{
  "name": "Segment players and predict churn with GPT-4o and reward pricing simulations",
  "nodes": [
    {
      "id": "34f5ed60-bc8b-4a4d-8b49-52b9e63c7360",
      "name": "Gameplay Logs Webhook",
      "type": "n8n-nodes-base.webhook",
      "position": [
        224,
        192
      ]
    },
    {
      "id": "4462e215-968f-4592-b407-af582ba8eecd",
      "name": "Player Segmentation Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        736,
        192
      ]
    },
    {
      "id": "73a491ca-c48f-4800-a305-84a7d65d4ace",
      "name": "Segmentation Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        416,
        528
      ]
    },
    {
      "id": "118c359a-7f43-4384-a083-8d0faba86de1",
      "name": "Segmentation Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        2400,
        544
      ]
    },
    {
      "id": "b60d30e4-c09b-44ea-8759-5b7fbac20c30",
      "name": "Player Behavior Embeddings",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        688,
        736
      ]
    },
    {
      "id": "5b2646b9-fccd-4c37-8baf-5772098e9c52",
      "name": "Player Behavior Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        608,
        528
      ]
    },
    {
      "id": "7844c123-a6f1-4ccb-bf38-0cc1f7ce9e58",
      "name": "Metrics Calculator",
      "type": "@n8n/n8n-nodes-langchain.toolCalculator",
      "position": [
        880,
        576
      ]
    },
    {
      "id": "510021be-4863-46ef-bb71-0c3be20c45c6",
      "name": "Statistical Analysis Tool",
      "type": "@n8n/n8n-nodes-langchain.toolCode",
      "position": [
        1024,
        528
      ]
    },
    {
      "id": "77c052b1-2866-451b-bbfe-e2fba14a5e85",
      "name": "Behavioral Prediction Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        1152,
        528
      ]
    },
    {
      "id": "2cf07839-4ed0-4334-a42d-0e02ccc62b69",
      "name": "Prediction Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1024,
        736
      ]
    },
    {
      "id": "ee0c57f8-082f-42db-9c5e-e6416aae377a",
      "name": "Prediction Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1248,
        736
      ]
    },
    {
      "id": "a8a10b94-5274-44b0-a9bd-e4c9511d2be4",
      "name": "Reward Redesign Simulation Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        1440,
        528
      ]
    },
    {
      "id": "98d8081f-175b-482a-bb79-05a815b543f9",
      "name": "Reward Simulation Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1424,
        736
      ]
    },
    {
      "id": "b12d778e-1018-4178-995c-291a7271edad",
      "name": "Reward Simulation Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1584,
        736
      ]
    },
    {
      "id": "f4251dc8-eb5d-423b-afe9-5ecddac4a774",
      "name": "Pricing Adjustment Simulation Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        1728,
        528
      ]
    },
    {
      "id": "2bc32b93-0810-4041-9c5a-6a09dfcc6d73",
      "name": "Pricing Simulation Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1728,
        736
      ]
    },
    {
      "id": "a2ec3179-7020-4cf9-84ba-78a5305a83ea",
      "name": "Pricing Simulation Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1888,
        736
      ]
    },
    {
      "id": "90e0a40a-f2fe-4013-bd91-6b346b0e9f6f",
      "name": "A/B Testing Roadmap Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        2112,
        544
      ]
    },
    {
      "id": "8afff0e9-b8df-441e-9248-70d23e80911c",
      "name": "Testing Roadmap Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        2128,
        752
      ]
    },
    {
      "id": "582af83b-9de0-486a-8c4d-701728f19830",
      "name": "Testing Roadmap Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        2320,
        752
      ]
    },
    {
      "id": "a9253a2b-4073-41f8-8e12-93af20cb3807",
      "name": "Prepare Analytics Results",
      "type": "n8n-nodes-base.set",
      "position": [
        2624,
        320
      ]
    },
    {
      "id": "c0468958-8ebd-4450-a092-9031fbf93d30",
      "name": "Store Analytics Results",
      "type": "n8n-nodes-base.dataTable",
      "position": [
        2832,
        320
      ]
    },
    {
      "id": "d877a3ca-70fe-4331-a1a7-7a5e44909458",
      "name": "Return Analysis Results",
      "type": "n8n-nodes-base.respondToWebhook",
      "position": [
        3040,
        320
      ]
    },
    {
      "id": "2cc12960-130a-4b71-98b9-1c43f7e237b2",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1488,
        -368
      ],
      "parameters": {
        "width": 416,
        "height": 384,
        "content": "\n## Prerequisites\n- LLM API key (OpenAI or compatible)\n- Game backend with webhook event support\n- Vector database for player behavior embeddings\n## Use Cases\n- Identify churning player segments and t"
      }
    },
    {
      "id": "6dbb34c2-36e5-4099-804b-c632efa09fba",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        912,
        -384
      ],
      "parameters": {
        "width": 544,
        "height": 288,
        "content": "## Setup Steps\n1. Configure the Gameplay Logs Webhook with your game backend event endpoint.\n2. Add LLM API credentials to all agent Chat Model nodes.\n3. Connect Player Behavior Vector Store to your e"
      }
    },
    {
      "id": "56b18716-81c0-45ba-b461-a33ee9008e86",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        192,
        -384
      ],
      "parameters": {
        "width": 704,
        "height": 336,
        "content": "## How It Works\nThis workflow automates player segmentation and game economy optimisation using a multi-agent AI architecture, targeting game designers, product managers, and data teams in mobile, PC,"
      }
    },
    {
      "id": "6232f2f9-dea2-4114-978f-aa4f89ef89c3",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1392,
        144
      ],
      "parameters": {
        "width": 624,
        "height": 752,
        "content": "## Reward Redesign & Pricing Simulation\n**What:** Simulates optimised reward structures and adjusted pricing models per segment.\n**Why:** Tests economic changes virtually before live deployment, reduc"
      }
    },
    {
      "id": "a0226214-01fa-4a99-8ad8-0e42368defb1",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -144,
        368
      ],
      "parameters": {
        "width": 1520,
        "height": 528,
        "content": "## Behavioral Prediction\n**What:** Predicts future player actions using statistical analysis and a dedicated prediction model.\n**Why:** Identifies at-risk or high-value players before churn or monetis"
      }
    },
    {
      "id": "1523751e-3204-4ad5-b4a8-b1b0e0ef5aeb",
      "name": "Sticky Note5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -144,
        -16
      ],
      "parameters": {
        "width": 1488,
        "height": 352,
        "content": "## Segment & Orchestrate\n**What:** Player Segmentation Orchestrator delegates tasks across specialist agents using behavioral embeddings and segmentation model.\n**Why:** Ensures each player segment re"
      }
    },
    {
      "id": "a5f114a8-ea91-4cce-bde1-f87a90e14615",
      "name": "Sticky Note6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2048,
        128
      ],
      "parameters": {
        "width": 464,
        "height": 816,
        "content": "## A/B Testing Roadmap\n**What:** Generates a structured A/B testing plan based on simulation outputs.\n**Why:** Provides an actionable experimentation framework grounded in predicted segment behaviour."
      }
    },
    {
      "id": "2e05a5d3-ce82-47ef-9800-bd76c225e68e",
      "name": "Sticky Note7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2560,
        144
      ],
      "parameters": {
        "width": 672,
        "height": 496,
        "content": "\n## Parse, Store & Return Results\n**What:** Segmentation Output Parser consolidates all findings; results are stored and returned via API.\n**Why:** Delivers a unified, structured analytics payload rea"
      }
    }
  ],
  "connections": {
    "Prediction Model": {
      "ai_languageModel": [
        [
          {
            "node": "Behavioral Prediction Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Metrics Calculator": {
      "ai_tool": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Segmentation Model": {
      "ai_languageModel": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Gameplay Logs Webhook": {
      "main": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Testing Roadmap Model": {
      "ai_languageModel": [
        [
          {
            "node": "A/B Testing Roadmap Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Reward Simulation Model": {
      "ai_languageModel": [
        [
          {
            "node": "Reward Redesign Simulation Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Store Analytics Results": {
      "main": [
        [
          {
            "node": "Return Analysis Results",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Prediction Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Behavioral Prediction Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Pricing Simulation Model": {
      "ai_languageModel": [
        [
          {
            "node": "Pricing Adjustment Simulation Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "A/B Testing Roadmap Agent": {
      "ai_tool": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Player Segmentation Agent": {
      "main": [
        [
          {
            "node": "Prepare Analytics Results",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Prepare Analytics Results": {
      "main": [
        [
          {
            "node": "Store Analytics Results",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Statistical Analysis Tool": {
      "ai_tool": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Player Behavior Embeddings": {
      "ai_embedding": [
        [
          {
            "node": "Player Behavior Vector Store",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Segmentation Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Behavioral Prediction Agent": {
      "ai_tool": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Player Behavior Vector Store": {
      "ai_tool": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Testing Roadmap Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "A/B Testing Roadmap Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Reward Simulation Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Reward Redesign Simulation Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Pricing Simulation Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Pricing Adjustment Simulation Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Reward Redesign Simulation Agent": {
      "ai_tool": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Pricing Adjustment Simulation Agent": {
      "ai_tool": [
        [
          {
            "node": "Player Segmentation Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    }
  }
}