Ever since Frederick F. Reichheld (the creator of NPS) published his article on NPS in “The One Number You Need to Grow” in the Harvard Business Review, NPS has become a growing trend in customer loyalty and customer relationship management. It’s continuing to gain traction, and is still a hot topic of discussion (and debate) around the business community.
A typical NPS survey.
Skip this section if you’re already familiar with NPS.
Most NPS surveys use a scale that range from 0 to 10, along with an open ended question asking the reason for the score.Clients who respond with a score of 9 to 10 are called Promoters. These are loyal customers who repeatedly buy from your company and recommend others to do the same. They’re more likely to forgive mistakes with your service or product, and are more engaged with your company brand.
Those that respond with 7 and 8 are called Passives. Passives are satisfied but unenthusiastic customers who can be lured by the competition to move or try other products and services.
If they find new products/services interesting they will move without much hesitation.Detractors are unhappy customers who are ‘at risk’ because of their perception about your company, and are those who spread the majority of negative word of mouth. They will give scores ranging from 0 to 6.
Net Promoter Score is calculated by subtracting the percentage of Detractors from the percentage of Promoters. It gives a good metric to track and work towards improving. Simple enough, right? So where do some companies trip up?
The open-ended question is where all the goodness lies — not the number which so many companies blindly chase after. While a bit over the top, Jared Spool says it quite humourously: “That number the survey respondents gave has no value. It’s like the skin of the mango. There’s nothing good about it. Just throw it away.”
The simple act of asking why the customer has given you their score is paramount in making NPS more than just a number and something to actually take action upon. It’s a small wording difference that triggers different responses from people. You’re much more likely to get useful, descriptive feedback from someone when you ask why they gave their score instead of what the reason was for their score. Asking “Why” encourages people to elaborate on their reasons rather than just stating what it is. Here’s a simplified example to illustrate the point:
Asking “what” can result in a rather unsubstantial response because it narrows the scope of the question:
“What is the reason for your score?” The fast service.
Asking “why” directs the person to elaborate more on their reason and broadens the scope:
“Why did you give us that score?” Because the service is faster than other stores.
Getting richer and more descriptive qualitative data is extremely important; better data collection means better results out of your voice of customer analysis. And yes, processing large amounts of long, free-text data can be a hard and time-consuming task. Well, it used to be.
In a world where we didn’t have the analytics technology we have now, asking “what” probably made life easier; it makes manual coding of surveys faster because the feedback is more likely to be short, quick statements which are easy to bucket into categories.
Historically, analysing text data has been notoriously difficult, but today we have access to a whole range of techniques, algorithms, and technologies that make it automated, more accurate, less biased, and extremely fast. We can afford to aim for long, descriptive answers to customer feedback nowadays. Not just aim for, but actively seek it. The payoff is huge, and companies live or die by understanding their customers.
Everyone wants to do customer insights right but they're setting themselves up to fail if they rely on tools that automate what humans already do manually. That's just automation and isn't true innovation. Customer insights teams today are allowing technologies to reveal the areas that need the most improvement. This video shows you how it's done. 🚀