{
  "abstract": "Marketing Mix Models (MMMs) are used to estimate channel-level Return on Ad Spend (RoAS) and guide marketing investment, yet competing specifications are usually compared on predictive-fit metrics such as out-of-sample R-squared, MAPE, or RMSE. We show that predictive fit can diverge from recovery of the causal quantities that drive marketing decisions. In simulations with known RoAS, the best-fitting specification is often not the one that recovers RoAS most accurately; replicated studies show that this disagreement is systematic and persists under media-spend collinearity, noise, and a misspecified carryover shape. Selecting specifications against experimental RoAS benchmarks, even imprecise ones, improves RoAS recovery relative to fit-based selection, and a real-world application with experimental benchmarks shows the same pattern. We propose an applied workflow: calibrate MMM specifications against estimand-aligned experimental benchmarks when they exist, and otherwise report RoAS sensitivity across plausible specifications rather than relying on a single best-fitting model.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.06111",
  "author_total": 2,
  "authors": [
    {
      "name": "Hao Chen"
    },
    {
      "name": "Alvin Lim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6ykcdd611r3n3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.06111",
  "fulltext_url": null,
  "id": "6ykcdd611r3n3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.06111",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.06111",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6ykcdd611r3n3.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:21:06Z",
      "native_id": "2611.06111",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.06111"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "When Predictive Fit Misleads: Selecting Marketing Mix Models Using Experimental RoAS Benchmarks",
  "title_text": "When Predictive Fit Misleads: Selecting Marketing Mix Models Using Experimental RoAS Benchmarks",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:32Z",
  "url": "https://research.draconitic.com/paper/6ykcdd611r3n3",
  "venue": "arXiv",
  "versions": []
}