{
  "docId": "019de074-02be-75fe-ba89-d286e984482b",
  "docSlug": "0751ea78f4edac7735750710a4f7e5ce",
  "documentTitle": "Lemonade | Investor Day Presentation Deck | 125 slides",
  "authorId": "lemonade",
  "authorName": "Lemonade",
  "documentKindSlug": "conference-presentation",
  "documentKindLabel": "Conference presentation",
  "sourceTypeSlug": "investor_relations",
  "sourceTypeLabel": "Investor relations",
  "presentationDate": "2022-11-01 00:00:00",
  "orientation": "landscape",
  "aspectRatio": 1.7777778,
  "pageNumber": 33,
  "pageCount": 125,
  "prevPage": 32,
  "nextPage": 34,
  "slideType": "kpi_overview",
  "function": "analyze_data",
  "density": "dense",
  "nDataPoints": 1,
  "notes": "The slide uses a sidebar navigation element indicating 'G&A, Marketing, Support' vs 'Fraud, LAE, Claims'.",
  "elementsJson": null,
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019de074-02be-75fe-ba89-d286e984482b/33",
  "deckHref": "/decks/019de074-02be-75fe-ba89-d286e984482b",
  "deckJsonHref": "/decks/019de074-02be-75fe-ba89-d286e984482b.json",
  "deckAnchorHref": "/decks/019de074-02be-75fe-ba89-d286e984482b#slide-33",
  "components": [
    {
      "bbox": {
        "h": 0.2,
        "w": 0.385,
        "x": 0.025,
        "y": 0.39
      },
      "kind": "chart",
      "text": "Projected LTV to spend distributions",
      "attrs": null,
      "subkind": "line",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "787895e3-0bea-4655-aad8-31e7c2cb3a09",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.208,
        "w": 0.078,
        "x": 0.027,
        "y": 0.044
      },
      "kind": "image",
      "text": "Sidebar navigation",
      "attrs": null,
      "subkind": "logo",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "163ff4c4-5cf4-451b-8f71-07b356438155",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.06,
        "x": 0.663,
        "y": 0.575
      },
      "kind": "metric",
      "text": "86%",
      "attrs": null,
      "subkind": "big-number",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "54b7cc5d-4590-4711-bb49-54c23c56da1a",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.13,
        "w": 0.41,
        "x": 0.403,
        "y": 0.378
      },
      "kind": "paragraph",
      "text": "Machine-learning models generate LTV predictions for every customer, every campaign, every product and every geo.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "39ffff21-6dec-4aa9-b92d-055549e01227",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.07,
        "w": 0.35,
        "x": 0.403,
        "y": 0.575
      },
      "kind": "paragraph",
      "text": "These models govern 86% of our marketing spend.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5ab97556-5b7d-4e3f-bc4d-ee356832cb82",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.042,
        "w": 0.203,
        "x": 0.397,
        "y": 0.058
      },
      "kind": "title",
      "text": "Expense Ratio",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e0cffca4-11b5-4481-acd9-f3deeeb6c65d",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Chart Selection Guide",
      "slug": "chart-selection-guide",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "2e3e98b1-31f0-40bf-b5a3-3b706db6bf1f",
      "evidence": "chart/line: Projected LTV to spend distributions",
      "confidence": 0.5
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 40,
      "from": 21,
      "beatId": "e088242d-96ca-49e7-80b8-69f93bfbdb5d",
      "arcName": "AIDA",
      "arcSlug": "aida",
      "beatName": "Desire",
      "beatSlug": "aida-desire",
      "evidence": "The presentation builds desire with a clear explanation of Lemonade's competitive advantage and customer-centric approach",
      "position": 2,
      "confidence": 0.8,
      "parentBeatName": "Evidence",
      "parentBeatSlug": "evidence"
    }
  ],
  "loops": [
    {
      "to": 40,
      "from": 20,
      "name": "Logic Chain",
      "slug": "01-logic-chain",
      "bestFor": "Skeptical audiences, controversial recommendations, rigorous analysis",
      "matchId": "d89597a2-285d-4cb8-842e-a26cc125d1e0",
      "evidence": "The presentation clearly explains the logic behind Lemonade's use of AI and machine learning",
      "position": 0,
      "objective": "How does Lemonade's use of AI and machine learning drive efficiency and customer experience?",
      "structure": "Premise 1 (Accepted truth) -> Premise 2 (Observed fact) -> Therefore... (Inevitable conclusion)",
      "confidence": 0.7,
      "description": "Build an airtight chain of logic where each premise leads inevitably to the conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}