You are here
Research data analysis that provides actionable insights to drive business change. All analysis is designed to meet research user needs and support our customer requirements.
Data analysis methods
Through a collaborative research design process, we ensure:
- easy identification of actions and improvement priorities and prioritised
- visibility of different respondent groups and research segmentation views
- advanced analysis techniques to reveal insight and opportunities for improvement.
We have full in-house analytical capability and experience in supporting customer requirements. Our team work with SPSS and other appropriate analysis packages, enabling high quality data transition, manipulation and analysis of data.
We have significant experience using statistical tools to uncover data trends and build insightful feedback from customers, employees and other stakeholder groups. More importantly, we are able to insightfully interpret and apply findings from different business environments.
Applying statistical knowledge we support the use of advanced data analysis techniques including:
- Significance testing
- Correlation analysis
- Regression models
- Factor analysis
- Discriminant analysis
- Conjoint analysis
- Key driver analysis
- Trade-off analysis
- Gap analysis
All data analysis specifications and formats are agreed with our clients to ensure their needs are met, in terms of data content and visual presentation.