{
  "docId": "019dd923-5de0-76bd-a169-7c3e8e51e816",
  "docSlug": "244a0de5e43cb644",
  "documentTitle": "What the Evolution of Travel Means for Business",
  "authorId": "BCG",
  "authorName": "BCG",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 9,
  "pageCount": 28,
  "prevPage": 8,
  "nextPage": 10,
  "slideType": "diagnosis",
  "function": "diagnose",
  "density": "overcrowded",
  "nDataPoints": 1,
  "notes": "Uses a 2x2 matrix to categorize consumer spending shifts (Short-term spikes vs Structural changes).",
  "elementsJson": [
    "headline_text",
    "matrix_2x2",
    "paragraph",
    "callout_box",
    "footnote"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5de0-76bd-a169-7c3e8e51e816/9",
  "deckHref": "/decks/019dd923-5de0-76bd-a169-7c3e8e51e816",
  "deckJsonHref": "/decks/019dd923-5de0-76bd-a169-7c3e8e51e816.json",
  "deckAnchorHref": "/decks/019dd923-5de0-76bd-a169-7c3e8e51e816#slide-9",
  "components": [
    {
      "bbox": {
        "h": 0.7,
        "w": 0.2,
        "x": 0.78,
        "y": 0.16
      },
      "kind": "callout",
      "text": "Demand sensing – lessons from travel. The travel sector faced massive volatility... Example: Car rental company implemented a real-time demand-forecasting platform and boosted demand-sensing accuracy by 30 pp",
      "attrs": null,
      "subkind": "primary",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "474faea7-7fd9-435c-b551-a4f74334606b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "callout",
      "text": "The travel sector faced massive volatility throughout the pandemic; increasing accuracy of demand forecasts is important to react quickly to changes.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-fefa-773b-abcd-2b05b40be7b1",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.45,
        "x": 0.08,
        "y": 0.24
      },
      "kind": "framework",
      "text": "2x2 matrix mapping 'Larger change during COVID-19' vs 'Greater lasting impact on industry'",
      "attrs": null,
      "subkind": "instance",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a3b71c8e-c88a-49b3-9aa8-27f40666e5ee",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "framework",
      "text": "2x2 Matrix",
      "attrs": null,
      "subkind": null,
      "toolName": "Structuring frame",
      "toolSlug": "structuring-frame",
      "confidence": null,
      "componentId": "019dd951-fefa-773b-abcd-3364a5d7c509",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "demand-sensing accuracy: 30 pp",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-fefa-773b-abcd-2c04453c21a2",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.2,
        "w": 0.65,
        "x": 0.08,
        "y": 0.66
      },
      "kind": "paragraph",
      "text": "Companies can prepare for volatility from demand fluctuations by bolstering demand-sensing capabilities. Companies must gather and analyze data from a variety of first- and third-party sources.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "0e3bbe7c-7cd5-4648-9264-efca418ca356",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.7,
        "x": 0.08,
        "y": 0.94
      },
      "kind": "source-note",
      "text": "1. Using BCG Lighthouse real-time demand-forecasting platform. Sources: BCG and Skift, How the Disruption of Air, Cruise, and Hotel Capacity Created Unique Opportunities article (April 2021), press search, BCG Lighthouse, BHI, BCG case experience",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "784e7d79-c770-46f2-8ddb-d4ed701de5da",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.65,
        "x": 0.07,
        "y": 0.04
      },
      "kind": "title",
      "text": "Implications for all companies | Prepare for volatility from consumer spending shifts with demand sensing",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "87e29998-902a-4a16-bcf8-dc446e4ad44b",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "2x2 matrix",
      "slug": "matrix-2x2",
      "agent": null,
      "layer": "slide",
      "matchId": "12dfac7f-165b-4f80-bf5e-e4d386bfda8a",
      "evidence": "The slide uses a 2x2 matrix to display data.",
      "confidence": 0.7
    },
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-163f-72bd-8a0b-a9e8ef0f4316",
      "evidence": "Title prescribes the action: 'Prepare for volatility... with demand sensing'.",
      "confidence": 90
    },
    {
      "name": "Annotation",
      "slug": "annotation",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-163f-72bd-8a0b-b3d51029d4a9",
      "evidence": "Side panel calls out the '30 pp accuracy boost' example.",
      "confidence": 70
    },
    {
      "name": "Gestalt Principles",
      "slug": "gestalt-principles",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-163f-72bd-8a0b-affde20d58c7",
      "evidence": "Quadrant cells grouped by enclosure and proximity around the 2x2 axes.",
      "confidence": 70
    }
  ],
  "frameworks": [
    {
      "name": "2x2 Matrix",
      "slug": null,
      "matchId": "019dd95a-1d21-7467-a461-19db2c76d9c5",
      "evidence": "Axes labelled 'Larger change during COVID-19' and 'Greater lasting impact on industry' with four examples.",
      "confidence": 85
    },
    {
      "name": "matrix-2x2",
      "slug": null,
      "matchId": "35d6db67-f6b0-47d4-a74a-303fab94a9fe",
      "evidence": "Visual 2x2 grid categorizing consumer spending shifts.",
      "confidence": 1
    }
  ],
  "arcBeats": [
    {
      "to": 12,
      "from": 9,
      "beatId": "019dd95a-0702-74a3-87e3-effb03509e2e",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Solution & Approach",
      "beatSlug": "consultants-gambit-solution-approach",
      "evidence": "Three implications for all companies + 5-pillar travel-company playbook.",
      "position": 3,
      "confidence": 88,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    },
    {
      "to": 11,
      "from": 9,
      "beatId": "019dd95a-0702-74a3-87e3-fcba889d125d",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Implications (So What)",
      "beatSlug": "triple-take-the-implications-so-what",
      "evidence": "Implications for all companies sequence (2A-C).",
      "position": 2,
      "confidence": 65,
      "parentBeatName": "Reflection",
      "parentBeatSlug": "reflection"
    }
  ],
  "loops": [
    {
      "to": 11,
      "from": 9,
      "name": "So What Cascade",
      "slug": "41-so-what-cascade",
      "bestFor": "Data presentations, executive summaries, driving to recommendations",
      "matchId": "019dd95a-088c-724c-b30f-eaeb030ec915",
      "evidence": "Slides 2A-2C each pair a stat (30pp, 42%, 50%) with an action (demand sensing, hybrid work, sustainability).",
      "position": 2,
      "objective": "Translate trend data into three so-what recommendations for all companies.",
      "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)",
      "confidence": 80,
      "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}