收集桌面应用程序上热图的使用点击次数
我们正在使用新的办公功能区界面创建桌面应用程序的实验版本，并希望进行一些可用性测试。我确信这里还有其他工程师也遇到过这个问题。您能否分享一下您为该过渡所做的测试的经验？我的想法是通过使用点击来捕获用户与应用程序的交互并构建热图。我可以使用什么样的工具来做到这一点？
We are creating an experimental version of a desktop application with the new office ribbon interface and want to do some usability tests. I am sure there are other engineers here who have faced the problem. Could you share your experience on what tests you did for that transition. My idea is to capture users' interaction with the application via usage clicks and build heat maps. What sort of tools can I use to do that?
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（1）
可用性测试分析软件将帮助您捕获用户对尚未准备好对外发布的实验性 Web 应用程序的点击。我使用过 Ovo Studios 的 OvoLogger。他们的事件跟踪器工具是 OvoLogger 的一个组件，可以捕获用户点击、突出显示它们，并生成各种格式的测试后报告。它还突出显示用户的光标和鼠标单击，因此观看可用性测试的观察者可以清楚地看到用户是只是将鼠标悬停在链接上还是实际上单击了它。
Google Analytics、Clicktale 和 CrazyEgg 等现场分析工具都提供热图，但您的情况会有所不同。 CrazyEgg 声称拥有比 Google Analytics 或 ClickTale 更准确的热图，因为它们捕获用户点击的精确位置，而不是捕获具有多个可能目标的页面级点击。如果您将来打算进行多变量分析，您将需要准确了解用户点击的位置（目标 A 或目标 B），以便您能够辨别哪个实验选项获得更多点击。
Usability testing analysis software will help you capture users' clicks on an experimental web app that is not yet ready to be published externally. I have used OvoLogger, by Ovo Studios. Their Event Tracker tool, a component of OvoLogger, captures user clicks, highlights them, and generates post-test reports in a variety of formats. It also highlights the user's cursor and mouse clicks, so observers watching the usability test can clearly see whether the user is just hovering over a link or actually clicking it.
Live-site analytics tools like Google Analytics, Clicktale and CrazyEgg all provide heatmaps, but your mileage will vary. CrazyEgg claims to have more accurate heat maps than Google Analytics or ClickTale, because they capture the precise location of the user's click rather than capturing page-level clicks with multiple possible targets. If you are looking at doing multivariate analysis in the future, you will want to know exactly where the user clicked (target A, or target B), so you will be able to discern which experimental option garners more clicks.