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How to Make Switching Costly: The Role of Marketing - iBrarian.net

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HOW TO MAKE SWITCHING COSTLY: AN INVESTIGATION OF THE DRIVERS
OF CONSUMER SWITCHING COSTS
Yolanda Polo and F. Javier SesГ© 1
Abstract
Customer switching costs have emerged as one of the fundamental drivers of customer
retention. Although the consequences of these costs have been well documented in the
literature, research on the determinants of switching costs remains limited. The present study
seeks to address this issue by investigating the extent to which switching costs are influenced
by marketing variables –price and advertising– and relationship characteristics. The authors
develop a conceptual framework about the drivers of switching costs and test the framework
empirically in the mobile phone industry using a hierarchical Bayes approach. The empirical
results show that by using price and advertising –both service and brand advertising– firms
are able to make switching costly for customers. Moreover, relationship characteristics
significantly contribute to explaining consumers’ differences in the cost of switching. Finally,
this study illustrates the key role played by competitors’ marketing actions in affecting the
cost of switching for customers of the focal firm. Implications for decision makers are
discussed.
Keywords: Switching costs, Customer retention, Price and advertising, Relationship
characteristics, Hierarchical linear model.
1
Yolanda Polo is Professor of Marketing, Faculty of Economics, Marketing Department, University of
Zaragoza, Spain, E-mail: ypolo@unizar.es. F. Javier SesГ© is an Assistant Professor, Marketing Department,
Faculty of Economics, University of Zaragoza, Gran VГ­a, 2, 50005, Zaragoza, Spain, Phone: +34 976761000,
Fax: +34 976761767, E-mail: javisese@unizar.es. The authors gratefully acknowledge the financial support by
the projects: SEC2005-05968 (MEyC, FEDER) and S09-PM062, from the Spanish Regional Government of
AragГіn.
2
INTRODUCTION
Switching costs have received increasing attention in the academic marketing literature.
From a theoretical point of view, switching costs are recognized as an important driver of
customer retention that leads to stable and long-lasting relationships (Dick and Basu, 1994;
Ganesan, 1994; Bendapudi and Berry 1997). From an empirical point of view, researchers
have shown the central role of switching costs in explaining customer retention in many
service industries including banking, airlines, credit card and telecommunications (Burnham,
Frels and Mahajan 2003; Harris and Uncles 2007; Jones, Mothersbaugh and Beatty 2000,
2002; Lam et al. 2004). In support of the theoretical arguments, most empirical studies
conclude that switching costs significantly affect customer decisions to maintain a
relationship either directly or indirectly through their moderating role on the associations
between customer relationship perceptions (like satisfaction, service quality, trust or
commitment) and customer loyalty (Bell, Auh and Smalley 2005; Patterson and Smith 2003;
Sharma and Patterson 2000). Based on both theoretical and empirical work, there is a general
consensus that switching costs are an important determinant of customer retention.
While marketing literature contains extensive research devoted to the consequences of
switching costs in terms of customer retention and customer switching behavior, little is
known about the drivers of these costs. However, knowledge about the factors that generate
and increase the level of switching costs can be of great interest to both academics and
practitioners. From the point of view of academics, understanding customer retention and its
antecedents is central to relationship marketing theories (Bolton, Lemon and Verhoef 2004;
Sheth and Parvatiyar 1995; Verhoef 2003). So far, marketing scholars have primarily focused
on satisfaction and/or commitment as the main motivations for customers to engage in longterm relationships (Garbarino and Johnson 1999; Mittal and Kamakura 2001; Morgan and
Hunt 1994; Szymanski and Henard 2001). Customer switching costs are also a key driver of
3
customer retention, but have received much less attention in the literature. From a practical
standpoint, executives are concerned about the negative consequences of customer switching
in terms of market share and profitability (Rust and Zahoric 1993). To keep their customers in
the relationship, firm strategies have mainly focused on satisfaction as the primary retention
tool (Anderson and Sullivan 1993; Fornell 1992). Although customer satisfaction has been
positively related to customer retention, satisfied or even very satisfied customers also defect
(Jones and Sasser 1995). In addition, recent research has shown that the impact of switching
costs on customer repurchase behavior is higher than that of satisfaction (Burnham, Frels and
Mahajan 2003). Thus, identifying the strategies and factors that make switching costly is an
important issue for marketing strategy.
Intuition and anecdotal evidence suggest that relationship history and marketing variables
influence the cost of switching providers. This is consistent with Verhoef’s (2003) hypothesis
that customer retention is a function of both consumers’ past behavior and marketing
practices. It is also consistent with recent empirical research on customer switching behavior
(Wathne, Biong and Heide 2001) which shows that marketing programs and relationship
variables are key elements in the choice of supplier. However, although these studies can
serve as guidelines about the factors that may influence the cost of switching providers, an
important question remains unanswered: How do marketing variables and relationship
characteristics affect switching costs?
Although some anecdotal evidence has been provided to support the thesis that firms can
control, to a certain extent, the magnitude of customer switching costs (Shapiro and Varian
1999), there is no general consensus about whether and to what extent firm marketing
practices affect these costs. Porter (1980) states that the impact of a firm’s strategy on
switching costs should be evaluated. Chen and Forman (2006) emphasize the central role of
firms’ strategies in locking their customers into the relationship. However, most researchers
4
have primarily focused on customer relationship perceptions as the key drivers of customer
retention, relegating marketing strategies to a secondary position (Gustafsson, Johnson and
Roos 2005; Verhoef, Franses and Hoekstra 2002). Competitors’ marketing strategies are also
a means of inducing customer switching (Keaveney 1995; Wathne, Biong and Heide 2001).
For instance, offering better economic conditions will make customers more willing to change
service providers. Although the important effect of competitors’ marketing strategy on
customer retention has been widely acknowledged, empirical research is limited mainly due
to the lack of available information. Thus, more research is required to clarify the role of
marketing –by both the focal firm and the competitors– in explaining customer switching
costs.
Customer past behavior is also a potential source of switching costs. Marketing scholars
indicate that the interactions between buyers and sellers generate close interpersonal
relationships and bonds that may act as a barrier to prevent customer switching (Lam et al.
2004). For instance, as the relationship develops, customers gain experience and familiarity
with the company and its employees, which enable them to make quicker and more effective
use of the services purchased. However, the literature does not offer much insight into
whether past behavior is a significant predictor of switching costs. A notable exception is
Burnham, Frels and Mahajan’s (2003) article, which incorporates some customer relationship
variables such as the breadth of the relationship. Given that both marketing instruments and
customer relationship characteristics are two potential sources of switching costs,
understanding their influence on these costs would be an interesting issue for marketing
theory and practice.
We intend to make two main contributions to the literature. First, we provide a
conceptual framework about the drivers of switching costs that simultaneously considers
marketing variables and relationship characteristics. In addition, we empirically investigate
5
the extent to which switching costs are influenced by these explanatory factors in order to
assess their relative importance. Second, by virtue of having data about all the firms in the
market, we can evaluate the impact of competitors’ marketing strategy on customer switching
costs in order to understand the role played by competition in explaining customer behavior.
The remainder of this manuscript is organized as follows. In the next section, we provide
the conceptual model and offer the research hypotheses. Subsequently, we discuss the
research methodology and describe the data. Then we present the results from the empirical
application and we conclude with a discussion of the main findings and contributions of the
article and an assessment of managerial implications, limitations and directions for future
research.
CONCEPTUAL MODEL AND HYPOTHESES
In this study, we investigate the cost that customers bear when switching service
suppliers. We define the cost of switching as the disutility a customer experiences from
changing product or service providers (Chen and Hitt, 2002). 2 Switching involves a process
that can be difficult and costly. Customers wanting to switch service suppliers have to give up
some relationship-specific investments (e.g. physical assets and organizational learning)
(Klemperer, 1995). There are many barriers that consumers must overcome if they are to
switch suppliers, including contractual, monetary, setup and psychological costs (Burnham,
Frels and Mahajan 2003; Jones, et al. 2007). Although the importance of each of these costs
varies with factors such as the industry or the customer-specific characteristics (Patterson and
Smith 2003), together they represent a clear disincentive to switch suppliers.
2
Although we mainly use the term “service” during the article, the discussion holds for both products and
services.
6
In this section, we provide a conceptual framework that describes and analyzes how and
why marketing variables (from the focal firm and from competitors), as well as customer
relationship characteristics, affect the cost of switching providers. This is a customer-specific
analysis because it studies the cost of switching at the individual level and it is a dynamic
model because longitudinal information on both marketing instruments and customer
behavior is used. Thus, the main goal of this model is to ascertain the drivers of customer
switching costs. The conceptual framework is displayed in Figure 1.
FIGURE 1
Conceptual Model. Drivers of the Customer Switching Cost
FOCAL FIRM’S
MARKETING VARIABLES
RELATIONSHIP
CHARACTERISTICS
COMPETITORS’
MARKETING VARIABLES
Price
Service Advertising
Brand Advertising
Length
Depth
Breadth
Price
Service Advertising
Brand Advertising
COST OF SWITCHING
PROVIDERS
CONTROL VARIABLES
Type of Setting
Gender
Age
In this model, we focus on two main groups of variables which potentially explain the
disutility that customers experience from switching: (1) marketing instruments and (2)
relationship characteristics. Marketing literature provides considerable insight into the key
role played by marketing instruments in explaining customer behavior (Bolton, Kannan and
7
Bramlett 2000; De Wulf, Odekerken-Schröder and Iacobucci 2001; Liu 2007; Rust, Zeithaml
and Lemon 2000; Verhoef 2003; Wirtz, Mattila and Lwin 2007). However, little is known
about the process through which these instruments influence the cost of switching. We focus
on two general-oriented marketing instruments: price and advertising. The latter is further
divided into communication focused on the service –service advertising– and that focused on
the brand –brand advertising. Price is one of the most important factors affecting customer
choice behavior (Keaveney 1995; Roos 1999) and the economics literature has highlighted its
important role in the management of customer switching costs (Farrell and Klemperer 2007).
Advertising is another marketing tool that has a potential impact on customer switching costs
and has been the subject of research in prior marketing studies dealing with switching costs
(Shum 2004). In addition to examining the effect of the focal firm’s marketing practices, we
assess the role of the competitors’ marketing instruments in explaining customer switching
costs. Competitors’ price and advertising (service and brand advertising) will therefore be
considered.
In the second group of variables, relationship characteristics describe the development of
a relationship over time. Three factors are included: (i) the duration of the relationship –
length–, (ii) service usage –depth– and (iii) cross-buying behavior –breadth–, which reflect
the progression of the relationship and describe the customer purchase behavior (Bolton,
Lemon and Verhoef 2004). This framework incorporates an additional group of variables in
order to control for some observed factors such as the type of setting (contractual vs. noncontractual) and demographics (gender, age).
Marketing Instruments
Organizations invest in a wide range of marketing activities aimed at influencing and
stimulating customer behavior. In the marketing literature, researchers have stressed the
8
important role of some marketing instruments in affecting customer behavior and loyalty
(Bolton, Kannan and Bramlett 2000; Bolton, Lemon and Verhoef 2004). Prior research has
also highlighted that, by using marketing strategies, firms are able to influence the magnitude
of switching costs (Chen and Forman 2006). In this section we discuss the associations
between two general-oriented marketing instruments, price and advertising, and the cost of
switching providers.
Price. To stimulate customer behavior, firms can act on this marketing instrument. The
impact of price on customer behavior has been well documented in economics (Einhorn 1994;
Smith and Brynjolfsson 2001). An important characteristic of these studies is that they usually
highlight the central role of price in the acquisition of new consumers. From the point of view
of marketing, researchers have recognized the importance of price in affecting the behavior of
existing customers (Bolton and Lemon 1999; Rao and Monroe 1989; Rust, Zeithaml and
Lemon 2000). Price has also been considered a key factor in the management of customer
switching costs (Klemperer 1995) and there is ample evidence in the marketing literature that
shows the key role played by price in customers’ decisions to switch service providers
(Keaveney 1995; Roos 1999; Roos, Edvardsson and Gustafsson 2004). Below, we discuss the
expected effect that the focal supplier’s and the competitors’ price have on the cost of
switching.
The focal firm can use price to strengthen their relationships with current customers by
offering them better tariffs. From an economic perspective, researchers have shown that the
lower the price, the higher the customer’s probability of purchasing the product or service
from the firm (Einhorn 1994; Smith and Brynjolfsson 2001). From a marketing point of view,
scholars have long recognized that a low price leads to better customer price perceptions and
expectations and to higher levels of satisfaction, which, in turn, leads to longer customer-firm
relationships (Bolton and Lemon 1999; Kalyanaram and Winer 1995; Keaveney 1995). These
9
arguments suggest a negative effect of price on customer switching costs. In addition, we can
argue that the magnitude of switching costs is also going to be affected by customer
expectations about future economic gains. Krishna et al. (2002) indicate that comparing the
price charged by the current firm with that charged by competitors influences perceived
savings. Thus, we expect that the higher the focal firm’s price, the higher the potential
monetary savings from switching service providers, which will lead to lower switching costs.
Competitors will also use price to stimulate customer behavior. They are interested in
attracting customers from the focal firm in order to increase their market share and
profitability. One way to do this is by affecting the cost of switching providers for customers
of competing companies. Competitors can use price to subsidize switching costs for these
customers (Chen 1997). By reducing this marketing instrument, the disutility that switching
generates will be partially compensated by the economic gains. Using the same argument as
in the previous paragraph about customer expectations, the lower the competitors’ price, the
higher the potential monetary savings of changing providers (leading to a lower cost of
switching). Based on the above discussion, we hypothesize the following:
H1a:
Focal provider’s price negatively influences the cost of switching.
H1b:
Competitors’ price positively influences the cost of switching.
Advertising. Marketing researchers have recognized that advertising plays a key role in
the acquisition of new customers (Bolton, Lemon and Verhoef 2004) and in affecting the
behavior of existing ones (Manchanda et al. 2006; Prins and Verhoef 2007). Advertising can
be used by firms with two main objectives (Prins and Verhoef 2007): (1) to inform existing
and prospective customers about service attributes and characteristics (service advertising)
and/or (2) to create favorable brand associations and brand preference (brand advertising). It
10
is important to consider the distinction between these two types of advertising as they are
expected to have different effects on customer switching costs.
Service advertising focuses mainly on informing existing and potential customers about
the advantages and benefits of the service (Prins and Verhoef 2007). On the one hand, it is
reasonable to think of focal supplier’s investments in service advertising as increasing
customer switching inconvenience because positive aspects of the service are highlighted
(Bolton, Lemon and Verhoef 2004). Customers will be aware of the benefits and advantages
that the service provides and the relationship between the customer and the firm will be
strengthened. On the other hand, focal supplier’s service advertising might have the opposite
effect. This is because it provides customers with valuable information which can be used to
better assess the different alternatives. The cost involved in searching for the information and
in evaluating and comparing the alternatives will, therefore, be substantially reduced. This
might make switching less costly. In support of this argument, Shum (2004) finds that
advertising has a negative impact on the purchase probabilities of loyal customers. Therefore,
there is no clear association between focal firm’s service advertising and the customer
switching cost.
Competitors’ service advertising is expected to reduce switching costs. Competitors will
use service advertising to create awareness and knowledge among the customers of the focal
firm of their services and to show their advantages and benefits. Exposure to this kind of
advertising will make customers of the focal firm more informed about the services supplied
by the competitors, reducing the cost involved in acquiring, evaluating and comparing the
information and, at the same time, reducing the risk involved in switching suppliers.
Moreover, highlighting the advantages and strengths of competitors’ services might induce
customers of the focal firm to switch, as they will be aware of the potential benefits of
11
switching providers. In this vein, Shum (2004) reports a significant reduction in the switching
barriers as a consequence of competitors’ advertising.
Following the previous reasoning, we do not posit any directional hypothesis on the focal
firm’s service advertising. We hypothesize that:
H2a:
The focal provider’s service advertising is related to the cost of switching.
H2b:
Competitors’ service advertising negatively influences the cost of switching.
Brand advertising aims to change brand feelings positively so as to influence purchase
behavior (Cramphorn 2006). The focal firm’s brand advertising will, therefore, focus on
increasing brand awareness, improving brand attitudes and creating a positive brand image.
This will reinforce the relationship between the customer and the firm, making the switching
process more costly (Burnham, Frels and Mahajan 2003). Studies in the marketing literature
have usually ignored the impact of brand advertising on retention or on the cost associated
with switching. Notable exceptions are studies by Rust, Zeithaml and Lemon (2000), who
acknowledge the effect of branding on customer equity, and by Bolton, Lemon and Verhoef
(2004), who contend that brand advertising has a positive impact on relationship length.
Taken together, these arguments suggest that brand advertising by the focal firm will make
switching more costly.
Competitors will also brand advertise in order to stimulate customer behavior. Exposure
to this form of advertising can lead to a greater awareness and knowledge of the competing
brands among existing customers of the focal firm. In addition, researchers have recognized
that brand name and corporate image are important quality signals, mainly for intangible
services (Davis, Buchanan-Oliver and Brodie 2000). All these factors will ultimately diminish
the uncertainty associated with switching providers, leading to a global reduction of the cost
12
of switching. So far, empirical evidence on these effects is almost absent in the marketing
literature. However, based on the above theoretical discussion, we hypothesize:
H3a:
The focal provider’s brand advertising positively influences the cost of
switching.
H3b:
Competitors’ brand advertising negatively influences the cost of switching.
Relationship Characteristics
Relationship characteristics describe the development of a relationship over time. In this
section we discuss the associations between the length, depth and breadth of the relationship
and switching costs. Prior theoretical and empirical research has highlighted the central role
of these variables in explaining various relational and behavioral constructs such as
satisfaction (Bolton 1998), number of services purchased and customer referrals (Verhoef,
Franses and Hoekstra 2002), profitable lifetime duration (Reinartz and Kumar 2003) and
customer switching costs (Burnham, Frels and Mahajan 2003). However, to our knowledge
this is the first study that integrates these three relationship characteristics into a single
framework about the drivers switching costs.
Length refers to the duration of a relationship. The early stages of a relationship are
characterized by high uncertainty and low customer experience (Verhoef, Franses and
Hoekstra 2002). In this phase of the relationship, specific investments are low and the
interdependencies between the parties are not greatly developed (Dwyer, Schurr and Oh 1987;
Geyskens, Steenkamp and Kumar 1999). As the relationship progresses over time: (1) trust
develops between the parties (Gwinner, Gremler and Bitner 1998); (2) customers gain
experience in using the products and services (Verhoef, Franses and Hoekstra 2001); (3)
13
familiarity and interdependencies between the parties grow (Dwyer, Schurr and Oh 1987);
and (4) psychological attachment increases (Burnham, Frels and Mahajan 2003).
Moreover, long-term customers are usually granted some benefits to enhance their
loyalty and to lock them into the relationship. Some are economic benefits in the form of
loyalty programs, which give customers financial rewards like discount coupons, points
redeemable for prizes or other economic advantages (Sharp and Sharp 1997). Continuing
customers also receive social benefits in the form of communication, cooperation, friendship
and rapport, which seek to increase social bonds and interdependencies between the parties
(Chiu et al. 2005). All these benefits are lost if the customer leaves the company and are not
readily available elsewhere.
Consistent with the previous reasoning, Verhoef, Franses and Hoekstra (2002) provide
some arguments supporting the idea that switching costs may increase with relationship age.
In addition, Shapiro and Varian (1999) offer some anecdotal evidence suggesting that
customers become locked into the relationship as a consequence of the increasing switching
costs. Hence, we hypothesize that:
H4:
The length of the relationship positively influences the cost of switching.
The depth of a relationship is the frequency of service usage over time. It might be
assumed that the depth of the relationship positively affects the cost of switching providers for
the following reasons. First, using the firm’s services provides customers with a better
knowledge of the company and the processes needed to use these services satisfactorily (Alba
and Hutchinson 1987). Second, those who use the service more can develop non-transferable
provider-specific skills from the time and effort invested in the relationship (Keaveney and
Parthasarathy 2001). Third, as customers use the service more, they become familiar with the
company and the interdependencies between the parties become stronger (Dwyer, Schurr and
14
Oh 1987). All these factors would make switching more costly. In support of these arguments,
Bell, Auh and Smalley (2005) emphasize that, as relationships deepen, consumers’ switching
costs increase; Keaveney and Parthasarathy (2001) report a negative effect of service usage on
customer switching behavior; and Chen and Hitt (2002), in an online context, show a positive
influence of service usage on the probability of customers staying with the company.
Together, these studies suggest a positive association between service usage and the cost of
switching.
The previous argument implies that an extremely heavy user should bear the highest cost
when switching suppliers. However, some researchers argue that intensive service usage
allows customers to become more informed about the products and services and to evaluate
options more rapidly and accurately (Alba and Hutchinson 1987). This might have the
opposite effect on the customer switching cost. Heavy users are expected to be able to better
evaluate and compare the available alternatives in the market, which will lead to a lower
dependence on the supplier (Bendapudi and Berry 1997; Ganesan 1994). In addition, heavy
users might also have greater incentives to look for the service provider that best fits their
needs and provides more value (Chen and Hitt 2002). Taking future benefits into account
would reduce switching costs and lead to a higher propensity to switch service providers. In
support of this argument, Johnson et al. (2004), in an online context, report that more active
shoppers tend to search across more alternatives.
Based on this discussion, the only way to accommodate both arguments is by means of a
non-linear relationship. Therefore, we propose an inverse U-shaped association between the
depth of the relationship and the cost of switching, where intermediate service usage is
associated with the highest switching costs. Thus:
H5:
The depth of the relationship is related to the cost of switching in an inverse U-
shaped manner. Intermediate service usage is associated with the highest switching cost.
15
The breadth of a relationship is reflected in the number of additional products or services
purchased from a company over time. Buying additional products or services from the firm
provides customers with a better knowledge of the company (McCracken 1986), which leads
to more accurate expectations and to less uncertainty (Keaveney and Parthasarathy 2001), and
increases the number of points where customer and vendor connect (Kamakura et al. 2003). In
addition, the switching process for customers who buy additional products or services is
expected to be more complicated for the following reasons: first, the risk associated with the
switching decision is higher because there are more services involved in the process
(Burnham, Frels and Mahajan 2003); second, switching providers implies the need to
compare alternative vendors in a greater number of attributes and characteristics (Shugan
1980); third, switchers need to invest additional resources in learning the new features of the
switched-to provider’s products and services. Thus, we propose the following hypothesis:
H6:
The breadth of the relationship positively influences the cost of switching.
Control Variables
In the conceptual model, we include a set of control variables that may affect the cost
associated with switching suppliers: type of setting, gender and age.
Type of setting. Understanding the differences between contractual and non-contractual
settings in terms of their impact on switching costs can be of interest to marketing literature.
Although some studies have already acknowledged the differences between them and their
different impacts on some relational constructs (Reinartz and Kumar 2000, 2003), empirical
research testing for these differences is lacking. A contractual setting refers to a relationship
which is governed by a contract or membership. Typical examples are Internet services or
magazine subscriptions. In contrast, non-contractual relationships are those that are not
16
governed by a contract. Examples of non-contractual settings are grocery store purchases or
hairdressing services. Due to the distinctive characteristics of these two settings, they are
expected to have different impacts on the cost of switching.
In a contractual setting, the customer signs a contract that specifies the details of the
arrangement and that normally commits her/him to stay in the relationship for a specified
period. Otherwise, the customer has to compensate the firm (normally by paying a monetary
penalty). In addition, uncertainty about future exchanges is lower because some eventualities
like price or service level are normally specified in the contract. In contrast, in a noncontractual setting customers are not committed to any firm and there is no penalty for
switching providers. In this setting, switching behavior is more common and customers
usually split their expenses among several firms (Dwyer 1997). Reinartz and Kumar (2000,
2003) have argued that switching costs are higher in contractual settings than in noncontractual ones. This discussion allows us to formulate the following hypothesis that
associates the type of setting and the cost of switching:
H7: Contractual settings positively influence the cost of switching.
Our model also includes customer demographics which capture observed customer
heterogeneity. The marketing literature has already stressed the important role played by
socio-demographic variables in explaining the differences in profitable lifetime duration
(Reinartz and Kumar 2003), perceived satisfaction levels (Mittal and Kamakura 2001),
repurchase intentions and switching behavior (Keaveney and Parthasarathy 2001). The main
motivation for introducing these variables is for statistical and segmentation purposes. We
examine heterogeneity in terms of customers’ age and gender. However, we do not posit any
directional association between these variables and the customer switching cost due to the
absence of an appropriate theory.
17
RESEARCH METHODOLOGY
Studies investigating customer retention have normally used regression analyses or
structural equation modeling to assess the impact of the independent variables on customer
repurchase intentions/behavior (Burnham, Frels and Mahajan 2003; Lam et al. 2004; Verhoef,
Franses and Hoekstra 2002), whereas studies aimed at analyzing switching behavior mostly
use techniques such as discriminant analysis or the critical incidence technique (CIT, or some
extensions) (Ganesh, Arnold and Reynolds 2000; Keaveney 1995; Keaveney and
Parthasarathy 2001; Roos 1999). In this study, we investigate the drivers of the cost of
switching by implementing a hierarchical linear model (Rossi and Allenby 2003; Snijders and
Bosker 1999).
Hierarchical linear modeling has been increasingly applied in recent years to a wide
variety of marketing problems (see Rossi and Allenby 2003) ranging from demand analyses
(Allenby, Arora and Ginter 1998) to heterogeneity in customer responses to marketing
interventions (Rust and Verhoef 2005). We employ this methodology to study customer
switching costs for the following reasons. First, the data set provides information at two
different levels: occasions (level 1) nested in individuals (level 2). Hierarchical data structures
require multilevel techniques to be applied instead of traditional regression methods (Snijders
and Bosker 1999). Second, this is a customer-specific analysis of switching costs. Bayesian
methods allow us to estimate choice model specifications with coefficients of explanatory
variables varying over decision units (Solgaard and Hansen 2003). Third, Bayesian methods
are able to estimate complex behavioral models taking into account the heterogeneity across
individuals and other sources of uncertainty (Allenby, Bakken and Rossi 2004). Finally,
recent developments in computational and modeling methods have made it possible to
calculate the posterior distribution of a wide variety of models, including the one employed in
this study.
18
We specify the hierarchical linear model as follows. At the occasion level (level 1), we
use a technique to measure switching costs similar to Lee et al. (2006). According to the
random utility framework (McFadden 1974), an individual derives utility by choosing the
alternative that gives her/him the highest value. Therefore, in each period t (out of T),
customer i (out of I) faces a choice of J alternatives in her/his choice set. The selected option
will be the one with the highest utility. The utility that customer i obtains from selecting firm j
in period t (Uijt) is formulated as a function of the attributes associated with the alternative j
(systematic component) and of a random disturbance (random component):
U ijt = X ijt ОІ i + Оµ ijt
(1)
where Xijt is a vector of service attributes associated with alternative j, ОІi is the parameter
vector that measures the impact of service attributes on customer choice decisions, and Оµijt is a
random term that is iid extreme value.
Similar to Lee et al. (2006), the cost of switching is introduced into the model as follows.
The X vector contains a switching dummy variable, labeled Sijt, which takes the value one in
period t if customer i chooses an alternative service provider to her/his present supplier, and
zero otherwise. Therefore, the parameter accompanying the variable Switching (Sijt), ОІ i , is
S
interpreted as the marginal utility that customer i derives from switching service providers. It
is important to note that this is a customer-specific parameter as a value per customer is
obtained from this model. To obtain the customer-specific parameters we adopt a Bayesian
mixed logit specification, as in Train (2003). 3
3
A Bayesian mixed logit model is a highly flexible model that overcomes the three main limitations associated
with the standard logit model as it allows for: (1) random taste variation, (2) unrestricted substitution patterns
and (3) correlation in unobserved factors over time (Train 2003).
19
At the second layer of the hierarchy, customer-related variables are introduced to explain
the heterogeneity in the first-level parameters. In this study, our focus is on the switching
costs parameters. This leads to the following expression,
ОІ i S = Z iО± + П„ i
(2)
where Zi is a vector containing customer-related variables (relationship characteristics,
control variables) and marketing instruments, О± is the parameter vector measuring the impact
of these variables on the customer switching cost, and П„i is a normally distributed disturbance
term.
Conventional marketing literature usually includes consumer characteristics at the second
level of the hierarchy in order to explain heterogeneity in the first level parameters. However,
in this study we also consider marketing variables at the second level as we aim to fully
capture the conditions under which switching took place for each customer. That is, price and
advertising are treated as customer-specific variables because a different value pertains to
each customer depending on (1) the period in which they switched providers and (2) the firm
they switched from. Thus, in order to better explain the heterogeneity in the cost of switching,
Equation 2 analyzes not only the role of customer characteristics in the switching period, but
also the role of marketing actions implemented by both the focal firm and the competitors in
that period.
Estimation of this hierarchical linear model is accomplished by using Markov Chain
Monte Carlo (MCMC) methods, which approximate the posterior distribution by sampling
from the full conditional distribution.
20
Data description
Our empirical study is carried out in the Spanish mobile phone industry using data from
three different sources. Information on relationship characteristics and control variables was
provided by TNS Global. The database covers a four-year window (from 2001 to 2004) on a
monthly basis (48 periods) and consists of a total number of observations of 287 mobile users
who switched service providers during the observation period. We combine this information
with monthly data on marketing instruments and firm attributes for all the firms competing in
the market during the period under analysis: Movistar (the former public monopoly),
Vodafone and Amena (currently Orange). This information was provided by two distinct
databases: the Global Wireless Matrix database and Infoadex.
Two important notes about the data are worth mentioning. First, although the available
sample covers only a portion of the market, namely, the customers who switch, it is important
to note that considering only these customers will probably provide a better picture of the
actual cost of switching, as they are completely aware of the cost and disutility that switching
providers causes. They have gone through the process of switching and have experienced the
inconvenience associated with it. For customers who have never switched, the exact
magnitude of the switching cost is not known: in the Spanish mobile phone industry, most
customers ignore how much information they need to get in order to take an informed
decision and/or they do not know much about how long the process of switching is. The real
cost is not known unless switching occurs. In addition, customers who have never switched
tend to overestimate the magnitude of these costs, leading to a biased analysis of switching
costs. Instead of focusing on switching cost perceptions, this study investigates the real cost of
switching for customers in the mobile phone industry. Second, most studies dealing with
switching costs use self-reported and/or cross-sectional data. Although this kind of data might
be useful for obtaining customer preferences for hypothetical alternatives (Lee et al 2006), it
21
may lead to an overestimation of the considered relationships and to serious methodological
problems (Verhoef 2003). In addition, switching is a dynamic process (Roos 1999).
Longitudinal information should therefore be used to better capture the underlying
mechanisms that generate the cost of switching for customers.
We have selected the mobile phone industry for the empirical analysis for the following
reasons. First, the mobile phone industry is considered one of the most important sectors, not
only in the communications field but also in the economy as a whole. Second, in most
developed countries the penetration rate is very high, 4 which indicates that the mobile phone
industry is reaching the mature stage of its lifecycle. Retention strategies, therefore, gain
importance over acquisition strategies, leading to an intense interest in the drivers of customer
retention. Third, the high fixed-cost structure of customer acquisition in information-intensive
markets makes customer retention a strategic mandate for firms in order to compensate the
initial sunk investments, which increases the relevance of studying retention mechanisms,
such as switching costs, in these contexts (Chen and Hitt 2007). Fourth, the Spanish mobile
phone market has recently been liberalized. Studying switching behavior in this type of
market is a subject of great interest to marketing theory and practice (Wieringa and Verhoef
2007). Fifth, from a public policy perspective, switching costs are considered a threat to
achieving competition in the mobile phone market (Grzybowski 2005). Finally, the mobile
phone industry has been the focus of attention in prior studies dealing with switching costs
and consumer retention (e.g. Watson et al. 2002; Lee et al. 2006), which shows the interest
among scholars in understanding customer behavior in this specific industry.
4
In the Spanish market, the penetration rate at the end of 2006 was 103% (Source: Global Wireless Matrix
database).
22
Variable operationalization
For each month in the study period, we observe the customers’ choice of mobile phone
operator. In Equation 1, utility is an unobserved latent variable that is manifested through
customer choices (the alternative selected in each period is the one that maximizes customer
utility). The dependent variable (Uijt) is then operationalized as a dummy variable that takes
two possible values: one if customer i chooses firm j in period t, and zero otherwise.
Customer choices are based on an evaluation of firm attributes over time. We introduce three
explanatory variables into the utility model: price, firm size and switching costs. These
variables have been widely used in prior studies that investigate customer choice behavior,
and the inclusion of them in the utility function is similar to Lee et al. (2006) and Birke and
Swann (2006). These variables are time-varying and are measured and updated each month.
Price (Pjt) is operationalized as the Average Revenue per User (ARpU). Mobile phone prices
are very complex and normally involve non-linear tariffs associated to different call types,
which makes it difficult to obtain a single price per firm. This has led market research
companies to develop some measures in order to approximate the price charged by each
mobile phone operator. One measure that has received particular attention among academics
is ARpU (McCloughan and Lyons 2006), and it has been previously used in research on
switching costs (Shy 2002). We define firm size (Tjt) as the logarithm of the number of
subscribers of firm j in each time period. The switching dummy variable (Sijt) has already
been defined.
In Equation 2, the criterion variable is the customer-specific estimate of the cost of
switching providers and, thus, only a single event associated with each customer exists.
Marketing instruments are introduced as follows. We define the focal firm’s price as the
ARpU of the focal firm in the period in which switching takes place for customer i.
Competitors’ price is measured as the average ARpU of the two competing firms in that
23
period. The variable introduced into the analysis (PRi) is the difference between them (Focal
Firm’s Price – Average Competitors’ Price). The advertising measurement follows the same
rule. We use data on service and brand advertising expenditures (in millions of euros) by the
focal firm in the switching period to test for the effect of the focal firm’s advertising on
switching costs. To analyze the effect of competitors’ advertising, we use the average service
and brand advertising expenditures by competitors in that period. We introduce the difference
between the focal firm’s and the competitors’ advertising expenses in both categories (service
advertising SAi, and brand advertising BAi) into the analysis.
Relationship characteristics are introduced in the following way. Relationship length
(LEi) is measured as the duration, in months, from the beginning of the relationship until the
period in which customer i switches to a competitor. To measure relationship depth (DEi), we
compute the average mobile phone consumption of customer i from the beginning of the
relationship until the switching period. We define the breadth of the relationship (BRi) as a
dummy variable taking the value one if customer i is subscribed to an additional service
offered by the company in the period when switching takes place, and zero otherwise. 5
In addition, our research includes a set of covariates for statistical control purposes. Type
of setting (Tsi) is operationalized as a dummy variable. It takes the value one if the customerfirm relationship takes place in a contractual setting (customer i has a post-paid subscription
in the switching period), and zero otherwise (the relationship takes place in a non-contractual
setting, that is, the customer has a pre-paid subscription in the switching period). With respect
to customer demographics, we define the variable age as the consumer’s age, in years, in the
switching period (Agi), and we set the gender dummy variable to zero for male customers
(Gei). Table 1 describes the variables included in the analysis.
5
Mobile phone companies offer additional services which allow customers to call in special conditions or to
benefit from special offers. For instance, Vodafone offers the A2 package, under which customers can
communicate with one selected contact in special conditions (lower rates).
24
TABLE 1
Variable Measurement
LEVEL 1: CHOICE MODEL
Variable
Utility
Hypothesized
Directional Impact
Label
Dummy variable: 1 = customer i chooses firm j in period t
0 = otherwise.
Uijt
Price
Pjt
No directional hypothesis
Tjt
No directional hypothesis
Sijt
–
Average Revenue Per User (ARpU) of firm j in period t
Firm Size
Logarithm of the number of subscribers of firm j in period t
Switching
Dummy variable: 1 = customer i chooses an alternative service provider in
period t.
0 = otherwise
LEVEL 2: DRIVERS OF THE COST OF SWITCHING
Variable
Label
Hypothesized
Directional Impact
Focal Firm ARpU – Average Competitors ARpU (both measured in the
switching period)
PRi
–
SAi
No directional hypothesis
BAi
+
Duration of the relationship (in months) from the beginning until the switching
period.
LEi
+
Depth
DEi
Marketing Variables
Price
Service Advertising
Focal Firm Service Advertising Expenses – Average Competitors Service
Advertising Expenses (both measured in the switching period and in millions
of euros)
Brand Advertising
Focal Firm Brand Advertising Expenses – Average Competitors Brand
Advertising Expenses (both measured in the switching period and in millions
of euros)
Relationship Characteristics
Length
Average mobile consumption (in euros) from the beginning of the relationship
until the switching period
Breadth
(DEi)
2
Inverse U-shaped
relationship
BRi
+
Tsi
+
Dummy variable: 1 = Female
0 = Male
Gei
No directional hypothesis
Age
Agi
No directional hypothesis
Dummy variable: 1 = Acquisition of an additional service from the company
0 = Otherwise
Control Variables
Type of Setting
Dummy variable: 1 = Contractual setting (post-paid subscription)
0 = Non-contractual setting (pre-paid subscription)
Gender
Age (in years) in the switching period
25
EMPIRICAL FINDINGS
All computations and simulations for this study were carried out using the WinBUGS
package (Spiegelhalter et al. 2003). We ran 60,000 iterations, of which 50,000 were used for
burn-in and the remainder (10,000) for estimation purposes. Autocorrelation in the sample
was reduced by thinning the iterations–taking only one in five. The 2,000 retained draws were
used to conduct inference.
The parameter estimates for the hierarchical linear model are reported in Table 2. Based
on both graphical and numerical techniques, we can conclude that all the parameters
converged to their posterior distributions. Statistical significance is not a concern of Bayesian
analyses, but we can construct an analog in order to assess parameter importance. The table
reports (1) the mean, (2) the standard deviation, (3) the Monte Carlo standard error and (4) the
mean divided by this error. Parameter significance depends on this quotient being higher than
two –in absolute value– (Rust and Verhoef 2005), which is satisfied by all the parameters
included in the model.
The parameters measuring the cost of switching providers are our main concern in this
analysis. We find that the marginal utility from switching service providers is negative
(ОІ3[Avg] = -4.24). That is, switching service providers is costly: it implies an important
reduction in the utility level. We also find that there are significant differences in switching
costs across customers, as shown by the standard deviation (ОІ3[Std] = 0.69). Figure 2
illustrates the distribution of switching costs in the sample. As we can see from the figure, the
marginal utility from switching suppliers is quite heterogeneous, ranging from 2.48 to 6.06 (in
absolute values). Although it is not the main concern of our research, price and firm size also
contribute to explaining customers’ choices. On average, price negatively influences
consumer utility (ОІ1[Avg] = -0.06), while firm size has a positive impact on it (ОІ2[Avg] =
0.54).
26
TABLE 2
Hierarchical Model Parameter Estimates
Parameter
Mean
Standard
Deviation
Monte
Carlo S.E.
Mean/MCSE
ОІ1[Avg]
ОІ2[Avg]
ОІ3[Avg]
-0.0561
0.5378
-4.2425
0.0201
0.0712
0.6893
0.0005
0.0042
0.0053
-98.77
128.63
-792.23
О±1
-0.0122
0.0022
0.0010
-12.21
О±2
0.0265
0.0038
0.0002
160.71
О±3
0.0321
0.0104
0.0005
69.63
О±4
О±5
О±6
0.0737
0.0053
-0.0006
0.0068
0.0036
0.0001
0.0002
0.0001
0.0000
312.51
33.36
-274.27
О±7
0.5578
0.1176
0.0047
117.43
О±8
0.0823
0.0908
0.0082
10.08
О±9
О±10
0.1868
0.0318
0.1375
0.0034
0.0016
0.0001
110.08
197.52
CHOICE MODEL
Price
Firm Size
Switching Cost
DRIVERS OF THE COST OF SWITCHING
Marketing Variables
Price (Focal firm ARpU – Competitors ARpU)
Service Advertising (Focal firm investments –
Competitors’ investments)
Brand Advertising (Focal firm investments –
Competitors’ investments)
Relationship Characteristics
Length (Duration)
Depth (Consumption)
(Depth)2
Breadth (Additional Service: 1 = acquisition; 0
= not acquisition)
Control Variables
Type of setting (1 = Contractual; 0 = Noncontractual)
Gender (1 = female; 0 = male)
Age
In order to ascertain the drivers of customer switching costs, two main groups of
variables were introduced into the model: marketing instruments and relationship
characteristics. To facilitate the analysis and discussion of the results, absolute values of
switching costs were taken. The results show that marketing instruments play a significant
role in explaining switching cost differences across customers. Consistent with hypotheses 1a
and 1b, the difference between the focal firm’s and the competitors’ price negatively impacts
customer switching costs (α1 = -0.01). That is, while the focal firm’s price negatively
influences the cost of switching providers, the competitors’ price has a positive impact on it.
27
The parameter accompanying the variable service advertising has a positive sign (О±2 = 0.03),
which means that the difference between the focal firm’s and the competitors’ expenditures
on service advertising positively influences customer switching costs. This is consistent with
hypothesis 2b in that competitors’ service advertising reduces the disutility from switching
providers. We find support for hypotheses 3a and 3b in that the difference between the focal
firm’s and the competitors’ expenditures on brand advertising has a positive effect on the
customer switching cost (α3 = 0.03). While the focal firm’s brand advertising increases the
cost of switching, the competitors’ brand advertising reduces it.
FIGURE 2
Switching Cost Distribution in the Sample
30%
Frequency %
25%
20%
15%
10%
5%
0%
< 3.0
3.1–3.5
3.6–4.0
4.1–4.5
4.6–5.0
5.1–5.5
> 5.6
Switching Costs (in absolute value)
Relationship characteristics play a significant role in explaining switching cost
differences as well. Consistent with hypothesis 4, we find that relationship duration is
positively associated with the customer switching cost (О±4 = 0.07). We find support for
hypothesis 5, in that there is a non-linear, inverse U-shaped relationship between mobile
phone consumption and the cost of switching (О±5 = 0.01; О±6 = -0.00). Hypothesis 6 is also
28
supported because cross-buying behavior positively influences the disutility derived from
switching service providers (О±7 = 0.56).
Finally, the estimation results show that the parameter accompanying the variable Type
of setting has the expected sign (О±8 = 0.08) and customers in a contractual setting (post-paid
subscription) experience higher switching costs than customers in a non-contractual one (prepaid subscription). This is consistent with hypothesis 7. The results also show that female
customers bear higher switching costs than males (О±9 = 0.19) and that customer age is
positively associated with the cost of switching providers (О±10 = 0.03).
DISCUSSION AND IMPLICATIONS
In this research we study the drivers of customer switching costs. In particular, we focus
on investigating the extent to which customer switching costs are influenced by marketing
instruments and relationship characteristics. Although marketing literature has long
emphasized the significant consequences of switching costs in terms of customer retention
and customer switching behavior, research on the drivers of switching costs remains limited.
To the best of our knowledge, this study represents the first attempt to integrate marketing
variables and relationship characteristics into a single framework about the drivers of
switching costs and to document the effect of competition on the magnitude of these costs.
This approach offers an interesting contribution to marketing literature as a better
understanding of customer retention and its drivers is central to relationship marketing
theories and to marketing strategy.
The present study reveals that marketing variables –price and advertising– are important
drivers of switching costs. This is an interesting result, because prior research has mainly
focused on relationship variables as the key elements leading to high switching barriers. Our
29
study results show that marketing instruments also contribute to making switching service
providers costly. Specifically, price has a negative influence and both service and brand
advertising have a positive effect on switching costs. While the negative effect of price and
the positive impact of brand advertising on switching costs are consistent with our
hypotheses, the positive effect of service advertising is more remarkable. There were reasons
for both a positive and a negative association between service advertising and the cost of
switching. On the one hand, service advertising highlights the advantages and benefits of
choosing the focal firm’s service, which makes switching less attractive. On the other hand,
this kind of advertising increases customers’ information about the characteristics and
attributes of the service, making switching easier. Our study results show that the first effect
dominates over the second and thus, investments in service advertising lead to high switching
costs. These findings are also important for the customer relationship management literature.
Some studies indicate that, after the customer-firm relationship has been established, the role
of price and advertising become less prominent (Kordupleski, Rust and Zahorik 1993; Rust,
Zahorik and Keiningham 1995). Our investigation shows that price and advertising are also
important to stimulate customer behavior once the relationship has been established.
In addition to investigating the effect of marketing strategies by the focal firm, we
explored the role of the competitors in customer switching costs. Marketing scholars have
long emphasized that understanding customer responses to competitive actions is essential to
marketing theory and practice. However, research in this area is very limited mainly due to
the lack of available information. We have access to a database that provides information on
price and advertising for all the companies operating in the market, which enables us to
extend current research on this subject by investigating the extent to which switching costs are
affected by competitors’ marketing strategies. Our study results reveal that, by acting on price
and advertising (both service and brand advertising), competitors are able to influence the cost
30
of switching providers for customers of the focal firm. Specifically, our results show that a
price cut is an effective strategy for competitors in order to attract customers from the focal
firm as it subsidizes the cost of switching they bear. This result is in line with previous
research showing that firms “pay customers to switch” (Chen 1997). We find a negative
association between both competitors’ service and brand advertising and the cost of switching
providers. An explanation for the first effect is that customers of the focal firm exposed to
competitors’ service communication efforts are more informed about the characteristics of the
service, which leads to a reduction of the switching costs. More notable is the finding of a
negative association between competitive brand advertising and the cost of switching as it
shows that even marketing communication efforts not directed at the service itself affect the
cost of switching providers for customers of the focal firm. A possible explanation for this
result is that customers of the focal firm who are exposed to this type of advertising can
develop positive feelings toward competing brands, leading to a decrease of the uncertainty
associated with changing providers. In a recent article, Prins and Verhoef (2007) show that
competitive communication efforts affect the adoption timing of a new product among
existing customers of the focal firm. We extend current knowledge on the effect of
competitive communication actions by showing that they also influence customer switching
costs.
We also examined the extent to which switching costs are influenced by relationship
characteristics. This is an interesting contribution to the literature as, to our knowledge, this is
the first study that simultaneously investigates the impact of the length, depth and breadth of
the relationship on customer switching costs. The results reveal that both relationship length
and breadth are positively related to the cost of switching. This is a rather interesting result as
it shows that increasing the duration of the relationship and/or cross-buying additional
products or services from the focal firm make switching more costly for customers. This is in
31
line with prior research showing that switching providers is easier in the early stages of the
relationship (Ongena and Smith 2001; Verhoef, Franses and Hoekstra 2002). We reasoned
that relationship depth might have a non-linear association with the cost of switching. The
results confirm this expectation, showing that relationship depth and switching costs are
related in an inverse U-shaped manner. This is a notable finding as it shows that intermediate
service consumption is associated with the highest switching cost, while both lowconsumption and high-consumption customers find switching providers less costly. Overall,
these results provide further evidence that the history of the relationship plays an important
role in explaining customer heterogeneity in relational constructs (Reinartz and Kumar 2003).
Although the control variables were not our primary focus, our study results show that
the type of setting is an important factor explaining the cost of switching as well. In a
contractual setting, where the relationship is governed by a contract or membership,
customers find switching providers more costly than in a non-contractual setting. While this
finding is consistent with our prior expectations, to the best of our knowledge no study to date
has simultaneously considered both types of setting in a single study and empirically
evaluated their different effects on relational variables.
Finally, from a database and modeling perspective, our study uses longitudinal
information on customer behavior and marketing variables and applies a hierarchical linear
model to study switching costs. The use of this kind of data avoids some methodological and
statistical problems associated with other sources of information (self-reported, crosssectional) and allows us to better capture the dynamic and heterogeneous nature of switching
costs. In addition, the availability of marketing data for all the firms in the market enables us
to examine the impact of competitors’ marketing practices on customer switching costs. In
terms of the hierarchical linear modeling, although there has been increasing interest in recent
years in using these techniques to study marketing problems, this has been less prominent in
32
the investigation of customer retention. Based on this modeling approach, we were able to (1)
handle the multilevel structure of the data available to us (occasions nested in individuals), (2)
obtain individual-specific parameters and (3) fully capture customer heterogeneity (Rossi and
Allenby 2003). Classical procedures often fail to capture customer heterogeneity and, with
complex behavioral models (like the one specified in this study), the maximization of the
simulated likelihood function can be difficult (Train 2003). Thus, our study extends current
knowledge on the use of hierarchical modeling by applying it to the investigation of customer
switching costs.
Managerial Implications
Achieving customer retention is essential for many firms in order to increase revenue and
profitability. This is especially true in information-intensive businesses where the high fixedcost structure of customer acquisition increases the importance of retaining customers in order
to overcome the initial sunk investments. However, successful customer retention requires a
proper understanding of its drivers. In the information economy, customer switching costs
have become one of the most powerful and effective mechanisms to achieve stable and longlasting relationships (Chen and Hitt 2007). The question is: how can firms make switching
costly? By understanding the determinants of switching costs, a firm can identify the key
factors that must be addressed to prevent future defections.
Our study results show that firms can use two general-oriented marketing instruments,
price and advertising, to increase the cost of switching suppliers. However, the effect of
advertising –both service and brand advertising– on switching costs is greater than that of
price. This suggests that making the process of switching costly for customers should be done
mainly by communicating the service and the brand. This strategy, at the same time, can help
the company to increase the size of its customer base as it reduces the cost of switching for
33
customers of competing vendors. Another strategy might be to focus on price. Our study
results reveal that, by reducing the level of this marketing instrument, switching becomes
more difficult for existing customers. Similar to advertising, this has an additional positive
effect on the firm’s customer base as it compensates the cost of switching for customers of
competing suppliers.
An important result of our research is that competitors’ marketing actions also influence
the cost of switching. In particular, price cuts and investments in service and brand
advertising by competitors may induce customers of the focal firm to switch providers
because these strategies subsidize, at least in part, their switching costs. This suggests that, to
prevent future defections, firms should react in a timely manner to competitors’ marketing
movements.
A proper understanding of the history of the relationship is also needed to optimally
manage customer switching costs. Our results suggest that deploying strategies based on the
customer relationship characteristics will also make switching costly. Relationship length and
breadth were found to have a positive influence on the disutility derived from switching.
Useful strategies might involve encouraging customers to stay in the relationship (e.g. giving
them economic or relational benefits), and/or cross-selling additional products or services
(e.g. extending the current offer of the firm and persuading customers to acquire new services
–Internet services, TV). Concerning the depth of the relationship, firms should be aware that
both customers who spend less and those who spend more find switching less costly.
Our study results emphasize the relevance of the type of setting in explaining the cost of
switching providers. In a contractual setting customers find switching providers more costly
than in a non-contractual setting. In our empirical application, this distinction was based on
the type of subscription the customer had: pre-paid or post-paid. Our results suggest that an
interesting strategy for mobile phone firms might be to encourage existing pre-paid customers
34
to change to post-paid subscriptions, as this will increase the cost of switching. In addition,
firms can persuade competing firms’ customers or prospective ones to choose post-paid
contracts.
This study also has implications for public policy. Customer switching costs are an
important threat to market competition (Grzybowski 2005). In most developed countries, the
regulator has implemented policies in recent years aimed at reducing the magnitude of these
costs. Number portability is a well-known example. However, this study clearly demonstrates
the existence of high switching costs in the mobile phone market in spite of these efforts. Our
results suggest that more measures should be taken by public policy officials in order to
further reduce switching costs and to increase competition in the market.
Finally, we should add a few words of caution regarding the management of customer
switching costs. Although increasing the magnitude of these costs is an effective strategy for
retaining customers in the relationship, it might lead to negative reactions from customers if
they feel locked into a firm in which they would prefer not to be (Jones et al. 2007).
Research Limitations and Further Research
Some limitations of our study should be noted and they suggest possible directions for
future research. First of all, in our empirical application, we only consider customers who
have switched service providers. Although this is an interesting contribution to the literature
because the actual cost of switching can be computed, the data set covers only a portion of the
market. Future research might also consider consumers who have never switched providers
and study the differences between switching and non-switching customers in order to obtain a
better understanding of customer retention and customer switching behavior.
35
Second, we tested our hypotheses in one particular context, namely, the mobile phone
industry. While this industry has some distinctive characteristics that make it relevant to the
study of switching costs (e.g. recently liberalized market, high cost of customer acquisition),
it would, of course, be important to investigate switching costs in other industries as well. In
addition, the mobile phone industry is in the mature stage of its lifecycle. Thus, it might be
interesting to study the cost of switching in less established industries where new consumers
progressively enter the market. A promising avenue for further research would consist of
analyzing firms’ trade-offs between acquiring new customers –creating long customer bases
to obtain higher rents in the future– and retaining existing ones –harvesting the current
customer base to obtain higher rents in the present (Farrell and Klemperer 2007). Clearly,
more research is required to address these issues.
Third, our study shows that switching costs are affected by marketing strategy.
Specifically, we focused on price and advertising. Clearly, firms have a wider range of
marketing activities at their disposal to influence and stimulate customer behavior. Customer
loyalty programs and/or product design strategies are good examples. Prior research has
already emphasized their importance in affecting customer behavior and value (Bolton,
Lemon and Verhoef 2004; Verhoef 2003). Thus, a promising avenue for future research
would be to study the role of relationship marketing instruments in customer switching costs.
Finally, public policy officials are very concerned about the negative consequences of
switching costs on market competition. We have acknowledged that regulatory policies might
also play an important role in explaining switching costs in this particular industry. For
instance, prior research has shown that mobile number portability has significantly reduced
the magnitude of these costs (Lee et al. 2006). Our study, however, does not include any
regulatory policy. Thus, an interesting extension of this research would be to analyze the role
of the regulator in driving customer switching costs.
36
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