Сравнение на методи

	Customer Journey Analysis	Online Controlled Experiment
Област	Маркетинг	Marketing Science
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2016	2020
Създател	Katherine N. Lemon & Peter C. Verhoef	Ron Kohavi, Diane Tang & Ya Xu (modern web experimentation practice)
Тип	Customer-experience mapping and measurement pipeline	Randomized-experiment pipeline for causal measurement of online changes
Основополагащ източник	Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96.	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265
Други названия	Customer Journey Mapping, Customer Experience Journey Analysis, Touchpoint Analysis, Journey Analytics	A/B Testing, Split Testing, Randomized Web Experiment, Controlled Experiment on the Web
Свързани	4	3
Резюме	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.	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?