A Holistic Examination of Net Promoter

Abstract

The measurement and management of customer loyalty and its link with firm growth have long been of interest to managers and researchers. One relatively recent word-of-mouth customer loyalty metric purported that the link to growth is the Net Promoter Score (NPS), a metric based on a likelihood to recommend question asked in customer surveys. This research provides a summary of the claims made regarding NPS, reviews the research conducted on this topic to date, and provides a holistic examination of two scientific studies that test the claims made. The two claims being tested are that (1) NPS is the single most reliable indicator of a company's ability to grow and (2) NPS is superior to customer satisfaction and the latter has no link to growth. Based on both macro- and micro-level investigations that test the link between NPS and firm growth and NPS and customer behaviour metrics respectively, our research finds that neither of these claims are supported.

Full text

© 2008 Palgrave Macmillan Ltd 1741-2439 $30.00 Vol. 15, 2, 79–90 Database Marketing & Customer Strategy Management 79
www.palgrave-journals.com/dbm
Timothy L. Keiningham
IPSOS Loyalty
Morris Corporate Center 2
1 Upper Pond Rd
Bldg D. Parsippany,
NJ 07054, USA
Tel: + 1 973 658 1719;
Fax: + 1 973 658 1701;
e-mail: tim.keiningham@
ipsos-na.com
A holistic examination of
Net Promoter
Received (in revised form): 10th December, 2007
Timothy L. Keiningham
is Global Chief Strategy Offi cer and Executive Vice President at Ipsos Loyalty. He is author of several management books, his
most recent being Loyalty Myths . He has received best paper awards from the Journal of Marketing (twice), the Journal of Service
Research , and Managing Service Quality (twice), and has received the Citations of Excellence ‘ Top 50 ’ award (top 50 management
papers of approximately 20,000 papers reviewed) from Emerald Management Reviews. Tim also received the best reviewer award
from the Journal of Service Research . His papers have appeared in such publications as Journal of Marketing , Sloan Management
Review, Journal of Service Research , Journal of Relationship Marketing , Interfaces , Marketing Management , Managing Service
Quality , and Journal of Retail Banking . He serves on the editorial review board of Journal of Marketing , Journal of Service Research ,
Journal of Relationship Marketing , and Cornell HRA Quarterly.
Lerzan Aksoy
is Associate Professor of Marketing at Ko ç University in Istanbul, Turkey. She is the co-author of the book Loyalty Myths (with
Keiningham, Vavra, and Wallard), 2005 by John Wiley and Sons. She has received best paper awards from the Journal of Marketing
and Managing Service Quality (twice), and has received the Citations of Excellence ‘ Top 50 ’ award (top 50 management papers of
approximately 20,000 papers reviewed) from Emerald Management Reviews. She was also awarded fi nalist for best paper in the
Journal of Service Research . Her papers have been accepted for publication in such journals as Journal of Marketing, Marketing
Science , Journal of Service Research , MIT Sloan Management Review, Journal of Relationship Marketing , International Journal of
Service Industry Management , Managing Service Quality , Journal of Consumer Marketing, and Marketing Management . She serves
on the advisory board of the Journal of Relationship Marketing , the editorial review board of the Journal of Ser vice Research , and the
International Journal of Service Industry Management and is an ad hoc reviewer for Journal of Marketing and Cornell HRA Quarterly .
Bruce Cooil
is the Dean Samuel B. and Evelyn R. Richmond Professor of Management at the Owen Graduate School of Management,
Vanderbilt University. His research interests include the adaptation of grade-of-membership and latent class models for marketing
and medical research, estimation of qualitative data reliability, large sample estimation theory, and extreme value theory. He has
also written and consulted on models for mortality, medical complications, medical malpractice, and automobile insurance claims.
His publications have appeared in business, statistics, and medical journals, including the Journal of Marketing Research , Journal
of Marketing , Psychometrika , Journal of the American Statistical Association , Annals of Probability , Circulation , and the
New England Journal of Medicine .
Tor Wallin Andreassen
is Professor and Chair Department of Marketing. He is founder and director of Service Forum and the founder of The Norwegian
Customer Satisfaction Barometer. He won the MSI / H. Paul Root Award from the Journal of Marketing , and has the Most
Downloaded Article Award, and the Citation of Excellence Award (twice). He is on the editorial review board of Journal of Marketing ,
Marketing Science , Journal of Service Research , and the International Journal of Service Industry Management .
Luke Williams
is Senior Research Analyst at Ipsos Loyalty. He holds a BA in Sociology from Rutgers University and an MA in Social Research
Methods from the University of Durham (UK). Luke has published editorial pieces in the fi eld of International Relations for the
School of Government and International Affairs Review , and is currently working on academic and trade articles in market research
and applied modern sociological theory. He has also reviewed for East Asia: An International Quarterly .
Keywords Net Promoter , customer loyalty , customer satisfaction , fi rm growth ,
recommend intention , word of mouth
Abstract The measurement and management of customer loyalty and its link with fi rm
growth have long been of interest to managers and researchers. One relatively recent
word-of-mouth customer loyalty metric purported that the link to growth is the Net
Promoter Score (NPS), a metric based on a likelihood to recommend question asked in
customer surveys. This research provides a summary of the claims made regarding NPS,

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INTRODUCTION
In recent years, there has been a distinctive,
fundamental shift in managerial thinking
that has prompted managers, consultants,
and academics to re-evaluate their
perceptions of the developing role of
customer satisfaction, retention, and loyalty.
There is a long history of corporate
investment into loyalty and customer
satisfaction, but most of these early
activities were based more on faith
than facts.
1
Adding to the complexity, loyalty models
were so differentiated that it made it
diffi cult for corporate managers to choose a
satisfaction or loyalty model with any real
sense of making an adequately informed
decision. Outside of the market research
sphere, there were few individuals who had
a strong grasp of exactly what it was that
the management teams should be looking at.
All that most managers really believed they
knew was that loyalty programmes worked
and that they needed one. But with such a
multidimensional concept as loyalty, it was
diffi cult to engage the idea itself, specifi cally
because it was so diffi cult to measure.
2 Such
was the status quo .
A December 2003 Harvard Business
Review article, however, noted that loyalty
consultant Fred Reichheld changed the
status quo with regard to how the value of
loyalty was perceived by management
teams.
3 Reichheld was able to accomplish
this status shift by emphasising a common
survey-based metric and introducing it as a
straightforward loyalty metric: the Net
Promoter Score (NPS).
Net Promoter was proclaimed to be the
single best predictor of fi rm growth.
3 This
development shook customer satisfaction and
loyalty space to its foundation and made the
import of loyalty an accessible concept to
CEOs and CMOs from companies such as
GE, American Express, Overstock.com ,
Intuit, and a number of other well-known,
publicly traded companies.
This paper is a summary of two separate
investigations into the claims attributed to
Net Promoter.
4,5 The research reported in
the Harvard Business Review comprised both
a macro-level and micro-level investigation
into the relationship between NPS and fi rm
growth and NPS and customer loyalty
behaviours, respectively. The purpose of this
paper is to unify scientifi c research in these
two distinct examinations into a
comprehensive scientifi c document.
THEORETICAL BACKGROUND
As early as the 1950s, a great deal of
importance has been placed on the value of
customer satisfaction. Additionally, many
management teams have now come to lay
considerable stock in the value of customer
loyalty. A myriad of books have been
published on the value of customer loyalty,
and what it means for a customer to be
both profi table and loyal.
Managers across the globe have
implemented loyalty strategies primarily
based on the evidence that loyalty schemes
reviews the research conducted on this topic to date, and provides a holistic examination
of two scientifi c studies that test the claims made. The two claims being tested are that
(1) NPS is the single most reliable indicator of a company ’ s ability to grow and (2) NPS
is superior to customer satisfaction and the latter has no link to growth. Based on both
macro- and micro-level investigations that test the link between NPS and fi rm growth
and NPS and customer behaviour metrics respectively, our research fi nds that neither of
these claims are supported.
Journal of Database Marketing & Customer Strategy Management (2008) 15, 79 – 90.
doi: 10.1057/dbm.2008.4 ; published online 12 May 2008

A holistic examination of Net Promoter
© 2008 Palgrave Macmillan Ltd 1741-2439 $30.00 Vol. 15, 2, 79–90 Database Marketing & Customer Strategy Management 81
work. For example, Tesco, Britain ’ s largest
retailer (and the third-largest retailer
globally), is the archetype for the value of
loyalty schemes. Prior to the launch of its
Clubcard in February of 1995, Tesco
struggled to keep its place as one of the top
four grocers in the UK.
6 Today, however,
nearly £ 1 out of every £ 6 in Britain is
spent at Tesco (TNS WorldPanel May 2007)
— earning over £ 2.5 billion in profi ts and
£ 42.6 billion in revenue according to 2007
fi gures — which Tesco ’ s management
attributes largely to the effects of Clubcard
and its loyalty scheme.
6
Tesco is not the only example to
emphasise and focus on satisfaction and
loyalty. There are several other famous
membership schemes that have proven to
benefi t their sponsors. American Express,
Neiman-Marcus, Microsoft, Kawasaki,
Volkswagen, CVS, Ford Motors, and
Hallmark are just some of the international
companies that boast strong response and
activity in conjunction with their loyalty
and membership programmes.
Word-of-mouth
There is a great deal of anecdotal evidence
that word-of-mouth can play a signifi cant
role in generating momentum for a group,
fi rm, or product. For example, the
continuing success of the American rock
group, Phish, epitomises the reach and
power of word-of-mouth support. Named
‘ the most important band of the 1990s ’ by
Rolling Stone (1st October, 1998), Phish ’ s
rise to cult status occurred largely without
the help of mainstream media outlets such
as MTV or syndicated radio play.
7 In an
effort to dodge the pitfalls of pop culture
popularity, the band shunned typical
advertising channels but grew its fan base
through the benefi ts of word-of-mouth.
There are numerous other examples of
companies that chose to defl ect more
traditional marketing approaches in favour
of word-of-mouth. One well-known
example that enjoyed such benefi ts in the
1990s is Napster. Today, social networking
(eg Facebook, MySpace) or quick-click
media sites garner popularity largely
without traditional marketing; even
YouTube has been incorporated into
CNN ’ s nationally televised US Presidential
debates ( NY Times 13th June, 2007).
Success stories of viral marketing highlight
the potency that seemingly unsolicited
praise wields.
8
Without a doubt, there are innumerable
instances where word-of-mouth has made
a signifi cant impact (positive or negative).
The value of word-of-mouth can be quite
large since (1) it costs the retailer virtually
nothing, (2) immediate communication
channels such as the internet and cell
phones permeate modern society, and (3) it
is perceived to have an immediate sense of
credibility.
9 Furthermore, consumers feel
like they are ‘ being sold ’ less by other
consumers than they are by traditional
advertising mechanisms, ultimately
alleviating some of the suspicions that
accompany vested interests.
9
Although a positive relationship between
word-of-mouth and sales is presumed, early
research shows that the linkage may be
more complex than imagined. In a study of
the effect of word-of-mouth on television
viewing, Godes and Mayzlin could not fi nd
a consistent relationship between the
volume of [word-of-mouth] and future
television ratings.
10 In addition, in a study
of a national US retailer, Godes and
Mayzlin fi nd that the expected additional
sales resulting from the word-of-mouth
activities of loyal customers did not create
anticipated additional sales.
11,12
On the other hand, managers and
researchers alike have come to realise the
pivotal role that customer loyalty and word-
of-mouth can play in the development of
a consumer base. The general consensus
is that word-of-mouth can have a major
impact on consumers ’ responses to a
product.
13 – 16 For example, Rust et al.
17
observe, ‘ the effect [of word-of-mouth] is

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notoriously hard to measure, but it is
frequently signifi cantly large ’ .
To date, however, only a small number of
researchers have proposed methods for
calculating the value of word-of-mouth
referrals.
16,18,19 Furthermore, there is no
peer-reviewed research that longitudinally
examines the relationship between word-of-
mouth activity and fi rm-level fi nancial
outcomes (eg revenue, profi ts) across
multiple industries. This has caused
researchers to call for additional
investigation into this relationship.
11,14,20
Net Promoter score and linkage to
fi rm growth
The Net Promoter concept was introduced
in a 2003 Harvard Business Review article.
3
One of the claims made by Net Promoter as
a metric was the positive relationship it was
purported to have with fi rm revenue growth.
The overarching message is that NPS is the
single most reliable indicator of a fi rm ’ s
ability to grow. NPS is derived from survey
responses to a likelihood to recommend
question on an 11-point scale. The
proportion of respondents rating the fi rm a 6
or less (called ‘ Detractors ’ ) is subtracted from
the proportion of respondents rating the fi rm
a 9 or 10 (called ‘ Promoters ’ ); this difference
represents a fi rm ’ s NPS.
3,21 The rationale was
that people highly likely to recommend a
fi rm were implied as being loyal to it. And
given the — at least anecdotal — evidence
of the power inherent in word-of-mouth
recommendation, the effect was that good
business produces loyal customers who sell
the business for you.
Nevertheless while the claims made with
respect to Net Promoter initially struck a
chord with management teams, the
following presented summary demonstrates
how and why the metric fails to satisfy the
claims it makes.
METHODOLOGICAL EVIDENCE
The 2003 Harvard Business Review article on
Net Promoter cites research conducted
beginning in 2001 on more than 400
companies in more than a dozen industries
as evidence of the superior power of this
metric relative to other survey questions in
predicting growth.
3 Although data from
customers of 400 + companies were
collected, inclusion in the actual analysis was
limited to fi rms that met specifi c criteria. As
a result, ‘ over 50 companies were included
across a dozen targeted industries ’ .
22 In the
Harvard Business Review article that
introduced Net Promoter, charts for three of
the examined industries were presented; the
sample sizes (in terms of number of fi rms)
were three, fi ve, and ten.
3 This would mean
that the sample sizes for each of the
remaining nine industries were approximately
3.6 on average (ie [50 − (3 + 5 + 10)] / 9 = 3.56)
. Therefore, industry sample sizes were small.
The analysis and results are described as
follows: ‘ Correlations were computed tying
… Net Promoter to each company ’ s
revenue growth rate for each targeted
industry ’ . Specifi cally:
(1) A mean NPS for each fi rm was computed
(two years of data were collected for
each fi rm in Reichheld ’ s and Satmetrix ’ s
analyses).
(2) An average revenue growth rate was
computed, which included the two years
for which NPS was available along with
an additional prior year (ie three-year
growth rates).
(3) Correlation coeffi cients were calculated
for each industry under investigation.
Several highly visible publications have
appeared regarding Net Promoter, including
a article in MIT Sloan Management Review
and a Wall Street Journal (2006) number-one,
best-selling business book, The Ultimate
Question .
23,24 In addition, numerous trade
journal papers have featured Net
Promoter.
25,26
Little reconnaissance is required to locate
claims of Net Promoters linkage to
growth.
24,27 The simple truth, however, is

A holistic examination of Net Promoter
© 2008 Palgrave Macmillan Ltd 1741-2439 $30.00 Vol. 15, 2, 79–90 Database Marketing & Customer Strategy Management 83
that these claims remained largely untested
by the scientifi c community. The primary
claim to be tested, of course, is that Net
Promoter is the ‘ single most reliable
indicator of a company ’ s ability to grow ’
and is a more competent metric at
estimating growth than other, multi-metric
methods and models.
28
Prior methodology-replication efforts
There have been few attempts at replicating
the fi ndings regarding NPS presented in the
Harvard Business Review . Details of these
studies are described below.
In the fi rst study — conducted by The
Listening Company in conjunction with the
London School of Economics — the
relationship between Net Promoter levels
collected in 2005 was compared to fi rm
growth rates for 2003 – 2004.
29,30 The study
reported a Pearson correlation of 0.484
when examining the relationship across the
entire data set. Marsden et al.
29 (p. 5) also
report ‘ a 7-point increase in the NPS
correlated with a 1 per cent increase in
growth (1-point increase = 0.147 per cent
more growth) ’ . This study, however,
(1) relied on cross-sectional Net Promoter
data and (2) linked Net Promoter to prior
period revenue growth rates. As a result, it is
not possible to determine a relationship
between NPS and fi rm revenue growth.
Another study by researchers Morgan and
Rego examined the longitudinal impact of
various customer satisfaction and loyalty
metrics in predicting business performance.
31
They labelled one such metric ‘ Net
Promoter ’ and found that their calculation
had no predictive value. The data used and
the calculation itself, however, differ
substantially from that which Reichheld and
Satmetrix advocate and test.
3,22,32 As a result,
the study does not examine Net Promoter in
its widely used and understood sense.
Therefore, conclusions regarding the claimed
effectiveness of Net Promoter as a predictor
of business performance cannot be made
from this study.
Our research
Different from the study performed by The
Listening Company and the one conducted
by Morgan and Rego, the research data that
our study provides are a result of replicating
the methodology used by Reichheld and
Satmetrix.
3,22,24 The results of our study,
however, contradict the claims made by
Reichheld, Bain & Co. and Satmetrix on
both the macro- and micro-levels.
4,5
The macro-data indicate that NPS is not
the superior metric (much less signifi cantly
so) when linked to fi rm revenue growth;
the replicated methodology is compared
with data from the Norwegian Customer
Satisfaction Barometer (NCSB) as well as
with data from the American Consumer
Satisfaction Index (ACSI).
Furthermore, the micro-data indicate that
the foundation of Net Promoter is not a
uniquely signifi cant singular indicator of
customer loyalty either. The Net Promoter
methodology is juxtaposed against data
drawn from a proprietary panel accessed by
a large, leading market research fi rm and
tested against a multi-metric solution.
MACRO-LEVEL DATA EXAMINATION
Comparison of different
loyalty metrics
The fi rst macro-examination is derived
from data published in the NCSB, which is
based on a national probability sample of
Norwegian households; the database
contains approximately 16,000 completed
telephone interviews pertaining to measures
of specifi c companies. Eligible interviewees
were considered to be ‘ qualifi ed ’ respondents
based on recent-purchase behaviours over
specifi c, indicated periods. Company
inclusion in the study was reliant upon
interviews with 100 – 200 of their existing
customers. For further methodology
support, see Keiningham et al. ,
4 Fornell,
33
Fornell et al .,
34 and Johnson et al .
35
Respondents were asked questions
regarding the following: (1) intention to

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84
recommend; (2) overall satisfaction; and
(3) repurchase intention. Firms were only
included in the analysis of NCSB data
if (1) respondents for a particular fi rm
were asked these three questions for two
or more consecutive years and (2) fi rm
revenue data could be obtained in the
analysis for the corresponding time-frame.
In total, 21 fi rms representing over
15,500 customer interviews met these
criteria.
The NPS was derived on the basis of
responses to the question, ‘ How likely is it
that you would recommend [company x] if
a friend or business relation asked for your
advice ’ ( ‘ very high probability / very low
probability ’ )? Because the data from the
NCSB are measured on a ten-point scale
(1 – 10), we subtracted the percentage of
respondents rating 1 – 6 from the percentage
rating 9 – 10. We also created ten other
commonly used satisfaction / loyalty metrics
for the analysis, including the NCSB score,
three measures (mean, top box, and top-two
box) for overall satisfaction measures,
repurchase intention measures, and
recommend intention.
For companies included in the analysis,
the North American Industrial Classifi cation
System (NAICS) was used to determine
industrial classifi cation, thereby ensuring
that the data groups were consistent with
Reichheld ’ s and Satmetrix ’ s methodology.
3,22
In total, fi ve industries (representing 17 of
the 21 fi rms in our data fi le) contained the
minimum threshold number of fi rms for
which Reichheld and Satmetrix conducted
their analysis.
3,22 We examined the following
industry classes independently from one
another in relation to relative change in
revenue: banking, gasoline stations (with
convenience stores), home furnishings
retailers, security systems, and transportation
(local / suburban transit). Depending on the
industry, data ranged from between the
years 2000 – 2005. Some industries contained
data for all years whereas others had data
for two years. Pooled correlations are
reported for industries with data of more
than two years.
From Table 1 , there is little statistical
evidence that the average levels of any
satisfaction / loyalty metrics shown are
signifi cantly correlated with the relative
change in revenue within the respective
industry.
36 Furthermore, it would seem
obvious that Net Promoter cannot
reasonably be categorised as the ‘ single
most reliable indicator of a company ’ s
ability to grow ’ .
28
Similarly, Table 1 also points towards the
inherent diffi culty of predicting fi rm
revenue growth within an industry on the
basis of a single attitudinally-based metric.
Despite this diffi culty, we might expect that
a robust and expansive longitudinal study
would show that changes in satisfaction /
loyalty metrics are at least somewhat
important predictors of relative changes in
revenue within fi rms.
We also conducted best-subsets analyses
in which we considered all 11 satisfaction /
loyalty metrics in Table 1 candidate
predictors for relative annual change in
revenue, and fi xed industry effects (in which
we represented industries as indicator
variables). The best scientifi c model in terms
of the Bayesian information criterion (see
Schwarz
37 ) did not include any of these
metrics; it included only the most
signifi cant industry effects.
Comparing NPS to ACSI
Net Promoter was also proclaimed to be
better than one of the most commonly
used metrics: customer satisfaction. As
evidence, the ACSI was asserted to have no
correlation with growth, most notably
quoted in a 2004 webinar: ‘ a Bain team
looked at the correlation between growth
and customer satisfaction, and found there is
none ’ . A scatter diagram was shown with
the x-axis labelled ‘ [ACSI] annual growth ’
and the y-axis labelled ‘ Sales annual
growth ’ .
3,24,38 The R
2 reported was 0.00,
indicating no correlation. Similar

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© 2008 Palgrave Macmillan Ltd 1741-2439 $30.00 Vol. 15, 2, 79–90 Database Marketing & Customer Strategy Management 85
indictments were also included in
Reichheld ’ s book, The Ultimate Question .
24
The claims regarding the lack of
relationship of satisfaction and growth,
however, are in stark contrast to several
papers that appear in The Journal of
Marketing , specifi cally examining this
relationship:
Anderson et al. fi nd a positive association
between the ACSI and Tobin ’ s q (the
ratio of a fi rm ’ s market value to the
replacement cost of its assets (Tobin,
1969)), the ratio of price to book value,
and equity prices.
39,40
Gruca and Rego use ACSI and
COMPUSTAT data and fi nd that
satisfaction creates shareholder value by
increasing future cash fl ow growth and
reducing its variability.
41
Aksoy et al.
42 and Fornell et al.
43 fi nd
that fi rms that performed better in terms
of their ACSI scores also performed
signifi cantly in terms of market returns.
Hence the second study in our macro-
examination was conducted in conjunction
with data accessed from the ACSI. The
objective was to replicate the Net Promoter
data used and compare the relationship of
NPS and growth to ACSI and growth.
In the appendix to The Ultimate Question ,
several scatterplots are provided to
demonstrate the NPS correlation to growth
rates.
24 These graphical plots were enlarged,
scanned, and imported into a charting
software package. This software tool allowed
us to preserve the scanned plots and save
them as background images. Over these
images, we reconstructed the exact
dimensions of the scanned plot image and
input data until the data points were
replicated. As an assurance check of the
replicated data, we compared the coeffi cient
of determination ( R
2 ) of the re-created data
with the reported R
2 from the original
scatterplots. All R
2 values were the same,
indicating a successful replication of the data.
—
—
—
Table 1 : Correlations between revenue change and satisfaction/loyalty metrics
Net
Promoter
NCSB Satisfaction
(Mean)
Repurchase
intention
(Mean)
Recommend
intention
(Mean)
Satisfaction
(Top Box)
Satisfaction
(Top 2 Box)
Repurchase
intention
(Top Box)
Repurchase
intention
(Top 2 Box)
Recommend
intention
(Top Box)
Recommend
intention
(Top 2 Box)
Banking
Pooled Y Y +1 (2001 – 2005) 0.40 0.17 0.02 0.37 0.32 − 0.05 − 0.29 0.37 0.40 − 0.02 0.29
p -value 0.20 0.60 0.95 0.24 0.32 0.88 0.37 0.23 0.20 0.96 0.36
Retail (gasoline)
Pooled Y Y +1 (2000 – 2003) − 0.45 0.09 0.49 0.62 − 0.32 0.42 0.84 0.63 0.45 0.38 0.21
p -value 0.55 0.91 0.51 0.38 0.68 0.58 0.16 0.37 0.55 0.62 0.79
Retail (home furnishings)
2003 – 2004 − 0.12 0.95 − 0.17 − 0.95 − 0.12 0.99 0.26 − 0.83 − 0.45 0.08 −
0.26
Security systems
2002 – 2003 0.86 0.17 0.05 − 0.56 0.95 0.64 0.16 − 0.44 − 0.99 0.76 0.98
Transportation
2000 – 2001 0.08 0.06 0.04 0.14 0.02 − 0.59 − 0.43 − 0.32 0.00 − 0.26 − 0.08
Note: The highest positive correlations between revenue change and satisfaction/loyalty metrics are in bold.

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Given the successful graphical replication
of Net Promoter and growth data, we
appended the ACSI data to the fi le. We
examined specifi cally the industries that were
used as Net Promoter exemplars and that
were also tracked by the ACSI: personal
computers, airlines, and life insurance. Where
possible, as many companies from each
industry were included in the ACSI
examination in a substantial attempt to
compare the link of ACSI and NPS to
growth. Based on the claims, one would
expect the results to reveal relationships in
which NPS is a superior predictor of growth
when juxtaposed with other metrics.
Table 2 compares the R
2 ’ s from the
original charts demonstrating the
relationship between NPS and growth to
the R
2 ’ s from ACSI scores versus
growth.
24,44 Immediately the similarity in
variance explained between the two is fairly
striking. In two of the three cases, the R
2 of
the ACSI / growth relationship is higher than
that of the NPS / growth relationship.
45
These data clearly contradict the claims
made on behalf of Net Promoter as (1) the
best predictor of growth and (2) that
customer satisfaction has no correlation
with growth.
MICRO-LEVEL DATA EXAMINATION
Data for the micro-level data juxtaposition
come from a longitudinal study of over
8,000 customers corresponding to fi rms in
one of three industries (retail banking,
mass-merchant retail, and internet service
providers).
46 These customers ’ ratings of
common satisfaction and loyalty metrics
were monitored over two years. Customers
were surveyed regarding their experiences
with a fi rm, and they later participated in a
follow-up survey that was conducted
approximately one year after the initial
survey. In the second year of the study,
customers ’ purchasing (retention, share-of-
category spending) and referral behaviours
were also tracked. Alternate calculation
approaches ( à la NPS classifi cation into
three groups) were also undertaken for
purposes of comparison.
We tested the two claims made by
Reichheld and Satmetrix on the micro-
level: (1) recommend intention alone is
an effective predictor of loyalty behaviour
and (2) a single-metric model is an
equivalent or better model than a multi-
metric one.
Our correlation analyses oppose the fi rst
claim made and that was tested in our
research: recommend intention alone is an
effective predictor of loyalty behaviour. The
claim that recommend intention is an
effective predictor of loyalty behaviours —
beyond other metrics — is not supported.
From Table 3 , we see that correlations of
recommend intention to recommend
behaviour and repurchase intention to
repurchase behaviour are signifi cant. It is
important to note that for the combined
recommend – repurchase variable, both
repurchase intention and recommend
intention were found to be almost
identical in terms of the strength of
association. Also, it is clear that industry
type has a dramatic effect upon
correlations, thereby calling the reliability
of any of these single metrics (with
respect to indicating loyalty behaviour)
into question for a cross-industry analysis.
Of more important note for the
validation testing for Net Promoter,
recommend intention has weak correlations
to change in share-of-wallet. This statistic is
crucial because Reichheld and Sasser
47
argued that ‘ profi t from increased purchases ’
is a major contributor to profi ts through
increased customer loyalty. According to the
Table 2 : R
2 of correlations: NPS and growth versus
ACSI and growth (ACSI-only companies)
Net promoter
and growth
ACSI and
growth
Wintel Personal
Computers
0.70 0.76
US Life Insurance 0.83 0.58
Airlines 0.57 0.70

A holistic examination of Net Promoter
© 2008 Palgrave Macmillan Ltd 1741-2439 $30.00 Vol. 15, 2, 79–90 Database Marketing & Customer Strategy Management 87
syllogism, then, the case for recommend
intention ’ s suffi ciency as an indicator of
loyalty behaviour is, again, unsubstantiated.
In general, however, none of the explored
variables account for more than 20 per cent
of the variance between variables. Thus,
using any one of these as a single predictor
of loyalty behaviour — especially across
industries — is not recommended.
Our regression analyses also oppose the
second claim that was tested in our research:
a single-metric model is an equivalent or
better model than a multi-metric one. We
analysed the incremental predictive value
of multiple-predictor models relative to
single-predictor models for retention within
each industry. The results indicate the virtue
of a multi-metric model versus a single-
metric model.
As candidate predictors for these multiple
logistic ordinal regression analyses, we used
all of the survey response variables. For the
ISP model, there was only a nominal
increase in the adjusted R
2 when a multi-
metric model was constructed or employed
over a single-metric model. In the other
two industry models, however, there was a
signifi cant improvement in the model by
employing the multi-metric method over
the single-metric method; there was an
average improvement of nearly 20 per cent
when moving from a single-metric model
to the best multi-metric one.
5
DISCUSSION AND CONCLUSION
This research provides a holistic
examination of Net Promoter research
conducted to date. It provides a review of
Table 3 : Correlations of loyalty metrics in t =1 and t =2
Change in
SOW
SOW Recommend
and retain
Retain Recommend
Share of wallet t − 1 (initial period)
Banking − 0.63 0.49 0.01 0.00 0.01
Retail − 0.34 0.37 0.10 0.08 0.08
ISP NA NA NA NA NA
Recommend Intention (recoded into three groups)
Banking 0.11 0.08 0.30 0.10 0.40
Retail 0.13 0.22 0.43 0.22 0.45
ISP NA NA 0.34 0.14 0.39
Recommend intention
Banking 0.12 0.10 0.31 0.12 0.38
Retail 0.13 0.23 0.43 0.23 0.43
ISP NA NA 0.35 0.17 0.37
Repurchase intention (recoded into three groups)
Banking 0.11 0.13 0.29 0.21 0.26
Retail 0.16 0.28 0.43 0.29 0.40
ISP NA NA 0.36 0.26 0.32
Repurchase intention
Banking 0.15 0.15 0.32 0.25 0.26
Retail 0.16 0.28 0.41 0.29 0.38
ISP NA NA 0.35 0.27 0.30
Overall satisfaction (recoded into three groups)
Banking 0.09 0.05 0.21 0.08 0.26
Retail 0.11 0.18 0.35 0.18 0.36
ISP NA NA 0.30 0.15 0.33
Overall satisfaction
Banking 0.09 0.06 0.22 0.10 0.26
Retail 0.12 0.21 0.36 0.20 0.36
ISP NA NA 0.30 0.16 0.32

Keiningham et al.
Database Marketing & Customer Strategy Management Vol. 15, 2, 79–90 © 2008 Palgrave Macmillan Ltd 1741-2439 $30.00
88
the claims made regarding Net Promoter,
other research conducted on this topic, and
summarises two pieces of scientifi c research
that test the claims made by Net Promoter.
The paper argues that while it is quite
tedious to have more variables and can
sometimes convolute the picture of what
you are researching, having too few
variables absolutely introduces the possibility
of peripheral blindness. And it would seem
to us that this is exactly where NPS has
fallen short: paring something complex
down to a single number or a single metric.
We investigated the claims made
regarding Net Promoter on both the
macro-level and micro-level instances, using
Reichheld ’ s and Satmetrix ’ s own
methodology.
3,22 In both of these foci, Net
Promoter failed to substantiate claims made
on its behalf, often by a signifi cant margin.
Both the NCSB and the ACSI matched or
outperformed NPS a majority of the time
using data that were meant to exalt Net
Promoter. The micro-level data
disconfi rmed that recommend intention
was an effective indicator of loyalty
behaviours, and the single-metric model
based on recommend intention was
ultimately outperformed by both dual-
metric and multi-metric ones.
Therefore, an apples-to-apples counter
proof negates the validity of the research
reported in the Harvard Business Review
regarding Net Promoter on three fronts:
(1) The results are not readily repeatable
according to the methodology indicated
in its publication (our results were
different using replicated methodology
as indicated in the claim that NPS
outperformed the ACSI and the ACSI was
uncorrelated to growth).
(2) The results of the metric ’ s original
publication are not generalisable to
the population at large (cross-industry
analysis reveals a signifi cant fl uctuation in
recommend intention-growth correlation
precision and accuracy).
(3) The construction of the metric itself
does not satisfy the claims that it is
credited with (the single-metric model is
signifi cantly outperformed by dual-metric
or multi-metric models).
What is at stake? Millions of dollars in
publicly traded companies, companies which
are basing corporate strategy on a metric
that does not perform as it is claimed to
perform. And given the complexity of
consumer behaviour, we question the value
of enforcing a single-metric model,
especially since we know that the claimed
predictive capacity of the metric
underperforms. Obviously, we would
expect to fi nd increased value in
broadening the parameters of how we
measure the behaviour of customers,
operating under the pretense that consumer
behaviours — at the very, very least —
might be multifaceted.
Recommend intention is not, by any
means, a useless metric or remotely a poor
one. In fact, it is an extremely useful tool
for helping to understand the research with
relation to its pragmatic application; we do
not question that the recommend intention
is valuable, we only question that it is the
‘ only ’ metric of true value. Obviously, we
would implore researchers to develop
models of a deeper variety.
While loyalty is a concept that all
managers want, we have found that it is not
straightforward to translate customers ’ loyalty
attitudes into customers ’ loyalty behaviours.
And attempting to understand the holistic
complexities of these connected facets —
how they cooperate with one another, how
they refl exively and dialectically alter one
another
48 — is a challenging (but ultimately
worthwhile) endeavour. As a result of these
complexities, though, fi rms are forced to
monitor and manage multiple customer
behaviours simultaneously. Alas, there are no
simple solutions for turning loyalty into
profi ts. If it were easy, everyone would
already be doing it.

A holistic examination of Net Promoter
© 2008 Palgrave Macmillan Ltd 1741-2439 $30.00 Vol. 15, 2, 79–90 Database Marketing & Customer Strategy Management 89
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