Usage Analytics

Usage analytics is about capturing data to generate predictive and explanatory knowledge of what people (users) are doing, how they behave and how they use products and services. It can be used for validation in prototyping and it can be implemented in products/services that are sold on the market to support improvements driven by knowledge about the user. Getting knowledge about humans can also be the main purpose of the product or service: think of data from sensors that is deployed to detect or predict health-related issues in users.

Usage analytics heavily relies on machine learning/AI and may also involve computational simulations as well as multimedia and VR technology. An important part is also data preprocessing or data wrangling, i.e., filtering, cleansing and reordering of data. It focuses on the processes that transform data into actionable knowledge, and is not so much concerned with designing the infrastructure (e.g., hardware, data-transfer protocols).

The contribution lies in the establishment of approaches and methods to support designers in finding opportunities to gain insights into users, to explore and help understanding the means to create knowledge out of data, and to validate the obtained knowledge. At a smaller scale, the research contributes by producing concrete concepts of products and services performing usage analytics, to serve as inspiration.

Usage Analytics News:

[February 7, 2023] | Wo Meijer published in ETIS ’22: Destructive Feedback: a user created strategy for collecting user feedback in shared systems. Read here.