Net Promoter

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Net Promoter or Net Promoter Score (NPS) is a management tool that can be used to gauge the loyalty of a firm's customer relationships. It serves as an alternative to traditional customer satisfaction research and claims to be correlated with revenue growth.[1]

Net Promoter Score is a customer loyalty metric developed by (and a registered trademark of) Fred Reichheld, Bain & Company, and Satmetrix. It was introduced by Reichheld in his 2003 Harvard Business Review article "One Number You Need to Grow".[2] NPS can be as low as −100 (everybody is a detractor) or as high as +100 (everybody is a promoter). An NPS that is positive (i.e., higher than zero) is felt to be good, and an NPS of +50 is excellent.

Net Promoter Score (NPS) measures the loyalty that exists between a provider and a consumer. The provider can be a company, employer or any other entity. The provider is the entity that is asking the questions on the NPS survey. The consumer is the customer, employee, or respondent to an NPS survey.

How it works

The Net Promoter Score, itself, is calculated based on responses to a single question: How likely is it that you would recommend our company/product/service to a friend or colleague? The scoring for this answer is most often based on a 0 to 10 scale.[3]

Those who respond with a score of 9 or 10 are called Promoters, and are considered likely to exhibit value-creating behaviors, such as buying more, remaining customers for longer, and making more positive referrals to other potential customers. Those who respond with a score of 0 to 6 are labeled Detractors, and they are believed to be less likely to exhibit the value-creating behaviors. Responses of 7 and 8 are labeled Passives, and their behavior falls in the middle of Promoters and Detractors.[3]:51 The Net Promoter Score is calculated by subtracting the percentage of customers who are Detractors from the percentage of customers who are Promoters. For purposes of calculating a Net Promoter Score, Passives count towards the total number of respondents, but do not directly affect the overall net score.[4]

Companies are encouraged to follow the likelihood to recommend question with an open-ended request for elaboration, soliciting the reasons for a customer's rating of that company or product. These reasons can then be provided to front-line employees and management teams for follow-up action.[2] Local office branch managers at Charles Schwab Corporation, for example, call back customers to engage them in a discussion about the feedback they provided through the NPS survey process, solve problems, and learn more so they can coach account representatives.[5]

Reichheld and Markey say the rating and answers to the "Why?" question provide all that is needed to identify reference customers and improvement opportunities. While this may be the case, the lack of any easy way to automatically analyze the verbatim answers without human bias is problematic. The response of many companies to the problem has been to add additional questions with rating scales.

Additional questions can be included to assist with understanding the perception of various products, services, and lines of business. These additional questions help a company rate the relative importance of these other parts of the business in the overall score. This is especially helpful in targeting resources to address issues that most impact the NPS. Companies using the Net Promoter System often rely on software as a service vendors that offer a full suite of metrics, reporting, and analytics.[3]:48–49

The primary purpose of the Net Promoter Score methodology is to evaluate customer loyalty to a brand or company.[citation needed] The ability to measure customer loyalty is a more effective methodology to determine the likelihood that the customer will buy again, talk up the company and resist market pressure to defect to a competitor.[6] Measuring loyalty can be done in several ways, and researchers have asserted that there are better predictors of actual recommendations than asking "likelihood to recommend."[7] Since the purpose of Net Promoter is not to predict actual recommendations alone, but to predict the full suite of financially-advantageous behaviors, proponents of the methodology do not find this troublesome.[3]:65–72

Net Promoter System also requires a process to close the loop. Closing the loop is a process by which the provider actively intervenes to learn more from customers who have provided feedback, and also to change a negative perception, often converting a Detractor into a Promoter.[3]:175–198 In order to do this, the survey respondent can not be anonymous (something that can have a negative impact in the willingness to take the survey or to give low grades).[citation needed] The Net Promoter survey will identify customers who need some follow-up, including Detractors, and should automatically alert the provider to contact the consumer and manage the followup and actions from that point.[8]

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 for consumers.[9] They further claim that a company's Net Promoter Score correlates with revenue growth.[10] The Net Promoter approach has been adopted by several companies, including Australia Post,[11] Siemens,[12] E.ON,[13] Philips,[3]:61–65 GE,[14] Apple Retail,[15] American Express,[16] and Intuit.[17] It has also emerged as a way to measure loyalty for online applications, as well as social game products.[18]

A customer is able to leave comments in the surveys sent to them. This is what allows a company to use the VOC (Voice of Customer) to ensure that company is meeting the expectations.[citation needed]

Some proponents of the Net Promoter Score also 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 maintain focus on 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.[3]:165

For some kinds of industries, notably software and services, it has been shown that Detractors tend to remain with a company and Passives leave.[19] This appears to be the case where switching barriers are relatively high.

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 similar in predictive power to other metrics, but fail to address the practical benefits of the approach, which are at the heart of the argument Reichheld put forth. Proponents of the approach also counter that analyses based on third-party data are inferior to analyses 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 of the approach.[17] Interestingly, 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.[3]:12–13

Criticism of NPS

While the Net Promoter Score has gained popularity among business executives, it has also attracted controversy from academic and market research circles.

Even Fred Reicheld, the inventor of NPS admits that the initial research for NPS was flawed: "A number of perspicacious readers have noted that the statistical evidence provided in my book The Ultimate Question is imperfect. It does not provide proof of a causal connection between NPS and growth. Nor are some of the timeframes ideal."[20] The lack of a proven causal connection is of course a feature of all use of statistical correllation and regression techniques. They suggest where to look for causal connections, but do not provide them on their own.[21]

NPS does not add anything compared to other loyalty-related questions Research by Keiningham, Cooil, Andreassen and Aksoy disputes that the Net Promoter metric is the best predictor of company growth.[22] Furthermore, Hayes (2008) claimed there was no scientific evidence that the "likelihood to recommend" question is a better predictor of business growth compared to 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.[23] The customer metrics included in this study perform equally well in predicting current company performance."[24]

NPS performs worse than satisfaction "Our results indicate that average satisfaction scores have the greatest value in predicting future business performance and that Top 2 Box satisfaction scores also have good predictive value." [25] “Overall, we find that the top-2-box customer satisfaction performs best for predicting customer retention and that focusing on the extremes is preferable to using the full scale”.[26]

NPS uses a scale of low predictive validity Daniel Schneider, Matt Berent, et al. found that out of four scales tested, the 11-point scale advocated by Reichheld had the lowest predictive validity of the scales tested.[27]

Culturally-insensitive The validity of NPS scale cut-off points across industries and cultures has also been questioned.[28]

Less Accurate than Composite Index of Questions "A single item question is much less reliable and more volatile than a composite index."[29] "Furthermore, combining CFMs (customer feedback metrics), along with simultaneously investigating multiple dimensions of the customer relationship, improves predictions even further."[24]

Fails to Predict 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." [30] ”….given the present state of evidence, it cannot be recommended to use the NPI as a predictor of growth nor financial performance.” [31]


See also

References

  1. ↑ Call Centers for Dummies, By Real Bergevin, Afshan Kinder, Winston Siegel, Bruce Simpson, p.345
  2. ↑ 2.0 2.1 Lua error in package.lua at line 80: module 'strict' not found.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 Lua error in package.lua at line 80: module 'strict' not found.
  4. ↑ Satmetrix Net Promoter web site The Net Promoter Score and System
  5. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  6. ↑ Answering the Ultimate Question
  7. ↑ "Measuring Customer Satisfaction and Loyalty: Improving the ‘Net-Promoter' Score" by Daniel Schneider, Matt Berent, Randall Thomas and Jon Krosnick ".
  8. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  9. ↑ Net Promoter Score
  10. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  11. ↑ http://auspost.com.au/annualreport2014/assets/downloads/AusPost_AR14_Our_Performance_Customers.pdf
  12. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  13. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  14. ↑ "With Its Stock Still Lackluster, G.E. Confronts the Curse of the Conglomerate," New York Times, 16 August 2006
  15. ↑ "Another Myth Bites The Dust: How Apple Listens To Its Customers," Forbes.com, 26 August 2011
  16. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  17. ↑ 17.0 17.1 "Would You Recommend Us?" Business Week, 29 January 2006.
  18. ↑ "Net Promoter Score for Social Gaming," 28 February 2011.
  19. ↑ http://uk.events.satmetrix.com/portfolio_page/maurice-fitzgerald-speaker-presentation/
  20. ↑ http://netpromoter.typepad.com/fred_reichheld/2006/07/questions_about.html
  21. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  22. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  23. ↑ Hayes (2008), "The True Test of Loyalty," Quality Progress, June 2008, 20–26.
  24. ↑ 24.0 24.1 Satisfaction as a Predictor of Future Performance: A Replication. Jenny van Doorn , Peter S.H. Leeflang, Marleen Tijs International Journal of Research in Marketing (Impact Factor: 1.71). 12/2013
  25. ↑ The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance. By Neil A. Morgan, Lopo Leotte Rego Marketing science Vol. 25, No. 5, September–October 2006, http://www.tsisurveys.com/morgan-rego.pdf
  26. ↑ The predictive ability of different customer feedback metrics for retention. By Evert de Haan, Peter C. Verhoef, Thorsten Wiesel. International Journal of Research in Marketing. 2015. http://portal.idc.ac.il/en/main/research/ijrm/documents/forthcoming_ijrm%20d-13-00011_vandoorn%20et%20al.pdf
  27. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  28. ↑ "Customer advocacy metrics: the NPS theory in practice" Admap, February, 2008. Archived March 12, 2008 at the Wayback Machine
  29. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  30. ↑ - "The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Customer Retention, Recommendation, and Share-of-Wallet" (Timothy L. Keiningham, Bruce Cooil, Lerzan Aksoy, Tor W. Andreassen, Jay Weiner).
  31. ↑ Nomological validity of the Net Promoter Index question. BY Birgit Leisen Pollack and Aliosha Alexandrov. Journal of Services Marketing, Vol. 27 Iss: 2, pp.118 – 129
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