Krahasoni metodat

	Customer Journey Analysis	Uplift Modeling
Fusha	Marketing	Marketing Science
Familja	Process / pipeline	Machine learning
Viti i origjinës	2016	2011
Krijuesi	Katherine N. Lemon & Peter C. Verhoef	Nicholas J. Radcliffe & Patrick D. Surry
Lloji	Customer-experience mapping and measurement pipeline	Heterogeneous-treatment-effect model for targeting incremental responders
Burimi themelues	Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96.	Radcliffe, N. J., & Surry, P. D. (2011). Real-World Uplift Modelling with Significance-Based Uplift Trees. Stochastic Solutions White Paper TR-2011-1.
Emërtime të tjera	Customer Journey Mapping, Customer Experience Journey Analysis, Touchpoint Analysis, Journey Analytics	Incremental Response Modeling, True-Lift Modeling, Net-Lift Modeling, Persuadable Targeting
Të lidhura	4	3
Përmbledhja	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.	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.