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Measuring success internally—analytics in sensitive environments
Analytics can help pinpoint where User Experience improvements make sense. This can be especially valuable for internal environments.
Internal User Experience
Internal tools such as intranets are used day by day by a large amount of people. Here, a good User Experience (UX) can raise the motivation and the efficiency of employees distinctively. The success of such UX efforts can be measured through analytics tools. If used in the right manner, analytics tools can also be employed in internal, sensitive environments.
Measuring success qualitatively and quantitatively
Several times, we have been asked to improve the User Experience of an intranet application. Each time, we encounter the same challenge: The intranet is heavily used, but there is hardly any data on how it is used. We can generate qualitative feedback about the use through user testings. However, such data needs to be supplemented with quantitative data, which allows us to log user journeys and validate assumptions. By using an analytics tool we can for instance identify whether certain features of the intranet are used at all.
Analytics in sensitive environments
Currently, about two thirds of all web applications use analytics tools. Google Analytics is by far the best known analytics tool. It offers a broad range of options to gather and evaluate data. However, it bears one problem for sensitive environments: The data is not locally stored. In sensitive environments such as intranet projects, compliance rules demand that all data is stored locally, i.e. in Switzerland and in the possession of the organisation itself. The solution: Piwik.
Piwik is the leading open-source analytics tool. It can be hosted locally, so that 100% of the data remains in your hands. Further, there are no licensing costs—although, supporting the open-source project is of course always welcome. Thanks to Piwik, we can use quantitative data even in sensitive environments, and sustainably improve the User Experience based on transparent data.