The original method is easy to use:
- Ask your customers a single question: "On a scale of 0-10, how likely are you to recommend us to a colleague or friend?"
- Count the number of 0-6 answers. These are your detractors.
- Count the number of 7-8 answers. These are your passives.
- Count the number of 9-10 answers. These are your promoters.
- Calculate the percentage of promoters and detractors.
- Subtract your detractors from your promoters to get your Net Promoter Score.
The result can be shown in a graph like the one above. A score by itself means little. The important thing is to track trends over time.
The following table shows (made up) raw NPS data:
I like to set up the spreadsheet so that the data is automatically color coded. It gives me a sense of where things are going while I enter the data.
Here is how my spreadsheet handles the conversion from raw data to NPS score:
The Net Promoter Score tells you how satisfied your customers are, but it does not tell you what to do to increase customer satisfaction. Let's fix that!
It would be interesting to know the distribution of our three customer categories. The table above tells us that, but it is better to visualize the data:
If we only knew what to do, we could use the staple diagram above to evaluate our actions. Let's add a second question to our survey:
What is the main reason for giving the score you did?Now we can figure out why we get the score we do. I'd recommend doing some serious analysis and synthesis here, because you cannot take customer responses at face value. You have to put them in context. For example, remember Apple´s Time Machine that I wrote about awhile ago. If you did a survey on the time machine, and asked only professional system administrators, the score is likely to be low, but in this case, that only indicates you are targeting the wrong user segment.
Based on the replies to the second question, and the NPS Breakdown table, you can decide whether to focus on moving detractors into the passives category, or moving passives into the promoters category, and how to do it.
It is also possible to find out other things. For example, you might have a product which the users love, but still does not sell. In that case, your constraint is not in customer satisfaction, but somewhere else. You might even have a product which people really don't like, but which sells anyway. Information like this can be extremely valuable.
For example, the Southland Corporation embarked on a costly program to improve customer service at its chain of 7-Eleven stores. The goal was that every customer should be greeted with a smile, eye contact, and thanks. They succeeded in increasing courtesy, but afterwards they discovered that the service improvements had little effect on profitability. (From Hard Facts, Dangerous Half-Truths & Total Nonsense, by Jeffrey Pfeffer and Robert Sutton.)
If the Southland Corporation had bothered to analyze the situation before acting, they could have avoided a costly mistake. An NPS survey would have revealed the same thing the Pfeffer-Sutton study did, that 7-Eleven customers want fast service, not insincere greetings.
Oh, and you may want to tack on a third question:
May we contact you again?This is not possible in all situations, but it is useful if you have a steady control group, so that you don't get hit by random variation in small samples. The thing to watch out for, of course, is that if you have a steady control group, you may get more and more positive answers over time just because you keep asking people what they think. But I know you already knew that.
Before using it, you should know that there is some controversy surrounding Net Promoter Score. Fred Reichheld, who invented the technique, claimed to have found a strong correlation between NPS and corporate growth. His claim was affirmed by some researchers, and refuted by others. (There is a Wikipedia article that references material from both camps.)
From a Theory Of Constraints perspective, if customer satisfaction is the system constraint, then you will benefit from improving it. If customer satisfaction is not the constraint, improving it will yield little benefit.
In either case, tracking it to know whether it is the constraint or not, is useful.