{
  "title": "Marketing consulting campaign measurement and claim evidence for AI",
  "source_page": "https://fiund.com/industries/marketing-consulting-attribution-basis-data",
  "authorship": "Original fictional Fiund editorial exercise. No actual operating records or third-party source assets are included.",
  "example": {
    "title": "More submission events do not establish more unique inquiries or causal lift",
    "context": "Two dummy funnel reports use synthetic aggregates and original definitions. No audience IDs, pixels, client budgets, account credentials or actual campaign appears.",
    "records": [
      [
        "MK1",
        "Practice period A report",
        "100 unique inquiry forms, deduplicated by the exercise rule."
      ],
      [
        "MK2",
        "Period B export",
        "140 submission events, including repeats. Unique inquiry count and cross-period deduplication absent."
      ],
      [
        "MK3",
        "Draft channel memo",
        "Attributes a 40% inquiry increase entirely to new channel C. No controlled comparison or source-attribution evidence supplied."
      ]
    ],
    "question": "What reported counts can be stated, and does the packet establish a 40% rise in unique inquiries caused by channel C?",
    "answer": "MK1 reports 100 unique inquiries in A, while MK2 reports 140 submission events in B under a different counting basis. Without B's deduplicated inquiry count, their difference does not establish a 40% unique-inquiry increase. MK3 also supplies no design isolating C's causal effect. Preserve counts, definitions and periods, and flag both the metric-comparability and causal-attribution gaps rather than repeating the memo's claim.",
    "failureModes": [
      "Comparing unique entities with repeat-including events as the same metric.",
      "Attributing an observed period difference entirely to one channel without supporting design or evidence."
    ],
    "fields": [
      [
        "Metric numerator and deduplication rule",
        "Defines what the count represents."
      ],
      [
        "Reporting period",
        "Keeps the time window explicit."
      ],
      [
        "Channel attribution and comparison design",
        "Separates descriptive movement from causal claims."
      ]
    ],
    "holdout": "Supply B's unique count but still no causal design. Require the model to resolve the descriptive change while preserving the attribution limitation.",
    "boundary": "Use synthetic aggregates and owned definitions. Exclude people, pixels, budgets and credentials."
  }
}