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genpark-rokt-transaction-moment-post-purchase-offer-arbiter-skill

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SUMMARY

Rokt-inspired Transaction Moment & Post-Purchase Retail Media Offer Arbiter. Leverages contextual multi-armed bandit sampling, customer brand affinity filtering, and eCPM yield arbitrage to serve the highest-converting third-party and nativ | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-rokt-transaction-moment-post-purchase-offer-arbiter-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)

Rokt-inspired Transaction Moment & Post-Purchase Retail Media Offer Arbiter. Leverages contextual multi-armed bandit sampling, customer brand affinity filtering, and eCPM yield arbitrage to serve the highest-converting third-party and native offers at checkout and thank-you pages.

🌐 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 RoktTransactionMomentPostPurchaseOfferArbiter

def main():
    arbiter = RoktTransactionMomentPostPurchaseOfferArbiter()
    tx = {
        "order_total_usd": 150.0,
        "merchant_category": "Luxury Retail",
        "loyalty_tier": "VIP"
    }
    offers = [
        {"offer_id": "r1", "title": "Equinox 1-Month Guest Pass", "cpa_bid_usd": 45.0, "historical_ctr": 0.05, "historical_cvr": 0.12, "is_premium": True},
        {"offer_id": "r2", "title": "$30 Off Uber Eats First Order", "cpa_bid_usd": 22.0, "historical_ctr": 0.06, "historical_cvr": 0.18, "is_premium": False},
        {"offer_id": "r3", "title": "Bargain Discount Outlet", "cpa_bid_usd": 15.0, "min_order_total_usd": 200.0}
    ]
    res = arbiter.arbitrate_offers(tx, offers, slot_capacity=2)
    print("--- Rokt Post-Purchase Offer Arbitration ---")
    print(f"Decision: {res['placement_decision']} | Total Projected eCPM: ${res['projected_slot_ecpm_usd']}")
    for idx, o in enumerate(res['selected_offers'], 1):
        print(f"Slot {idx}: {o['title']} (eCPM: ${o['ecpm_usd']}, Arbitration Score: {o['arbitration_score']})")

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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