The analysis of User Activity Data in software applications is now a common technique. For example, data is mined from large volumes of logs that record how users interact with web-application sites like amazon.com. Taking a similar approach, our research question is whether the analysis of this data from a Computer-aided Translation (cat) tool used in large running translation projects can help us better understand how translators interact with machine translation (mt). In the short term, these productivity analyses help buyers and translators base per-word pricing conversations for projects that use Machine Translation on hard data. In the long term, we believe the analysis of User Activity Data may help optimise translation technology development and translator training using various computational linguistic aids like predictive typing, interactive mt, full-sentence mt and automatic speech recognition. To solve this problem, we have developed an instrumented version of a well-known free open-source desktop-based cat tool called OmegaT we called iOmegaT. In this chapter, we describe iOmegaT in more detail, including design decisions we made. We also discuss some data we have analysed, how the system is used in commercial translation projects and how we think the data could be gathered from a wider range of cat tools while accounting for data privacy concerns.