
    {
      "segment": "New User - Non-technical Background",
      "content_type": "step_by_step_guide",
      "content": "Interactive tutorial guiding teachers step-by-step through course creation, content editing, and student invitation",
      "trigger": "Display immediately after registration"
    }
  ],
  "ab_tests": [
    {
      "test_id": "ONB_TEST_001",
      "hypothesis": "Step-by-step guidance vs. free exploration",
      "target_segment": "Non-technical background users",
      "expected_lift": "15%"
    }
  ]
}

A/B Test Design Template

test_id: "ONB_TEST_{sequence_number}"
name: "Test name"
hypothesis: "Optimization hypothesis description"
target_segment: "Target user group"
variants:
  control:
    name: "Control group"
    description: "Current plan description"
  treatment:
    name: "Treatment group"
    description: "Optimization plan description"
metrics:
  primary: "Primary metric definition"
  secondary: ["Secondary metric list"]
  guardrail: ["Guardrail metric list"]
design:
  min_sample_per_variant: 2000
  runtime_days: 14
  mde: 0.05
success_criteria:
  - primary_metric_lift: ">=10%"
  - guardrail_metrics: "No significant decline"
  - statistical_significance: 0.95

Output Validation Rules

| Field Path | Type | Required | Description | |----------|------|------|------| | currenteffectiveness | object | Yes | Current effectiveness assessment, must include overallcompletionrate/dropoffpoints | | currenteffectiveness.overallcompletionrate | number | Yes | Overall completion rate, range 0-1 | | currenteffectiveness.stagecompletionrates | object | No | Stage completion rates | | currenteffectiveness.dropoffpoints | array | Yes | Drop-off point list, each item must include stage/dropoffrate | | currenteffectiveness.dropoffpoints[].stage | string | Yes | Drop-off stage name | | currenteffectiveness.dropoffpoints[].dropoffrate | number | Yes | Drop-off rate, range 0-1 | | segmentstrategies | array | Yes | Segment strategy list, at least 1 segment strategy | | segmentstrategies[].segment | string | Yes | Segment name | | segmentstrategies[].size | number | No | Segment user proportion | | segmentstrategies[].characteristics | string[] | No | Segment characteristic description | | segmentstrategies[].strategy | string | Yes | Strategy description | | segmentstrategies[].expectedimprovement | string | No | Expected improvement effect | | personalizedcontent | array | No | Personalized content list, each item must include segment/contenttype/content/trigger | | personalizedcontent[].segment | string | Yes | Target segment | | personalizedcontent[].contenttype | string | Yes | Content type, enum: stepbystepguide/video/tooltip/checklist | | personalizedcontent[].content | string | Yes | Content description, cannot be empty | | personalizedcontent[].trigger | string | Yes | Trigger condition, cannot be empty | | abtests | array | No | A/B test list, each item must include testid/hypothesis | | abtests[].testid | string | Yes | Test ID, cannot be empty | | abtests[].hypothesis | string | Yes | Test hypothesis, cannot be empty | | abtests[].targetsegment | string | No | Target segment | | abtests[].expectedlift | string | No | Expected lift |

Decision Rules

| Situation | Action | |------|----------| | Onboarding completion rate 30% | Optimize guidance content for that stage | | Technical user completion rate significantly lower than non-technical | Provide self-service exploration path | | A/B test primary metric lift <5% | Adjust test hypothesis or expand sample |

Quality Checks

P0 Checks (must pass for quick/standard/deep)

• [ ] Onboarding stage definitions are complete (Welcome → Activation Complete)
• [ ] Drop-off analysis covers all stages and user segments

P1 Checks (must pass for standard/deep)

• [ ] Personalized guidance matches user segments
• [ ] A/B tests include guardrail metrics (subsequent retention, payment conversion)

P2 Checks (only deep must pass)

• [ ] Extended analysis is complete (deep inference and roadmap generated)
• [ ] Decision records are complete (key decisions have rationale and alternatives)

Degradation Strategy

Upstream File Missing Degradation Plan

| Missing Upstream Input | Degradation Plan | Output Impact | Data Acquisition Instructions | |----------|----------|----------|------------| | Onboarding data missing | User describes current Onboarding flow → Generate optimization recommendations | Optimization recommendations based on qualitative description rather than data-driven | Request user to provide current Onboarding flow steps and completion rate data per step | | Aha Moment missing | Skip Aha Moment guidance optimization, based on general best practices | Onboarding optimization lacks Aha Moment anchor | Request user to provide Aha Moment definition or upload activation-aha output file | | Both Onboarding data and Aha Moment missing | User describes current Onboarding flow → Generate optimization recommendations | Output optimization recommendations based on best practices, marked as "awaiting data validation" | Request user to provide current Onboarding flow description and core user behaviors | | User segment data missing | Skip segment Onboarding optimization, output general guidance plan only | Cannot customize differentiated Onboarding for different user groups | Request user to provide user segment tags and characteristics data per group |

Data Acquisition Instructions

When upstream files are missing, the user needs to provide the following information to support degraded generation:

• Current Onboarding Flow: Steps and content of new user guidance
• Completion Rate Data (optional): Completion rate per guidance step
• User Feedback (optional): New user feedback on the guidance flow

Upstream Change Response

Upstream Change Impact Table

| Upstream Source | Change Type | Impact Scope | Response Action | |----------|----------|----------|----------| | activation-aha | Primary Aha Moment change | Onboarding endpoint and guidance path | Redesign guidance path to point to new Aha | | activation-aha | Reach rate data update | Expected improvement of segment strategies | Adjust expected improvement and priorities | | User provided - Onboarding data | Data definition change | Effectiveness assessment and drop-off analysis | Re-evaluate effectiveness using new definition |

Downstream Notification Mechanism Table

| Downstream Consumer | Notification Condition | Notification Method | Notification Content | |------------|----------|----------|----------| | retention-management | Activation rate change | Write to output file | New user activation rate and Onboarding completion rate | | activation-orchestrator | Onboarding strategy output completed | Output file updated | Onboarding optimization completion status and key conclusions |

Key Success Metrics

| Metric | Current Value | Target Value | |------|--------|--------| | Onboarding completion rate | 45% | ≥60% | | Activation rate | 35% | ≥50% | | Average completion time | 12.5 minutes | ≤10 minutes | | Guidance satisfaction | 3.2 | ≥4.0 |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: LuckyOneTwoThree
• Source: LuckyOneTwoThree/vibe-skill
• License: MIT
• Homepage: https://luckyonetwothree.github.io/all-skill-html/

Install and usage instructions live in the source repository linked above.