Compare methods

	Uplift Modeling	Customer Journey Analysis	Multi-Touch Media Attribution	Online Controlled Experiment
Field	Marketing Science	Marketing	Marketing Science	Marketing Science
Family	Machine learning	Process / pipeline	Machine learning	Process / pipeline
Year of origin	2011	2016	2016	2020
Originator	Nicholas J. Radcliffe & Patrick D. Surry	Katherine N. Lemon & Peter C. Verhoef	Eva Anderl et al. (graph/Markov attribution); Ron Berman (Shapley-value attribution)	Ron Kohavi, Diane Tang & Ya Xu (modern web experimentation practice)
Type	Heterogeneous-treatment-effect model for targeting incremental responders	Customer-experience mapping and measurement pipeline	Credit-assignment model for converting multi-channel exposure paths into channel contributions	Randomized-experiment pipeline for causal measurement of online changes
Seminal source	Radcliffe, N. J., & Surry, P. D. (2011). Real-World Uplift Modelling with Significance-Based Uplift Trees. Stochastic Solutions White Paper TR-2011-1.	Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96.	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.	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265
Aliases	Incremental Response Modeling, True-Lift Modeling, Net-Lift Modeling, Persuadable Targeting	Customer Journey Mapping, Customer Experience Journey Analysis, Touchpoint Analysis, Journey Analytics	Multi-Touch Attribution, Data-Driven Attribution, Markov-Chain Attribution, Shapley-Value Attribution	A/B Testing, Split Testing, Randomized Web Experiment, Controlled Experiment on the Web
Related	3	4	3	3
Summary	Uplift modeling targets the people a marketing action actually changes, not the people most likely to buy anyway. Where a conventional response model predicts the probability of purchase, an uplift model predicts the difference a treatment makes — the incremental effect of, say, sending a coupon — and uses it to find 'persuadables' while avoiding 'sure things,' 'lost causes,' and especially 'sleeping dogs' who react negatively to contact. Nicholas Radcliffe and Patrick Surry, pioneers of the technique, formalized significance-based uplift trees that split on the difference in treatment-versus-control response rather than on response alone, and introduced the Qini curve to evaluate incremental gain. Pierre Gutierrez and Jean-Yves Gerardy's literature review situates uplift modeling squarely within causal inference, organizing the main estimation strategies and metrics. Because the quantity of interest is a conditional average treatment effect, uplift modeling is most reliable when built on randomized treatment and control data. The payoff is sharper, more profitable targeting: spend marketing effort where it produces genuine incremental response instead of rewarding behavior that would have happened regardless.	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.	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.	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?