NPS : Net Promoter Score<\/span><\/h2>\nI have always put customer\u2019s satisfaction with our product or service first. Indeed, when I started my career in the 90s, it was a very hot topic. Various measuring methods have been developed and implemented in order to evaluate and track the users and customers\u2019 satisfaction, like the Mystery shoppers, satisfaction surveys, etc.<\/p>\n
However, despite the success these concepts have reached their time, the priorities have changed. These have adapted to today\u2019s accelerated changes. As a consequence, the new important indicator is the\u00a0NPS<\/strong>, or\u00a0Net Promoter Score<\/em>.<\/p>\nThus, it is worth wondering what has exactly changed since then. The boom of ICTs is one of the most important reasons, but giving more details about the origin of that change is not the goal of this post. The only thing known for sure is that today having satisfied customers is no longer enough, and it is important to go a step further in our relation with them. Consequently, it is important to keep in mind that an \u2018only satisfied\u2019 customer can be lost eventually. He can easily stop being seduced or become totally attracted by other channels or by your competitors.<\/p>\n
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How do you calculate NPS?<\/span><\/h2>\nLet\u2019s take an example:
\n\u201cConsidering your experience with our product\/service\/brand, how likely would you recommend\u00a0<\/em>it to a friend or your family?\u201d<\/em>
\nThis is a typical question you may find in a customer satisfaction survey. The answer would be chosen among a scale of values, for example from 1 (not at all likely) and 10 (extremely likely). Resumining it, the 10 values could be grouped as follows:
\n\u2013 Low probability (values 1-3)
\n\u2013 Medium probability (values 4-7)
\n\u2013 High probability (8-10)<\/p>\nNonetheless, the criteria to be followed with the NPS is not the same. In the case of the NPS, medium values are simply ignored; they are not taken into account. The criteria to define a \u2018medium value\u2019 change as well. To calculate the NPS, the grouping would be the following:
\n\u2013 Low probability (values 1 \u2013 6)
\n\u2013 High probability (values 9-10)<\/p>\n
Indeed, the categories are polarized. The customers who gave us neutral marks (7 and 8) are not important. The most important are those who give us the best marks (9 and 10, these respondents are\u00a0Promoters<\/em>) and those who give us the lowest marks (6 and below, they are\u00a0De<\/em>tractors<\/em>).<\/p>\nBefore the NPS, we would only have done a statistical average and got an average value X. With the NPS, we also calculate an average but we only take into account the values described above (from 1 to 6 and 9-10). The result (Y) is the store, establishment, company or product\u2019s NPS score.<\/p>\n
<\/p>\n
NPS : Example<\/span><\/h2>\nIt is going to be clearer with an example.
\nLet\u2019s suppose that to the same question as in the previous example, we have obtained the following answers from 10 respondents:
\n{9, 7, 8, 6, 6, 9, 8, 10, 4, 9}<\/p>\n
With these values, the average is 7.6\/10. However in the case of the NPS, as we only take the values given by\u00a0promoters<\/em>\u00a0and\u00a0detractors<\/em>, the calculation has to be done as following:
\n\u2013 Highest marks [9-10]: 4 respondents, 40% of the total.
\n\u2013 Lowest marks [1-6]: 3 respondents, 33% of the total.
\n[40% \u2013 33% = 7] => So the store\u2019s NPS is 7.
\nThe results have to be interpreted differently as well. For instance, an NPS score above 0 starts to be acceptable, while a score above 50 is considered as excellent.<\/p>\n <\/p>\n
![\"NPS\"](\"https:\/\/icidigital.eu\/capkelenn-old\/wp-content\/uploads\/2015\/11\/NPS-english-1-1024x378.jpg\")
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<\/p>\n
What does the NPS means?<\/strong><\/span><\/h2>\nWhat could explain that?<\/p>\n
Thus, it is worth wondering what has exactly changed since then. The boom of ICTs is one of the most important reasons, but giving more details about the origin of that change is not the goal of this post. The only thing known for sure is that today having satisfied customers is no longer enough, and it is important to go a step further in our relation with them. Consequently, it is important to keep in mind that an \u2018only satisfied\u2019 customer can be lost eventually. He can easily stop being seduced or become totally attracted by other channels or by your competitors.<\/p>\n
[\/vc_column_text][vc_column_text]<\/p>\n
How do you calculate NPS?<\/span><\/h2>\nLet\u2019s take an example:
\n\u201cConsidering your experience with our product\/service\/brand, how likely would you recommend\u00a0<\/em>it to a friend or your family?\u201d<\/em>
\nThis is a typical question you may find in a customer satisfaction survey. The answer would be chosen among a scale of values, for example from 1 (not at all likely) and 10 (extremely likely). Resumining it, the 10 values could be grouped as follows:
\n\u2013 Low probability (values 1-3)
\n\u2013 Medium probability (values 4-7)
\n\u2013 High probability (8-10)<\/p>\nNonetheless, the criteria to be followed with the NPS is not the same. In the case of the NPS, medium values are simply ignored; they are not taken into account. The criteria to define a \u2018medium value\u2019 change as well. To calculate the NPS, the grouping would be the following:
\n\u2013 Low probability (values 1 \u2013 6)
\n\u2013 High probability (values 9-10)<\/p>\n
Indeed, the categories are polarized. The customers who gave us neutral marks (7 and 8) are not important. The most important are those who give us the best marks (9 and 10, these respondents are\u00a0Promoters<\/em>) and those who give us the lowest marks (6 and below, they are\u00a0De<\/em>tractors<\/em>).<\/p>\nBefore the NPS, we would only have done a statistical average and got an average value X. With the NPS, we also calculate an average but we only take into account the values described above (from 1 to 6 and 9-10). The result (Y) is the store, establishment, company or product\u2019s NPS score.<\/p>\n
<\/p>\n
NPS : Example<\/span><\/h2>\nIt is going to be clearer with an example.
\nLet\u2019s suppose that to the same question as in the previous example, we have obtained the following answers from 10 respondents:
\n{9, 7, 8, 6, 6, 9, 8, 10, 4, 9}<\/p>\n
With these values, the average is 7.6\/10. However in the case of the NPS, as we only take the values given by\u00a0promoters<\/em>\u00a0and\u00a0detractors<\/em>, the calculation has to be done as following:
\n\u2013 Highest marks [9-10]: 4 respondents, 40% of the total.
\n\u2013 Lowest marks [1-6]: 3 respondents, 33% of the total.
\n[40% \u2013 33% = 7] => So the store\u2019s NPS is 7.
\nThe results have to be interpreted differently as well. For instance, an NPS score above 0 starts to be acceptable, while a score above 50 is considered as excellent.<\/p>\n <\/p>\n
<\/p>\n
<\/p>\n
What does the NPS means?<\/strong><\/span><\/h2>\nWhat could explain that?<\/p>\n
\n\u201cConsidering your experience with our product\/service\/brand, how likely would you recommend\u00a0<\/em>it to a friend or your family?\u201d<\/em>
\nThis is a typical question you may find in a customer satisfaction survey. The answer would be chosen among a scale of values, for example from 1 (not at all likely) and 10 (extremely likely). Resumining it, the 10 values could be grouped as follows:
\n\u2013 Low probability (values 1-3)
\n\u2013 Medium probability (values 4-7)
\n\u2013 High probability (8-10)<\/p>\n
Nonetheless, the criteria to be followed with the NPS is not the same. In the case of the NPS, medium values are simply ignored; they are not taken into account. The criteria to define a \u2018medium value\u2019 change as well. To calculate the NPS, the grouping would be the following:
\n\u2013 Low probability (values 1 \u2013 6)
\n\u2013 High probability (values 9-10)<\/p>\n
Indeed, the categories are polarized. The customers who gave us neutral marks (7 and 8) are not important. The most important are those who give us the best marks (9 and 10, these respondents are\u00a0Promoters<\/em>) and those who give us the lowest marks (6 and below, they are\u00a0De<\/em>tractors<\/em>).<\/p>\n Before the NPS, we would only have done a statistical average and got an average value X. With the NPS, we also calculate an average but we only take into account the values described above (from 1 to 6 and 9-10). The result (Y) is the store, establishment, company or product\u2019s NPS score.<\/p>\n <\/p>\n It is going to be clearer with an example. With these values, the average is 7.6\/10. However in the case of the NPS, as we only take the values given by\u00a0promoters<\/em>\u00a0and\u00a0detractors<\/em>, the calculation has to be done as following: <\/p>\n <\/p>\n What could explain that?<\/p>\nNPS : Example<\/span><\/h2>\n
\nLet\u2019s suppose that to the same question as in the previous example, we have obtained the following answers from 10 respondents:
\n{9, 7, 8, 6, 6, 9, 8, 10, 4, 9}<\/p>\n
\n\u2013 Highest marks [9-10]: 4 respondents, 40% of the total.
\n\u2013 Lowest marks [1-6]: 3 respondents, 33% of the total.
\n[40% \u2013 33% = 7] => So the store\u2019s NPS is 7.
\nThe results have to be interpreted differently as well. For instance, an NPS score above 0 starts to be acceptable, while a score above 50 is considered as excellent.<\/p>\n<\/p>\nWhat does the NPS means?<\/strong><\/span><\/h2>\n