Segment User V2

Action ID: segment_user_v2

Description

Segment user V2

Input Parameters

Name	Type	Required	Default	Description
users	string	✓	-	List of users in JSON format. Should be a JSON array of user objects.

View JSON Schema

{
  "description": "Segment user",
  "properties": {
    "users": {
      "description": "List of users in JSON format.",
      "title": "List of Users",
      "type": "string"
    }
  },
  "required": [
    "users"
  ],
  "title": "SegmentUserV2NodeInput",
  "type": "object"
}

Output Parameters

Name	Type	Description
segments	array	User segments with categorized user groups using enhanced segmentation logic.

View JSON Schema

{
  "description": "Segment user output",
  "properties": {
    "segments": {
      "description": "User segments.",
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "User Segments",
      "type": "array"
    }
  },
  "required": [
    "segments"
  ],
  "title": "SegmentUserV2NodeOutput",
  "type": "object"
}

How It Works

This is an enhanced version of the user segmentation node with improved algorithms and capabilities. It analyzes user data with advanced segmentation logic, providing more granular and accurate user groupings. The V2 version includes better handling of edge cases, more sophisticated pattern recognition, and enhanced segment quality. It processes the user list and returns optimized segments with detailed characteristics for each group.

Usage Examples

Example 1: Advanced Behavioral Segmentation

Input:

users: '[
  {"id": 1, "name": "Alex", "last_login": "2024-01-14", "session_count": 120, "avg_session_time": 45, "feature_adoption": 0.85},
  {"id": 2, "name": "Beth", "last_login": "2023-12-01", "session_count": 5, "avg_session_time": 10, "feature_adoption": 0.20},
  {"id": 3, "name": "Chris", "last_login": "2024-01-13", "session_count": 80, "avg_session_time": 35, "feature_adoption": 0.70},
  {"id": 4, "name": "Dana", "last_login": "2024-01-15", "session_count": 200, "avg_session_time": 60, "feature_adoption": 0.95}
]'

Output:

segments: [
  {
    "segment_name": "Champions",
    "description": "Highly engaged users with strong feature adoption",
    "count": 2,
    "characteristics": {"avg_sessions": 160, "avg_feature_adoption": 0.90},
    "users": [
      {"id": 1, "name": "Alex", "engagement_score": 92},
      {"id": 4, "name": "Dana", "engagement_score": 98}
    ]
  },
  {
    "segment_name": "Promising",
    "description": "Active users with growth potential",
    "count": 1,
    "characteristics": {"avg_sessions": 80, "avg_feature_adoption": 0.70},
    "users": [{"id": 3, "name": "Chris", "engagement_score": 75}]
  },
  {
    "segment_name": "At Risk",
    "description": "Low engagement users requiring attention",
    "count": 1,
    "characteristics": {"avg_sessions": 5, "avg_feature_adoption": 0.20},
    "users": [{"id": 2, "name": "Beth", "engagement_score": 25}]
  }
]

Example 2: Revenue-Based Segmentation

Input:

users: '[
  {"id": 501, "email": "[email protected]", "lifetime_value": 15000, "avg_order": 500, "frequency": "weekly"},
  {"id": 502, "email": "[email protected]", "lifetime_value": 2400, "avg_order": 120, "frequency": "monthly"},
  {"id": 503, "email": "[email protected]", "lifetime_value": 350, "avg_order": 70, "frequency": "rarely"}
]'

Output:

segments: [
  {
    "segment_name": "Platinum Tier",
    "description": "Top revenue generators",
    "count": 1,
    "total_value": 15000,
    "users": [{"id": 501, "email": "[email protected]", "tier": "platinum"}]
  },
  {
    "segment_name": "Gold Tier",
    "description": "Consistent mid-value customers",
    "count": 1,
    "total_value": 2400,
    "users": [{"id": 502, "email": "[email protected]", "tier": "gold"}]
  },
  {
    "segment_name": "Bronze Tier",
    "description": "Low-value occasional customers",
    "count": 1,
    "total_value": 350,
    "users": [{"id": 503, "email": "[email protected]", "tier": "bronze"}]
  }
]

Example 3: Multi-Dimensional Segmentation

Input:

users: '[
  {"id": 701, "age": 28, "location": "urban", "device": "mobile", "plan": "premium", "satisfaction": 4.5},
  {"id": 702, "age": 45, "location": "suburban", "device": "desktop", "plan": "free", "satisfaction": 3.2},
  {"id": 703, "age": 32, "location": "urban", "device": "mobile", "plan": "premium", "satisfaction": 4.8}
]'

Output:

segments: [
  {
    "segment_name": "Premium Mobile Urban",
    "description": "High-satisfaction urban premium mobile users",
    "count": 2,
    "avg_satisfaction": 4.65,
    "users": [
      {"id": 701, "profile": "young_professional"},
      {"id": 703, "profile": "young_professional"}
    ]
  },
  {
    "segment_name": "Free Desktop Suburban",
    "description": "Moderate-satisfaction suburban desktop users",
    "count": 1,
    "avg_satisfaction": 3.2,
    "users": [{"id": 702, "profile": "mature_user"}]
  }
]

Common Use Cases

• Advanced Customer Lifecycle Management: Segment users across complex lifecycle stages with multiple factors

• Predictive Churn Analysis: Identify at-risk segments using sophisticated behavioral patterns

• Personalized Engagement Strategies: Create highly targeted campaigns based on multi-dimensional segments

• Product Development Insights: Understand user segments to guide feature prioritization

• Revenue Optimization: Identify high-value segments and optimize monetization strategies

• Customer Success Programs: Allocate resources efficiently based on detailed segment characteristics

• Cross-sell and Upsell: Target specific segments with relevant product recommendations

Error Handling

Error Type	Cause	Solution
Invalid JSON Format	Users parameter contains malformed JSON	Validate JSON syntax and ensure proper formatting
Empty User Array	No users provided in the array	Include at least one user object for segmentation
Insufficient Data	User objects lack enough attributes for meaningful segmentation	Provide richer user data with multiple attributes
Data Type Mismatch	User attributes have incorrect data types	Ensure numeric fields are numbers, strings are text, etc.
Segmentation Error	Advanced algorithm cannot process the data	Verify data quality and consistency across user objects
Memory Limit	Too many users to process	Consider batching large user lists into smaller groups

Notes

• Version 2 Enhancements: This version includes improved segmentation algorithms, better pattern recognition, and more detailed segment metadata compared to V1.

• Multi-Dimensional Analysis: V2 can analyze multiple user attributes simultaneously for more sophisticated segmentation.

• Segment Quality: Enhanced algorithms produce higher quality segments with clearer distinctions and more actionable insights.

• Performance: While more sophisticated, V2 maintains good performance even with larger user datasets.

• Rich Metadata: Segments include additional information like characteristics, scores, and descriptions for better understanding.

• Backward Compatibility: Output format is compatible with V1, with additional optional fields for enhanced functionality.

• Best Practices: Provide comprehensive user data including behavioral, demographic, and transactional attributes for optimal segmentation results.

• Use Case Selection: Use V2 when you need more nuanced segmentation compared to the basic V1 implementation.