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I have a survey responses data. It has various columns, but one of the column is the age of the product usage by the user. The other column is the response given by the user on a 0-10 point scale as used to calculate NPS.

Now I have calculated NPS using this response variable but what I also want is how does Age variable impact NPS score. Any visualization or model which can help explain that.

NPS is calculated based on the below function I wrote which takes in responses of all users and finds Promoters_Proportion(9 &10) and Detractors_proportion (0-6)

def nps_score(responses): 
    """Function to get the NPS score from the 
     Survey responses 

    """
    global sample_size,promoters_proportion,detractors_proportion,sample_NPS

    sample_size = len(responses)
    promoters_proportion = sum([1 for x in responses if x >=9])/sample_size
    detractors_proportion= sum([1 for x in responses if x<=6])/sample_size

    sample_NPS= promoters_proportion - detractors_proportion

    print("Sample Net Promoter Score(NPS) is {} or {}%".
          format(round(sample_NPS,2),round(sample_NPS*100,2)))

I can plot correlation or a regression model between age and responses, but NPS is calculated from all the responses (%promoters- %detractors). So not sure how to do this.

Any ideas/help wud be appreciated.

Sample Data below is Age (in days), Response of a user

array([['161.791365740741', 8.0],
       ['40.7387384259259', 7.0],
       ['1,289.49306712963', 8.0],
       ['32.3515277777778', 5.0],
       ['1,082.74270833333', 8.0],
       ['538.689490740741', 7.0],
       ['276.823541666667', 10.0],
       ['1,249.75631944444', 10.0],
       ['75.5923611111111', 10.0],
       ['248.181539351852', 6.0],
       ['2,427.55869212963', 8.0],
       ['772.878356481481', 10.0],
       ['1,475.76710648148', 10.0],
       ['1,560.73618055556', 10.0],
       ['211.118738425926', 10.0],
       ['686.884409722222', 10.0],
       ['1,377.74114583333', 9.0],
       ['1,548.9194212963', 10.0],
       ['20.0154166666667', 5.0],
       ['1,039.62513888889', 3.0],
       ['84.2223032407407', 4.0],
       ['182.914525462963', 10.0],
       ['34.0710648148148', 3.0],
       ['771.650115740741', 10.0],
       ['1,107.72900462963', 8.0],
       ['900.200601851852', 10.0],
       ['1,402.0755787037', 9.0],
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  • $\begingroup$ I wonder whether you are asking for something that has any meaning. For instance, suppose older people tended to have larger responses. Including one middle-aged person in a group of younger people would increase the NPS, while including the very same person in a group of older people would decrease the NPS. In this sense NPS cannot be attributed to age at all, because it is a kind of "emergent" property of an entire group. Why not just study the relationship at the subject level between age and response? $\endgroup$ – whuber Dec 11 '18 at 23:37
  • $\begingroup$ Thanks @whuber for response. Pleasure and hv learnt a lot from ur answers here. The age here is the age of using the product. Not the age of the user. You have a very valid point. The idea we are trying to explore is that does age of usage of product has some impact on overall NPS. Now my confusion is that age has a relationship with the response one gives. That response along with all the other response gives NPS. So how to find the effect of age to an overall NPS is am trying to figure out. Like is there some visualization or model I can do to get some sense? $\endgroup$ – Baktaawar Dec 11 '18 at 23:43
  • $\begingroup$ What do u mean at a subject level? Like u r saying just do regression on age and responses? Or do a correlation? That can be done. Is there some way we can use that info to deduce some impact on NPS? Need to talk about this to executive leadership and we need some way of checking if we can relate impact of individual attributes (like age etc) on overall NPS of the survey or not. $\endgroup$ – Baktaawar Dec 11 '18 at 23:48

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