{
  "docId": "019dd923-5f69-752d-a9bb-54473db5e334",
  "docSlug": "kZ7iFdhNsLIgawNi1nFF",
  "documentTitle": "Alexis Ohanian's Initialized Capital backed an 18-year-old Y Combinator alumnus after seeing this pitch deck",
  "authorId": "Pitchdecks",
  "authorName": "Depict.ai",
  "documentKindSlug": "pitchdeck",
  "documentKindLabel": "Pitch deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 6,
  "pageCount": 10,
  "prevPage": 5,
  "nextPage": 7,
  "slideType": "problem_statement",
  "function": "frame_problem",
  "density": "balanced",
  "nDataPoints": 0,
  "notes": "Uses yellow highlighting for emphasis on the core problem (lack of data).",
  "elementsJson": [
    "paragraph"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5f69-752d-a9bb-54473db5e334/6",
  "deckHref": "/decks/019dd923-5f69-752d-a9bb-54473db5e334",
  "deckJsonHref": "/decks/019dd923-5f69-752d-a9bb-54473db5e334.json",
  "deckAnchorHref": "/decks/019dd923-5f69-752d-a9bb-54473db5e334#slide-6",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Individual e-commerce stores do not ever have enough data to be able to do recommendations in the right way, just like with any single e-commerce store to detect which credit card will be fraudulent, they will not have enough data to predict which products will sell.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-d066-720a-9bf9-7dbf76277e1e",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.38,
        "w": 0.52,
        "x": 0.24,
        "y": 0.31
      },
      "kind": "paragraph",
      "text": "Individual e-commerce stores do not ever have enough data to be able to do recommendations in the right way, just like with any single e-commerce store to detect which credit card will be fraudulent, they will not have enough data to predict which products will sell.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "27c1113d-77b5-4d1b-a954-05462076e7b0",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 6,
      "from": 6,
      "beatId": "19df2276-9195-46f2-ac9f-d99a0fb1bc76",
      "arcName": "The Sequoia Pitch",
      "arcSlug": "sequoia-pitch",
      "beatName": "Problem",
      "beatSlug": "sequoia-pitch-problem",
      "evidence": "The problem statement is clearly presented on page 6",
      "position": 0,
      "confidence": 0.8,
      "parentBeatName": "Complication",
      "parentBeatSlug": "complication"
    }
  ],
  "loops": [
    {
      "to": 6,
      "from": 6,
      "name": "Cost Of Inaction",
      "slug": "27-cost-of-inaction",
      "bestFor": "Urgent budget requests, compliance, risk mitigation",
      "matchId": "003c00ee-57dd-4214-a6ce-cfcfdeb03fa4",
      "evidence": "The problem statement on page 6 implies the cost of inaction",
      "position": 0,
      "objective": "Highlight the cost of not having sufficient data for recommendations",
      "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point",
      "confidence": 0.7,
      "description": "Quantify what happens if the audience does nothing"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}