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While the results will certainly reflect employees’ overall satisfaction, the most valuable insights will come from Key Driver Analysis (KDA). KDA will identify employees’ satisfaction with each aspect of their experience, but more importantly, it will reveal which aspect (driver) has the greatest impact on their likelihood of giving a high overall satisfaction rating.<\/p>\n
KDA Calculations<\/h3>\n
The performance of each driver is calculated by simply taking the weighted average of all responses received. <\/p>\n
In the above case, assuming the weights assigned to the 7 answer options are on a scale of 1 to 7, 1 being \u2018Very Dissatisfied\u2019 and 5 being \u2018Very Satisfied\u2019, the weighted average for the driver \u2018Professionalism of Staff\u2019 might look like this:<\/p>\n
Driver<\/strong><\/td>\n| Very Dissatisfied<\/td>\n | Dissatisfied<\/td>\n | Neutral<\/td>\n | Satisfied<\/td>\n | Very Satisfied<\/td>\n | Weighted score<\/td>\n | Total responses<\/td>\n<\/tr>\n | Professionalism of Staff<\/strong><\/td>\n | 2<\/td>\n | 2<\/td>\n | 1<\/td>\n | 0<\/td>\n | 1<\/td>\n | 2.33<\/td>\n | 6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n | Performance is measured as a % of the weighted score: (2.33 \/ 5) x 100 = 46.67%<\/strong><\/p>\n The importance of a driver variable is calculated based on correlation. Correlation describes how much the main metric (ESAT) score changes based on the change in a particular driver variable. Take the below example:<\/p>\n The correlation of 0.75 for the driver \u2018Professionalism of staff\u2019 means that for every 1 point change in the weighted score of the driver variable, the overall ESAT will change by 0.75 in the same direction. <\/p>\n Correlation values are between 0 and +1.<\/p>\n<\/div>\n One way to better understand the insights provided by Key Driver Analysis is to view data on a 2\u00d72 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. <\/p>\n Key Driver Analysis allows you to instantly identify which areas of improvement offer the greatest impact to improve your Employee Satisfaction Score, Employee Net Promoter Score, or Employee Effort Score. A Key Driver Analysis Report allows you to identify the opportunities to make the biggest difference in your customers\u2019 experience so you can prioritize and allocate resources accordingly. A Key Driver Analysis requires two elements: An EX metric question (ESAT, EES, eNPS) that represents an important goal. Are you trying to build satisfaction? Choose ESAT. 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 the results will certainly reflect employees’ overall satisfaction, the most valuable insights will come from Key Driver Analysis (KDA). KDA will identify employees’ satisfaction with each aspect of their experience, but more importantly, it will reveal which aspect (driver) has the greatest impact on their likelihood of giving a high overall satisfaction rating. KDA Calculations 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 7 answer options are on a scale of 1 to 7, 1 being \u2018Very Dissatisfied\u2019 and 5 being \u2018Very Satisfied\u2019, the weighted average for the driver \u2018Professionalism of Staff\u2019 might look like this: Driver Very Dissatisfied Dissatisfied Neutral Satisfied Very Satisfied Weighted score Total responses Professionalism of Staff 2 2 1 […]<\/p>\n","protected":false},"author":30,"featured_media":0,"parent":0,"menu_order":494,"comment_status":"closed","ping_status":"closed","template":"faq_php.php","meta":{"footnotes":""},"categories":[434],"tags":[],"yoast_head":"\n |
![](\"\/help\/wp-content\/uploads\/2024\/10\/EXKDA4.png\")
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