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genpark-klaviyo-predictive-clv-omnichannel-journey-trigger-skill

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SUMMARY

Klaviyo-inspired Predictive Customer Lifetime Value (CLV) & Omnichannel Journey Trigger. Estimates 365-day CLV using recency-frequency-monetary models, forecasts churn probability, and orchestrates personalized cross-channel flows (SMS, Ema | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-klaviyo-predictive-clv-omnichannel-journey-trigger-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Klaviyo-inspired Predictive Customer Lifetime Value (CLV) & Omnichannel Journey Trigger. Estimates 365-day CLV using recency-frequency-monetary models, forecasts churn probability, and orchestrates personalized cross-channel flows (SMS, Email, Push) while enforcing fatigue limits.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import KlaviyoPredictiveCLVOmnichannelJourneyTrigger

def main():
    trigger = KlaviyoPredictiveCLVOmnichannelJourneyTrigger()
    profile = {
        "customer_id": "VIP-8812",
        "total_orders": 8,
        "total_spend_usd": 1280.0,
        "days_since_last_order": 20,
        "days_since_last_touchpoint": 5
    }
    result = trigger.evaluate_customer_journey(profile)
    print("--- Klaviyo Predictive CLV & Journey Trigger ---")
    print(f"Customer: {result['customer_id']} | 365d Predicted CLV: ${result['predictive_metrics']['predicted_next_365d_clv_usd']}")
    print(f"Churn Risk: {result['predictive_metrics']['churn_probability']*100:.1f}%")
    print(f"Flow Triggered: {result['automated_journey_trigger']['flow_name']} via {result['automated_journey_trigger']['channel']}")
    print(f"Incentive: {result['automated_journey_trigger']['incentive_offer']}")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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