Introduction to Key Driver Analysis
Key Driver Analysis allows you to instantly identify which areas of improvement offer the greatest impact to improve your Customer Satisfaction Score, Net Promoter Score, or Customer Effort Score. A Key Driver Analysis Report allows you to identify the opportunities to make the biggest difference in your customers’ experience so you can prioritize and allocate resources accordingly.
A Key Driver Analysis requires two elements:
- A CX metric question (CSAT, CES, NPS) that represents an important goal. Are you trying to build satisfaction? Choose CSAT.
- A Key Driver or rating question that includes possible variables that may impact your overall goal. Are you trying to check in on Product, Service, and Value? These are your variables.
While results will certainly show patients’ overall satisfaction, the most valuable insights will come from Key Driver Analysis. KDA will identify patients’ satisfaction with each aspect of their experience, but even more importantly, it will show which aspect (driver) has the greatest impact on patients’ likelihood of giving a high overall satisfaction rating.
The performance of each driver is calculated by simply taking the weighted average of all responses received.
In the above case, assuming the weights assigned to the 5 answer options are on a scale of 1 to 5, 1 being ‘Very Dissatisfied’ and 5 being ‘Very Satisfied’, the weighted average for the driver ‘Professionalism of Staff’ might look like this:
|Professionalism of Staff
Performance is measured as a % of the weighted score: (2.33 / 5) x 100 = 46.67%
The importance of a driver variable is calculated based on correlation. Correlation describes how much the main metric (CSAT) score changes based on the change in a particular driver variable. Take the below example:
|Professionalism of Staff
The correlation of 0.75 for the driver ‘Professionalism of staff’ means that for every 1 point change in the weighted score of the driver variable, the overall CSAT will change by 0.75 in the same direction.
Correlation values are between 0 and +1.
Understanding Key Drivers
One way to better understand the insights provided by Key Driver Analysis is to view data on a 2×2 matrix. This visualization allows you to investigate potential relationships between two data points: the impact or importance of a driver variable (y-axis); and the performance of the driver variable (x-axis), as seen in the example below.
- A median value (mid-point) of the performance values of all driver variables is represented by the vertical dotted line. A median for the importance (correlation values) of all driver variables is represented by the horizontal dotted line. This divides the graph into 4 quadrants.
- The first quadrant (where the red points appear) consists of drivers for which the performance is lower than the median performance. In other words, the performance of these items is below average. Also, this quadrant suggests that the importance (correlation value) for these drivers is higher than average (median importance). This means that the driver variables represented by red points should be seen as critical fixes because they offer the greatest opportunity for improvement.
- The second quadrant (green points) consists of drivers which are performing better than the average and are also of high importance compared to the rest. Maintaining high performance of these variables is very valuable in sustaining and improving overall.
- The third quadrant (yellow points) consists of drivers which are performing lower than the average, but also indicate a lower than average level of importance. While fixing these areas might be nice, it won’t have a major impact on overall satisfaction.
- The fourth quadrant (blue points) consists of drivers which are performing better than the average but the level of importance is lower than the average importance.