Claes Fornell, Forrest V. Morgeson III, & G. Tomas M. Hult
Stock Returns on Customer
Satisfaction Do Beat the Market:
Gauging the Effect of a
Marketing Intangible
A debate about whether firms with superior customer satisfact ion earn superior stock returns has been persistent in the
literature. Using 15 years of audited returns, the authors find convincing empirical evidence that stock returns on
customer satisfaction do beat the market. The recorded cumulative returns were 518% over the years studied
(2000–2014), compared with a 31% increase for the S&P 500. Similar results using back-tested instead of real returns
were found in the United Kingdom. The effect of customer satisfaction on stock price is, at least in part, channeled
through earnings surprises. Consistent with theory, customer satisfaction has an effect on earnings themselves. In
addition, the authors examine the effect of stock returns from earnings on stock returns from customer satisfaction. If
earnings returns are included among the risk factors in the asset pricing model, the earnings variable partially mitigates
the returns on customer satisfaction. Because of the long time series, it is also possible to examine time periods when
customer satisfaction returns were below market. The reversal of the general trend largely resulted from short-term
market idiosyncrasies with little or no support from fundamentals. Such irregularities have been infrequent and
eventually self-correcting. The authors provide reasons why irregularities may occur from time to time.
Keywords: customer satisfaction, customer lifetime value, intangibles, stock portfolio returns, abnormal returns
A
mong the many potential performance outcomes for
marketing, there has been a rapid rise in interest in
measures of stock performance and customer sat-
isfaction over the past severa l years (e.g., Katsikeas et al.
2016). The literature on customer satisfaction and stock
returns has also become fairly extensive (Aksoy et al. 2008;
Anderson, Fornell, and Mazvancheryl 2004; Anderson and
Mansi 2009; Bell, Ledoit, and Wolf 2014; Fornell, Mithas,
and Morge son 2009a, b; Fornell et al. 2006; Gruca and Rego
2005; Ittner and Larcker 1998; Ittner, Larcker, and Taylor
2009; Jacobson and Mizik 2009a, b; Lambert 1998; Luo,
Homburg, and Wieseke 2010; Malshe and Agarwal 2015;
Ngobo, Casta, and Ramo nd 2012; O’Sullivan, Hutchinson,
and O’ Connell 2009; O’Sullivan and McCallig 2009; Peng
et al. 2014; Tuli and Bha radwaj 2009). Although most of
these studies examine customer satisfaction using the same
data source (American Customer Satisfaction Index [ACSI];
www.theacsi.org) and find predominantly positive risk-
adjusted stock returns, their conclusions differ with respect
to the statistical signi ficance of the abnormal returns and
whether there is evidence of mispricing.
Some researchers (Bell, Ledoit, and Wolf 2014; Jacobson
and Mizik 2009a; O’Sullivan, Hutchinson, and O’Connell
2009) have argued that abnormal returns cannot be dis-
tinguished from random variation, or that there is no evidence
that customer satisfaction predicts long-term stock returns
(Ittner, Larcker, and Taylor 2009). These arguments and pre-
dictions can now be put to the test. If they are correct, then
customer satisfaction returns subsequent to these cited studies
should approximately equal market returns. The new empirical
evidence, however, points to anything but equal to market
returns. For the full 15-year time period (2000–2014), the
model-free audited cumulative gross returns on customer sat-
isfaction amounted to 518%.
1
By comparison, the Standard
& Poor’s [S&P] 500 grew by 31% over the same period
(2000–2014). On an annual basis, the customer satisfaction
portfolio outperformed the S&P in 14 out of 15 years. The
magnitude of such a return disparity over a period of 15 years
Claes Fornell is Chairman, CFI Group (e-mail: cfornell@cfigroup.com).
Forrest V. Morgeson III is Director of Research, American Customer
Satisfaction Index, LLC (e-mail: morgeson@theacsi.org). G. Tomas M. Hult
is Professor and Byington Endowed Chair, Eli Broad College of Business,
Michigan State University (e-mail: [email protected]du). The authors appreciate
the input, feedback, and assistance provided by Joshua Blechman,
Xueming Luo, and David Durfee.
1
The lead author is the owner and founder of the U.S. fund studied
and the American Customer Satisfaction Index (ACSI). As regis-
tered under the Investment Advisers Act of 1940, the fund ’s
investment manager is regulated by the SEC. This should not be
taken to imply a certain level of skill or training. The returns were
audited by an independent third-party accounting firm, unaffiliated
with the authors and registered with the Public Company
Accounting Oversight Board. The audited returns have also been
provided to the Editor in Chief as a part of the review process.
© 2016, American Marketing Assoc iation Journal of Marketing
ISSN: 0022-2429 (print) Vol. 80 (September 2016), 92–107
1547-7185 (electronic) DOI: 10.1509/jm.15.022992
implies that known risk factors or sector biases are very unlikely
explanations because investors would have had enough time to
discover the anomalies and adjust to them accordingly.
These findings do not mean, however, that the returns
cannot be mitigated by known factors from time to time. In-
deed, we find that this is the case. Moreover, and similar to
the impact of employee satisfaction on stock prices (Edmans
2011), there is evidence that the effect of customer satisfaction
is channeled, at least to some extent, through earnings surprises.
That is, to the extent that the market does not react to news
about customer satisfaction, there is a reaction to its materi-
alization in corporate earnings and effect on earnings surprises.
We find significant relationships between customer satisfac-
tion and earnings and that customer satisfaction is predictive of
earnings surprises. In turn, stock returns on earnings mitigate
the effect on customer satisfaction returns. That is, some of the
effect on stock prices from customer satisfaction seems to be
absorbed by the effect of earnings on stock prices.
In addition, we identify time periods under which the
abnormal returns are negative (even though the absolute re-
turns remain positive), but they are too short in duration to be
statistically significant and are overwhelmed by the predom-
inance of positive risk-adjusted alphas. Nevertheless, there
are lessons to be learned from the return reversals. They seem
to happen largely without assistance from a corresponding
reversal in fundamentals but as a reaction to price increases of
hitherto strong performing stocks in favor of less expensive
stocks (i.e., stocks with lower price-to-earnings [P/E] ratios).
The remainder of the study is structured as follows. We
begin with the theoretical impetus behind the proposition that
customer satisfaction is germane to firm value and demon-
strate how a small increase in customer retention can have a
large effect on equity value. This is of critical importance
because the equity value grows exponentially at higher levels
of customer retention, whereas the value of an increase in
customer retention from a low level to a somewha t higher
level is much smaller. The implication, which we test in this
study, is that changes in customer satisfaction are much more
likely to have an effect when they are accompanied by high
levels of customer satisfaction.
Next, we present the data from the ACSI and the stock
portfolio. We describe the relevant properties of each—the
ACSI, with respect to the degree to which it is representative
of the U.S. stock market (S&P 500 index), and the stock
portfolio, with respect to how it is constructed wi th ACSI data
as input. In both cases, we take these data sets as they are. The
same is true with respect to the U.K. customer satisfaction data,
but not the U.K. portfolio returns. The U.K. portfolio returns
result from back testing that we performed. Because of the low
credibility of back testing due to the risk of data snooping (Lo
and MacKinlay 1990), we selected the simplest and most
transparent of trading rules, as we detail subsequently.
Following this, we turn to the analyses and findings. We
employ the standard capital asset pricing model (CAPM) to
estimate the market risk premium and add the momentum
risk premium (Carhart 1997), the size risk prem ium, and the
growth risk premium (Fama and French 1993). The Barra
model, controlled for market returns, is used for additional
attribution analysis and shows that only 2 (technology and
retail) out of 16 style factors have a significant effect, and
approximately 80% of the return variance is due to idio-
syncratic effects. Next , we estimate the effect of customer
satisfaction on earnings and earnings surprises as well as the
effect of earnings returns on customer satisfaction returns.
We find that customer satisfaction has a signi ficant effect on
both earnings and earnings surprises. As we hypothesize,
changes in customer satisfaction have an effect on earnings
only if they are complemented by high levels of customer
satisfaction. We also use the new five-factor Fama–French
(2015) model to estimate the effect of earnings returns on
customer satisfaction returns, finding that returns on earnings
reduce returns on customer satisfaction to some degree.
Finally, we examine the extent to which the returns to
customer satisfaction can be generalized. Confirmatory evidence
from the United Kingdom shows results similar to those in the
United States. There is also disconfirmatory evidence: over the
15-year time period, stocks of companies with strong customer
satisfaction did not always outperform the market. In particu-
lar, there was a time period in 2012–2013 marked by under-
performance. We examine market behavior during this period and
find that it deviates substantially from the norm in the sense that
companies that have weak balance sheets, are heavily shorted,
and have low price-to-earnings ratios outperformed the market.
Theoretical Impetus
Major shifts in economic activity over the past several decades
have caused intangible assets to become a major force for value
creation, economic growth, and performance assessment (e.g.,
Katsikeas et al. 2016). These assets loom large in the modern
economy and are often valued higher than the assets on balance
sheets. They are generally not, however, capitalized like other
investments, thereby disconnecting the timing of income from
expenditure in financial statements. For example, even though
the benefits accrue in the future, investments in customer service
are usually fully expensed when they occur. As a result, they
might become visible in income statements as an input variable
without much information about the nature or timing of actual
outputs. Under such circumstanc es, it is difficult to ascertain the
real value contribution from having satisfied customers. For-
tunately, there is output information available, but even so, the
economic value of customer satisfaction still might not become
manifest until there is an impact on other outputs (e.g., earnings).
Perhaps more than any other intangible, satisfied cus-
tomers are essential for any seller in a competitive market
if repeat business is a significant portion of total revenue.
Accordingly, customer satisfaction occupies a central place
in both micro and macro analysis. At the micro level, it is
a leading indicator of favorable (high level/low volatil ity)
net cash flows (e.g., Gruca and Rego 2005). At the macro level,
it is related to economic growth through consumer spend-
ing and to the efficiency by which capital is allocated (e.g.,
Fornell, Rust, and Dekimpe 2010). Allocative efficiency, in
this sense, depends on the joint ability of consumer and equity
markets to punish (reward) firms that fail (succeed) in sat-
isfying their customers. That is, consumer markets reward
high-consumer-utility-produ cing firms with repeat busi-
ness and punish low-consumer-utility-producing ones by
Stock Returns on Customer Satisfaction / 93
defection. By rewarding high-consumer-utility-producing
firms and their shareholders with higher stock prices an d
penalizing low-consumer-utility-producing firms with capital
withdrawal, capital markets would be in line with consumer
markets under the notion of allocative efficiency. It is in this
sense that buyer satisfaction plays a vital role in the individual
company’s ability to generate wealth at the micro level and in
allocative efficiency at the macro level.
Most consumer markets are characterized by numerous
purchase alternatives and by repeat sales as a large portion of
firm revenue. High customer satisfaction, relative to competi-
tion, is associated with repeat purchase, market share protec-
tion, lower price elasticity, lower transaction costs, and lower
selling/marketing costs (Anderson, Fornell, and Lehmann 1994).
Satisfied customers are therefore important for earnings, return
on investments, return on assets, and cash flows (Aksoy et al.
2008; Anderson, Fornell, and Mazvancheryl 2004; Fornell et al.
2006; Gruca and Rego 2005; Tuli and Bharadwaj 2009). Be-
cause of its influence on buyer loyalty, customer satisfaction is
also beneficial for risk reduction. Systematic, idiosyncratic, and
downside risk are lower for firmswithstrongcustomersat-
isfaction (Fornell, Mithas, and Morgeson 2009a, b; Tuli and
Bharadwaj 2009). In addition, customer satisfaction is asso-
ciated with other benefits, such as positive word of mouth,
higher reservation prices, more cross-buying, fewer consumer
complaints, lower warranty and field service costs, and less
customer defection and employee turnover (Fornell et al. 2006).
Most of these effects have positive impacts on acceleration,
stability, size, and—by implication (from loyal customers)—
risk reduction regarding future cash flows.
Although there is substantial empirical support for many of
the aforementioned effects, the most fundamental finding has to
do with repeat business. There is a large literature on customer
lifetime value (CLV), or customer equity, whereby the eco-
nomic value of repeat business is determined by the discounted
net present value of future cash flows from current customers.
Consistent with the proposition that customer satisfaction has
a positive impact on stock price, research has shown that an
increase in CLV can lead to an increase in stock price (Kumar
and Shah 2009, 2011) and to higher future profits (Venkatesan
and Kumar 2004). Although there are several ways to measure
CLV (Holm, Kumar, and Rohde 2012; Kumar and George
2007), for our purposes and without loss of generalization,
assuming that profit margins are constant, the time period is
infinite, and the customer retention rate does not vary over time,
we can theoretically express customer equity value as follows:
CEV = m
r
1 + i - r
,(1)
where CEV = customer equity value, m = the profit margin
multiple, r = the proportion of retained customers, and i = the
discount rate. By way of example, consider a company in which
75% of customers return as future buyers and the discount rate
is 5%. Solving Equation 1 gives a margin multiple of 2.5. If
customer satisfaction increased and led to a growth in the
proportion of retained customers by, say, 5 percentage points
(r = .8), the corresponding margin multiple would be 3.2—a
growth in equity value of 28%. Note that a fairly small increase
in retention (5 percentage points) leads to a much larger growth
in the value of customer equity (28%). The effect obviously
becomes more pronounced at higher levels of r, but even at
moderate levels, it is quite substantial and of relevance to
investors. Because customer satisfaction is a major contributor
to buyer retention, it follows that it too may affect stock price.
Practically, it may be useful to illustrate a few short examples
of relationships between customer satisfaction and share price
before turning to formal analysis. Consider four firms—Apple,
Netflix, Costco, and Home Depot—over an identical ten-year
time period. In Figure 1, Panels A–D, we depict the relationship
between ACSI and stock price for these companies.
The most dramatic relationship is the one for Apple. As its
customer satisfaction increased from a middle-of-the pack com-
pany to best in class, Apple’s stock price soared (Figure 1, Panel
A). Netflix depicts a reversal of fortunes in both ACSI and stock
price (Panel B). In contrast to Apple, Netflix already had a
very high level of customer satisfaction when the ACSI began
tracking it. As its ACSI score continued upward, its stock price
did too. However, Netflix frustrated customers by increasing its
prices by 30%–50% in 2011. Both its ACSI and stock price
plummeted. Costco illustrates yet another type of relationship
(Panel C). Here, it is change in the ACSI that is most relevant.
Even though Costco customer satisfaction is high, it is not as
high as that for many other companies. Finally, Home Depot
shows what can happen to a company that starts out with a
reasonable level of customer satisfaction, drops back signifi-
cantly (to a low ACSI score of 66), and later not only recovers
but also achieves a higher ACSI score than it had before the
slump (Panel D). As customer satisfaction deteriorates, Home
Depot’s stock price falls. When customer satisfaction increases
sharply later on, so does the firm’s stock price. These com-
panies illustrate how ACSI and stock price move together in
different ways: from good to superb (Apple); from superb to
weak (Netflix); from good to better (Costco); and from good, to
bad, to better (Home Depot). Although these are just examples
of individual companies, they do show a strong relationship
between customer satisfaction and stock price. Next, we ex-
amine the extent to which these examples can be generalized
in a portfolio of companies, selected on the basis of ACSI data.
Data
Customer Satisfaction Data
The customer satisfaction data used in this study come from
the ACSI, which provides annual customer satisfaction scores
from a latent variable structural equation model estimated from
survey data on customer satisfaction for approximately 300 of
the largest companies, across 45 distinct industries, in the U.S.
consumer market (e.g., Fornell et al. 2006).
2
Data are collected
on a quarterly basis for different industries, with approximately
2
Customer satisfaction is measured for the largest companies in
each industry. Within each industry, those with the largest U.S.
market shares are covered. The sample of measured ACSI com-
panies includes mostly larger consumer goods firms. Although this
means that the ACSI is not perfectly representative of the broader
universe of publicly traded companies, it is in keeping with the prior
literature investigating the impact of satisfaction (and other intan-
gibles) on market performance (Edmans 2011).
94 / Journal of Marketing, September 2016
25% of the total annual probability sample of about 70,000
households interviewed each quarter, resulting in annual satis-
faction scores for each company.
3
Although the ACSI is obvi-
ously skewed toward consumer markets, it seems to represent the
overall stock market quite well.
4
Audited Returns and Back-Tested Returns
The other major data source is audited stock portfolio returns
obtained from the same stock fund exami ned in Fornell et al.
(2006). Previous research has not detected an announcement
effect regarding ACSI news releases and press coverage
(Fornell et al. 2006; Raithel et al. 2011; Srinivasan and Hanssens
2009) or has found only a limited effect (Ittner, Larcker, and
Taylor 2009). Accordingly, the trading is based on both levels
and changes in a company’s customer satisfaction score. Stocks
are purchased both before and after the ACSI release date, with
a blackout period of 48 hours surrounding the announcement.
5
In general, access to audited returns data, which is what
this study relies on for its major analysis, is advantageous be-
cause data snooping bias is eliminated (Black 1993; Lo and
MacKinlay 1990; Merton 1987). Data snooping refers to the
reuse of data for model testing. For example, trying out various
rules for stock portfolio construction in a back-testing scenario
is almost always subject to data snooping bias—leading to
stock-picking rules based on sample uniqueness but without
predictive superio rity. There is substantial e vidence that
FIGURE 1
Examples of the ACSI–Stock Price Relationship
A: Apple B: Netflix
spApple ACSI Apple tock rice
Year
$0
$100
$200
$300
$400
$500
$600
65
70
75
80
85
90
Stock Price
ACSI
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Netflix ACSI Netflix stock price
Year
$0
$40
$80
$120
$160
$200
65
70
75
80
85
90
Stock Price
ACSI
2007
2008
2009
2010
2011
2012
2002
2003
2004
2005
2006
Home Depot ACSI Home Depot stock price
Year
$0
$10
$20
$30
$40
$50
$60
$70
60
64
68
72
76
80
Stock Price
ACSI
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Costco ACSI Costco stock price
Year
$0
$20
$40
$60
$80
$100
$120
72
74
76
78
80
82
84
Stock Price
ACSI
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
C: Costco D: Home Depot
3
The ACSI database contains approximately 3,200 firm-year
observations over a 20-year period, with an average customer sat-
isfaction sco re of 76.44 and an average annual standard deviation
of 2.43.
4
For the time period 1997–2003, Fornell et al. (2006) examined
the top 20% ACSI firms and found that they outperformed the Dow
Jones Industrial Average (DJIA) by 21%–40%, but that the bottom
80% ACSI firms had a return virtually identical to the DJIA (20.4%
and 21%, respectively). For this study, the correlation between the
returns of the ACSI universe and the S&P 500 (for the time period
2000–2014) was .88. The reason for the high correspondence between
the ACSI universe returns and S&P returns is probably that consumer
spending is such a large proportion of the gross domestic product and
that many firms covered by ACSI compete in both consumer and
nonconsumer markets.
5
The blackout period is not imposed by regulation, but it is used
to remove any reasonable risk of trading on announcements. Even
though most prior research has not detected a significant
announcement effect, its existence cannot be completely ruled out,
especially as the returns on customer satisfaction become more
widely known. For example, a recent study by Ivanov, Joseph, and
Wintoki (2013) finds that stock trading volume is 2.8% higher on
average during a ten-day period around the ACSI announcements
and that there is a market reaction to these announcements over six
trading days.
Stock Returns on Customer Satisfaction / 95
data-driven discovery leads to seriously distorted findings and
that it is endemic in the literature (White 2000). The lack of
credibility is so serious that the SEC prohibits the use of back-
tested results in presentations of stock funds to investors. In
academia, however, back testing is common, presumably
because real returns data are difficult to obtain. The advantage
of back-tested results is that the portfolio construction rules can
be made explicit, but that is of limited value if the process of
how these rules were established is not disclosed. If the rules
were established through trial and error, the predictive powers
of back-tested results are greatly curtailed.
However, even though actual returns are generally pref-
erable, they are not without limitations. They can almost never
be perfectly replicated because, in practice, stock trading rules
are almost never truly formulaic. Most professional inves-
tors require different risk exposure, stop-loss criteria, portfolio
diversification , leverage, and so on. Yet perfect nume rical
replication is not required for generalization or for theory
testing—verification and general principles are. Only audited
returns can provide true numerical verification.
The general trading principle can be succinctly stated as
follows: go long in firms with strong customer satisfaction
(i.e., high and rising levels of ACSI scores relative to other
firms in the same industry), and short in firms with weak
customer satisfaction (i.e., low and declining ACSI scores
relative to other firms in the same industry). This principle
may be operationalized in slightly different ways; the issue is
the extent to which different studies using the same theo-
retical principle, with diff erent operationalizations, arrive at
similar results. Among the studies that h ave examined the
customer satisfaction–stock return relationship, most have ob-
tained similar numer ical results. Indeed, all previous studies
that have analyzed stock returns of firms with strong customer
satisfaction (high levels and positive changes) have reported
numerically large abnormal returns. When the regression esti-
mates, which typically reflect monthly returns in these studies,
are expressed as annual returns, it is evident that the abnormal
returns are very large.
For example, Jacobson and Mizik (2 009a) report risk-
adjusted monthly abnormal returns of .007. Had they expressed
these returns in annual terms, it would have been obvious that a
monthly above-market return of .007 is very high, equaling a
market outperformance of 8.4% per year over ten years, even
after adjustments for additional risk factors—better than almost
any mutual fund over the same time period. Aksoy et al. (2008)
report abnormal annual returns of 10.6% over a different (but
somewhat overlapping) ten-year period. As we show herein,
these estimates are similar to our findings over a time period
50% lon ger. W e retur n to other st udies s ubsequen tly an d
discuss circumstances under which some of them reached
conclusions different to ours, but for now it is important to point
out that even though our main study does not include formulaic
trading rules, it does not follow that the general principle behind
the portfolio construction is insufficient for verification or that
the results cannot be approximated in a meaningfu l way.
In our case, then, we expect that the general principle of
going long in firms with strong customer satisfaction and
short in firms with weak customer satisfaction would lead
to similar returns even though operationalizations of the
principle may differ. We test this prediction by using data
from an additional market and country and, in so doing,
mitigate the strengths and weaknesses of real data versus
back-tested data. In this case, we back test a paper portfolio
based on customer satisfaction data in the United Kingdom
and compare its returns with the Financial Times Stock Ex-
change (FTSE) 100 index. Notwithstanding the limitations
of back test ing, it serves as a complement to the actual and
audited returns data in the U.S. study. By combining actual
and back-tested returns, the weakness in one approach is
compensated by the stre ngth of the other, and vice versa. The
estimated annual abnormal return (12%) in the U.K. study is
fairly similar to what we and others find in the United States
(8.4%–10.8%). The difference is most likely due to the fact
that there are more variables available for adjusting the
returns for risk in the United States and that the U.K. returns
do not take dividends into account. If dividends had been
included, the returns would have been reduced by approx-
imately 2%, bringing the U.K. returns in line with the U.S.
results.
Portfolio Returns
Let us begin with the returns on the U.S. customer satisfaction
portfolio described previously. Figure 2 shows the cumulative,
model-free returns, expressed as the value of $100 invested
from April 2000 through June 2014. The investment grew to
$618 (+518%). Although it might appear that the 2009 re-
cession dip in the portfolio returns in Figure 2 was larger than
market, the opposite is actually the case in terms of percent-
age changes. This finding is consistent with Merrin, Hoffman,
and Pennings (2013), in that customer satisfaction is found to
act as a buffer after the bursting of price bubbles. It is also
evident that the returns are not due to spectacular performance
in a few years and underperformance in others, as is often the
case with mutual funds. As Figure 2 illustrates, the customer
satisfaction returns were higher than the returns for the S&P
500 in 14 of the 15 years, with 2013 as the sole outlier. In view
of the large difference between the S&P 500 and the customer
satisfaction portfolio performance over a long period of time, it
is very unlikely that the result can be explained by known (risk)
factors or sector bias (whether retail, technology, or otherwise).
With a short time period, the impact of such factors could be
substantial, but this is not true in the long run. This is because
investors would have had sufficient time to react, and the
abnormal returns would have been eliminated. In the absence
of these factors, it would seem reasonable to conclude that the
results were essentially driven by customer satisfaction and
that investors in the main were unaware of its effect or its
manifestation. It may be possible, however, that known fac-
tors, to a limited extent, mitigate customer satisfaction returns.
This is what we examine next.
Let us begin the analysis of risk-adjusted returns by using
the standard Sharpe–Lintner CAPM (Lintner 1965a, b; Sharpe
1965):
SAT
it
=a
it
+ b
1
MKT
t
+e
it
,(2)
where SAT
it
= the portfolio return minus the risk-free rate at
time t (month), a
it
= the estimate of risk-adjusted above-market
96 / Journal of Marketing, September 2016
returns at time t, MKT
t
(market risk premium) = the market
returns in excess of the risk-free rate at time t, and e=the error
term. We obtained the risk data for this and subsequent equa-
tions from Kenneth French’s website and data library (http://mba.
tuck. dartmouth.edu/pages/faculty/ken.frenc h/data_library.html).
The results (presented in Table 1) indicate a significant alpha
of .009, equivalent to annualized market risk-adjusted returns of
10.8%, as well as a significant market beta of .692.
Jacobson and Mizik (2009a) claim that that abnormal
returns from satisfaction are limited to the technology sector,
where they found adjusted abnormal annual returns of 32.4%–
38.4% over ten years. No commercially available portfolio of
common stock has ever even been close to outperforming the
market by 32%–38% annually over a period of ten years, so
there is reason to be somewhat skeptical about these num-
bers. This finding also seems inconsistent with other results
reported by Jacobson and Mizik. When technology (and
utilities) is removed from the portfolio, the abnormal returns
are reduced by only 1.0%–3.3% for the full sample (depending
on the model they use), which must be statistically insignificant
if 6.4%–8.4% is insignificantly different from zero (or nearly
so for the latter alpha), as they report in their study.
Even though we could not find any theoretical or empirical
justification for treating technology differently, we did check
to determine whether the results changed under narrower mar-
ket indices, such as NASDAQ and the Dow Jones Industrial
Average (DJIA). The estimated annual alpha (adjusting for
dividends as an average of 2%) relative to NASDAQ is 11.2%,
slightly higher than the Fama–French benchmark (MKT) at
10.8%, and the alpha with the DJIA is 10.0%, slightly lower.
If technology stocks were much more sensitive to customer
satisfaction, an even larger alpha would be expected.
FIGURE 2
Cumulative Returns on $100 Invested in Customer Satisfaction: Port folio Versus the S&P 500 (April 2000
Through June 2014)
$617.59
$130.81
Mar 2000
Nov 2000
Jul 2001
Mar 2002
Nov 2002
Jul 2003
Mar 2004
Nov 2004
Jul 2005
Mar 2006
Nov 2006
Jul 2007
Mar 2008
Nov 2008
Jul 2009
Mar 2010
Nov 2010
Jul 2011
Mar 2012
Nov 2012
Jul 2013
Mar 2014
$0
$100
$200
$300
$400
$500
$600
$700
Portfolio S&P 500
TABLE 1
Risk-Factor Model Results
Sharpe–Lintner
CAPM
Fama–French
Three-Factor CAPM
Carhart Four-Factor
CAPM
Winsorized (95%) Carhart
Four-Factor CAPM
a .009*** .009*** .009*** .009***
a (%) 10.8% 10.8% 10.8% 10.8%
MKT .692*** .654*** .622*** .608***
SMB — .171* .183** .072
HML — .005 -.003 .028
MOM ——-.062* .050
*p < .05 (one-tailed).
**p < .01 (one-tailed).
***p < .001 (one-tailed).
Notes: MKT = monthly market returns excess of the risk-free rate; SMB = size risk-factor returns; HML = value risk-factor returns; MOM = momentum
risk-factor returns.
Stock Returns on Customer Satisfaction / 97
Other known factors cannot be expected to do much
better, but we examine the extent to which they mitigate the
returns. Adding risk factors proposed by Fama and MacBeth
(1973) and Fama and French (1993, 2004), we specify a three-
factor model:
SAT
it
=a
it
+ b
1
MKT
t
+ b
2
SMB
t
+ b
3
HML
t
+e
it
,(3)
where the additional factors are SMB
t
(stock size risk
premium) = the size risk-factor returns at time t and HML
t
(stock growth risk premium) = the value risk-factor returns
at time t.
With respect to alpha, the result is identical to the original
CAPM (an alpha of .009, or 10.8% annual return). The mar-
ket beta is somewhat smaller at .654, as some of the effect is
picked up by the small stock risk factor (.171). The effect of
the growth risk premium is not significant. We are not aware
of any theoretical basis for interpreting the size risk-factor
returns effect, even though it is statistically significant at .05.
The ACSI is, by design, dominated by large cap stocks. The
returns of small stocks minus big stocks do covary with the
customer satisfaction returns (of large stocks), but that asso-
ciation is most likely a spurious one.
Finally, we add another risk factor, suggested by Carhart
(1997)—MOM
t
(stock momentum risk premium) = the mo-
mentum risk-factor returns at time t—but this factor is not
significant and the results do not change.
SAT
it
=a
it
+ b
1
MKT
t
+ b
2
SMB
t
+ b
3
HML
t
+ b
4
MOM
t
+e
it
:(4)
To summarize, the results show an annual four-factor alpha of
10.8% over a 15-year period from a stock fund long in strong
customer satisfaction stocks and short in weak customer
satisfaction stocks. The results are unchanged even when
more risk factors are added to the model, but the results are at
odds with the conclusions, though not necessarily with the
numerical results of Jacobson and Mizik (2009a), Bell,
Ledoit, and Wolf (2014), and Ittner, Larcker, and Taylor
(2009). There are several reasons for this discrepancy beyond
the longer time period we used.
First, consider Ittner, Larcker, and Taylor (2009),
who report that customer satisfaction has incremental
informational value beyond financial information but find
no abnormal returns or evidence of mispricing. However,
levels of customer satisfactionaredisregardedintheir
portfolio construction; only changes are deemed relevant.
The assumption is that customer satisfaction levels are
already impou nded in stock prices—consistent with the
efficient market hypothesis of zero alpha. As ide fr om
ignoring information implied by CLV (Equation 1),
which posits that (high) levels are critical because of
their exponential impact on value, there is the logic al
challenge of assuming that levels of customer satisfaction
are already reflected in share prices when that very
assumption (market efficiency) is being tested. High serial
correlation notwithstanding, the importance of levels (in
combination with changes) is that firms with high levels of
customer satisfaction (and thus, retention) create much greater
customer equity value than firms with low levels of customer
satisfaction, even if the latter firms also have increasing
satisfaction.
Two other studies (Bell, Ledoit, and Wolf 2014; Jacobson
and Mizik 2009a) obtain large positive alphas but likewise
conclude that these abnormal returns cannot be distinguished
from chance. Jacobson and Mizik (2009a) also apply a con-
ditional model with time-varying parameters and find an
alpha of zero.
6
However, such a result would require the
correlation between alpha and beta to be “enormous” (to cite
Lewellen and Nagel 2006). We find that correlation to be no
greater than .36, but that may not matter because time has
passed and the prediction of zero alpha is now testable and
shown to be inaccurate.
The Earnings Effect
Systematic underpricing of customer satisfaction could be due
to multiple factors, but recent research has suggested earnings
surprises as a major factor (Ngobo, Casta, and Ramond 2012;
O’Sullivan and McCallig 2009). That is, investors do not re-
act to information about customer satisfaction per se either be-
cause they are no t aware of it or because they place little trust
in it until it is substantiated by financials. Inst ead, they react to
earnings surprises, which, in turn, may have been caused by
strengthening or weakening customer satisfaction. Thus, we
examine the impact of customer satisfaction on both earnings
and earnings surprises.
Over time, the portfolio has held substantially more longs
than shorts (920 vs. 88). In Table 2, the first two mean scores
reflect average earnings surprises for the long (3.3 cents per
share) and short (.9 cents per share) books, respectively. The
long positions had positive quarterly earnings surprises 60%
of the time. That may not, in itself, suggest anything out of the
ordinary, because most S&P stocks tend to beat earnings
estimates (Jakab 2012). However, only 37% of the longs had
negative earnings surprises. The short positions had negative
6
Unlike the unconditional (or static) CAPM, the conditional
CAPM suggests that beta (or exposure to market risk) varies over
time, as rational investors anticipate future investment conditions
that could change adversely and thus alter their investment strategies
intertemporally. According to the logic of the conditional CAPM,
investor expectations lead to a correlation between beta and market
risk premium that also varies over time, and that can help better
explain contemporaneous market risk premium (or alpha). Either
short-window regressions testing for the existence of intertemporal
variation in beta (Adrian and Franzoni 2009; Lewellen and Nagel
2006) or a version of the CAPM that integrates lagged terms for
market risk premium and beta (alpha
t-1
· beta
t-1
) (Jagannathan and
Wang 1996) are used to test the conditional CAPM. Although results
from these studies also vary, and although a debate in the finance
literature over the usefulness of the conditional CAPM in better
explaining asset pricing anomalies remains active, most research has
found only a marginally better explanation of risk premiums through
the conditional CAPM. Some studies have found that the conditional
CAPM decreases the size of the market risk premium moderately or
slightly but without alpha falling to zero or becoming statistically
insignificant (Adrian and Franzoni 2009; Jagannathan and Wang
1996). Other studies have found virtually no impact of the condi-
tional CAPM, suggesting that the correlation between alpha and beta
over time would need to be “enormous” to explain away asset
pricing anomalies (Lewellen and Nagel 2006). Yet all agree that the
intertemporal covariance between beta and alpha needs to be very
large for the conditional CAPM to improve explanation of market
risk premiums from the CAPM.
98 / Journal of Marketing, September 2016
quarterly earnings surpr ises 49% of the time, much more
frequently than S&P 500 firms; they also had 49% positive
earnings surprises, less common than S&P 500 firms. At first
glance, these findings would seem to suggest that strong
customer satisfaction is more closely associated with posi-
tive earnings surprises than weak customer satisfaction with
negative earnings surprises. Yet looking at quarterly earnings
surprises in dollar terms (the second set of mean scores in
Table 2, with mean earnings per share [EPS] multiplied by
common shares outstanding), negative earnings surprises are
larger: approximately –$62.4 million on average for the shorts
versus +$26.1 million for the longs.
Accordingly, firms with strong (weak) customer sat-
isfaction are more likely to have positive (negative) earnings
surprises, suggesting that investors react to tangible con-
sequences of customer satisfacti on and not necessarily to
information about customer satisfaction itself. This effect
seems to overwhelm the potentially countervailing impact
from the accounting practice of exp ensing, rather than
capitalizing, investments in customer service improve-
ments. Unless the increased service improvement is due to
technology investment or some other capital expenditure,
expensing can lead firms with strong short-term earnings
growth and weak customer satisfaction to be overvalued by
the market, whereas firms with weak short-term earnings
growth but strong customer satisfaction would be under-
valued. It is easy to find conditions under which this can
happen.
For example, on the one hand, employee turnover or cost
cutting in customer service may have a positive effect on
short-term earnings but a detrimental impact on customer
satisfaction, thus eroding future earnings power. On the other
hand, a firm’ s investment in better customer service may
have a positive effect on customer satisfaction and lead to
greater future earnings potential, but—if the investment is
expensed instead of capitalized over time—short-term earn-
ings would be negatively affected. If, under these circum-
stances, investors pay attention to accounting information but
not to prior and subsequent customer satisfaction, the resulting
valuation bias might be considerable. Even if 37% of the
companies with strong customer satisfaction did indeed have
negative earnings surprises, that is not enough to offset the
cumulative positive earnings surprises for companies with
strong customer satisfaction.
In light of these results, it seem s that there is a relationship
between customer satisfaction and earnings surprises, which
we confirm by combining long and short positions and esti-
mating the following regression:
SUR
it…t+3
= b
0
+ b
1
CS
it
+ b
2
DCS
it
+ b
3
ðCS
it
·DCS
it
Þ +e
it
,(5)
where SUR is earnings surprise for company i at time t, mea-
sured each quarter following the inclusion of the stock in
either the long or the short book as quarterly EPS minus the
Institutional Brokers Estimate System quarterly median
analyst forecast; CS is the annual ACSI customer satisfac-
tion score; DCS is changes from the prior year in the annual
ACSI customer satisfaction score; and CS ·DCS is the
interaction effect between the two.
7
Table 3 presents the
results. The effect of ACSI and the interaction between ACSI
changes and levels are significant. The ACSI changes alone
are not significant. This result u nderscores the importance of
the standard customer value equation (Equation 1) in that
changes are conditional on high values to have an effect.
Unless there is a change from a level that is already high, there
is no effect on earnings surprises from a change in customer
satisfaction.
If customer satisfaction leads to earnings surprises in
the manner suggested by the estimates in Equation 5 and in
TABLE 2
Customer Satisfaction Portfolio Earnings Surprises
N M Min Max SD
Long Positions
EPS - median EPS forecast 920 .0330 -6.3867 3.9900 .5999
EPS - median EPS forecast ($ millions) 920 26.1132 -6,663.75 2,477.3910 432.8846
Short Positions
EPS - median EPS forecast 88 .0086 -2.1033 3.7200 .6029
EPS - median EPS forecast ($ millions) 88 -62.3988 -3,497.84 697.6367 447.2678
Notes: Earnings surprise = quarterly earnings per share - median earnings per share forecast (source: Bloomberg L.P.). A t-test of the difference
between long and the short positions shows that the long positions had significantly more positive earnings surprises than the short positions
(p < .05, one-tailed).
TABLE 3
The Effect of Customer Satisfaction on
Earnings Surprises
Coefficient SE
a .030 .019
CS .006* .003
DCS -.795 .582
CS ·DCS .120* .062
*p < .05 (two-tailed).
Notes: CS = annual ACSI customer satisfaction score; DCS = changes
in the annual ACSI customer satisfaction score; CS x DCS = the
interaction effect between CS and DCS.
7
Because ACSI scores are constant from the quarter they are
released until new results come out 12 months later, and because
earnings surprises are quarterly, the same independent variable is
applied to four different dependent variables further and further into
the future (current quarter, next quarter, two quarters ahead, etc.).
Stock Returns on Customer Satisfaction / 99
Tables 2 and 3, it might also have an impact on earnings
themselves, as we po stulate in Equation 6:
EPS
it…t+3
= b
0
+ b
1
CS
it
+ b
2
DCS
it
+ b
3
ðCS
it
·DCS
it
Þ +e
it
,(6)
where EPS is earnings for company i at time t, measured each
quarter following the inclusion of the stock in either the long or
the short book as quarterly earnings per share; CS is the annual
ACSI customer satisfaction score; DCS is changes in the annual
ACSI score; and CS ·DCS is the interaction effect between the
two. Table 4 provides the results, which are consistent with the
effect on earnings surprises. The level of customer satisfaction
is significant, as is the interaction between levels and changes.
Changes by themselves are not significant. Unless changes are
accompanied with high levels of customer satisfaction, they do
not seem to have an impact on earnings.
We have examined earnings, earnings surprises, and how
they are affected by customer satisfaction. Next, we investi-
gate whether stock returns on earnings explain the effect of
customer satisfaction returns. If they do, the substantial alphas
shown previously might be reduced. A new five-factor model
proposed by Fama and French (2015) enables us to estimate
the effect of earnings returns on customer satisfaction returns.
In the new model, earnings are defined as annual revenues
minus cost of goods sold; interest expense; and selling,
general, and administrative expenses at time t - 1. The factor,
termed RMW by Fama and French, is the difference in
returns between diversified portfolios with robust versus
weak profitability. In that sense, it is similar to the customer
satisfaction portfolio, which, by going long and short, respec-
tively, also becomes the difference in returns between robust
and weak custom er satis fa cti on. The Fam a–French model
also includes an investment factor (CMA); however, we do
not discuss it further because it turns out to be irrelevant in
this circumstance. Accordingly, we specify the five-factor
model as follows:
SAT
it
=a
it
+ b
1
MKT
t
+ b
2
SMB
t
+ b
3
HML
t
+ b
4
RMW
t
+ b
5
CMA
t
+e
it
:
(7)
An important finding is that earnings mitigate the effect of
customer satisfaction on stock returns. The estimate of abnormal
returns is reduced from .009 to .007, or from an annualized
return of 10.8%–8.4%. However, it is not surprising that cus-
tomer satisfaction and profitability are related. The literature
supports such a finding (Morgan and Rego 2006), though it is
not always the case that high customer satisfaction has a positive
impact on profitability. Yet when buyers have choice and are
reasonably well informed, it is usually the case.
The Winsorized estimates in Table 5 are consistent with
the full sample results, but the effect of earnings is smaller
and the abnormal returns larger. This means that the outliers
are more strongly correlated to earnings returns (as mea-
sured by RMW). Again, this follow s from the exponential
effects encapsulated in Equation 1: stocks with the highest
(lowest) return had the highest (lowest) correlation to earn-
ings returns.
In addition to the models common in the academic fi-
nancial literature, there are also commercially available mul-
tifactor models. As we have seen, when controlling for risk
factors such as market, size, value, profitability, momentum,
and so forth, the customer satisfaction alpha survives. Does it
also survive when using an expanded multifactor model? The
most well-known of these types of models is the model
introduced by Rosenberg and McKibben (1973) and made
available by Barra Inc., until the firm was acquired by MSCI in
2004. Table 6 presents the results from applying the most
current commercially available Barra USE4 comprehensive
trading model, using customer satisfaction returns from July
2006 to November 2013. This analysis was performed by
Morgan Stanley.
Of these factors, the estimated factor loading betas for
technology and retail are significant at the .05 level. This is not
surprising, because technology stocks in general did well during
this time period and because the ACSI tracks both consumer
technology and many retail firms. No other factors were sig-
nificant. Approximately 80% of the variance was unaccounted
for, taking all factors into account. Accordingly, there seems to
be no known factor that can explain the market outperformance;
it is due to either customer satisfaction or some yet unknown
factor that is correlated with customer satisfaction.
Generalizability
We are not aware of previous research reporting higher risk-
adjusted returns for an intangible asset other than those found
TABLE 4
The Effect of Customer Satisfaction on EPS
Coefficient SE
a .630*** .028
CS .032*** .005
DCS -.673 .882
CS ·DCS .249** .094
**p < .01 (two-tailed).
***p < .001 (two-tailed).
Notes: CS = annual ACSI customer satisfaction score; DCS = changes
in the annual ACSI customer satisfaction score; CS x DCS = the
interaction effect between CS and DCS.
TABLE 5
Five-Factor Model Results
Fama–French
Five-Factor CAPM
Winsorized (95%)
Fama–French
Five-Factor CAPM
a .007*** .008***
a (%) 8.4% 9.6%
MKT .718*** .626***
SMB .213** .124*
HML -.142 -.059
RMW .392** .234*
CMA .117 .059
*p < .05 (one-tailed).
**p < .01 (one-tailed).
***p < .001 (one-tailed).
Notes: MKT = monthly market returns excess of the risk-free rate;
SMB = size risk-factor returns; HML = value risk-factor returns;
RMW = profitability risk-factor returns; CMA = investment risk-
factor returns.
100 / Journal of Marketing, September 2016
in this study. For example, annual abnormal returns from
research and development have been estimated at 4.6% (Lev
and Sougiannis 1996) and 3.5% (Edmans 2011) from employee
satisfaction. Bec ause the customer satisfaction returns are so
different than the returns on other intangibles, they call for extra
efforts in validation. We are fortunate in the sense that we have
access to comparable data in which both settings and time
periods are different and in which we can use trading rules based
on the same principle but with a different operationalization.
Ultimately, observed empirical regularities need not only to be
confirmed in time and space but also to be understood in terms
of theory such that the conditions under which they occur can
be specified (competitive consumer markets, repeat purchases,
market failure, etc.) and under which they do not. Next, we
discuss the former condition.
Confirmatory Evidence
Do these results generalize across time and space? As to the
question of whether the results hold in a different stock
market and are robust with respect to simpler but perfectly
replicable trading rules, the analysis is extended to the United
Kingdom, where the returns are not obtained from audited
profits, but from back testing. Although customer satisfaction
data comparable to the United States do not exist for many
countries, there is a similar customer satisfaction index in the
United Kingdom: the National Customer Satisfaction Index
(NCSI-UK, www.ncsiuk.com). It uses the same latent struc-
tural equation model as the ACSI and it is also updated
quarterly. The U.K. data were available from 2007 to 2011
from the customer satisfaction universe of publicly traded
firms (74 companies). We obtained the returns with simple
trading rules that generated two portfolios: the first portfolio
comprised the top 50% of companies in customer satisfac-
tion, and the second contained the bottom 50%, all equally
weighted.
8
We purchased stocks at the end-of-month closing
price for the quarter in which NCSI-UK results were announced
and held all stocks for one calendar year. Each quarter, we
examined the stocks with new customer satisfaction infor-
mation and adjusted the portfolios accordingly. If a stock was
no longer in the top 50%, we moved it from the first portfolio
to the second portfolio, and vice versa. Figure 3 provides
the model-free returns (trading costs excluded) on the two
portfolios compared with returns on the FTSE 100.
Except for a short period following inception, the top
50% customer satisfaction portfolio outperformed both the
FTSE and the bottom 50% portfolio. From August 2007 to
April 2011, the top 50% portfolio earned a (model-free)
return of 59%. The bottom 50% portfolio returned 13%,
and the FTSE had a return of –6%, all model free. The
estimated CAPM alpha was .010 (12% annualized) for the top
50% portfolio, compared with .005 (6% annualized) for the
bottom 50% portfolio. The former was significant at the .01
level; the latter was insignificant. The market beta coefficient
was significant at the .001 level for both portfolios; it was very
high for the bottom 50% portfolio at 1.163 and substantially
lower for the top 50% portfolio at .759.
The estimated alpha for the top 50% customer satisfaction
portfolio is large and, at an annualized rate of 12.0%, similar
to the U.S. alpha at 10.8%. The alpha of the bottom 50%
customer satisfaction portfolio is not significant, but it might
still seem odd that stocks of the weakest 50% customer sat-
isfaction companies did better than market. The reason is
probably an effect of beta (i.e., the regression coefficient for
the FTSE 100), as the market moved up sharply after the
2007–2008 recession. As we have mentioned, the beta for
the bottom 50% portfolio was 1.163, compared with .759 for
the top 50% portfolio. In other words, high customer sat-
isfaction reduced market exposure, whereas low customer
satisfaction exacerbated it. Overall, the real audited profitab-
normal returns to customer satisfaction in the United States
correspond well to the back-tested paper profits returns in the
United Kingdom.
TABLE 6
Barra USE4 Model Results
Factor Coefficient
Technology .30*
Retail .24*
Small-large .20
Volatility large cap .12
OTM put .24
EM-dev. .10
Momentum .06
Financials -.13
Japan -.05
Energy -.07
Industrials .11
Health care -.05
Europe .06
Illiquidity .02
Credit .04
Market -.06
*p < .05 (two-tailed).
Notes: Technology = long technology growth stocks; Retail = long con-
sumer, developed market bias; Small-large = long small caps,
short large caps; Volatility large cap = long volume, large cap
exposure; OTM put = long tail protection , downside exposure
management; EM-dev. = emerging markets bias, commodity bias;
Momentum = long momentum stocks, trading style; Financials =
short financials/real estate, long growth/short value; Japan = short
bias Japan, long carry trades; Energy = short global growth, long
transports/developed markets; Industrial s = long cyclicals/long
deep value stocks; Health care = short health care biotech,
short cyclicals; Europe = long bias Europe, long dollar protection;
Illiquidity = long less liquid stocks, long credit; Credit = long credit
sensitive equities; Market = short global equity. The Barra model
results were prepared by Morgan Stanley.
8
We assigned all subsidiaries covered by the NCSI-UK the stock
prices of their parent company and deleted all private or government-
owned compa nies from the dat a set prior to analysis. The U.K. data
include o nly one manufacturing industry (autos), so the concen-
tration bias that would result from taking the top 50% firms in the
ACSI does not exist. Even though data relating to the Fama–French
risk factors a re not readily available in the United Kingdom, there
is no a priori reason to suggest that these factors would be relevant
when they were not significant in the U.S. study (see also Griffin
2002). Accordingly, we analyze t he U.K. data with the s tandard
Sharpe–Lintner CAPM.
Stock Returns on Customer Satisfaction / 101
Boundary Conditions
As for boundary conditions and disconfirmatory evidence
relating to our findings, let us examine the periods of time
when the results deviated from the overall time period.
Figure 4 shows that from September 2012 to August 2013,
the stocks of companies with strong customer satisfaction
underperformed the S&P 500. As a result, the satisfaction
portfolio returns for the full year of 2013 were lower than
market. In other words, strong customer satisfaction did not
produce a positive alpha during this period or during 2013.
Because this was the only year of underperformance, it might
be justified to consi der it a chance incident. Nonetheless, the
time period seems long enough to warrant closer examina-
tion, albeit not persistent enough for statistical inference.
In 2013 , the long-s hort customer satisfaction portfolio
returned 21%, its long book was up 25% (both model free), and
the S&P 500 was up 30%. According to Xydias (2013), not only
was the stock market rally one of the broadest in history, but
firms with weak balance sheets outperformed those with strong
balance sheets. There were other idiosyncrasies as well. The
most heavily shorted stocks dramatically outperformed the
market indices. Specifically, the 100 most heavily shorted stocks
in the Russell 3000 index were up 34%, while the index itself
was up 18%. The best performing stocks also had lower cus-
tomer satisfaction than the average stock. The best 25 stock
performers in the ACSI universe had an exceptionally high
average return of 95%, but their average ACSI score was only
75, lower than the average of the longs in the portfolio (82) as
well as the overall ACSI average (77).
With little or no economic justification, there were also
massive reversals in stock returns, from high to low and vice
versa. During the 2011–2012 time period, the top 25 ACSI
universe stock s in terms of returns—with an average return of
95% in 2012–2013—returned only 1.7% on average over the
previous 12 months. The S&P 500 stocks had a similar re-
versal. As Table 7 shows, the top 100 stock performers in the
12-month 2012–2013 period had dramatically greater returns
when compared with the prior 12-month 2011–2012 period
and greatly outperformed the S&P average. Specifically, the
top 100 returned 60% in 2012–2013, but these stocks
returned only 8% in 2011–2012. The corresponding returns
for the remainder of the S&P were 16% and 13%, and for the
total S&P 500, 14% and 24%.
Given that there was a significa nt reversal in stock returns
both for the ACSI universe and for the S&P 500, what do the
data suggest regarding the justification of these reversals on
the basis of revenue and earnings? As Table 7 shows, the top
25 ACSI universe stock performers in 2012–2013 had an
average trailing 12-month revenue growth of 6.7%. This was
lower than the long stocks in the portfolio, which had a
revenue growth of 7.8%, but slightly higher than both the
average ACSI stock (at 5.6%) and the average S&P 500
stock (at 5.7%). Accordingly, the higher share prices could
hardly be justified from revenue growth. Were they driven by
earnings growth? As Table 7 shows, the trailing 12-month
EPS growth for the top 25 ACSI stock performers was 33%
during the underperformance period, compared with 12% for
the ACSI universe, 11% for the long portfolio, 10% for the
average S&P 500 stock, and 17% for the top 100 S&P 500
stock performers in 2012–2013. That is, the top stock
performers did have substantially higher EPS growth.
However, the multiples for the EPS increase on stock price were
FIGURE 3
Cumulative Returns on £100 Invested in Customer Satisfaction: High NCSI-UK Portfolio, Low NCSI-UK
Portfolio, and the FTSE 100
£159.3
£112.7
£94.3
–60%
–40%
–20%
0%
20%
40%
60%
80%
Top 50% Bottom 50% FTSE
Apr 2007
Aug 2007
Dec 2007
Apr 2008
Aug 2008
Dec 2008
Apr 2009
Aug 2009
Dec 2009
Apr 2010
Aug 2010
Dec 2010
Apr 2011
Notes: The high NCSI-UK portfolio consists of the top 50% of measured companies in customer satisfaction; the low NCSI-UK portfolio consists of
the bottom 50% of measured companies in customer satisfaction.
102 / Journal of Marketing, September 2016
extraordinarily high. For example, the top 100 S&P 500 stock
performers had EPS growth 63% greater than the average S&P
firm, but a stock return 153% greater. The top 25 ACSI stock
performers had EPS growth 194% greater than the long stocks,
but a stock return 417% greater.
If increasing stock price cannot be justified by revenue
growth but rather is driven by a very large increase in multiples
of EPS growth, did these stock prices rise because they were
“cheap”? Table 8 provides some statistics answering this
question.
The top 25 ACSI stocks in 2012–2013 had an average P/E
ratio of 14.98 in 2011–2012. This compares with a P/E ratio
of 17.64 for the long book and a P/E ratio of 18.24 for the
S&P 500. Thus, if the P/E is interpreted as the relative price
of a stock, then the long stocks were notably more expensive
than the top 25 stock performers. Again, the S&P shows a
similar pattern. The average S&P 500 stock was 28% higher
in P/E ratio than the subsequent top 100 stock performers.
Overall, the evidence suggests that the market exhibited
unusual characteristics during the 2012–2013 time period.
In addition to an unusually broad rally and above-market
performance by stocks with weak balance sheets and by those
most shorted, there was a significant reversal in stock returns
between 2011–2012 and 2012–2013 for both the ACSI
universe and the S&P 500. It is not that the stocks that did
well in 2011–2012 did poorly in 2012–2013, but they did not
do as well as the (previous) underperformers of 2011–2012.
There is no evidence to suggest that the reversal is due to
differences in revenue increases, but some of the reversal
seems to be due to differences in earnings increases. There
were lower P/E ratios for the stocks that gained the most
and evidence that the investors preferred lower-priced stock
even without relating price to earnings. In 2011–2012, the
long-book customer satisfaction portfolio stocks were priced
(P/E) 100% higher than the 2012–2013 top 25 ACSI stock
performers. Even though the underperforming period began
FIGURE 4
Customer Satisfaction Portfolio Versus the S&P 500 (September 2012–August 2013)
3.3%
16.1%
–10%
–5%
0%
5%
10%
15%
20%
25%
Satisfaction portfolio S&P 500
Sep 2012
Oct 2012
Nov 2012
Dec 2012
Jan 2013
Feb 2013
Mar 2013
Apr 2013
May 2013
Jun 2013
Jul 2013
Aug 2013
TABLE 7
Satisfaction Portfolio and S&P 500 Stock Returns, Revenue Growth, and EPS Growth
2011–2012
Avg. Ret. %
2012–2013
Avg. Ret. %
2013 T12M
EPS Growth
2013 T12M
Rev Growth
ACSI
Absolute D
ACSI %
D
ACSI
Mean
ACSI
Median
Satisfaction Long Portfolio
Top 25 in ACSI 1.7% 94.6% 33.2% 6.7% .63 .9% 75.0 77.0
Bottom 111 in ACSI 17.3% 15.3% 7.3% 5.3% .87 1.2% 78.8 80.0
Total ACSI 14.4% 29.9% 12.1% 5.6% .82 1.2% 76.9 79.0
Total satisfaction portfolio 17.0% 18.3% 11.3% 7.8% 2.24 2.9% 81.9 83.0
S&P 500
Top 100 S&P 500 8.4% 60.1% 16.5% 7.8% ————
Bottom 349 S&P 500 15.6% 13.4% 8.3% 5.1% ————
Total S&P 500 14.0% 23.8% 10.1% 5.7% ————
Notes: Avg. Ret. % = average stock return percentage; T12M EPS Growth = trailing 12-month revenue growth; T12M Rev Growth = trailing 12-month
revenue growth. For the satisfaction portfolio, among the 235+ companies measured in ACSI 136 were both publicly traded and had data
available during the entire time period examined, resulting in a comparison between the Top 25 in satisfaction and the Bottom 111 in
satisfaction. For the S&P 500, 449 companies were available during the entire time period examined, resulting in a comparison between the
Top 100 in returns and the Bottom 349.
Stock Returns on Customer Satisfaction / 103
in the fourth quarter of 2012, the calendar year returns for
2012 were consistent with prior years, with an above-market
return of 12%.
In summary, the large number of market idiosyncrasies
during the 2012–2013 time period accounts for a large por-
tion of the customer satisfaction underperformance during
this time period. It was an unusual market in many ways:
stocks with weak balance sheets did better than stocks with
strong balance sheets; companies with weak customer sat-
isfaction did better than companies with strong customer
satisfaction; there was a massive reversal in stock price
growth from compa nies that previously had strong growth
to companies that had had weak growth; the most heavily
shorted stocks had much higher return than market; stocks
with low P/E ratios performed bett er than stocks with high
P/E ratios; and stocks with low absolute prices also did bet-
ter. The better-performing stocks did not have superior revenue
growth, but they did show greater percentage earnings growth.
However, they also appear to have been unusually well-rewarded
by investors for those earnings.
Even with the many oddities of the 2012–2013 stock
market, it does suggest that a stock-picking strategy based on a
rather limited universe (firms tracked by the ACSI) is not likely
to outperform the market all the time. Unless there is large
variance in temporal customer satisfaction and, thus, much
turnover in the portfolio, holdings will eventually be domi-
nated by high-priced stocks. The price differential, whether in
absolute price or relative to earnings, may well make lower-
priced stocks appear to be bargains, largely independent of
their fundamentals. At some point, however, fundamentals will
matter again and demand will shift back. It is not clear how
long that usually takes, but in view of the finding that the
portfolio produced higher-than-market returns for 14 years and
less-than-market returns for 12 months, the period is probably
rarely much longer than 1 year.
Discussion
The findings presented in this study suggest that risk-adjusted
stock returns on customer satisfaction are significantly above
market and that these abnormal returns are robust to a variety of
alternative explanations such as size, value, and momentum
risk factors as well as data snooping. Strategically, this places
emphasis on customer satisfaction as an important intangi-
ble marketing asset, also labeled as an operational “customer
mindset” performance variable by Katsikeas et al. (2016).
Importantly, it also highlights marketing’s value to the firm
(e.g., Feng, Morgan, and Rego 2015) and, by at least indirect
extension, supports the notion that chief marketing officers
matter (e.g., Germann, Ebbes, and Grewal 2015) if these firm
managers are aligned in their understanding (levels and drivers)
of the customers’ satisfaction (Hult et al. 2016). The find ing that
stock returns on customer satisfaction do beat the market is
robust in that the 15-year time period studied (2000–2014)
should be long enough to eliminate sector bias as an explanation.
As an additional check, we also estimated models with different
benchmarks, especially for technolog y and industrial sectors.
The difference in overperformance was marginal whether we
used NASDAQ or the DJIA.
There is a statistically significant relationship between
customer satisfaction and lagged earnings surprises, and cus-
tomer satisfaction is largely without influence on contempora-
neous share prices until its effects are manifested in earnings
reports. Customer satisfaction also has an effect on earnings
thems el ves . P e rh ap s even more notabl e i s t he finding that
earnings returns have an effect on customer satisfaction returns.
When lagged earnings returns are included among the risk
factors, the abnormal returns on customer satisfaction are
reduced from 10.8% per annum to 8.4%.
Equation 1 alludes to the expectation of major abnormal
returns because (1) equity value, expressed as the discounted
net present value of future cash flows from current customers,
can increase greatly even with modest growth in customer
retention, and (2) investors in general do not seem to be aware
of this (see Gupta, Lehmann, and Stuart 2004). Because the
market does not generally value customer satisfaction until
its effects show up in improved company financials, it is
noteworthy that a marketing intangible can produce returns
much higher than many other types of intangibles, further
underscoring the conclusion that familiarity with marketing
information is not widely spread among equity market par-
ticipants. This may be particularly true with respect to the
huge leverage from loyal customers. For example, Gupta and
Lehmann (2005) find that a 1% improvement in customer
retention has an effect on customer equity value that is 5 times
greater than a comparable gain in profit margin and 50 times
TABLE 8
Average Returns and P/E Ratios
2011–2012 Avg. Ret. % 2012–2013 Avg. Ret. % 2011–2012 P/E Ratio 2012–2013 P/E Ratio
S&P 500 14.9% 23.2% 18.24 18.57
ACSI 17.2% 23.9% 15.72 18.25
Longs 15.5% 18.9% 17.64 19.24
S&P Top 100 9.7% 54.4% 14.26 18.25
ACSI Top 25 7.5% 65.9% 14.98 20.28
S&P Bottom 349 16.4% 13.6% 19.46 18.66
ACSI Bottom 111 19.8% 12.6% 15.92 17.71
Notes: Avg. Ret. % = average return percentage (source: Compustat); P/E Ratio = price-to-earnings ratio (source: Compustat). As in Table 6, “ S&P
500” = the total sample of 449 measures available during the period; “ACSI” = the 136 companies in the ACSI universe for which data were
available; “Longs” = the long positions within the satisfaction portfolio; “S&P Top 100” = top 100 performing companies in the S&P 500 (vs. the
“Bottom 349”); “ACSI Top 25” = top 25 performing companies in the ACSI (vs. the “Bottom 111”).
104 / Journal of Marketing, September 2016
greater than the same magnitude of improvement in cu stomer
acquisition cost.
The returns are so different from expected returns that it
might not be appropriate to label them as yet another asset
pricing anomaly. According to Fama and French (2008), all
stock return anomalies are proxies for intangibles and for
net cash flows. The reason that they are considered anomalies
is that they cannot be explained by the CAPM. This is, of
course, true for customer satisfaction as well. It is a predictor
of future cash flows, represents an intangible asset, and is
not explained by the CAPM. However, the similarities end
there. The contribution of customer satisfaction to value
creation—especially if viewed as consumer utility in the
classical economics sense—is a more momentous economic
variable relative to anomalies such as value, size, and momentum,
all of which originate from observation, followed by post hoc
conjecture (Fama and French 2004).
The analys is of customer satisfaction and stock returns,
by contrast, did not begin with empirical observation. The
reasoning is not post hoc, but ex ante. Its origins can be traced
to utility theory (Kahneman, Wakker, and Sarin 1997), for-
tified by the marketing models of customer equity (Gupta
and Lehmann 2005; Kumar 2008; Rust, Zeithaml, and
Le mon 20 0 0; Villanueva and Hanssens 2007). It is not that
post hoc reasoning is omitted in its entirety, but empirical
testing has generally trailed theory, not the other way around.
Yet one might ask whether various intangibles and the
anomalies they give rise to could be correlated to each other
and therefore have a common cause. Because our study
does not control for all known anomalies, might omitted
variable bias have affected our results? This is possible, but
unlikely. Green, Hand, and Zhang (2013) identified more
than 300 anomalies and found that they were predominantly
orthogonal, a finding also confirmed by McLean and Pontiff
(2015).
Implications
The most obvious implication—from the finding that com-
panies that treat their customers well tend to produce better
returns to their investors—is that firms should generally try to
improve customer satisfaction along with the volatility of
future customer cash flow risks. This is hardly a revolutionary
idea. Indeed, it has been a normative fixture in marketing
literature and education for more than 50 years. However, it has
taken on increasing exigency as a result of the proliferation of
the Internet and information and communications technologies
as well as global competition and the rise in global division of
labor. Yet paradoxically, marketing has not gained stature in
the business organization. Rather, the opposite seems to be the
case (Nath and Mahajan 2008; cf. Feng, Morgan, and Rego
2015; Germann, Ebbes, and Grewal 2015).
As suggested by the sheer size of the abnormal returns, the
reward for having satisfied customers is much greater than is
generally known. By the same token, the value of marketing
information (and specifically information about customer sat-
isfaction), which generates abnormal stock returns of approx-
imately 10% per annum, seems underappreciated. According to
Edmans (2011), abnormal returns for other types of intangible
assets (e.g., research and development, advertising, quality of
organization, patents, human capital) range betwee n 4% and
6%. For employee satisfaction, he estimates an alpha of 3.5%.
These numbers suggest that it would be beneficial to allocate
more resources to the marketing function, especially because
many of th e most effective ways to increase c ustomer sat-
isfaction lie not in product quality but in ta rgeting, market
segmentation, customer service and the management of cus-
tomer relationships, and CLV (e.g., Fornell 2007; Kumar et al.
2008).
If the economic benefits of high customer satisfaction in
terms of improved consumer utility and shareholder value
seem apparent, why do many companies fail to improve cus-
tomer satisfaction? Indeed, overall customer satisfaction, on
average, has decreased over the past two years, according to
the ACSI. Social and portable media, online data collec-
tion, voice-recorded automated telephone interviewing and
computer-assisted t elephone interviewing, Big Data, and so
forth provide companies with access to more data on cus-
tomers than ever before. The information and communica-
tions technologies revolution has led to extraordinary growth
in data storage, transmission, and displaying. Yet it is
probably safe to say that there has not been corresponding
progress in data processing. Instead, raw data are often used
to calculate the number of “likes,” or the proportion of cus-
tomers who say they will or will not recommend. Validity and
reliability are concepts rarely mentioned. Calibration toward
objectives is virtually unheard of in this context. The same is
true with corrections for sampling and measurement error,
which would be problematic not only in terms of measurement
accuracy but also with respect to identifying what companies
need to do to increase their customers’ satisfaction. Along with
measures and analysis that may be too simplistic, it is also
possible that firms thereby invest too little in customer retention.
This could be particularly damaging because underspending on
customer retention tends to have a more harmful effect than
underspending on customer acquisition (Reinartz, Thomas, and
Kumar 2005).
However, it should be recognized that customer satisfaction
information is not without interpretational challenges. There-
fore, it would be unrealistic to expect that equity markets would
be frictionless with respect to such information. In addition
to the friction associated with arbitrage costs, imperfect infor-
mation, limitations on investors’ cognitive and reasoning skills,
and institutional rigidities that impair market efficiency, cus-
tomer satisfact ion is not included in the analysis models most
investors use (Gupta and Lehmann 2005). Consequently, it
would be difficult for equity markets to instantly incorporate
customer satisfaction information. It is also not the case that
strong customer satisfac tion always le ads to above-ma rket
stock returns. As our data demonstrate, in 2012–2013 there
was a period when the returns, thou gh strong in an absolute
sense, underperformed the market. Even though the market
exhibited several unusual characteristics during this time,
the somewhat limited stock universe provided by the ACSI,
combined with small temporal variance in customer satis-
faction, eventually produces a portfolio of highly priced stock
vulnerable to temporary reversals in relative fortune during
broad stock rallies. Nevertheless, we may well be at a point
Stock Returns on Customer Satisfaction / 105
when one can envision a debate between marketing and finance
about whether earnings or customer satisfaction belongs among
the risk factors in asset pricing models. Empirically, they are
correlated; therefore, one mitigates the effect of the other. But
which one should be mitigated? What comes first, earnings or
customer satisfaction? We are not aware of research suggesting
that earnings per se cause customer satisfaction, but there is
ample evidence pointing in the other direction.
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