From Wikipedia
The Net Promoter Score is obtained by asking customers a single question on a 0 to 10 rating scale, where 10 is "extremely likely" and 0 is "not at all likely": "How likely is it that you would recommend our company to a friend or colleague?" Based on their responses, customers are categorized into one of three groups: Promoters (9–10 rating), Passives (7–8 rating), and Detractors (0–6 rating). The percentage of Detractors is then subtracted from the percentage of Promoters to obtain a Net Promoter score (NPS). NPS can be as low as -100 (everybody is a detractor) or as high as +100 (everybody is a promoter).
Now we want to scale it down to 1-5 for our purposes, because frankly our users will get confused on the 0-10 scale. Now we are considering 1,2,3 to be detractors , 4 to be passive and 5 to be a promoter
. Now In the Standard 0-10 scale, it is actually a 11 point scale – so promoters (9,10) is 2/11th of the distribution which is 18%. In the 1-5 scale we use promoters (5) is 1/5 which is 20%
. So more chances of getting a promoter score. By same logic, 5 point scale will also show less detractors than 0-10 point scale (3/5=60% vs 7/11 = 63.6%)
. So a positive skew looks possible mathematically. But the fact is a user has only 1 option to choose from on the 1-5 scale to be a promoter, but 2 on the 1-10 scale. So someone who might be a 9 on a 10 scale might be a 4 on the 5 scale and become a passive and never show up and kill our NPS score to go to -100(if no one votes 5 , and a lot of people vote 4) . Three questions
1.) Which one of the two math arguments should be considered for scaling down
2.) What should be the right passive pivot for the 1-5 scale?
3.) Are there other tests that can be done on this data to get an accurate measure of customer feedback?
[self-study]
tag & read its wiki. $\endgroup$