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Net Promoter or Net Promoter Score (NPS) is the percentage of customers rating their likelihood to recommend a company, a product, or a service to a friend or colleague as 9 or 10 ("Promoters") minus the percentage rating this at 6 or below ("Detractors") on a scale from 0 to 10. Respondents who provide a score of 7 or 8 are referred to as "Passives" and do indeed enter into the overall percentage calculation. The result of the calculation is expressed without the percentage sign. The core "how likely would you be to recommend..." question is sometimes accompanied by additional open-ended or so-called "driver" questions.
It is a management tool used as a measure of customer loyalty and has been shown to correlate with revenue growth relative to competitors. NPS has been widely adopted by Fortune 500 companies and other organizations. Companies, employers, or other bodies ask the questions of customer, employee, or other respondents such as resellers, implementation partners, and suppliers. NPS ranges between −100 (all respondents are "Detractors") and +100 (all respondents are "Promoters"). Scores vary substantially between industries, so a good score is simply one whose trend is better than that of competitors in the same industry, as measured by double-blind benchmark research.
The metric was developed by (and is a registered trademark of) Fred Reichheld, Bain & Company and Satmetrix. It was introduced by Reichheld in his 2003 Harvard Business Review article, "The One Number You Need to Grow". Its popularity and broad use have been attributed to its simplicity and its openly available methodology.
NPS has its critics. Some correctly point out that more sophisticated metrics based on answers to multiple questions can be better predictors of market share. Other critics essentially prove some of what the creators' research demonstrated: it is a good predictor for most, but not all the industries.
A short history of the Net Promoter Score. Fred Reichheld wrote The Loyalty Effect in 1996. Impressed by the work, André Schwager, CEO of Satmetrix, approached Reichheld in 2001, and they decided to collaborate to develop a simple indicator of customer loyalty. At the time, Satmetrix already had a more complex four-category satisfaction metric that resulted in four categories: Promoters, Passives, Disappointed, and Lost customers.
Bain became involved quite quickly adding resource to the research work. The work had two parts. In the first, over four thousand customers in six industries were asked to provide instances where they had either purchased more or referred others to a company. The respondents were asked the approximately 20 questions that Fred and Bain had developed in their work on what they called the Loyalty Acid Test. The intention was to work out which of the questions had the most statistically significant ability to predict actual repurchase and referral behavior. The result was 14 statistically significant case studies of individual companies in the six industries. The team was surprised to discover that willingness to recommend performed best, finishing either first or second for 11 of the 14 companies over the twelve months of that stage of the study. When comparing the results to the existing Satmetrix customer categories the team discovered that the behaviors of Disappointed and Lost customers were the same. The Detractor term was the result of a brainstorming session the joint team held on the subject.
The other major effort also started in early 2001. Satmetrix had already been asking thousands of customers to respond to a short survey in which they gave their ratings for one or two of forty companies covered by the research. They had been doing the survey quarterly for a number of years. A total of about 15,000 responses were gathered and the results compared with revenue trends from 1999 to 2002. Once again, the recommendation question performed best for most, but not all of these companies. Reichheld then submitted the Harvard Business Review article The One Number You Need To Grow.
The Net Promoter Score was born.
The HBR article got huge attention. Two key forums and an NPS certification course helped the metric to grow quickly. The forums were the Bain NPS Loyalty Forum, which still exists today, and the Satmetrix Net Promoter Conference. The certification course was created in classroom and online versions and is described in detail at netpromotermasterclass.com and is still the only class directly owned by one of the holders of the NPS trademark (Satmetrix).
The primary objective of the Net Promoter Score methodology is to predict customer loyalty (as evidenced by repurchase and referral) to a product, service, brand, or company.:49–51 Reichheld, a Satmetrix team led by Dr. Laura Brooks, and additional resources from Bain developed the methodology by comparing the ability of several different questions to predict future purchases and referrals of individual respondents. They chose the likelihood to recommend question based on the observation that it best predicted these customer behaviors in 11 of 14 industries studied.:49–51 They also found that differences in Net Promoter Scores among direct competitors in a market could explain substantial differences in revenue growth rates among competitors in that market.:61–65:77–81 Importantly, Markey points out that "competitive benchmark" Net Promoter Scores collected through a carefully constructed double-blind Quantitative marketing research methodology provide the most reliable basis for comparing scores.
Reichheld, Brooks, and Markey all note that the best predictions are therefore based on competitive comparisons. If Company A's score improves, it can only expect to gain market share if its main competitors scores do not improve to the same extent. Revenue trend comparisons also need to take industry growth rates and other factors into account. Maintaining flat revenue in an industry that is in decline can be good news, for example. There are also industries where other factors have a far larger effect on revenue. The market price of a barrel of oil has far more effect on the revenue any petroleum-related business than anything else, for instance.
For some industries, notably annuity-based business-to-business software and services, it has been shown that Detractors tend to remain with a company and Passives leave. This appears to be the case where switching barriers are relatively high, and where companies do a good job in closing the loop with Detractors. Passives receive relatively little attention and may be more amenable to being persuaded to switch by competitive offers.
Artificial Intelligence and Machine Learning applied to NPS
Artificial Intelligence and Machine Learning techniques are already starting to greatly influence NPS. The main challenge of the approach that has been used since 2003 is that it depends on people answering surveys. Email is the principal method companies use to distribute surveys and response rates have been declining steadily, according to multiple peer-reviewed studies.   Respondents therefore represent a fraction of the true target populations. Concerns are addressed and loops closed for Detractors who answer the surveys, but not for those who don’t. However, AI can use companies’ operational and other data to identify Promoter, Passive, and Detractor attributes and identify shortcomings and improvement opportunities for 100% of customers, rather than just respondents. Surveys, particularly double-blind competitive benchmark surveys, are then used for occasional calibration and competitive comparisons.
Building on the Net Promoter Score
As distinct from the Net Promoter Score, the Net Promoter System (a registered service mark of Bain & Co.) adds various processes, including a process to close the loop. In closing the loop, the provider actively intervenes to learn more from customers who have provided feedback, and also to change a negative perception, attempting to convert Detractors into Passives and Passives into Promoters.:175–198 The Net Promoter survey will identify customers who need follow-up, including Detractors, and should automatically alert the provider to contact the consumer and manage the follow-up and actions from that point, a practice followed by companies such as Scotiabank.
Proponents of the Net Promoter approach claim the score can be used to motivate an organization to become more focused on improving products and services.:199–200 The Net Promoter Score is now used by two-thirds of the Fortune 1000 companies according to the May 2020 Fortune article The Simple Metric That’s Taking Over Big Business. The article mentions that “Some 40,000 employees use it at IBM, and the executive who oversees its use, Michelle Peluso, says, ‘It’s more than a metric. One could use the word religion’”. The article also goes to some lengths to position NPS as a long-term metric. They include a quote from Diego Rodriguez, Chief Product and Design Officer at Intuit, who says, “This is a long-term metric. You can drive yourself crazy if you get hung up on the day-to-day or month-to-month. But if you use it for improving long-term, people will never go back.”
Some proponents of the Net Promoter Score suggest that the same methodology can be used to measure, evaluate and manage employee loyalty. They claim that collecting the feedback from employees in a manner similar to Net Promoter customer feedback can provide companies a way to improve their culture. What is sometimes called the "employee Net Promoter Score" or eNPS has been compared to other employee satisfaction metrics and some companies have claimed that it correlates well with those other metrics.:165
In the face of criticisms of the Net Promoter Score, the proponents of the Net Promoter approach claim that the statistical analyses presented prove only that the "recommend" question is of similar predictive power to other metrics, but fail to address its practical benefits, which are at the heart of the argument Reichheld put forth. Proponents also counter that analyses based on third-party data are inferior to those conducted by companies on their own customer sets, and that the practical benefits of the approach (short survey, simple concept to communicate, ability to follow up with customers) outweigh any statistical inferiority. They also allow that a survey using any other question can be used within the Net Promoter System, as long as it meets the criteria of sorting customers reliably into Promoters, Passives and Detractors.:12–13
While the Net Promoter Score has gained popularity among business executives and is considered a widely used instrument for measuring customer loyalty in practice, it has also generated controversy in academic and market research circles.
Research by Keiningham, Cooil, Andreassen and Aksoy disputes that the Net Promoter metric is the best predictor of company growth. It has to be pointed out that the non-correlating data they identified was entirely with retail gas stations in Norway. Gas stations are a great example of a sector where it should be obvious that revenue trends depend above all on the price of a barrel of oil and that customer satisfaction has little or no influence on financial results. The conclusions of the paper are therefore invalid, since they are not supported by the data from the other industries it covered. Furthermore, Hayes (2008) claimed there was no scientific evidence that the "likelihood to recommend" question is a better predictor of business growth than other customer-loyalty questions (e.g., overall satisfaction, likelihood to purchase again). Specifically, Hayes stated that the "likelihood to recommend" question does not measure anything different from other conventional loyalty-related questions. The customer metrics included in this study perform equally well in predicting current company performance." In defence of NPS, it must be said that the objectors actually state that the superior predictive ability of the recommendation question in most industries has never been demonstrated in a peer-reviewed journal. That was the case at the time those papers were written, since the HBR is not such a journal. The objectors did not show that any other single-question metric was superior.
No evidence for 11-point scale superiority
While several studies, such as one by Preston and Colman, have shown that there is little statistical difference in reliability, validity, or discriminating power, an unpublished paper by Schneider et al (2008) found a more nuanced pattern. Out of four scales tested in two studies (the original LTR with neutral label, a 7-point version with neutral label, a 7-point fully labeled, and 5-point fully labeled), the 7-point scales were better than the 11-point scale advocated by Reichheld, with partly-labeled scale outperforming all others by a smaller margin in predicting stated historical recommendations .
Reliability compared to a composite index of questions
"A single item question is much less reliable and more volatile than a composite index." "Furthermore, combining CFMs (customer feedback metrics), along with simultaneously investigating multiple dimensions of the customer relationship, improves predictions even further." Addressing this objection in one of their Bain Net Promoter System podcasts, Reichheld and Markey point out that they have never said otherwise. They simply find composite indices far more challenging to implement and communicate.
Imperfect predictive power for loyalty behaviors
"Recommend intention alone will not suffice as a single predictor of customers' future loyalty behaviors. Use of multiple indicators instead of a single predictor model performs significantly better in predicting customer recommendations and retention." "…given the present state of evidence, it cannot be recommended to use the NPI as a predictor of growth nor financial performance."
Not suitable as key performance indicator
As part of a critical analysis of the Net Promoter Score, Ralf Lisch highlighted that "it is a weak point of the approach that the so-called passives are shut out from the score although they have an influence on the percentage of the remaining two categories." Furthermore, losing detractors as customers can improve the NPS while the business eventually suffers. Lisch comes to the conclusion that contrary to the claim that the Net Promoter Score is "the one number you need to grow", the NPS is not suitable as key performance indicator. . Lisch’s first point about Passives is of course totally incorrect, as Passives are indeed included when calculating the score. NPS is quite easy to game, like all customer satisfaction metrics, and as Lisch points out. This is why Reichheld, Bain, and Satmetrix, all recommend double-blind competitive benchmark research as the main way of calibrating NPS and predicting market share trends.
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