{
  "docId": "019dd923-5e87-7479-8208-4d8db59d6593",
  "docSlug": "f65935f61924cbcf",
  "documentTitle": "IAB State of Data 2023",
  "authorId": "IPSOS",
  "authorName": "IAB",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "industry_analyst",
  "sourceTypeLabel": "Industry analyst",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 35,
  "pageCount": 47,
  "prevPage": 34,
  "nextPage": 36,
  "slideType": "initiative_list",
  "function": "present_solution",
  "density": "dense",
  "nDataPoints": 0,
  "notes": "The slide defines a 'conditional sequence data approach' as a non-linear sequence where next steps are based on conditions from previous steps.",
  "elementsJson": [
    "headline_text",
    "paragraph",
    "numbered_list",
    "callout_box",
    "footnote"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e87-7479-8208-4d8db59d6593/35",
  "deckHref": "/decks/019dd923-5e87-7479-8208-4d8db59d6593",
  "deckJsonHref": "/decks/019dd923-5e87-7479-8208-4d8db59d6593.json",
  "deckAnchorHref": "/decks/019dd923-5e87-7479-8208-4d8db59d6593#slide-35",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "When evaluating DCR use cases, it is optimal to take a conditional sequence data approach.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-2300-70cc-b42e-78ff1eec3ba2",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.75,
        "w": 0.48,
        "x": 0.48,
        "y": 0.16
      },
      "kind": "list",
      "text": "1. Audience insights and segmentation: Collaboration in a DCR between the buy- and sell-sides of the digital advertising industry allows both parties to comingle their first-party data and improve data enrichment to understand advanced consumer insights, such as behavioral and contextual signals, in a privacy-compliant way.\n2. Media campaign measurement and attribution: By matching customer and performance data, DCRs enable companies to measure cross-media performance and develop attribution and measurement models for optimization. DCR users can conduct a variety of analyses: consumer journey, audience overlap, and reach and frequency, as well as mix modeling and scenario planning with the use of artificial intelligence (AI) and machine learning (ML).\n3. Audience modeling and activation: Advertisers can use DCRs for scalable audience activation by creating probabilistic or look-a-like audiences for targeting in a privacy-first ecosystem. Publishers and retailers can uncover and create audiences that ad buyers may find more appealing. DCR collaboration capabilities open the possibilities for improved data enrichment to understand consumer insights such and behavioral and contextual signals.",
      "attrs": null,
      "subkind": "numbered",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "b705a239-923b-4d57-9ae3-c363c754903c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.45,
        "x": 0.03,
        "y": 0.16
      },
      "kind": "paragraph",
      "text": "When evaluating DCR use cases, it is optimal to take a conditional sequence data approach. Outline the various use cases sequences based on your hypothesis which will branch out based on the business needs, operational lift, cost, and data maturity.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "22c3d04f-ae72-4ea7-9641-6ca705b57a88",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.09,
        "w": 0.45,
        "x": 0.03,
        "y": 0.61
      },
      "kind": "paragraph",
      "text": "In the future, as DCRs evolve and become more sophisticated, additional use cases will be unlocked and offer companies across the ecosystem more flexible, sophisticated, interoperable, and scalable opportunities.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a0bd3630-0083-4b3d-b1e7-d569fe1cb464",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.45,
        "x": 0.03,
        "y": 0.41
      },
      "kind": "paragraph",
      "text": "Data monetization in a privacy-preserving environment is a core capability for retailers in DCRs. This allows for the democratization of data to improve personalization, enhance audience insights, and offers a comprehensive view of measurement and outcomes.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "eceba19c-a369-4ed8-8a64-4d4727786e44",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.35,
        "x": 0.13,
        "y": 0.93
      },
      "kind": "source-note",
      "text": "*Conditional sequence data approach: a non-linear sequence where the next steps are based on if conditions from previous steps are met.",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e38e437a-ca56-4db3-98ec-265edcb4be32",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.06,
        "w": 0.75,
        "x": 0.03,
        "y": 0.03
      },
      "kind": "title",
      "text": "Use Case Framework: Develop a conditional sequence data approach",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "03c4a7aa-a1a6-4746-ac61-6c24f8fd456b",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "2x2 matrix",
      "slug": "matrix-2x2",
      "agent": null,
      "layer": "slide",
      "matchId": "49fc5222-8ca7-4eb6-9dee-e20ba34ae0dd",
      "evidence": "Outline the various use cases sequences based on your hypothesis which will branch out based on the business",
      "confidence": 0.5
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 35,
      "from": 27,
      "beatId": "7685aa5b-efa2-4a40-a44b-1e4f39c5b0ac",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Solution & Approach",
      "beatSlug": "consultants-gambit-solution-approach",
      "evidence": "The document offers best practices and considerations for privacy-preserving technology",
      "position": 3,
      "confidence": 0.8,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    }
  ],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}