A statistical method to identify which factors have the greatest impact on a critical outcome, like customer satisfaction or retention. Often the starting point for targeted qualitative research.
Definition: A statistical method to identify which factors have the greatest impact on a critical outcome, like customer satisfaction or retention. Often the starting point for targeted qualitative research.
Key Driver Analysis (KDA) is a quantitative technique used to identify which variables have the strongest relationship with an outcome of interest.
KDA uses statistical methods (typically regression analysis) to determine which factors most strongly predict or "drive" a key metric. For example:
KDA is particularly powerful as a starting point for qualitative research. The workflow:
A KDA on support ticket data might reveal that customer satisfaction is driven by:
This statistical insight tells you where to focus, but not what "clarity" looks like in practice. That requires qualitative research to discover.
KDA exemplifies the power of triangulation: quantitative methods identify patterns at scale, while qualitative methods provide the contextual understanding needed to act on those patterns.
Research focused on numerical measurement with the goal of generalizing findings from a sample to a broader population. Answers 'how much,' 'how many,' and 'how often.'
The practice of combining multiple data sources, methods, or perspectives to build more robust research findings. Reduces reliance on any single source and increases confidence in conclusions.
This term is referenced in the following articles: