归因与分析
FullStory
企业级数字体验平台，自动录制所有用户会话，支持跨页面用户旅程分析和 DX Data
主要用途
Fullstory captures how real people actually use a website or app by combining pixel-perfect session replay with automatically indexed behavioral events, so teams do not have to predefine every interaction they might want to analyze later. Once instrumented, it records clicks, navigation, form interactions, errors, and rage-click or dead-click friction signals, then exposes them through funnels, journeys, heatmaps, and product analytics dashboards. Because the underlying data is captured retroactively, a team can build a new funnel or segment and immediately watch the replays behind any drop-off. AI-assisted investigation surfaces anomalies and summarizes sessions. The result connects quantitative metrics to the lived experience, helping product, design, engineering, and support teams diagnose bugs, prioritize usability fixes, and improve conversion and retention.
所在链路
Fullstory sits at the analytics stage of the experience funnel, supplying the behavioral context that attribution and conversion tools quantify but cannot show.
核心功能
- High-fidelity session replay with retroactive, autocaptured events
- Funnels, journeys, and conversion analysis tied to underlying replays
- Heatmaps and friction signals like rage clicks and dead clicks
- Frustration and error tracking that links UX issues to revenue impact
- Mobile and web analytics under one behavioral data model
- AI-assisted session summaries and anomaly investigation
适合谁用
- Product and UX teams diagnosing where and why users drop off
- Support and engineering teams reproducing reported bugs via replay
- Conversion teams connecting quantitative funnels to real session behavior
新手提示
- Autocapture records broadly, so configure element exclusion and data privacy rules early to keep sensitive fields out of replays.
- Define a few key conversion funnels first rather than exploring everything; the retroactive data lets you refine them without re-instrumenting.
- Watch a handful of full session replays before trusting aggregate metrics; the qualitative context often reframes what the numbers imply.