The Tale of Two Products<\/h2>\n
After purchasing a product on an online e-commerce store, customers are asked the typical NPS survey question \u201cOn a scale of 0-10, how likely is it that you would recommend [product name] to your friends, family or business associates?\u201d for two different products.<\/p>\n
Product A Customer Ratings: [6,6,6,6,6,6,6,6,10,10] (Average Customer Rating 6.8)<\/p>\n
Product B Customer Ratings: [0,0,0,0,9] (Average Customer Rating 1.8)<\/p>\n
Their ratings are listed above. Look at the two data sets. Consider carefully. Which product do you think has a higher NPS (Net Promoter Score).<\/p>\n
The answer is their NPS is exactly the same.<\/p>\n
Wait that\u2019s unbelievable\u2026 the first product has an average customer rating of 6.8\/10 while the second has an average customer rating of 1.8\/10. How is that possible?<\/p>\n Welcome to the wonderful world of NPS, one of the most misguided scoring calculations ever invented.<\/p>\n Before we start, what is NPS (Net Promoter Score) and how is it calculated?<\/p>\n Take your data set of ratings between 0-10. Divide these ratings into three groups. Anyone with a rating of 7 or 8 is a \u201cPassive.\u201d Throw these scores out. Anyone with a score of 9 or 10 is a \u201cpromoter\u201d and anyone with a score of 0-6 is a \u201cdetractor.\u201d Take your % of detractors out of the total ratings and subtract it from your % of promoters out of the total ratings. Whatever % comes out of that, negative or positive, turns into your NPS score.<\/p>\n That\u2019s it, you\u2019re done.<\/p>\n So, if 50% of respondents were promoters and 10% were detractors, your NPS is 40. Well sounds straightforward enough\u2026 what\u2019s wrong with that?<\/p>\n In the above example, product A has two 10s (promoters) and eight 6s (detractors). So you subtract the % of detractors (80%) from the % of promoters (20%) to get -60% as a total which turns into an NPS of -60.<\/p>\n Product B has one 9 (promoter) and four 0s (detractors). So you subtract the % of detractors (80%) from the % of promoters (20%) to get -60.0% as a total which turns into an NPS of -60.<\/p>\n So product A has a final NPS of -60 and product B has a final NPS of -60. The products have the exact same NPS.<\/p>\n When working with ratings you will almost always have to lose some accuracy to achieve an average \u201crating.\u201d Often this involves rounding a final number.<\/p>\n Rounding is a form of data grouping. When you are saying 49.469 will be rounded to 49.47 you are grouping data based on\u00a0 a set of parameters or rules. Usually this is done at the very last possible stage to avoid, as much as possible, abstracting out the detail in data too early to prevent inaccuracies.<\/p>\n The sooner in the process you group the data, the more divergent comparable data sets will become in the final calculation because you stack small inconsistencies into bigger and bigger ones, creating a cascading chain of building inaccuracies. That is why most scoring systems round at the last possible step before producing a score.<\/p>\n Additionally, the wider the range of this \u201cgrouping\u201d the more inaccurate, and less reflective of the original data set, your final rating becomes.<\/p>\n NPS has one of the widest groupings imaginable. Every rating between 0-6 is considered exactly the same. That\u2019s right, 60% of all possible ratings are grouped and considered \u201cequivalent.\u201d A further, less egregious, grouping happens when 7 is considered equivalent to 8 and 9 is considered equivalent to 10.<\/p>\n NPS does not employ any mechanism of considering the volume or number of data points when creating a calculation. So a data set with one 0 and one 10 is considered exactly equivalent to a data set with one million 0\u2019s and one million 10\u2019s. This is also a weakness shared with traditional averaging. The average of the above two data sets (one with two data points and the other with two million data points) will also come out to be exactly the same.<\/p>\n What is the solution to this? The solution is to produce two scores, one an average that does not take into consideration the number of data points and the other a Wilson score which does weight the number of data points.<\/p>\n![\"Spray](\"https:\/\/loopinput.com\/wp-content\/uploads\/2019\/11\/image2-1.png\")
What is NPS?<\/h2>\n
![\"Net](\"https:\/\/loopinput.com\/wp-content\/uploads\/2019\/11\/image1-1024x553.png\")
NPS Example<\/h2>\n
![\"Girl](\"https:\/\/loopinput.com\/wp-content\/uploads\/2019\/11\/image1-1.jpg\")
<\/p>\nWhy NPS Sucks<\/h2>\n
Arbitrary First-In Data Grouping<\/h2>\n
No Consideration for Predictive Accuracy or Volume of Data Set<\/h2>\n
![\"\"](\"https:\/\/loopinput.com\/wp-content\/uploads\/2019\/11\/image4.png\")
![\"big](\"https:\/\/loopinput.com\/wp-content\/uploads\/2019\/11\/bigknight-233x300.jpg\")