揭开用户旅程的神秘面纱：通过自动跟踪革新故障排除

Troubleshooting critical issues by deciphering a user’s journey on the Grab app is an extremely challenging task. With countless user journeys and multiple paths through the User Interface (UI), it’s akin to searching for a needle in a vast haystack. This challenge frequently resonates with us, the dedicated developers at Grab, as we strive to understand user behaviors, views, and interactions.

通过解读用户在Grab应用上的旅程来排查关键问题是一项极具挑战性的任务。由于用户旅程无数且通过用户界面（UI）的路径多样，这就像在浩瀚的干草堆中寻找一根针。这一挑战常常与我们Grab的专注开发者产生共鸣，因为我们努力理解用户的行为、观点和互动。

The challenge

挑战

The distinction between resolving an issue effectively versus spending hours on a wild goose chase is understanding our user journey in real-time.

有效解决问题与在无果的追逐中花费数小时之间的区别在于实时理解我们的用户旅程。

The development team initially attempted to address the issue of the incomplete user journey tracking by implementing a system where a click stream event would be sent with every user interaction. However, this approach presented significant challenges due to the sheer volume of UI components—often numbering in the hundreds—and the reliance on individual developers to correctly instrument each one.

开发团队最初试图通过实施一个系统来解决不完整的用户旅程跟踪问题，该系统会在每次用户交互时发送点击流事件。然而，由于UI组件的数量庞大——通常达到数百个——并且依赖于个别开发人员正确地对每个组件进行监测，这种方法面临着重大挑战。

A common pitfall was that developers would occasionally overlook or forget to instrument certain user interactions, leading to breaks in the recorded user journey. This created a highly frustrating situation for both the development and product teams, as the integrity of the user journey data was consistently compromised. Despite continuous efforts to patch these bugs and address the omissions, the team found themselves in a perpetual state of reaction, constantly trying to catch up with newly discovered breaches rather than proactively preventing them. This reactive approach consumed valuable resources and hindered the ability to gain a complete and accurate understanding of user behavior.

一个常见的陷阱是开发人员有时会忽视或忘记对某些用户交互进行监测，从而导致记录的用户旅程出现中断。这给开发和产品团队带来了极大的挫败感，因为用户旅程数据的完整性始终受到影响。尽管团队不断努力修复这些错误并解决遗漏问题，但...