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The value of different customer
satisfaction and loyalty metrics in
predicting customer retention,
recommendation, and
share-of-wallet
Timothy L. Keiningham
IPSOS Loyalty, Parsippany, New Jersey, USA
Bruce Cooil
Owen Graduate School of Management, Vanderbilt University,
Nashville, Tennessee, USA
Lerzan Aksoy
College of Administrative Sciences and Economics, Koc¸ University,
Istanbul, Turkey
Tor W. Andreassen
Norwegian School of Management, Department of Marketing, Oslo, Norway, and
Jay Weiner
IPSOS Insight, Minneapolis, Minnesota, USA
Abstract
Purpose – The purpose of this research is to examine different customer satisfaction and loyalty
metrics and test their relationship to customer retention, recommendation and share of wallet using
micro (customer) level data.
Design/methodology/approach – The data for this study come from a two-year longitudinal
Internet panel of over 8,000 US customers of firms in one of three industries (retail banking,
mass-merchant retail, and Internet service providers (ISPs)). Correlation analysis, CHAID, and three
types of regression analyses (best-subsets, ordinal logistic, and latent class ordinal logistic regression)
were used to test the hypotheses.
Findings – Contrary to Reichheld’s assertions, the results indicate that recommend intention alone will
not suffice as a single predictor of customers’ future loyalty behavior. Use of a multiple indicator instead
of a single predictor model performs better in predicting customer recommendations and retention.
Research limitations/implications – The limitation of the paper is that it uses data from only
three industries.
Practical implications – The presumption of managers when looking at recommend intention as
the primary, even sole gauge of customer loyalty appears to be erroneous. The consequence is
potential misallocations of resources due to myopic focus on customers’ recommend intentions.
Originality/value – This is the first scientific study that examines recommend intentions and its
impact on retention and recommendation on the micro (customer) level.
Keywords Customer retention, Customer loyalty, Customer satisfaction
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0960-4529.htm
Customer
satisfaction and
loyalty
361
Managing Service Quality
Vol. 17 No. 4, 2007
pp. 361-384
qEmerald Group Publishing Limited
0960-4529
DOI 10.1108/09604520710760526
Introduction
Enhancing customer loyalty has become a popular topic for managers, consultants,
and academics. The arguments in support of loyalty are simple to understand. Loyal
customers are reported to have higher customer retention rates, commit a higher share
of their category spending to the firm, and are more likely to recommend others to
become customers of the firm (Reichheld and Earl Sasser, 1990; Zeithaml, 2000).
To monitor their performance and guide improvement efforts with regard to
customer loyalty, managers frequently rely on customer feedback systems. This
feedback typically is obtained through customer surveys that contain measures of
satisfaction, repurchase intention, and word-of-mouth intention (Morgan and Rego,
2006). The inherent belief among managers is that these measures serve as leading
indicators of customers’ future firm-related behaviors (e.g., retention,
share-of-wallet allocation, and word-of-mouth).
Research has examined most of these commonly used customer satisfaction/loyalty
metrics and subsequent customer behaviors. These examinations however have
tended to focus on bivariate relationships such as repurchase intention and repurchase
behavior (Chandon et al., 2005; Morwitz et al., 1997), customer satisfaction and share-of
wallet (Keiningham et al., 2003), complaint intention and complaining behavior (Oh,
2006).
Additionally, there is no consensus as to the best means of gauging customer
loyalty (Uncles et al., 2003). Since the goal of managers is to enhance different customer
loyalty outcomes simultaneously (e.g., retention, share-of-wallet, customer referrals),
however, there is a desire among managers to find the optimum gauge of customer
loyalty that will result in favorable outcomes on multiple behavioral criteria.
Noted loyalty consultant, Frederick Reichheld, argues that of all commonly used
loyalty metrics, recommend intention is by far the best at predicting customers’ actual
loyalty behavior (purchase and recommendations) (Reichheld, 2003). Reichheld bases
this assertion on research conducted in partnership with Satmetrix Systems and Bain
& Company (Reichheld, 2003; Satmetrix, 2004). In particular, Reichheld (2003, p. 50)
states, “The data allowed us to determine which survey questions had the strongest
statistical correlation with repeat purchases or referrals ... One question was best for
most industries. ‘How likely is it that you would recommend [company X] to a
colleague or friend?’ ranked first or second in 11 of the 14 cases studied...Interestingly,
creating a weighted index – based on the responses to multiple questions and taking
into account the relative effectiveness of those questions – provided insignificant
predictive advantage”[1]. This research served as the micro-level (customer-level)
analysis from which the Net Promoter loyalty metric was ultimately created[2].
Thus far, however, there have been no peer-reviewed, scientific investigations
examining the relationship between recommend intention and customer behaviors
(outside of customer referral/complaining behavior). This research seeks to examine
the relationship between responses to commonly used satisfaction and loyalty survey
questions, including recommend intention, and their relationship to future customer
behavior: purchasing (retention and share-of-spending), and recommendations.
The data for this study comes from a longitudinal study of over 8,000 customers of
firms in one of three industries (retail banking, mass-merchant retail, and Internet
service providers (ISPs)). Customer ratings of common satisfaction and loyalty metrics
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were monitored over two years; in the second year of the study, customers’ purchasing
(retention and share-of-category spending) and referral behaviors were also tracked.
Customer metrics
Customer satisfaction
By far, the most commonly used customer perceptual metric by managers is
satisfaction (Gupta and Zeithaml, 2007). Zeithaml et al., 2006 (p. 170) observe that this
is “because it is generic and can be universally gauged for all products and services
(including nonprofit and public services)[3]. Even without a precise definition of the
term, customer satisfaction is clearly understood by respondents, and its meaning is
easy to communicate to managers.” With regard to satisfaction’s relationship to
customer behavior, research has shown a link been satisfaction and customer retention
(Anderson and Sullivan, 1993; Bolton, 1998; Jones and Earl Sasser, 1995; LaBarbera
and Mazursky, 1983; Loveman, 1998; Mittal and Kamakura, 2001; Newman and
Werbel, 1973; Rust and Zahorik, 1993; Sambandam and Lord, 1995) and customers’
share of category spending (i.e. share-of-wallet) (Keiningham et al., 2005; Keiningham
et al., 2003; Perkins-Munn et al., 2005).
Customer expectations
Customer satisfaction is strongly influenced by customer expectations. The gap
between perceived quality and expected quality, called “expectancy disconfirmation”
is a strong predictor of customer satisfaction (Oliver, 1980; Rust et al., 1995). As a
result, many managers and researchers have chosen to explicitly measure the extent to
which a product/service meets customers’ expectations.
The seminal SERVQUAL framework of Parasuraman et al., 1988; Parasuraman
et al., 1991, 1993; Zeithaml et al., 1996) conceptualized and operationalized service
quality as the gap between customers’ expectations and perceptions (Parasuraman
et al., 1985; 1994). Zeithaml et al. (1996) propose a methodology for linking service
quality measures to financial outcomes: in particular, service quality to repurchase
intention to retention to firm financial outcomes.
Additionally, the American Customer Satisfaction Index (ACSI) measures customer
expectations as a component of its satisfaction index (Merz, 2005).
Customer value (worth what paid for)
According to Zeithaml (1988), “perceived value is the customer’s overall assessment of
the utility of a product based on perceptions of what is received and what is given”.
Consumers’ perceptions of value are influenced by differences in monetary costs,
non-monetary costs, customers’ tastes, and customers’ characteristics (Bolton and
Drew, 1991).
Consultant Bradley Gale popularized the use of a technique called Customer Value
Analysis (CVA) (Gale, 1994). The relative performance of companies on a “perceived
value” metric used in CVA was linked to firms’ relative market share (Clark et al.,
1999). As a result, many managers adopted the CVA approach.
The value metric was typically defined as customers’ responses to a “worth what
paid for” question (Bowden, 1998; Clark et al., 1999; Varki and Colgate, 2001).
Specifically, Gale (1994, p. 80) recommends a value question similar to the following,
Customer
satisfaction and
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363
“Considering the products and services that your vendor offers, are they worth what
you paid for them?”
Brand preference
Marketers have long understood the importance of a brand’s inclusion in a consumer’s
“evoked set” (the subset of brands that will be considered for purchase on any given
occasion) to the ultimate success of the brand. As such, the degree to which consumers
prefer specific brands relative to competing alternatives is an important component of
customers’ brand loyalty (Rundle-Thiele and Mackay, 2001). Additionally, brand
preference has been shown to interact with customer satisfaction to impact customers
behavioral loyalty (as measured by share-of-wallet) (Keiningham et al., 2005).
In marketing literature, attitudinal loyalty is often described as preference for the
brand (Bennett and Rundle-Thiele, 2002). Therefore, brand preference may in fact be
regarded as a higher order construct in the sense that “preference” would likely be an
outcome based upon customers’ expectations or experience (i.e. satisfaction).
Repurchase intention
Researchers have long used repurchase intentions to help predict future purchasing
behavior. While the correlation between intentions and repurchase is not perfect, a
number of researchers have examined various factors influencing this relationship
(Bemmaor, 1995; Chandon et al., 2005; Jamieson and Bass, 1989; Morrison, 1979;
Morwitz et al., 1993; Morwitz et al., 1997).
Recommend intention
Word-of-mouth intention has been of importance to researchers for at least the past
thirty years. Early research regarding word-of-mouth tended to focus on complaining
behavior (for example, Gronhaug and Kvitastein, 1991; Singh, 1988). More recently,
however, the focus has shifted to recommendations and customer advocacy (for
example, Brown et al., 2005; Christopher et al., 1991; Jones and Earl Sasser, 1995).
Thus far, there is very little scientific research relating recommend intention to
actual recommendations. In an analysis of actual conversations in numerous
discussions forums on the Internet, Andreassen et al. (2006) documented
recommendations as one of four unique dialogues taking place. As noted earlier,
loyalty expert Fred Reichheld (2003) argues that recommend intention is the best
metric at predicting not only customers’ recommending behavior, but also their
purchasing behavior.
Customer retention
In our investigation, customer retention is defined as customers’ stated continuation of
a business relationship with the firm. For Internet service providers (ISPs), it is
continuing to use the same provider. For retail banks, it is continuing to maintain an
account relationship with the bank. And for discount retailers, it is the continued repeat
shopping with the retailer.
Much of the research regarding customer satisfaction and customers’ actual
behavior has focused on the relationship between satisfaction and retention. This
emphasis is largely the result of early research, which identified customer retention as
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a key driver of firm profitability (Reichheld, 1993, 1996; Reichheld and Kenny, 1990;
Reichheld et al., 2000; Reichheld and Earl Sasser, 1990).
Share-of-wallet
For retail banking, share-of-wallet is defined as the stated percentage of total assets
held at the bank being rated by the customer[4]. For discount retailers, it is the stated
percentage of total purchases from discount retailers conducted at the retailer being
rated by the customer. Because customers of ISPs overwhelmingly use only one service
provider (outside of their work environment), share-of-wallet is not measured for this
industry.
Researchers Jones and Earl Sasser (1995, p. 94) assert, “the ultimate measure of
loyalty, of course, is share of purchases in the category” (i.e. share of wallet). While this
is likely an overstatement, as share of wallet is not as forward looking as other
measures of loyalty (Oliver, 1999), it is frequently used by researchers to operationalize
loyalty behavior (for example, Bowman et al., 2000; Bowman and Narayandas, 2004;
Brody and Cunningham, 1968; Cunningham, 1956; Cunningham, 1961; Wind, 1970).
Managerially, a focus on improving customers’ share-of-wallet has been found to
have greater financial impact than by focusing on customer retention. McKinsey &
Company reports that efforts to improve customers’ share of spending and customer
retention can add as much as ten-times greater value to a company than focusing on
retention alone (Coyles and Gokey, 2002).
Customer recommendations
Arndt (1967, p. 190) in his seminal work defined word-of-mouth as “oral,
person-to-person communication between a perceived non-commercial communicator
and a receiver concerning a brand, a product, or a service offered for sale”. Two
decades later Westbrook (1987, p. 261) defined word-of-mouth as: “informal
communication directed at other consumers about the ownership, usage or
characteristics of particular goods and services and/or their sellers”. For all
industries investigated, customer recommendations represents whether or not the
respondent actually recommended the firm or brand to another person.
Trend in spending
It is a well-known maxim in marketing that past customer behavior tends to be a
relatively good predictor of future customer behavior (Sheeran et al., 1999; Soderlund
et al., 2001). In fact, the widely used RFM (recency, frequency, monetary value)
segmentation approach used by direct marketers is based upon this truth (Keiningham
et al., 2006; Miglautsch, 2002). To provide a gauge of past behavior, respondents to the
bank and retail surveys were asked to report their recent trend in spending in the
category with the specific firm under investigation.
Hypothesis development
Most models of satisfaction and loyalty tend to view the relationship among metrics as
being hierarchical (for example, Anderson and Mittal, 2000; Heskett et al., 1994;
Parasuraman et al., 1988; Rust et al., 1995). Simplistically, the hierarchy would be
expected to flow as follows: customer perceptions to behavioral intentions to customer
Customer
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loyalty
365
behavior. This logic has a close resemblance to the theory of reasoned action (Ajzen
and Fishbein, 1980) from attitude theory.
Consultants and business managers frequently ignore or misunderstand the
hierarchical and differentiating characteristics of each link in the chain of effects from
satisfaction to intentions to behavior. As a result, it is not uncommon to hear
consultants and managers say that something to the effect that they have gone
“beyond” customer satisfaction to measuring customer loyalty; For example, book
titles such as Customer Satisfaction Is Worthless: Customer Loyalty Is Priceless
(Gitomer, 1998) and Beyond Customer Satisfaction to Customer Loyalty (Bhote, 1996)
reflect this common management perception.
Reichheld (2003) makes a similar argument in his research regarding the
relationship between responses to various questions asked in a customer survey and
customers’ subsequent loyalty-based behaviors. In particular, Reichheld argues that
recommend intention performs better than many questions designed to assess
customers’ perceptions of their experience, most notably singling out customer
satisfaction measurement as inferior. Oliver (1999), however, finds that satisfaction is a
necessary step in loyalty formation, and that for many firms is the only feasible goal to
enhance loyalty for which they can strive. Reichheld’s (2003) position in effect argues
that firms should manage customer intentions, as opposed to perceptions of their
experience; in other words, manage an outcome (i.e. intentions) instead of a cause (i.e.
customer experience).
While the logic of a hierarchical process is both commonsensical and theoretically
supported, in the case of the findings presented by Reichheld (2003) regarding the
superiority of recommend intention in linking to customers’ loyalty behaviors than
other metrics, there are notable gaps in the current literature from which to accurately
gauge the reasonableness of the findings. Therefore we offer five hypotheses, based
upon the current literature, which address fundamental aspects of Reichheld’s (2003)
findings.
Hypotheses
As noted earlier, customer perceptions, behavioral intentions, and customer behavior
are widely believed to be hierarchical constructs. Since are hierarchical, the strength of
the relationship between extremes in the continuum should be less than for adjacent
constructs. In other words, in the chain of effects, variables that are closer to the
outcome should have a stronger relationship compared to variables that are earlier in
the chain. This would appear to be the case based upon Reichheld’s (2003) finding that
a behavioral intention metric was more closely linked to customer behavior than
measures of customers’ satisfaction with the product/service experience. Hence, we
hypothesize:
H1. Intentions (repurchase and recommendation) will be more strongly correlated
to customer behavior than customers’ perceptions of satisfaction, value, and
expectations.
Reichheld (2003) and Satmetrix (2004) specifically measured two types of behavioral
intentions: repurchase intention and recommend intention. A number of researchers
have examined the relationship between repurchase intention and repurchase behavior
(Bemmaor, 1995; Chandon et al., 2005; Jamieson and Bass, 1989; Morrison, 1979;
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Morwitz et al., 1993; Morwitz et al., 1997). A body of research similarly exists
examining the relationship between word-of-mouth intention and word-of-mouth
behavior (Brown et al., 2005; Christopher et al., 1991; Gronhaug and Kvitastein, 1991;
Jones and Earl Sasser, 1995; Singh, 1988).
While seemingly obvious, it is important to point out that each intention metric is
designed to predict a specific customer behavior (i.e. repurchase or recommendation).
Reichheld (2003) argues, however, that recommend intention suffices as a predictor for
both types of customer behavior.
Currently, Reichheld (2003) and Satmetrix (2004) provide the only research into this
specific issue. Despite their findings, however, we believe that each intention metric is
gauging a distinct customer behavior. Therefore, we hypothesize:
H2. Repurchase intention will be more strongly correlated to repurchase behavior
than recommend intention, and customers’ perceptions of satisfaction, value,
and expectations.
H3. Recommend intention will be more strongly correlated to recommend
behavior than repurchase intention, and customers’ perceptions of
satisfaction, value, and expectations.
Share-of-wallet is a topic of increasing importance among both managers and
academics (Zeithaml, 2000). Researchers Jones and Earl Sasser (1995, p. 94) assert,
“the ultimate measure of loyalty, of course, is share of purchases in the category”
(i.e. share of wallet). Reichheld and Earl Sasser (1990) argue that “profit from
increased purchases” (i.e. increased share of category spending/share of wallet) is a
major contributor to profits through increased customer loyalty.
This would appear to be supported by empirical research. Coyles and Gokey (2002)
find that efforts to improve customers’ share of spending and customer retention can
add as much as ten-times greater value to a company than focusing on retention alone.
Therefore any metric designed to best gauge customer loyalty would need to assess its
relationship to share of wallet.
Perkins-Munn et al. (2005) found a strong relationship between repurchase
intentions and actual repurchase, and that retention and share-of-wallet, while not
identical, are closely related and hence can at times be used as proxies for one
another. Reichheld (2003), however, argues that recommend intention is the best
metric for gauging customers’ loyalty behaviors; therefore, based upon this
assertion, recommend intention would be expected to more closely correlate to
share of wallet.
Again, Reichheld (2003) and Satmetrix (2004) provide the only research into this
specific issue (and it is unclear as to how they integrated share of wallet into their
measure of customers’ purchasing behaviors). Therefore, given the findings of
Perkins-Munn et al. (2005), we hypothesize:
H4. Repurchase intention will be more strongly correlated to share-of-wallet than
recommend intention, and customers’ perceptions of satisfaction, value, and
expectations, and customers’ recommend intention.
Reichheld (2003, p. 50) states, “creating a weighted index – based on the responses to
multiple questions and taking into account the relative effectiveness of those questions
Customer
satisfaction and
loyalty
367
– provided insignificant predictive advantage” when compared to the use of a single
recommend intention question. This finding is highly unexpected. As we expect
recommend intention to be more strongly correlated to recommend behavior, and
repurchase intention to be more highly correlated to customers’ repurchasing behavior,
it would appear more logical to expect that these two behavioral intention metrics
would significantly contribute to a model designed to predict these customer
behaviors.
Furthermore, researchers have shown that typically single item measures are less
reliable than multi-item scales/constructs (Wanous and Hudy, 2001; Wanous and
Reichers, 1996; Wanous et al., 1997).
Therefore, we hypothesize that:
H5. A multivariate model will be significantly better at predicting both customers’
repurchase and recommend behaviors than a univariate model containing
only recommend intention.
The data
The data for this study comes from a longitudinal study of over 8,000 customers of
firms in one of three industries (retail banking, mass-merchant retail, and Internet
service providers (ISPs)). The panel is proprietary to a large market research provider,
and is structured and maintained so that market researchers can obtain and survey
USA consumers based upon their desired demographic profiles. The research firm
provides incentives to members to continue participation in the panel. In the case of
this research, respondents were screened based upon being active customers of one of
the firms/brands under investigation.
Customers were surveyed regarding their experience with the brand/firm. A
follow-up survey was conducted approximately one year after the initial survey. In
addition to the questions surveyed in the initial study, customers’ stated purchasing
(retention and share-of-category spending) and referral behavior were also tracked.
Survey researchers frequently use customer attitudinal and perceptual metrics to
aid in predicting customers’ future behaviors. Our research examined several of the
most common customer perception metrics (customer satisfaction, customer
expectations, customer value (defined as “worth what paid for”), and brand
preference) and two widely used behavioral intention metrics (repurchase intention
and recommend intention). Table I presents the attitudinal questions used and their
corresponding rating scales.
Additionally, our study examined customer behaviors associated with customer
loyalty. As predictor variables, two stated behavior metrics were investigated (from the
initial survey period): recent trend in spending within the industry and for the firm (see
Table II). As dependent variables, four behaviors were investigated: change in
share-of-wallet (i.e. SOW
t
2SOW
t21
), SOW, customer retention, and customer
recommendations.
Analysis
Creation of recoded variables
Reichheld (2003) and Satmetrix (2004) used Pearson correlations to test the strength of
the relationship between various satisfaction/loyalty survey questions and subsequent
customer behaviors (purchase and recommendations). Satmetrix (2004) reports:
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Taking into account your total experience, overall, how satisfied are you with (Company or Brand X)?
(1-10 scale)
10 Completely satisfied
1 Completely dissatisfied
How well has (Company or Brand X) met your expectations? (1-10 scale)
10 Completely failed to meet expectations
1 Greatly exceeded expectations
Using a scale from 1 to 10 with 1 being Strongly Disagree and 10 being Strongly Agree please tell me
how much you agree with the statement (Company or Brand X) is worth what I pay for it (1-10 scale)
10 Strongly agree
1 Strongly disagree
Six/twelve months from now, how likely are you to still be using (Company or Brand X)? (1-5 scale)
5 Definitely will be using them
4 Probably will be using them
3 Might or might not be using them
2 Probably will not be using them
1 Definitely will not be using them
How likely would you be to recommend (Company or Brand X) to friends and colleagues? (1-5 scale)
5 Definitely would recommend them
4 Probably would recommend them
3 Might or might not recommend them
2 Probably would not recommend them
1 Definitely would not recommend them
Of the following list of statements, please select the one that comes closest to your feelings (regarding
Company or Brand X) (1-5 scale)
5 I prefer them to all the other (firms/brands in category)
4 They are one of a few I prefer to other (firms/brands in category)
3 They are acceptable, but I have no particular preference for them
2 I somewhat prefer other (firms/brands in category)
1 I strongly prefer other (firms/brands in category)
Table I.
Satisfaction/loyalty
questions investigated
Over the last (year (bank), three months (retail)) would you say that the total value of your (savings and
investments/purchases) at (all firms/brands in category) you use has...? (1-5 scale)
5 Increased a lot
4 Increased a little
3 Stayed the same
2 Decreased a little
1 Decreased a lot
Over the last (year (bank), three months (retail)) would you say that the total value of your (savings and
investments/purchases) at (Company or Brand X) has...? (1-5 scale)
5 Increased a lot
4 Increased a little
3 Stayed the same
2 Decreased a little
1 Decreased a lot
Table II.
Trend in spending
questions investigated
(banking and retail
surveys only)
Customer
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369
... the likelihood to recommend question proved to be the top correlate to actual customer
behavior 80 percent of the time. More explicitly, if customers reported that they were likely to
recommend a particular company to a friend or colleague, then these same customers were also
likely to actually repurchase from the company, as well as generate new business by referring the
company via word-of-mouth ... [the] results of this analysis also led to the discovery of a
classification scheme, whereby customers can be grouped according to their joint loyalty and
behavioral profiles ... Customers were segmented into three categories based on their
“recommend” ratings and their combined purchase and referral rates. Using these groupings,
customers can be characterized in terms of their joint profile of “what they say” and “what they
will actually do”.
As a result, so that we can better compare and contrast our findings with those of
Reichheld (2003) and Satmetrix (2004), we created a new three-segment repurchase
intention variable (i.e. the five-point scale was recoded into a three-point scale). Because our
scales differ from those used by Reichheld (2003)/Satmetrix (2004), however, we wanted to
be certain that our three cluster groupings were not only as similar as possible, but also
that the groupings demonstrated high empirical validity in terms of the recoded variable’s
relationship to the customer behaviors investigated by Reichheld (2003).
Reichheld (2003) uses a 0-10 scale where the end anchors are labeled “extremely
likely-not at all likely”. The scale was segmented into three groups: ratings of 9-10, 7-8,
and 0-6. The repurchase intention variable in our study, however, used a five point
rating scale. Based upon the Reichheld (2003) groupings, it would appear that the
comparable groupings would be ratings of 1-3, 4, and 5.
To empirically confirm the validity of this three-segment grouping vis-a
`-vis the
variable’s relationship to customer behavior, we conducted two separate chi-square tests.
In the first analysis, chi-square tests were conducted for customer recommendations by
recommend intention level for each industry (Figure 1). In each case, the groupings based
upon the 1-3, 4, 5 rating levels were highly significant (i.e. p,0.0001). In the second
analysis, chi-square tests were conducted using a combined variable of customer
recommendations and retention[5] by recommend intention level for each industry
(Figure 2). (Note that Reichheld (2003) and Satmetrix (2004) report examining the
correlation of variables on repurchase (retention) and referral (recommendation) behavior).
Again, in each case, the groupings based upon the 1-3, 4, 5 rating levels were found to be
highly significant (i.e. p,0.0001). As a result, the analyses strongly support this
three-segment grouping.
To be able to make apples-to-apples comparisons for all variables under
investigation, new three-segment (recoded) variables were created for all attitudinal
variables under investigation. Variables that used a five-point scale (repurchase
intention, recommend intention, and brand preference) were recoded as follows: 1-3
recoded to 1, 4 recoded to 2, and 5 recoded to 3. Variables that used a ten-point scale
(overall satisfaction, expectations, and customer value (worth what paid) were recoded
as follows: 1-6 recoded to 1, 7-8 recoded to 2, and 9-10 recoded to 3.
Correlation analysis
Table III presents the median within industry correlations of the attributes under
investigation with subsequent customer behaviors associated with customer loyalty.
All values greater than 0.022 are significant at the 0.01 level (2-sided).
The first thing to note is that the vast majority of variables investigated explain less
than 10 percent of the variance in the relationship (i.e. r,SQRTð0:1Þ¼0:32). Given
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the relatively modest correlation strength, it appears questionable that any single
attitudinal measure alone would best gauge future customer behavior.
With regard to H1, that repurchase and recommend intentions will be more closely
related to customer behavior than customer perceptions of satisfaction, value, and
expectations, this hypothesis is not supported. The correlations in Table III clearly show
that attitudes and intentions associated with customer loyalty differ in the strength of
association to various customer behaviors. Furthermore, industry type impacts the
association between customer attitudes and their subsequent behaviors.
In general, H2 and H3 that repurchase intention best predicts retention, and
recommend intention best predict future recommendations, are supported. The finding,
Figure 1.
Chi-squared tests for
customer
recommendations by
recommend intention level
Customer
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371
Figure 2.
Chi-squared tests for
retention-
recommendations
combined by recommend
intention level
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Change
in SOW SOW
Recommend
and retain Retain Recommend
Banking
Share of wallet t21 (initial period) 20.63 0.49 0.01 0.00 0.01
Recommend intention (recoded into 3 groups) 0.11 0.08 0.30 0.10 0.40
Recommend intention 0.12 0.10 0.31 0.12 0.38
Repurchase intention (recoded into 3 groups) 0.11 0.13 0.29 0.21 0.26
Repurchase intention 0.15 0.15 0.32 0.25 0.26
Overall satisfaction (recoded into 3 groups) 0.09 0.05 0.21 0.08 0.26
Overall satisfaction 0.09 0.06 0.22 0.10 0.26
Worth what paid (recoded into 3 groups) 0.03 0.13 0.21 0.06 0.29
Worth what paid 0.06 0.11 0.24 0.10 0.30
Expectations (recoded into 3 groups) 0.06 0.06 0.19 0.07 0.25
Expectations 0.09 0.09 0.23 0.10 0.27
Brand Preference (recoded into three groups) 0.07 0.14 0.31 0.16 0.34
Brand preference 0.10 0.16 0.32 0.19 0.33
Trend in total spend/savings in category 0.09 20.12 0.04 20.01 0.08
Trend in spending/savings with individual firm 0.12 0.03 0.17 0.13 0.14
Retail
Share of wallet t21 (initial period) 20.34 0.37 0.10 0.08 0.08
Recommend intention (recoded into 3 groups) 0.13 0.22 0.43 0.22 0.45
Recommend Intention 0.13 0.23 0.43 0.23 0.43
Repurchase intention (recoded into 3 groups) 0.16 0.28 0.43 0.29 0.40
Repurchase intention 0.16 0.28 0.41 0.29 0.38
Overall Satisfaction (recoded into 3 groups) 0.11 0.18 0.35 0.18 0.36
Overall iatisfaction 0.12 0.21 0.36 0.20 0.36
Worth what paid (recoded into 3 groups) 0.09 0.16 0.31 0.14 0.33
Worth what paid 0.09 0.20 0.34 0.17 0.35
Expectations (recoded into 3 groups) 0.09 0.16 0.32 0.15 0.33
Expectations 0.10 0.19 0.35 0.19 0.35
Brand Preference (recoded into three groups) 0.15 0.30 0.41 0.22 0.41
Brand preference 0.15 0.33 0.42 0.25 0.41
Trend in total spend/savings in category 0.07 0.00 0.06 0.03 0.07
Trend in spending/savings with individual
firm 0.08 0.14 0.17 0.13 0.15
ISP
Share of wallet t21 (initial period)
Recommend intention (recoded into 3 groups) 0.34 0.14 0.39
Recommend intention 0.35 0.17 0.37
Repurchase intention (recoded into 3 groups) 0.36 0.26 0.32
Repurchase intention 0.35 0.27 0.30
Overall Satisfaction (recoded into 3 groups) 0.30 0.15 0.33
Overall satisfaction 0.30 0.16 0.32
Worth what paid (recoded into 3 groups) 0.28 0.12 0.31
Worth what paid 0.28 0.14 0.31
Expectations (recoded into 3 groups) 0.31 0.14 0.35
Expectations 0.31 0.15 0.34
Brand Preference (recoded into three groups) 0.33 0.15 0.37
Brand preference 0.34 0.19 0.36
Trend in total spend/savings in category
Trend in spending/savings with individual
firm
Table III.
Correlations of survey
responses in Time 1 to
customer behavior in
Time 2
Customer
satisfaction and
loyalty
373
however, is not universal, and varies by industry and the type of customer behavior. 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.
H4 stated that repurchase intention would be more strongly correlated to share of
wallet than customers’ perceptions of satisfaction, value, and expectations, and customers’
recommend intention. While directionally this is true, the differences were not always
statistically significant.
With regard to share of wallet, however, the correlations in Table III reveal two
other interesting findings. First, past share of wallet tends to be a better predictor of
future share of wallet than attitudinal variables. Second, brand preference showed
equal or stronger relationships to share of wallet than other intention or attitudinal
metrics. This would appear to in part support the marketing literature that defines
attitudinal loyalty as a preference for the brand (Bennett and Rundle-Thiele, 2002).
Single or multi-item measures
As noted earlier, Reichheld (2003, p. 50) states that models using multiple items to predict
customers’ purchase and recommend behavior provided “insignificant predictive
advantage” when compared to the use of a single recommend intention question. As
single item measures have been shown to be less reliable than multi-item scales/constructs
(Wanous and Hudy, 2001; Wanous and Reichers, 1996; Wanous et al., 1997), H5 proposes
that a multivariate model will perform better than a model consisting only of recommend
intention.
To test the difference between single-predictor and multi-predictor models, we
conducted two types of analyses. First we analyzed the incremental predictive value of
multiple-predictor models relative to single-predictor models for Retention within each
industry. We summarize these results in Table IV. As candidate predictors, we used all
15 of the survey response variables (listed in the rows of Table III). Among the ISP
firms there is only a marginal increase in R-squared (adjusted) as one goes beyond the
best single-predictor model, but the increase in R-squared (adjusted) is greater than 20
percent in the other two industries (Banks: 25 percent; Retail: 21 percent). Nevertheless,
these models all have relatively modest predictive value.
Table V summarizes the results for the best single- and multiple-logistic ordinal
regressions when using the combined Recommend-Retention variable as the dependent
variable (0: not retained; 1: retained but did not recommend; 2: retained and
recommended). Here we focus on models with recommend intention only and with both
recommend intention and repurchase intention as predictors. Repurchase intention is
used as the second predictor here because:
(1) it is one of the most promising predictors based on the analysis in Table IV and
an examination of the correlation tables for the Recommend-Retention variable
(see Table III); and
(2) it is the other variable explicitly examined by Reichheld (2003) and Satmetrix
(2004).
In Table V, percent concordance represents the percentage of times that customer pairs
are correctly ranked by the model on the Recommend-Retention scale, while the
receiver-operating characteristic curve (ROCC) area represents the percentage of
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Banking Retail ISP
Model type R
2
% Predictor(s) R
2
% Predictor(s) R
2
% Predictor(s)
Best single-predictor model 5.9 Repurchase-intent
a
8.7 Repurchase-intent (3 level)
a
7.4 Repurchase-intent
a
Best two-predictor model 6.5 Repurchase-intent
a
Brand preference
9.4 Repurchase-intent
a
Brand preference
a
7.5 Repurchase-intent
a
Recommend-intent (3 level)
Best predictive model 7.3 Repurchase-intention
a
Brand preference
b
Satisfaction
b
10.3 9-Predictor model 7.5 Same as above:
Repurchase-intent
a
Recommend-intent (3 level)
Notes: All R
2
values are adjusted; the Best Predictive Model is the model that minimizes the Akaike Information Criterion (AIC);
a
p,0.01 (two-sided);
b
p,0.05 (two-sided)
Table IV.
Single versus
multiple-predictor models
for retention by industry
Customer
satisfaction and
loyalty
375
Banking Retail ISP
Predictors % Concordance ROCC area % Concordance ROCC area % Concordance ROCC area
Recommend intention only versus repurchase
intention only 52 65 59 72 55 67
Recommend intention and repurchase intention 62 67 65 74 64 70
Notes: In each model, all predictor coefficients are highly significant; p,0.001
Table V.
Prediction of
recommend-retention
based on recommend
intention and repurchase
intention in ordinal
logistic regressions
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376
correct rankings of customer pairs when predicted ties are resolved randomly. In every
case repurchase intention has significant incremen tal value ( p,0.001), and when it is
added to the model, the percent concordance increases by 18, 11, and 16 percent for
firms in the Banking, Retail and ISP industries, respectively.
Finally, we studied multi-segment models for the Recommend-Retention dependent
variable, by fitting latent class regression models. In this case, we looked at all firms
together, using firm indicators as covariates, and using a reweighted maximum
likelihood procedure to give each firm equal representation in the analysis. We
experimented with employing each of the basic attitudinal variables as either a
covariate (for classifying customers to segments) or as a within-segment predictor of
Recommend-Retention. We found the best models, in terms of the Bayesian
Information Criterion (BIC), using backward and forward stepwise analyses. The best
single-predictor model uses Recommend-Intention across three customer segments
(only firm indicators are used as covariates in this case). It misclassifies customers at a
rate of 24 percent. The best multiple-predictor model is better in terms of BIC, and it
uses recommend intention and worth what paid as predictors across four segments. In
this model, firm indicators are used as covariates along with the three attitudinal
covariates: repurchase intention, expectations and recommendation intention (coded
into three groups). This model misclassifies customers at a rate of 11 percent.
Discussion
Our investigation found that recommend intention does provide insight into
customers’ future recommend behavior. The assertion that recommend intention
alone will suffice as a predictor of customers’ future loyalty behavior (Reichheld, 2003;
Satmetrix, 2004), however, is not supported.
We reach this conclusion based upon three primary findings. First, bivariate
correlations of all the attitudinal variables and customer behaviors investigated tended
to be modest. Second, when examining the three primary behaviors associated with
customer loyalty (retention, share of wallet, and recommendations) (Reichheld and Earl
Sasser, 1990; Zeithaml, 2000), recommend intention was generally not the best
predictor for each of these variables. Third, multivariate models universally
outperformed models that use only recommend intention.
These findings have clear implications for managers. In large part, because of the
current popularity of Net Promoter, many firms look at recommend intention as the
primary, even sole gauge of customer loyalty. The belief is that this metric best tracks
customers’ future loyalty behavior (and ultimately firm growth), and therefore it
supercedes and makes irrelevant other measures. Based upon our research, however,
the presumptions of these managers appear to be erroneous. Our findings call into
question the rigor of the research reported by Reichheld (2003) and Satmetrix (2004)
with regard to the relationship between various survey-based metrics and subsequent
customer behavior. Without question, Reichheld and colleagues have done a service by
stimulating debate and research on customer loyalty behaviors. Their findings,
however, do not appear to be generalizable. (Our findings on the micro-level analysis of
Reichheld (2003)/Satmetrix (2004), taken in conjunction with the findings of
Keiningham et al. (2007) on their macro-level analysis, call into question the
robustness of the entire study). The consequences are the potential misallocation of
Customer
satisfaction and
loyalty
377
resources due to flawed strategies that are guided by a myopic focus on customers’
recommend intentions.
Additionally, our findings clearly show that aggregate level attitudinal metrics are
not strong predictors of customers’ future behaviors as noted by the modest R-squares.
This is not to discount their importance, but to point to the fact that any single metric
designed to explain customer behavior across a diverse customer base is unlikely to be
an adequate gauge upon which managers can act. Cooil et al. (2006) demonstrate the
importance of segmenting customers based upon their characteristics when attempting
to link customer perceptions to customer behaviors, as they have been found to
moderate this relationship.
Furthermore, our findings demonstrate that customers’ loyalty-based behaviors are
multidimensional. In particular, no one metric best predicts all behaviors associated
with customer loyalty. This implies that firms must balance and manage different
aspects of the customer experience simultaneously if they are to optimize the loyalty
behaviors they desire from their customers. For researchers, this implies that holistic
models of loyalty will need to be developed to model the impact of these various
dimensions of customers’ loyalty behavior on firm financial outcomes. The impact of
these dimensions is likely to vary by industry and customer characteristics.
Furthermore, our research implies that each dimension is likely to be affected by
differing aspects of the customer experience.
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
behaviors. As a result, there are no simple solutions for turning loyalty into profits. If it
were easy, however, everyone would already be doing it.
Notes
1. More recently, Reichheld has modified this claim, stating that Net Promoter yields slightly
less accurate predictions for the behavior of individual customers, but a far more accurate
estimate of growth for the entire business than models consisting of data from multiple
survey items to predict firm growth (Reichheld, 2006).
2. It is important to note that a macro-level analysis of Net Promoter was also conducted by
Reichheld, Bain & Company, and Satmetrix that linked firm-level Net Promoter scores to
relative firm growth rates within their respective industries. Researchers, however, have
reported being unable to replicate the findings reported by Reichheld (2003), Satmetrix (2004)
and Keiningham et al. (2007).
3. Johnson and Fornell (1991) make the same argument in their work.
4. Specifically, it is the stated percentage of the total value of savings and investments at all
financial institutions used by the respondent (excluding work related retirement plans and
excluding the value of the respondent’s home) held at the bank.
5. Respondents were required to be actual customers of the firm in the initial period to qualify
for participation in the study. As a result, recommendations only occurred if the customer
was actually retained, i.e. defectors did not recommend the brand...this does not mean that
they did not engage in WOM, but that their WOM was not a recommendation. As we are
seeking to examine the robustness of the Reichheld (2003)/Satmetrix (2004) findings and
these papers investigated recommendations, we examine recommendation behavior.
Therefore, there were three categorical outcomes in our retained-recommended variable:
defection, retention-only, and retention with recommendations.
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Further reading
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menu_url ¼publications_results.asp
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About the authors
Timothy L. Keiningham is senior vice president and head of consulting at Ipsos Loyalty. He is
author of several management books and numerous scientific papers. His most recent book,
Loyalty Myths (with Vavra, Aksoy, and Wallard), 2005 by John Wiley and Sons, poses the
fallacies of most of the conventional wisdom surrounding customer loyalty. He has received best
paper awards from the Journal of Marketing, the Journal of Service Research, and Managing
Service Quality, and has received the Citations of Excellence Top 50 award (top 50 management
papers of approximately 20,000 papers reviewed) from Emerald Management Reviews.
Additionally, two papers that he coauthored were finalists for best paper in Managing Service
Quality. Tim also received the best reviewer award from the Journal of Service Research. His
articles have appeared in such publications as Journal of Marketing,Sloan Management Review,
and Journal of Service Research. He is the corresponding author and can be contacted at:
tim.keiningham@ipsos-na.com
Bruce Cooil is 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.
Lerzan Aksoy is assistant professor of marketing at Koc¸ University in Istanbul, Turkey. She
is the co-author of the book Loyalty Myths (with Keiningham et al., 2005) by John Wiley and Sons.
The Globe and Mail (Toronto, Canada) counted Loyalty Myths as the Number 4 best business
book of the year; Soundview Executive Book Summaries chose Loyalty Myths as one of the 30
best business books of 2006. She is co-editor of the book, Customer Lifetime Value (with
Keiningham and Bejou), 2006 by Haworth Press. Her article The Brand-Customer Connection,
was selected by Emerald Management Reviews as one of the top 50 management articles of 2005,
from among 20,000 articles reviewed by that organization in that year. She has received the
Outstanding Paper (Best Paper) award from Managing Service Quality and was a finalist for best
paper in the Journal of Service Research. Her articles have been accepted for publication in such
journals as Journal of Marketing, Journal of Service Research, and MIT Sloan Management
Review. She serves on the advisory board of the Journal of Relationship Marketing.
Tor W. Andreassen is Professor and Chair Department of Marketing. Professor Andreassen
is the founder and director of Service Forum and the founder of The Norwegian Customer
Satisfaction Barometer at the Norwegian School of Management. He holds a Sivilokonom degree
from The Norwegian School of Economics and Business Administration, a MSc in marketing
(with honors) from the Norwegian School of Management, and a Doctor of Economics from
Stockholm University, School of Business. He has received the Highly Commended Paper award
from Managing Service Quality and the Most Downloaded Article Award – Top 200 from the
Emerald Group Publishing Limited and the Citation of Excellence of Highest Quality Rating by
Anbar Electronic Intelligence. His research has been published in such journals as Journal of
Customer
satisfaction and
loyalty
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Marketing,Quality & Quantity,Journal of Economic Psychology,Journal of Public Sector
Management, and Journal of Service Research.
Jay Weiner is Senior Vice President, Marketing Sciences at Ipsos Insight. Jay consults with
many Fortune 500 corporations on marketing and marketing research issues. He specializes in
applying advanced methods to help companies make better marketing and business decisions.
Jay’s expertise and work includes pricing, segmentation, customer and employee loyalty,
conjoint analysis, discrete choice analysis, in addition to multivariate statistical analyses. He
received his doctorate in marketing from the University of Texas at Arlington. Jay has published
and presented numerous papers on conjoint, choice, and pricing research in refereed conference
proceedings.
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