Net Promoter Score

By Gojko Adzic

The Net Promoter Score® (NPS®) is a popular but widely disputed and misused customer satisfaction index. It is mostly applicable as a relative, directional signal for customer experience, and as a trigger for in-depth follow up with individual customers.

This article explains the key aspects of running NPS research, limitations and applicability for people who want to use this method in practice.

How to calculate the NPS Score?

The original method to calculate the NPS score is to ask customers to rate on the scale of 0 to 10 how likely they would be to recommend a product or service to their friends or colleagues (with zero typically labelled “not at all likely”, five labelled “neutral” and ten labelled “extremely likely”).

Net Promoter Scale Survey

1. How likely is it that you would recommend [company or product] to a friend or colleague?

0 1 2 3 4 5 6 7 8 9 10
Not at all likely Extremely likely
The NPS survey uses a 11-point Likert scale to capture likelihood to recommend, as a measurement of loyalty and a predictor of growth.

The responses are then grouped into 3 buckets:

The Net Promoters score is the percentage of promoters minus the percentage of detractors.

Alternative questions

When asking about recommendation is not applicable (see the section on limitations of NPS), use one of the following two questions instead:

Interpreting NPS results

NPS is an example of Likert Scale research, with top-box/bottom-box scoring, so typical methods for evaluating statistical relevance can be applied to NPS.

Origins of NPS

Net Promoter Score was invented by Frederick F. Reichheld, while working at Bain & Company, around 2001. It became widely popular after Reichheld introduced it in the The One Number You Need to Grow paper in 2003.

The original idea came from the assumption that companies waste money on complex satisfaction surveys, and that asking a single question is a good enough predictive signal for growth. Since asking a single question is much simpler and faster than complex surveys, companies would be able to use it for fast feedback and as a relative measurement over time, and across different business units.

As the inspiration for the NPS, Reichheld quotes customer research ideas of Andy Taylor, the CEO of Enterprise Rent-A-Car, who decided to poll customers each month with just two simple questions. The first asked about the recent rental experience, and the other about the likelihood that the customer would rent from the Enterprise again. Asking just two questions enabled Enterprise Rent-A-Car to quickly collect relative comparison metrics for its 5 thousand branches in the United States, and provide almost real-time feedback on how individual offices or even employees were doing, “and the opportunity to learn from successful peers”. Another important aspect of Enterprise Rent-A-Car surveys was that they mostly focused on measuring the number of “enthusiastic” customers.

By concentrating solely on those most enthusiastic about their rental experience, the company could focus on a key driver of profitable growth: customers who not only return to rent again but also recommend Enterprise to their friends.

– Frederick F. Reichheld, The One Number You Need to Grow

The theory behind NPS

Reichheld introduced NPS as a “way to measure and manage customer loyalty without the complexity of traditional customer surveys.” The method builds on Reichheld earlier work on managing customer loyalty, particularly The Loyalty Effect.

Defining loyalty as the “willingness of someone […] make an investment or personal sacrifice in order to strengthen a relationship”, Reichheld suggests that loyal customers will stay with a supplier who treats them well and provides good value in the long term, even if for a particular transaction the supplier does not offer the price or terms related to competitors. Loyalty “drives top-line growth”, due to repeated purchases, but according to Reichheld it also reduces customer acquisition costs because “loyal customers talk up a company to their friends, family, and colleagues”. Since customers recommending the product to others “risk their reputations”, they will do that only “if they feel intense loyalty”.

Reichheld suggested measuring loyalty through likelihood to recommend more than repeated purchases because some products naturally have a long purchase cycle, so customers would not repeatedly purchase them frequently enough to make frequent surveys relevant.

Following that thinking, intense loyalty drives growth, and measuring intense loyalty can help to predict growth. NPS, in theory, helps companies measure loyalty quickly and simply, in a way that can be used to benchmark and compare how customers experience a product over time, or in different environments. A simple, one question survey, is likely to result in much higher completion rates than a complex survey, providing a more complete picture. A single number makes it easily relatable, and consumable by executives.

Unlike the Enterprise Rent-A-Car research that mostly focused on tracking the enthusiastic customers, Reichheld’s research also points to the importance of tracking disappointed customers (“detractors”). The negative effects of detractors damaging the reputation of a company in their conversations with colleagues and friends offset the positive effects of enthusiastic customers promoting it, so both numbers need to be tracked in combination. Reichheld then claims that the “Net” difference between both groups is a good predictor of growth.

Arguments for NPS

Reichheld’s original paper quotes research by Satmetrix, who tracked likelihood to recommend scores from 400 companies in more than a dozen industries (notably, the paper does not explicitly claim that Satmetrix tracked NPS scores, but “would recommend” scores), and imply that “a strong correlation existed between net-promoter figures and a company’s average growth rate over the three-year period” for some classes of researched companies, such as airlines, where revenue data was readily available.

Problems with NPS

NPS was, according to Net Promoter 3.0 used “by two-thirds of the Fortune 1000” in 2011. Still, the relevance of the NPS score as a predictor of growth was never confirmed by peer-reviewed research.

Due to simplicity and ease of measurement, NPS scores are also relatively easy to falsify. For example, when NPS metrics are directly linked to employee’s income, people find creative ways to game the metric, reducing its validity. Reichheld warns against directly linking it to financial compensation as one of the biggest problems in practice:

The most common and most damaging misuse of Net Promoter Score is linking it to front-line employee compensation.

Fred Reichheld, from the Account Experience Podcast

Reichheld and co-authors of Net Promoter 3.0 claim that one of the most common types of abuse by inexperienced practitioners is to link NPS scores to “bonuses for frontline employees, which made them care more about their scores than about learning to better serve customers.”

Limitations of NPS

The relevance of NPS scores as a predictor of growth heavily depends on customer’s actually being able to sensibly recommend a product or service, and then measuring loyalty that way. Markets where likelihood to recommend does not apply are not a good fit for NPS. In particular, even the original paper recognises that NPS scores do not predict growth in markets that are “monopolies and near monopolies, where consumers have little choice”.

Separately, there is a problem with the standard NPS question for product types that people don’t casually recommend to friends or colleagues. A typical example is a software system an employee is forced to use as part of their job, and do not have a choice in selecting an alternative. Reichheld recognized this issue in the original paper, and recommended using alternative framing around loyalty:

Asking users of the system whether they would recommend the system to a friend or colleague seemed a little abstract, as they had no choice in the matter. In these cases, we found that the “sets the standard of excellence” or “deserves your loyalty” questions were more predictive.

– Frederick F. Reichheld, The One Number You Need to Grow

Addressing the limitations and problems

Following on almost two decades of NPS applications, Reichheld and colleagues published an update called NPS 3.0 in 2021 (for references, see Net Promoter 3.0 and Winning on Purpose), which complements the customer-provided NPS score with “earned growth”, as an accounting-based counterpart. According to the authors of the paper, this metric “reinforces the effectiveness of NPS” and provides a “clear, data-driven connection between customer success… and business results”. They define two metrics:

The authors warn against reporting NPS scores to investors and focusing on increasing NPS numbers as a target, and suggest that Earned Growth is a much better lagging metric to track and report for such cases.

NPS Licensing and Trademarks

While Reichheld claims he released the method as “open source” (from an interview Adam Dorrell on the Account Experience Podcast), the names NPS®, Net Promoter Score®, and Net Promoter System® are registered trademarks, owned by NICE systems in the US. “Net Promoter” was also a registered trademark until June 2026, but it then expired.

The method itself is free to use (without any fee or permission); but using the trademarked names requires attribution even for non-commercial projects, and commercial use of the registered trademarks requires a license, according to Net Promoter System Trademarks and Licensing Page.

An umbrella term for similar research systems is Likelyhood to recommend, often used to avoid the registered NPS trademark.