Activation Aha

Automated Aha Moment Engineering

Core Principles

1. Aha is Causation Not Correlation: The Aha Moment must be a causal relationship between behavior and retention, not merely a correlation
2. Reach Rate Determines the Ceiling: No matter how strong an Aha Moment is, if the reach rate is low it cannot scale; the reach path must be optimized simultaneously
3. Shortest Path First: The shorter the path from registration to Aha Moment, the higher the activation rate

Interaction Mode

🤖→👤 AI Suggests, Human Approves

Input

| Input Item | Type | Required | Source | Description | |--------|------|------|------|------| | Retention data | object | Yes | output/pm-metrics-ops/analysis-retention/retention_analysis.json | D1/D7/D30 retention rates | | User behavior data | object | Yes | User provided | Event logs, behavior sequences | | User segment data | object | ○ | User provided | User segment data |

Aha Moment Definition

The Aha Moment is the critical moment when a user first experiences the core value of the product. After completing this behavior, users are more likely to retain long-term and become active users.

Aha Moment = Specific Behavior + Specific Time Window + Retention Lift Effect

Execution Steps

Step 1: Aha Moment Candidate Search [Core]

Candidate Behavior Enumeration

Scan all user behaviors to find those highly correlated with retention:

1. Behavior Type Classification:

• Core feature usage
• Key path completion
• Social interaction behaviors
• Content creation behaviors
• Settings configuration behaviors

1. Time Window Analysis:

• Within 1 hour of registration
• Within 24 hours of registration
• Within 7 days of registration

1. Correlation Calculation:

`` Correlation = Degree of correlation between user performing behavior and retention Lift = Retention rate of users who performed the behavior - Retention rate of users who did not ``

Candidate Filtering Criteria

• Correlation ≥ 0.5
• Reach rate ≥ 10%
• Retention lift ≥ 15%

Step 2: Reach Rate Measurement [Core]

Analyze the actual reach of each candidate Aha Moment:

| Metric | Description | |------|------| | Overall reach rate | Proportion of users who reached this behavior out of total registered users | | Time distribution | Time distribution of users reaching this behavior | | Path analysis | User path from registration to this behavior | | Drop-off nodes | Drop-off points before users reach this behavior |

Step 3: Shortest Path Identification [Core]

Analyze how to get users to the Aha Moment fastest:

1. Path analysis: Identify typical paths from registration to Aha Moment
2. Friction identification: Find friction points and drop-off points in the path
3. Optimization recommendations: Design shorter reach paths

Step 4: Onboarding Optimization Recommendations [Deep]

Based on Aha Moment analysis, generate Onboarding optimization recommendations:

Direct Guidance Strategy

• Directly guide users to complete the Aha Moment behavior in the Onboarding flow
• Design a "one-click experience core feature" shortcut path

Incentive Strategy

• Provide rewards for users who complete the Aha Moment behavior
• Lower the barrier to completing the Aha Moment

Education Strategy

• Strengthen the value demonstration of the Aha Moment
• Provide value preview before reaching the Aha Moment

Output Depth Tiering

| Depth Level | Output Scope | Description | |----------|----------|------| | quick | Aha moment and activation path | Core conclusions + minimum viable output | | standard | Full output (current default) | Complete output including all Step outputs | | deep | Full strategy + Aha moment quantitative validation + Activation path optimization + Activation funnel deep analysis | Full output + extended analysis + deep inference |

Output

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

Output Files: aha_moment.json

Output Schema:

{
  "type": "object",
  "required": ["candidates", "primary_aha"],
  "properties": {
    "candidates": {"type": "array", "description": "Aha Moment candidate list, including behavior, correlation, reach rate, and retention lift"},
    "primary_aha": {"type": "object", "description": "Primary Aha Moment, including behavior, reach rate, retention lift, and confidence"},
    "secondary_ahas": {"type": "array", "description": "Secondary Aha Moment list"},
    "onboarding_optimization": {"type": "object", "description": "Onboarding optimization recommendations, including target behaviors and optimization funnel"}
  }
}

aha_moment

{
  "candidates": [
    {
      "behavior": "First create and publish an online course",
      "behavior_type": "action|feature_engagement",
      "retention_lift": 0.35,
      "correlation": 0.78,
      "reach_rate": 0.45,
      "time_to_aha": "Within 24 hours of registration",
      "recommendation": "Guide users in Onboarding to use course templates to quickly create their first course"
    }
  ],
  "primary_aha": {
    "behavior": "First create and publish an online course",
    "reach_rate": 0.45,
    "retention_lift": 0.35,
    "confidence": 0.92
  },
  "secondary_ahas": [
    {
      "behavior": "First invite students to join a course",
      "reach_rate": 0.25,
      "retention_lift": 0.28
    }
  ],
  "onboarding_optimization": {
    "target_behaviors": ["Create online course" // ... same structure extensible],
    "current_funnel": {...},
    "optimized_funnel": {...},
    "expected_activation_lift": "15%"
  }
}

Aha Moment Analysis Example

Candidate Aha Moment Analysis:

1. "First create a project"
   - Correlation: 0.82
   - Reach rate: 35%
   - Retention lift: +42%
   - Recommendation: Optimize project creation flow, lower creation barrier

2. "First share content with friends"
   - Correlation: 0.65
   - Reach rate: 18%
   - Retention lift: +28%
   - Recommendation: Guide users to share, add sharing incentives

3. "First use core analytics feature"
   - Correlation: 0.75
   - Reach rate: 25%
   - Retention lift: +35%
   - Recommendation: Demonstrate core feature value in Onboarding

Output Validation Rules

| Field Path | Type | Required | Description | |----------|------|------|------| | candidates | array | Yes | Aha Moment candidate list, at least 1 candidate | | candidates[].behavior | string | Yes | Behavior description, cannot be empty | | candidates[].behaviortype | string | No | Behavior type, enum: action/completion/social/discovery | | candidates[].correlation | number | Yes | Correlation coefficient, range 0-1 | | candidates[].reachrate | number | Yes | Reach rate, range 0-1 | | candidates[].timetoaha | string | No | Time required to reach Aha | | candidates[].retentionlift | number | Yes | Retention lift, must be >0 | | candidates[].recommendation | string | No | Recommended action | | primaryaha | object | Yes | Primary Aha Moment, must include behavior/reachrate/retentionlift/confidence | | primaryaha.behavior | string | Yes | Aha behavior description, cannot be empty | | primaryaha.reachrate | number | Yes | Reach rate, range 0-1 | | primaryaha.retentionlift | number | Yes | Retention lift, must be >0 | | primaryaha.confidence | number | Yes | Confidence, range 0-1 | | secondaryahas | array | No | Secondary Aha Moment list | | secondaryahas[].behavior | string | Yes | Aha behavior description, cannot be empty | | secondaryahas[].reachrate | number | Yes | Reach rate, range 0-1 | | secondaryahas[].retentionlift | number | No | Retention lift | | onboardingoptimization | object | No | Onboarding optimization recommendations, must include targetbehaviors | | onboardingoptimization.targetbehaviors | array | Yes | Target behavior list | | onboardingoptimization.targetbehaviors[].behavior | string | Yes | Behavior description | | onboardingoptimization.expectedactivation_lift | string | No | Expected activation lift |

Decision Rules

| Situation | Action | |------|----------| | Aha candidate correlation ≥0.5 and reach rate ≥10% | Add to priority validation list | | Primary Aha reach rate <20% | Optimize Onboarding guidance path | | Multiple Aha candidates point to different segments | Design differentiated guidance by segment | | Aha behavior has no causal relationship with retention | Exclude the candidate, continue searching |

Quality Checks

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

• [ ] Aha candidates pass correlation filtering (≥0.5) and significance testing
• [ ] Reach rate analysis includes time distribution and path analysis

P1 Checks (must pass for standard/deep)

• [ ] Shortest path identification includes friction point analysis
• [ ] Onboarding optimization recommendations are directly actionable

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 | |----------|----------|----------|------------| | Retention data missing | User provides user behavior list → Infer Aha Moment candidates | Aha Moment based on inference rather than data validation | Request user to provide retention rate difference data for users with different behaviors | | Behavior data missing | User provides user behavior list → Infer Aha Moment candidates | Cannot perform behavior-retention correlation analysis | Request user to provide user behavior event logs (including event name, timestamp, user ID) | | Both retention data and behavior data missing | User provides user behavior list → Infer Aha Moment candidates | Output Aha Moment candidate list, marked as "awaiting data validation" | Request user to provide user behavior list and retention rate data | | User segment data missing | Skip segment comparison analysis, output overall Aha Moment only | Cannot identify differentiated Aha Moments for different user groups | Request user to provide user segment tags and behavioral characteristics per group |

Data Acquisition Instructions

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

• User Behavior List: Core behaviors users can perform in the product
• Retention Rate Data (optional): Retention rate differences for users with different behaviors
• New User Typical Path (optional): Most common action sequences for new users

Upstream Change Response

Upstream Change Impact Table

| Upstream Source | Change Type | Impact Scope | Response Action | |----------|----------|----------|----------| | analysis-retention | Retention metric definition change | Correlation calculation and retention lift assessment | Recalculate correlation and retention lift using new definition | | User provided - Behavior data | Event definition change | Candidate behavior enumeration and path analysis | Update behavior classification, re-search candidates | | User provided - Segment data | Segment dimension change | Segment-level Aha Moment identification | Re-identify segment Aha using new dimensions |

Downstream Notification Mechanism Table

| Downstream Consumer | Notification Condition | Notification Method | Notification Content | |------------|----------|----------|----------| | activation-onboarding | Primary Aha Moment change | Write to output file | New Aha behavior, reach rate, and guidance path | | activation-orchestrator | Aha Moment identification completed | Output file updated | Aha identification completion status and key conclusions |

Notes

• Aha Moments may change as the product iterates and should be periodically re-evaluated
• Different user segments may have different Aha Moments
• Aha Moment optimization requires balancing reach rate and retention lift

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.