手法を比較

	Online Controlled Experiment	Multi-Touch Media Attribution
分野	Marketing Science	Marketing Science
系統	Process / pipeline	Machine learning
提唱年	2020	2016
提唱者	Ron Kohavi, Diane Tang & Ya Xu (modern web experimentation practice)	Eva Anderl et al. (graph/Markov attribution); Ron Berman (Shapley-value attribution)
種類	Randomized-experiment pipeline for causal measurement of online changes	Credit-assignment model for converting multi-channel exposure paths into channel contributions
原典	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265	Anderl, E., Becker, I., von Wangenheim, F., & Schumann, J. H. (2016). Mapping the customer journey: Lessons learned from graph-based online attribution modeling. International Journal of Research in Marketing, 33(3), 457-474.
別名	A/B Testing, Split Testing, Randomized Web Experiment, Controlled Experiment on the Web	Multi-Touch Attribution, Data-Driven Attribution, Markov-Chain Attribution, Shapley-Value Attribution
関連	3	3
概要	Online controlled experiments, commonly called A/B tests, randomly split live web or app traffic between a control and one or more treatment variants to measure the causal effect of a change on user behavior. Ron Kohavi, Diane Tang, and Ya Xu — who built and ran experimentation platforms at Microsoft, Google, and LinkedIn — set out the modern theory and best practice in their 2020 Cambridge book, and Kohavi's earlier survey with colleagues established the practical foundations of running trustworthy web experiments at scale. The discipline centers on a clearly defined Overall Evaluation Criterion (OEC) that captures long-term value, rigorous randomization, adequate statistical power, and a battery of trustworthiness checks such as the Sample Ratio Mismatch test. Because users are randomized, the difference in metrics between variants is an unbiased estimate of the change's causal impact — the gold standard for marketing and product decisions that attribution and observational analysis can only approximate. The output is a confident ship/no-ship decision: did this headline, layout, price, or feature actually move the metrics that matter, by how much, and with what certainty?	Multi-touch media attribution distributes credit for a conversion across the sequence of marketing touchpoints a customer encountered, replacing crude heuristics like 'last click gets everything' with models that respect the whole journey. Two principled approaches dominate: graph-based Markov-chain models, advanced by Eva Anderl and colleagues, which represent customer paths as transitions between channels and value a channel by its 'removal effect' on the probability of conversion; and Shapley-value attribution, analyzed by Ron Berman, which treats channels as players in a cooperative game and assigns each its average marginal contribution across all possible coalitions. Both reject single-touch rules because those rules systematically misvalue channels — Berman shows that last-touch over-incentivizes the final exposure and can lower advertiser profit, while Anderl et al. demonstrate that Markov models recover credit allocations markedly different from simple heuristics. The result is a defensible, data-driven map of which channels actually move customers toward conversion, used to reallocate budget and compute channel-level return on ad spend. Because attribution is fundamentally about the incremental effect of exposures, it sits at the boundary of measurement and causal inference.