Compare methods

	Multi-Touch Media Attribution	Customer Journey Analysis
Field	Marketing Science	Marketing
Family	Machine learning	Process / pipeline
Year of origin	2016	2016
Originator	Eva Anderl et al. (graph/Markov attribution); Ron Berman (Shapley-value attribution)	Katherine N. Lemon & Peter C. Verhoef
Type	Credit-assignment model for converting multi-channel exposure paths into channel contributions	Customer-experience mapping and measurement pipeline
Seminal source	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.	Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96.
Aliases	Multi-Touch Attribution, Data-Driven Attribution, Markov-Chain Attribution, Shapley-Value Attribution	Customer Journey Mapping, Customer Experience Journey Analysis, Touchpoint Analysis, Journey Analytics
Related	3	4
Summary	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.	Customer journey analysis is the systematic mapping and measurement of the full sequence of touchpoints a customer experiences with a firm, across the prepurchase, purchase and postpurchase stages, in order to understand and improve the end-to-end customer experience. It reflects a shift from evaluating isolated interactions or single satisfaction scores toward seeing the customer experience as a dynamic, cumulative, multi-touchpoint process that unfolds over time and recurs in loops. Katherine Lemon and Peter Verhoef's influential 2016 Journal of Marketing synthesis provided the field's organizing framework, defining customer experience as a customer's cognitive, emotional, sensory, social and behavioral responses across the journey, and classifying touchpoints as brand-owned, partner-owned, customer-owned and social or external. The analysis inventories these touchpoints stage by stage, measures the experience at each, traces the paths customers actually take through them, and identifies the moments and pain points that most shape outcomes such as conversion, satisfaction and loyalty. The result is a diagnostic that connects specific interactions to overall experience and guides where to invest in redesign, integrating behavioral analytics with qualitative experience research.