This article outlines a comprehensive experience walkthrough process for designers, covering its origins, a six‑step execution workflow, practical methods for uncovering usability issues, severity assessment, and follow‑up strategies to improve product experience across diverse user groups.
This article explains why user satisfaction matters, outlines a five‑step process for constructing a satisfaction model—including goal definition, metric collection, definition, attribution, and weighting—and describes how to validate the model through content and structural validity checks.
This article explains how Kuaishou applies causal inference frameworks, such as Rubin's potential outcomes and Pearl's causal graphs, together with machine‑learning techniques like double‑machine learning, causal forests, and meta‑learners to evaluate product features, recommendation strategies, and user behavior under complex network effects in live streaming.
This article surveys recent advances such as running Node.js directly in the browser with WebContainers, GitHub's large‑scale adoption of Web Components, the principles of Clean Architecture, the zx shell‑script tool, and a product‑reliability "3+1" evaluation method.
This article details a two‑month quantitative study of the "CheShangTong" B2B app redesign, comparing pre‑ and post‑release surveys using Customer Effort Score, Net Promoter Score, and emotional experience metrics to evaluate service process, outcome, and user sentiment.
This article explains the evolution, responsibilities, and evaluation methods of the product‑effect assessment team at Amap, covering offline testing, AB experiments, metric analysis, automated scoring models, and the tools that support a comprehensive product‑performance framework.
This guide walks you through a concise, four‑step product evaluation method—finding expert users, crafting story scripts, role‑playing the experience, and prioritizing iterations—to quickly uncover real‑world usage insights and drive focused product improvements.
This article outlines Baidu's comprehensive product evaluation framework, describing its multi‑layer assessment system, the combination of subjective and objective metrics, and a suite of common evaluation methods such as indicator analysis, AB testing, user feedback, behavior analysis, big‑data profiling, and competitor comparison.
This article explains how to build a resource evaluation model by defining assessment dimensions, selecting metrics for attracting and retaining users, choosing objective and subjective data collection methods, and quantifying each indicator with thresholds and scoring rules, using an O2O food‑delivery example.
