Net Promoter Score (NPS): The One Metric You Need to Grow...or Not?
The Benefits and Limitations of This Popular Metric
You've probably heard of the Net Promoter Score (NPS), a simple metric that claims to measure customer loyalty with one question: "How likely are you to recommend us?" It was introduced by Fred Reichheld in 2003 and has been widely adopted by many companies across diverse industries. Sounds great, right? Well, as the great Lee Corso would say, not so fast my friend! There’s more to it than that. So let’s take a closer look at NPS and try to sift through the good and the not-so-good.
The score itself is kinda weird
NPS asks customers to rate their likelihood to recommend on a scale of 0 to 10 and then splits them into promoters (9 or 10), passives (7 or 8), and detractors (6 or less). To calculate it, you subtract the percentage of detractors from the percentage of promoters to get the final score. The higher the NPS, the more loyal and satisfied customers are. Easy peasy.
But what does it mean? For example, a score of 0 could mean that everyone is a passive, or that half are promoters and half are detractors. It’s also sensitive: a small change in one group can have a big impact on the score. And it doesn't account for how different customers may interpret the scale differently depending on their industry, market, or culture. For example, a customer who gives a 6 may be very satisfied in one industry but very dissatisfied in another.
The main problem with this scoring system is that it ignores the nuances and differences within each group of customers. It treats all promoters as equally loyal, all passives as equally indifferent, and all detractors as equally unhappy. But this is not the case in reality. Some promoters may be more enthusiastic, vocal, or influential than other promoters. Some passives may be more satisfied, loyal, or generate more revenue than other passives. And some detractors may be more dissatisfied, vocal, or influential than others detractors. By lumping them all together into broad categories, NPS loses valuable information and insights that could help companies understand and improve customer loyalty.
The question is narrow
NPS only measures one aspect of customer loyalty: the likelihood to recommend. But loyalty is more than that. It also includes retention, expansion (e.g. did a customer upgrade?), advocacy, and engagement. NPS doesn't tell you why customers are loyal or not, what they like or dislike about your product or service, or what you can do to improve their experience and outcomes. It's a vague and unactionable metric that doesn't give you much guidance on how to grow your business. For example, a customer who gives a 10 may be very likely to recommend you but may not buy from you again or may switch to a competitor if they offer a better deal. Or a customer who gives a 6 may be unlikely to recommend you but may buy from you again or may stay with you if they have no better alternatives.
By focusing only on advocacy, NPS misses out on other opportunities to measure and improve customer loyalty.
What if the response rate is low?
When you see an NPS survey, do you respond? Probably not.
According to research by CustomerGuage, B2B brands can expect a response rate of anywhere between 4.5% and 39.3%. The average NPS response rate was 12.4%.
Low response rates can skew the results and limit their usefulness.
Non-responders may have different levels of satisfaction or loyalty than responders, which can lead to an overestimation or underestimation of the NPS score. Non-responders may also represent important customer segments that are not captured by the NPS survey, which can reduce the representativeness and generalizability of the results. And non-responders may signal problems with the design or delivery of the NPS survey, such as frequency, timing, channel, or incentives.
The problem with low response rates is that they can introduce biases and errors into the NPS results. Biases can occur when non-responders have different characteristics or opinions than responders, which can distort the true picture of customer loyalty. Errors can occur when non-responders are not randomly distributed across different customer segments or groups, which can affect the validity and reliability of the results. And problems with survey design or delivery can affect non-responders' willingness or ability to participate, which can affect the quality and quantity of the data.
On the other hand, the above chart may look like it shows a positive correlation between response rate and average NPS score but because NPS uses a Likert scale, it is easily susceptible to extreme response bias.
Does NPS predict future growth?
And if so, how well?
To answer these questions, MeasuringU in 2019 analyzed 158 companies across 14 industries from an independent NPS data source. They compared NPS scores with revenue growth rates for the immediate two-year and four-year future periods. Here are some of the key findings and takeaways from their analysis.
They found a correlation between NPS and growth. But it is modest.
It’s important to note that the method of delivery of NPS surveys for this study is not typical of how a customer would encounter it naturally while using a product or service. See the method below:
The Temkin report has NPS benchmark data from 10,000 U.S. consumers collected from an online panel in October 2013. Sampling was matched to U.S. Census data to mirror the U.S. population on age, income, ethnicity, and region. Respondents provided NPS data on a “random” sample of organizations they had interacted with in the prior 90 days (meaning not all responses are independent). There were 269 companies, with sample sizes between 100 and 2,500 per company, in 19 industries.
In their study, they found a modest correlation between NPS and future growth in 11 of 14 industries for both the two-year and four-year periods. This means that companies with higher NPS scores tend to have higher growth rates than companies with lower NPS scores.
The correlation coefficient r measures the strength and direction of the linear relationship between two variables. The value of r can range from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation at all.
The correlation was stronger for the four-year period (r = .31) than for the two-year period (r = .25), suggesting that NPS may have a longer-term impact on growth. The correlation was also stronger when using relative ranks instead of absolute scores (r = .44 for two-year and r = .29 for four-year growth), indicating that NPS may be more useful for comparing companies within an industry than across industries.
However, not all industries showed a positive or significant relationship between NPS and future growth. The correlation ranged from non-existent/negative to very strong depending on the industry. It's safe to say that companies should not blindly rely on NPS as a proxy for future growth without considering the industry context and other factors that may affect customer loyalty.
One of the stronger correlations found in their analysis was for the software industry (r = .40 after two years and r= .69 after four years). This means that software companies with higher NPS scores tend to have higher growth rates than software companies with lower NPS scores.
And again, I would emphasize that respondents were asked to survey multiple companies across multiple industries, all at once, which has the potential to introduce anchoring bias (which I talk about more in the below post).
Conclusion
NPS is a popular metric that asks customers how likely they are to recommend a product or service. But it has some serious flaws.
NPS uses an odd scoring system that splits customers into three groups, ignoring the differences within each group
NPS only measures one aspect of loyalty: the likelihood to recommend. But loyalty is much more than that.
NPS surveys have low response rates which can skew results.
NPS is not a magic bullet for predicting future growth.
You need to use other metrics and methods that complement NPS and give you a more holistic and actionable view of customer loyalty and experience. For example, you can ask more specific and relevant questions about customer satisfaction, retention, expansion, advocacy, and engagement. You can also collect and analyze data on engagement, churn rate, and referral rate.
I hope you found this post helpful. If you have any questions or comments, please let me know in the comments section below. And if you liked this post, please share it with your friends or colleagues who might find it useful.
Thanks for reading!