Data Analysis & Insights
The collected data enable a wide range of evaluation and analysis options to generate deeper insights. In addition to standardised evaluation approaches, we also offer a variety of advanced analytical methods that provide a “look behind the scenes.”
Our data analysts support your project from the very beginning. Depending on your specific research objectives and the structure of the data, different analytical methods are appropriate. These include:
- Data exploration: A range of exploratory methods (e.g. cluster analysis, latent class analysis, etc.) help uncover relationships within the collected data and identify new, actionable insights.
- Data visualisation: Presenting abstract data and relationships in a visually accessible format (charts, dashboards, etc.) supports clear and complexity-reducing data preparation.
- Descriptive statistics: Descriptive statistical methods allow you to gain an overview of the collected data and identify key tendencies and distributions.
- Inferential statistics: Inferential statistical methods are used to test sample-based probabilistic statements and assess their validity for entire populations.
These analytical approaches form the basis for a wide range of deeper insights, such as identifying segments or homogeneous groups with typical patterns of characteristics, as well as assessing the explanatory power of the collected data. The resulting insights provide you with new and valuable options for answering your research questions and making well-informed decisions. Depending on the research objectives, different analytical methods can also be combined.
Has this sparked your interest? Please feel free to contact us.