Table Table 2 From the research bench 2 September 2026

How does marketing mix modeling compare to multi-touch attribution?

Dimension	Marketing Mix Modeling (MMM)	Multi-Touch Attribution (MTA)
Data granularity	Macro aggregate time-series (weekly, regional)	Micro user journeys (click-stream logs, user IDs)
Privacy resilience	Completely immune to cookie loss and tracking bans	Highly vulnerable to iOS privacy changes and ad blockers
Offline coverage	Evaluates TV, radio, print, OOH, and macro trends	Blind to offline channels; measures only digital clicks
Carryover modeling	Explicitly models multi-week adstock and memory decay	Assumes linear touchpoint decay or arbitrary lookback windows
Saturation modeling	Models diminishing marginal returns and channel capacity	Treats all touches as having constant linear returns
Causal validity	Moderate (vulnerable to endogeneity if uncalibrated)	Extremely poor (confuses correlation with ad persuasion)
Execution speed	Strategic (quarterly/annual budget planning)	Operational (daily keyword and campaign adjustments)

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Source: Table from this essay. Sources and interpretation are given in the article.

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What is marketing mix modeling?

Marketing mix modeling applies econometric regression to aggregate time-series data to estimate marketing effectiveness and optimize media budgets.

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Isoglu, S. (2026). How does marketing mix modeling compare to multi-touch attribution? [Table]. isoglu.com. https://isoglu.com/insights/exhibits/isoglu-2026-what-is-marketing-mix-modeling-fig02/

BibTeX

@misc{isoglu-2026-what-is-marketing-mix-modeling-fig02,
  author = {Isoglu, Sinan},
  title = {{How does marketing mix modeling compare to multi-touch attribution?}},
  year = {2026},
  howpublished = {Table 2, isoglu.com},
  url = {https://isoglu.com/insights/exhibits/isoglu-2026-what-is-marketing-mix-modeling-fig02/},
  note = {From the essay {What is marketing mix modeling?}}
}

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