UPCOMING WEBINAR: Faster (and better) survey analysis The Net Promoter Score is most people's go-to measure for evaluating companies, brands, and business units. However, the standard way of computing the NPS - subtract the promoters from the detractors - is a bit of a pain. This article describes how easy it is to recode the data so that you can compute the NPS by computing the average, and you get exactly the same answer as you do when using the standard way.
For example, the table below shows data for a particular campsite. If we add up the first 7 categories, so 0 through to 6, 18.13% of people in this data set are Detractors. Adding up categories 7 and 8 gives us 28.94% Passives and the remaining 52.94% are Promoters. So, in this data set, the NPS score is 34.8 which is OK.
This automation looks for likelihood to recommend questions in your project and creates new variables that are recoded to display the NPS score.
Requirements
- Familiarity with the Structure and Value Attributes of Variable Sets.
- A data set containing standard Net Promoter Score data (i.e., ordinal data on a 0-10 scale where 0 is not all likely to recommend and 10 is extremely likely to recommend).
Method 1 - Automatic recoding
- Hover over the variable set containing the likelihood to recommend data in the Data Sources tree.
- Click + > Numeric Transformations > Net Promoter Score (NPS). This will create a new variable set with recoded data.
- Drag the new variable set onto the page to create a summary table of the average of each variable, which is the NPS.
Method 2 - Manual recoding
- Select the variable set containing the likelihood to recommend data from the Data Sources tree.
- From the object inspector, go to Data > Properties and click on the Values button.
- Change the entries in the Value column as follows, and press OK.
- 0-6: change to -100
- 7-8: change to 0
- 9-10: change to 100
- Click OK.
- Change the Structure to Numeric or Numeric - Multi. This will cause the table to show the Average, which gives us the NPS.