customer win back role of attributions and perceptions in customers 2

7/26/2019 Customer Win Back Role of Attributions and Perceptions in Customers 2

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ORIGINAL EMPIRICAL RESEARCH

Customer win-back: the role of attributions and perceptionsin customerswillingness to return

Doren Pick1 &Jacquelyn S. Thomas2&Sebastian Tillmanns

3&Manfred Krafft

3

Received: 9 January 2014 /Accepted: 28 May 2015 /Published online: 16 June 2015# Academy of Marketing Science 2015

Abstract Interest in customer reacquisition has increased as

firms embrace the concept of customer relationship manage-ment. Using survey and transactional data from defected sub-

scribers of a publishing company, we investigate how defected

customers evaluate their propensity to return to the company

prior to any win-back offer. We introduce a new variable for

relationship marketing, general willingness to return (GWR),

and show that it is strongly and positively related to the actual

return decision and the duration of the restarted relationship.

Combining attribution theory elements with existing win-back

explanations, which focus on economic, social, and emotional

value perceptions, provides a more comprehensive understand-

ing of the factors that influence the GWR to a former relation-

ship. Importantly, we learn that regardless of whose fault it is, if

the reasons for the relationship termination can change or are

preventable and the firm can control those changes, then the

defected customer has a higher general willingness to return to

the former relationship. Also, we show that the duration of time

absence before relationship revival moderates the impact of

GWR on second relationship duration. Furthermore, we dem-

onstrate that satisfaction prior to defection and the length oftime absence provide a reasonable basis for distinguishing

defected customers who differ in their GWR. By applying

our findings, we derive recommendations for firms on how to

position marketing communications to recapture defected

customers according to their general willingness to return.

Keywords Customerrelationship management. Relationship

revival . Consumer attributions

Introduction

Win-back, or customer reacquisition, is the process of revital-

izing relationships with customers with whom the company

has failed to maintain an active relationship (Thomas et al.

2004). As reacquisition becomes a more prominent part of a

firms customer marketing strategy, it is important to under-

stand the mechanisms that drive customer return and assess

the process with relevant metrics. Consequently, metrics that

are analogous to the popular measures applied to customer

acquisition or retention have been extended to the win-back

context. For example, Bsecond lifetime value^ (SLTV), de-

fined as the expected LTV of a customer who has returned to

a former relationship, has been discussed as a valuable metric

for targeting and assessing the quality of a recaptured custom-

er (Griffin and Lowenstein 2001; Stauss and Friege 1999;

Thomas et al. 2004). Using SLTV as an objective, Thomas

et al. (2004) present a two-stage econometric model to devel-

op a pricing strategy for recapturing lost customers. In addi-

tion to SLTV, there is a long history in direct marketing for

firms to use recency, frequency, and monetary value (RFM)

models when determining who to target for reacquisition or

repurchase (e.g., Elsner et al. 2004; Hughes 1996) or to

* Manfred Krafft

[email protected]

Doren Pick

[email protected]

Jacquelyn S. [email protected]

Sebastian Tillmanns

[email protected]

1 Freie Universitt Berlin, Boltzmannstr. 20, 14195 Berlin, Germany

2 Cox School of Business, Southern Methodist University,

P.O. Box 750333, Dallas, TX, USA

3 Institute of Marketing, University of Mnster, Am Stadtgraben

13-15, 48143 Mnster, Germany

J. of the Acad. Mark. Sci. (2016) 44:218240

DOI 10.1007/s11747-015-0453-6
http://crossmark.crossref.org/dialog/?doi=10.1007/s11747-015-0453-6&domain=pdfhttp://orcid.org/0000-0002-9394-9519


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determine the allocation of marketing expenditure on cus-

tomers (Reinartz and Kumar2000).

A common strength of all of these metrics is that they are

objective assessments of customerspast behaviors, or objec-

tive predictions of future behavior and economic value, based

on past interactions with a firm. Additionally, a notable limi-

tation of these measures is that they do not reflect or measure

the customers cognitivestate or disposition at defection, northe development of a return decision. This distinction high-

lights an opportunity to consider the behavioral and cognitive

aspects of customers as they progress from defection to an

actual return decision.

A review of the customer win-back literature shows that

research has focused on the defection intention or decision

(e.g., Capraro et al.2003), has taken the firms point of view,

has focused on the conceptual aspects of winning customers

back (e.g., Reichheld and Sasser Jr.1990; Stauss and Friege

1999), has considered customers return intentions (Tokman

et al. 2007) and the classification of revocable relationships

(Roos1999), has investigated customersperceptions of spe-cific win-back offers or activities (Homburg et al. 2007;

Tokman et al.2007), and has paid attention to behaviors after

win-back (Thomas et al. 2004). In general, these streams of

research reveal that researchers usually choose to address a

specific phase of the customer defection to win-back process.

Unifying these disparate examinations of customer win-back,

we posit that customer win-back can be conceptualized into

stages that represent the interplay between external customer

actions, internal customer processing, and firm actions toward

the customer. This approach is particularly relevant for con-

tractual relationships where customer defection can be deter-

mined by firms. To frame this research, we present the follow-

ing stages of win-back:

Stage 1: customer defection decision and relationship

termination

Stage 2: customer rationalization of the defection decision

Stage 3: win-back offer extended

Stage 4: customer processing of win-back offer

Stage 5: customer return decision

Stage 6: second lifetime relationship

We seek to expand research that relates to customers ra-

tionalization resulting from internal processing of the defec-

tion decision (stage 2). In this research on contractual relation-

ships, the defection involves an action and some involvement

on the part of the consumer. Thus when rationalizing the de-

cision, customers might evaluate the effort involved with the

relationship termination and also consider their willingness to

return independent of a win-back offer by the firm. This con-

templation reflects their affinity to return after a defection.

This reasoning is in line with the theory of cognitive disso-

nance that argues that individuals generally assess former

decisions and their implementation and might revise a prior

decision. Accordingly, in this study we focus on the willing-

ness to return withouta given win-back offer by the prior firm.

In order to support our assertions and findings we also exam-

ine actual outcomes (i.e., stages 5 and 6). To our knowledge,

the re-examination and rationalizing that occurs in stage 2

represents an opportunity in the literature that warrants addi-

tional research. Through this research, we will demonstratehow insights at this stage 2 can form future firm actions and

potentially enhance the probability of successful win-back.

Rationalizing defection and relationship revival

Consumer perceptions reflect the consumers understanding

or interpretation of a situation or information. Thus percep-

tions inform how the consumer rationalizes the defection de-

cision before any (potential) win-back offer is provided. While

focused on a different stage of the process, Tokman et al.

(2007) study the drivers of consumers perceptions of thevalue of an actual win-back offer. Specifically, these authors

leverage the theory of social capital to explain how consumers

think about reacquisition actions. This theory describes an

individuals sense of obligation to an organization, which is

based on past experiences with the organization, and special

treatment received from them (Coleman1990). Tokman et al.

(2007) maintain that this social capital influences consumers

when they evaluate a win-back offers value. They find that

while the characteristics of an offer affect consumers per-

ceived value of a win-back offer, the perceived importance

of the service and the social capital in the customerfirm rela-

tionship moderate consumers value perception. Thus, these

authorsresearch demonstrates that value perceptions are crit-

ical in the reacquisition process.

Similar to Tokman et al. (2007), Homburg et al. (2007)

investigate relationship revival activities. Their research is dis-

tinct in that it references equity theory to explain consumers

perceptions of revival activities and links this to actual revival

performance. According to equity theory, individuals evaluate

outcomes and inputs of both the relationship partners and seek

a balanced relationship (Adams 1963, 1965). These authors

empirically demonstrate that a perception of equity or fairness

affects customer satisfaction, which is ultimately linked to

customer win-back. Their analysis also shows that customer

characteristics (e.g., age and variety seeking) and relationship

characteristics (e.g., the duration of the relationship prior to

termination) also predict the actual relationship revival.

Thus, using different theories, Tokman et al. (2007) and

Homburg et al. (2007) investigate how consumers interpret

actual win-back actions, finding that consumers consider the

value and equity of a win-back offer. Interestingly, the notion

of value perception has been conceptualized as a multi-

dimensional construct in other literature (Sheth et al. 1991a,

J. of the Acad. Mark. Sci. (2016) 44:218240 219


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b). Sweeney and Soutar (2001), for example, believe that sev-

eral dimensions of value may be simultaneously considered.

While their context is not specific to customer return or win-

back, these authors focus on how consumers assess an actual

product on the basis of the following value dimensions: qual-

ity, emotional value, price, and social value. They find that

each dimension affects a persons willingness to buy. Howev-

er, the emotional value dimension has a particularly strongeffect on willingness to buy. Their findings are consistent with

Gassenheimer et al.s(1998) argument that asserts that eco-

nomic and social values are both critical for successful ex-

change relationships.

Jointly, all of these research streams suggest that the study

of how consumers rationalize the possibility of rejuvenating

pas t rela tions hips cou ld be enhance d by inves tigat ing

economic-oriented value perceptions in the same framework

as social- or emotional-oriented value perceptions. Attribution

theory, which focuses on how individuals evaluate or assess

circumstances, has also been suggested as a possible theoret-

ical framework that might explain defected customers pro-pensity to reactivate former relationships, (e.g., by how cus-

tomers attribute their defection reasons to the firm or them-

selves, i.e., locus) (Homburg et al.2007).

In this research, we make use of these suggestions by

integrating different theoretical perspectives. Specifically,

we integrate attribution theory and prior theories on custom-

er win-back into a systematic study, using both survey and

transactional data from defected subscribers of a publishing

house, and test the appropriateness of our theoretical frame-

work. Theory combination is therefore an important aspect

of our research. However, since we primarily focus on the

neglected effects of attribution theory on customerswilling-

ness to return, phenomena identified in other theoretical con-

cepts such as equity theory will be addressed only to a

limited degree.

This research differs from prior work on switchback inten-

tions in that we do not examine intentions in light of a specific

win-back offer (i.e., stage 4). Instead, we focus on consumers

willingness to returnprior to a potential win-back attempt or

specific offer (i.e., stage 2). We refer to this attitude as a

defected clients general willingness to return (GWR) to a

former supplier, which is independent of expectations of a

specific offer from such a firm. Specifically, we define GWR

as the unconditionalwillingness of a customer to return to a

former supplier.

While there clearly is an overlap between GWR and mea-

sures of intentions, we posit that GWR is distinct from tradi-

tional constructs of customer intention, namely repurchase

intentions, revisit intentions, and loyalty. First, an important

distinction of the GWR measure is that it pertains to the

revision of a previously established decision, which may or

may not be the case with a measure of intention to repurchase.

This is a significant distinction because changing a prior

decision, which was contractually binding in our case, is more

involving than stating a desire or intention to continue a prior

behavior such as repurchase. Thus, one can conceptualize in-

tentions as a broad concept and GWR as a specific concept

that is highly relevant for contexts in which a customer must

actively revise a prior decision or behavior.

Additionally, unlike GWR, measures of repurchase and

revisit intentions often capture only the current relationshipsand thus are void of the defected customers perspective.

GWR is also distinct from loyalty measures because loyalty

often relates not only to purchase-related behaviors but also to

WOM (e.g., Zeithaml et al.1996) and entails multiple cogni-

tive sentiments and behavioral actions that may or may not be

in response to firm actions. Thus, GWR is a measure of a

specific disposition that is relevant for customers who have

explicitly terminated their relationship.

Understanding the rationalization of defected customers

and investigating their disposition or GWR to a former rela-

tionship has several benefits. Specifically, if firms know the

drivers of GWR, they might be better able to influence thosedrivers and increase the odds of customer return. For example,

some customers might return without any win-back offer but

are more interested in an apology and thus this knowledge can

help to reduce investments in win-back activities. Related to

this, by understanding defectorsrationales and their motivat-

ing factors, firms can more effectively design win-back activ-

ities. We further elaborate on this in our section on managerial

implications. Understanding how customers differ in their

willingness to return could allow firms to prioritize customers

for reacquisition activities. Thus, this paper advances market-

ing theory and practice in three key areas:

(1) We propose the new construct, GWR, and show that it is

strongly and positively related to both aspects of revival

performance of firms, i.e., return decision and second

relationship duration. This construct can be used to mea-

sure a defected customers generalpropensity to return

and thus assist firms in targeting customers for reacqui-

sition and developing win-back offers.

(2) We provide evidence for several antecedents of GWR to

a prior contractual relationship and show how the dura-

tion of time absence moderates the relationship between

GWR and second relationship duration. This is very rel-

evant for managers in optimizing marketing win-back

measures.

(3) By integrating theoretical explanations, we demonstrate

the importance of defected customers perceptions and

attributions on their GWR that drives their return behav-

ior. Applying the results from our model we provide

distinct implications for customer groups that differ in

their status (prior satisfaction and duration of time ab-

sence). For managers, these insights can help to refine

and target win-back marketing communications.

220 J. of the Acad. Mark. Sci. (2016) 44:218240


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Drivers of general willingness to return

Economic value perceptions

Two main economic arguments have been suggested in the

context of customer win-back. The first relates to customers

perceptions of switching costs when changing providers (or, in

terms of this research, reactivation costs) (Ping 1993; Sharmaand Patterson2000). The perception ofreactivation costsmight

emerge in a variety of monetary or non-monetary ways. For

example, returning to a former relationship might necessitate

the termination of a current relationship, spending time, money,

and cognitive effort. Furthermore, defectors might rationalize

that reactivating a former relationship is risky due to the fear

of incurring additional costs if the relationship once again fails.

Accordingly, we suggest that if defected customers perceive the

costs of reactivating a relationship as high, it is unlikely that they

would be willing to return to their former supplier. Thus, we

assume that the costs of reactivating a former relationship de-

crease customersgeneral willingness to return.Theattractiveness of alternatives has also been conceived

as an economic consideration in the context of how consumers

assess the equity of alternatives. Specifically, the attractive-

ness of alternatives refers to customersperception of the de-

gree to which the products or services that competitors offer

are more interesting, beneficial, and valuable. Several studies

indicate that the attractiveness of alternatives drives cus-

tomers decisions to maintain or leave a relationship (e.g.,

Sharma and Patterson 2000; Bansal et al. 2005; Pick and

Eisend 2014). Accordingly, a lack of attractive alternatives

could stimulate defected customers willingness to return to

their former supplier. Hence, we suggest that the attractiveness

of the alternatives that competitors offer decreases customers

general willingness to return.

Emotional and social value perceptions

Prior literature has also suggested that commitment, switching

experiences, and variety seeking are central drivers of cus-

tomers intention and behavior (Garbarino and Johnson

1999; Homburg et al. 2007; Sheth and Parvatiyar 1995;

Snchez-Garca et al.2012). We assert that affective commit-

ment, switching experiences, and variety seeking are reflec-

tions of consumers assessments and perceptions of an emo-

tional or a social value that they derive from relationships.

Although we focus on the emotional and social components

of variety seeking and switching experience, we have to em-

phasize that they also entail some economic facets, such as

risk and benefit considerations.

In the context of our study, affective commitmentcan be

defined as defected customersemotional attachment to a sup-

plier. It has been empirically shown that commitment is one of

the main drivers of intention to repurchase (Johnson et al.

2006), of retention (Verhoef 2003), or of the propensity to

leave a firm (Morgan and Hunt1994). If a former relationship

with a supplier was emotionally important for customers, this

commitment may exacerbate or ignite post-relationship disso-

nance, since these customers behavior (i.e., defection) and

attitude (i.e., commitment) do not fit (Thomas et al. 2004).

Applying this line of thought to our study, we argue that cus-

tomers who perceive themselves as strongly and affectivelycommitted to a former supplier would be more willing to

return in order to eliminate or reduce the cognitive dissonance

resulting from their defection.

Lam et al. (2010) propose that customersswitching deci-

sions might be based on sociopsychological rather than func-

tional utility. Accordingly, an important facet of social value

perceptions is the extent to which consumers have accumulat-

edswitching experiences over time. Switching experiences are

intensive if customers have often changed suppliers. Addition-

ally, by accumulating these experiences, customers are be-

lieved to have obtained greater knowledge and expertise of

the quality of suppliers in the marketplace (Burt1997). Ingeneral, the more experienced consumers are with switching,

the lower they might perceive the risks related to dealing with

a product, service, or person. Thus, consumers with more

switching experiences may feel more confident about their

behavior when terminating their current relationships and

returning to their former suppliers. Accordingly, consumers

are more likely to return to their former relationship if they

have switching experience.

Finally, emotional value perceptions are also reflected in

customers tendency toward variety seeking. Prior research

argues that satiety with a product can lead to variety seeking

behavior on subsequent purchases (Inman2001). Interesting-

ly, variety seeking is considered a major driver of customers

tendency to abandon relationships, even if those relationships

are satisfactory (Bansal et al.2005). In general, variety seek-

ing affects customer loyalty negatively. However, this gener-

ally negative effect of variety seeking (Homburg et al.2007)

changes in the context of defected customers. For defected

customers, their former supplier can become attractive again

if it can demonstrate new orBnovel^differentiated products or

services. Accordingly, Snchez-Garca et al. (2012) find that

variety seeking customers develop higherrevisitintentions in

the long run (in a non-contractual setting). Since customers

who are prone to variety seeking search actively for new of-

fers, a former supplier can again become relevant and thus

customers are more willing to return.

Attribution theory

How consumers make attributions can affect their perceptions,

rationalization of the defection decision, and propensity to

reactivate former relationships (Homburg et al. 2007). Attri-

bution the ory des cribe s peo ple as rationa l inf orm ation



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processors whose causal inferences influence their attitude

and behavior (Bettman1979; Weiner2000). To date, attribu-

tion research has been applied to help understand the drivers

of customersattitudes and behaviors in theircurrentrelation-

ships (e.g., customer satisfaction and word-of-mouth) (Tsiros

et al.2004) but, to the best of our knowledge, not to contrac-

tual relationships with a prior firm.

Attribution theory suggests that judgments are based onthree major dimensions of causal attributions: locus, stability,

and controllability (Weiner1985).

Locus The locus of causality is the consumers perception of

where the responsibility for an incident lies. From a customer

perspective, this involves answering the following questions: Is

defection due to a factoroutside the customer, such as compe-

tition or the former supplier (external locus), ordue to the

customer(internal locus)? There is evidence that attribution to

an external locus leads to lower loyalty intentions toward the

former supplier (Wagner et al. 2009). Therefore, customers who

perceive and rationalize the defection as company-related, suchas a service failure, should have a lower willingness to return.

However, research also indicates that people tend to be

biased by attributing their own failings to external factors

(e.g., Folkes1988). Once a relationship has been terminated,

the expectation is that defected customers will generally avoid

attributing the relationships termination reasons to them-

selves. This might also hold true in cases where the firm has

no locus on the defection reasons. In addition, as customers

perceive that they have power or influence, it can be argued

that customers expect a company to deal with their defection

reasons in its customer orientation strategy, or customer reten-

tion strategy. And, if a business relationship takes a negative

development, customers are more likely to externalize the

reasons to their provider than to themselves, also known as

the self-serving bias (Heider1958). Additionally, arguments

from cognitive dissonance theory further emphasize that cus-

tomers will attribute the reasons for their defection to external

objects rather than to themselves (Festinger1962). Therefore,

companies may be seen as mostly responsible for customer

defection. For example, the firm should have lowered product

prices if the customers did not have the financial resources to

maintain their subscription. Thus, when defection occurs, the

resulting rationalization can lead to a negative attitude toward

the firm due to the necessity to terminate the relationship.

Consequently, we offer the following hypothesis:

H1: The more customers perceive the causes of defection as

the former suppliers fault (external locus), the lower

their willingness to return to this prior relationship.

Stability Stability refers to the perception that the circum-

stances of a relationship termination will either be fairly

permanent (stable), or relatively temporary (unstable). Stable

outcomes are presumed to reoccur in the future, while un-

stable conditions create uncertainty about future outcomes. If

customers perceive the major reasons for their defection as

rather permanent and unchangeable, i.e., stable over time,

their willingness to revive a former relationship with a sup-

plier will be low (Folkes et al.1987; Homburg et al. 2007).

This reasoning can be expanded: if customers defection isattributed to an own locus (e.g., a low budget for buying a

product or service) or external locus (e.g., the prices of the

supplier are perceived as too high) and these reasons are

perceived to be stable, customers willingness to return will

be low. Therefore, in both situations, the willingness to re-

turn is expected to be low. The underlying reason for this is

that customers find no changes in the companys offerings

and there is consequently no improvement in the relation-

ship value. Their return would represent losing emotional,

cognitive, and social value in their relationship with the

former supplier. While the proposed directional relationship

between stability and GWR is reasonable, establishing thelink between stability and GWR is important because its

significance reveals whether defectors are predisposed to

reevaluating their defection decision early on in the return

process and not simply after an offer is made. A lack of

significance would suggest that defectors are less open to

reexamining the conditions that lead to their defection and

may only be open to reexamination after a win-back offer is

made. Under these circumstances, the offer and its perceived

value becomes the focal point and increases the importance

of the actual offer in the win-back process. Therefore, we

hypothesize about stability to formally establish the link to

GWR and propose the following:

H2: The more customers perceive the circumstances of de-

fection as stable (stability), the lower their general will-

ingness to return to this prior relationship.

Controllability Controllability is a third dimension that

needs to be analyzed with regard to customer attributions.

The causes of defection can involve choice (controlled), or

are constrained and/or non-volitional (uncontrolled). From a

customers point of view, the perception of whether a supplier

could control a cause, or prevent its consequences, is pivotal

for their attitude and behavior (Wagner et al. 2009). For ex-

ample, it can be argued that customers will be relatively more

willing to reactivate a former relationship if the causes of their

defection are under the suppliers control (Folkes et al.1987).

With such a controllability perception, a defected customer

might expect that the firm is able to prevent and change as-

pects that refer to the defection reason (e.g., price). If these

aspects are changed, the customer will be more willing to

return. Hence, we hypothesize:



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H3: The more customers perceive the causes of defection as

being under the control of the former supplier (control-

lability), the higher their general willingness to reacti-

vate this prior relationship.

In summary, the synthesis of the antecedents to general

willingness to return that are derived from attribution theory

dimensions, as well as from economic, emotional, and socialvalue perceptions, serves as the perceptual model in this re-

search. Supplementing our perceptual model, we also develop

a transactional model of actual return behavior. The purpose of

the transactional model in this research is to demonstrate the

link between GWR and actual return behavior of defected

customers. By establishing this linkage we can support the

use of GWR as a crucial measure in the win-back process.

Figure 1 depicts the perceptual and transactional models.

The next section hypothesizes about the factors that impact

actual return behavior. This set of hypotheses will be tested in

our transactional model.

Drivers of customer return behavior

General willingness to return

Because people seek consistency in their attitudes, intentions,

and behavior (see Fishbein and Ajzen 1975), we expect that

GWR will be linked to actual return behavior. By investigating

the relationship of GWR with two types of customer behavior,

we respond to the concern that attitudes or intentions may not

always be related to the behavior of customers. More specifi-

cally, in our study we investigate whether general willingness

to return is related to both, the actual return to the former

relationship and the duration of the reactivated relationship.

We therefore hypothesize:

H4: General willingness to return has a positive effect on (a)

the actual return to the former relationship and (b) the

duration of the second relationship.

In our transactional model, we relate measures from our

survey to actual customer behavior before and after the survey.

Time absenceafterthe survey is the measure of the amount of

time elapsed from when a defected customer was surveyed

and his/her return to the firm. A long time absence after the

survey suggests that the customer might be doubtful or facing

impediments to returning. Additionally, these customers mayevaluate benefits and costs of a potentially renewed relation-

ship more thoroughly. However, if a customer returns after

such a long consideration time, it suggests that the impedi-

ments or possible cognitive dissonance may have been satis-

factorily resolved after sufficient efforts were exerted. Given

the efforts required to get to the favorable return decision, one

might expect that the customers may be more convicted in

Emotional and social valueperceptions

Affective CommitmentSwitching experiencesVariety seeking

Perception of behavior: Attribution

Theory

LocusStabilityControllability

Economic value perceptions

Reactivation costsAttractiveness of alternatives

Explaining general willingness to return

(Perceptual model)

Explaining customer return behavior

(Transactional model)

Relationship characteristics

Number of trial subscriptions

Number of regular subscriptions

Relationship duration beforedefection

Differentiation variables

SatisfactionTime absence before the survey

Time absenceafter the survey

Generalwillingness to

return

Customerwin-back

Secondrelationship

duration

Fig. 1 Research framework containing the perceptual and transactional model



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their return decision and as a result the second relationship

duration would be longer than if the return process was shorter

and the return process was easier. We therefore state:

H5: Time absenceafterthe survey has a positive effect on

the duration of the second relationship.

The effect of GWR on customers actual return behaviormight be attenuated by the temporal distance between cus-

tomers expression of their GWR and their actual return

behavior. In a similar vein, Mittal and Kamakura (2001) claim

that there is likely an attenuation of the relationship between a

satisfaction rating and the repurchase behavior of a customer

as the time lag increases.

Following from the argument that the length of time ab-

sence after the survey is an indicator of the efforts necessary to

return to the former relationship, then one might expect that

the length of the time absence might moderate the association

of GWR with second relationship duration. We have argued in

H4b that GWR is positively related to second relationshipduration. However, when defectors have a high GWR, a lon-

ger time absence after the survey reflects a contrast to the

defectors return propensity and therefore could indicate a

deep reconsideration process. Though this finally leads to

returning to the supplier, doubts can remain, leading to aver-

age durations of second lifetime.

This same argument can apply to the case when defectors

have a low GWR and a long time absence. Specifically, the

decision of low GWR defectors to recommit after a long time

absence may reflect thoughtful reconsideration and result in

stronger convictions about this reversal of the decision and a

longer second lifetime. Therefore we hypothesize:

H6: Time absenceafter the survey and before relationship

revival moderates the impact of general willingness to

return (GWR) on second relationship duration such that

the effect of GWR is attenuated when the duration of

absence after the survey is long.

Transactional model control variables

Our first control variable in the transactional model is custom-

er satisfaction prior to defection. Homburg et al. (2007) and

Tokman et al. (2007) find empirical support for defected cus-

tomers who were satisfied with a prior relationship being more

likely to revive this relationship. Moreover, Tokman et al.

(2007) find that satisfaction interacts with some antecedents

of switch-back intentions (e.g., price attitude) and not with

others (e.g., service benefits). Thus, customer satisfaction

can be considered one of the most important factors in a cus-

tomer retention strategy. Nevertheless, even the most satisfied

customers might defect for reasons beyond the firms control,

or even that of the customers themselves (Ganesh et al.2000).

For example, the customers financial situation might have

changed, or there is simply no longer a need for a firms

products and services. Furthermore, customers might defect

because a competitors specific offer might generate greater

benefits. In specific contexts such as internet or cable TV

providers, clients may defect because services by the current

provider are not available in their new location. Accordingly,some studies question the link between customer satisfaction

and customer retention (Jones and Sasser Jr.1995), or cannot

verify this empirically (Verhoef2003).

Ganesh et al. (2000) propose that customers who switched

from their former supplier for reasons other than dissatisfac-

tion are less likely to have negative attitudes and feelings

toward this supplier. Accordingly, we expect that customers

who were satisfied with their former relationship are more

likely to return and stay for a longer period of time.

The second control variable is the recency of the last

purchase. This is considered one of the most important mea-

sures in customer relationship value estimation (Reinartz andKumar2000; Neslin et al.2013). Neslin et al. (2013) find that

recency (i.e., time since last purchase) is negatively related to

purchase probability. Thomas et al. (2004) find empirical sup-

port that the probability of recapturing a defected customer

lessens with the time being absent. However, Tokman et al.

(2007) find that time absence has no significant direct effect

on switch-back intentions. Nevertheless, in the context of

defected contractual relationships, a former suppliers chance

of retrieving customers is higher as long as the customer does

not enter into an alternative contract.

The third control variable isrelationship duration prior to

defection. Some studies find that relationship duration is pos-

itively related to the social and financial bonds between the

customer and the firm (e.g., Chiu et al. 2005; Reinartz and

Kumar 2003), and could increase positive behavioral inten-

tions (van Birgelen et al.2006). In spite of the termination of a

relationship, customers with a long relationship duration

might specifically feel they have a bond with their former

supplier. Processes and interactions learned in a previous re-

lationship with a firm might support customers return deci-

sions. Furthermore, lapsed customers with long prior relation-

ship durations might be relationship prone and thus have lon-

ger second relationship durations.

Given that our research context is for subscriptions (i.e.,

contractual relationships), we attempt to account for the con-

sumers prior use of trial and regular subscriptions. Trial sub-

scriptions are a common means of customer acquisition in the

publishing industry (Picard and Brody1997). These subscrip-

tions might be highly attractive due to reduced prices or re-

duced contract durations. Accordingly, customers might feel

more flexible and therefore return more easily in the near future.

In line with the social exchange theory, customers who often

make use of trial subscriptions might have a higher likelihood



8/24

of returning to a relationship because they might feel an obli-

gation to give the firm something back (Bagozzi 1995). Ac-

cordingly, these customers might subscribe for a longer period

of time and thus have longer second lifetime durations.

One can also argue that customers with a long history of

defecting from and returning to regular subscriptions might

get used to the processes of canceling and renewing their sub-

scriptions. Accordingly, these customers should have much ex-perience with switching suppliers. In accordance with our as-

sumptions about switching experience in our perceptual model,

we expect that customers who are highly experienced with

switching or renewing their contracts have a higher probability

of returning to their former relationship after they have

defected. Given these contextual considerations, we introduce

number of prior trial subscriptions and number of prior regular

subscriptionsas control variables in our transactional model.

Methodology

Data collection

To test our research hypotheses, we surveyed subscription

customers of a publishing house that sells a variety of ro-

mance novels in Germany. A survey of 600 regular romance

readers found that they mirror the general population in terms

of age, education, and marital and socioeconomic status

(Thurston 1983).

Specifically, we surveyed customers who had canceled

their subscriptions and had been inactive for at least

12 months. The firms database contained about 25,000

defected customers, from which we drew a random sample

of 6411 individuals. We sent each individual a questionnaire

and incentivized their participation by offering them an oppor-

tunity to win one of 20 vouchers worth EUR 20 for a purchase

from a major retailer. Two weeks after our initial mailing,

reminder postcards were sent to the non-responding defectors.

Those who responded after our reminder postcard were

marked as late respondents. A comparison between the early

and late respondents regarding all the key variables indicated

that they were similar (p>.10). Accordingly, non-response

bias does not seem to be an issue in the study.

A total of 748 questionnaires were returned, yielding a

response rate of 12%. After eliminating 205 surveys due to

incomplete answers and removing three participants as out-

liers in the subsequent cluster analysis, we achieved a final

sample of 540 respondents. We are aware of higher re-

sponse rates in other study contexts and attribute this to

the home mailing procedure and the specific context of

defected customers in our study.

Of these respondents, 99.6% were female and 56% were

married. Our participantsmean age was 40 years (range: 17

to 82 years), and the median annual household income was

EUR 12,000 to EUR 18,000. Because our sample is not rep-

resentative of the German population in gender and income,

our findings may not be generalized to the overall population.

However, this is not unusual as magazine subscriptions are

typically targeted to specific customer groups.

In addition to our survey data, we obtained transactional

data about the surveyed customers from the cooperating

publishing house. Hence, we were able to derive whetherour respondents actually returned and when they did, the

duration of their second relationship. Information on two

surveyed customers in the cooperation partners database

was missing. Hence our transactional model accounts for

538 out of 540 customers we examined in the perceptual

model. We also obtained transactional data from 789 cus-

tomers who did not receive the survey but did defect in the

same manner as the customers who did participate in our

survey in order to account for a potential measurement bias.

Despite our attempt to rigorously collect both survey data

and post-defection transactional data from defected cus-

tomers, the product category of this research presented anatural limitation. This limitation is a result of the fact that

some customers could purchase the novels at retail locations

without a subscription. While executives of the publishing

house judged that the impact of the retail sales on the sub-

scription sales were minimal, we acknowledge this as a

potential limitation of our data.

Statistical analysis overview

To test our hypotheses, we took several steps in our statistical

analyses. First, in order to deeply understand the value of inte-

grating attribution theory with existing theories on customer

return and win-back, we sought to identify differences in

defected customers that may blur interesting insights. Thus we

decided to perform a cluster analysis to account for heterogene-

ity among defectors. Second, we tested the proposed anteced-

ents of the general willingness to return with regression analysis

(perceptual model). Finally, we set up regression models to

derive how the general willingness to return related to the actual

return of defected customers and, if they did return, how long

they maintained the relationship (transactional model).

Perceptual model

Measures and validation

With the exception of a measure for GWR, we made use of

established measures, which we either applied directly or

adapted for our survey. Details about the exact items, their load-

ing, the construct composite reliability, and the source of the

scale are described in the Appendix. All the scales were assessed

using a seven-point Likert scale. Their respective means, stan-

dard deviations, and correlations are provided in Table1.



9/24

Table1

Descriptivestatisticsandcorrelations

Variables

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Constructs

1.Generalwillingnesstoreturn

2.Priorsatisfaction

0.25*

3.AffectiveCommitment

0.39*

0.42*

4.Varietyseeking

0.07

0.03

0.08

5.Switchingexperiences

0.15*

0.04

0.04

0.39*

6.Locus

0.05

0.51*

0.11*

0.13*

0.12*

7.Stability

0.45*

0.17*

0.22*

0.17*

0.16*

0.13*

8.Controllability

0.11*

0.18*

0.04

0.08

0.13*

0.37*

0.05

9.Reactivationcosts

0.27*

0.37*

0.09*

0.12*

0.13*

0.39*

0.19*

0.20*

10.Attractivenessofalternatives

0.05

0.22*

0.10*

0.26*

0.38*

0.25*

0.00

0.05

0.23*

Transactionalmeasures

11.Timeabsencebeforethesurvey0.12*

0.06

0.03

0.14*

0.17

0.05

0.06

0.03

0.08

0.07

12.Timeabsenceafterthesurvey

0.03

0.02

0.09

0.08

0.06

0.06

0.03

0.50

0.03

0.01

0.02

13.Trialsubscriptions

0.08

0.02

0.07

0.04

0.19*

0.01

0.10*

0.04

0.00

0.06

0.04

0.06

14.Regularsubscriptions

0.03

0.00

0.03

0.13*

0.21*

0.03

0.09*

0.09*

0.03

0.04

0.15

0.02

0.46*

15.Priorrelationshipduration

0.02

0.04

0.05

0.14*

0.19*

0.07

0.11*

0.06

0.07

0.12*

0.14*

0.01

0.34*

0.65*

16.Secondrelationshipduration

0.13*

0.03

0.13*

0.01

0.00

0.08

0.05

0.02

0.07

0.05

0.04

0.10

0.10

0.10

0.07

Mean

3.62

6.16

4.29

3.91

3.91

1.66

3.91

3.23

2.22

2.58

970.30

256.78

0.85

0.66

738.54

631.02

StandardDeviation

1.66

0.91

1.70

1.54

1.53

1.21

1.53

1.81

1.17

1.22

277.86

312.86

0.91

0.75

1,151.11

972.44

*p.05). We find that the number of trial

subscriptions in which customers were enrolled before their

defection showed a strong positive effect (=0.84, p .05). The number

of regular subscriptions neither showed an effect on the return

decision (=0.33, p>.05), nor on the relationship duration

after returning (=54.49,p >.05).

Discussion

This research investigates the perceptions and rationalizations

of defected customers in order to understand the factors that

influence their revival of former relationships. An important

characteristic of the current research is that we investigate the

customer evaluations priorto any of the firms win-back ac-

tions. We refer to the disposition of the consumer at this stage

as the consumers GWR, or general willingness to return to

their former supplier. The investigation of such a variable is of

interest because it can reveal customers overall affinity and

relationship evaluations indicating their propensity to revise a

previous decision and return to a firm from which they have

defected. Importantly, identifying GWR and understanding its

impact shows thatactual return behavior is NOT simply areflection of the firms offers that are extended to revive the

relationship. Rather, GWR is a major driver of relationship

revival and potentially instrumental in designing and targeting

revival offers to defected customers. Thus, our finding that

GWR impacts actual return behavior is not only a critical

support for the usage of our GWR measure and our research

findings, but it also is a notable contribution to the existing

knowledge in customer win-back research as will be ex-

plained in the following.

Theoretical implications

Combining theories improves our understanding of cus-

tomer willingness to return Given that customer win-back is

arguably one of the least researched areas of CRM, integrating

theories and then testing the applicability of hypotheses de-

rived from those theories contributes to a better understanding

of this important field of research. In detail, we draw upon a

theory that has not been applied in a customer win-back con-

text (i.e., attribution theory) and compare our model to abase-

line model that reflects individual theories that are focused on

Table 5 Logistic regression and censored normal regression explaining revival performance

Return Second relationship duration

b SE b SE

Intercept 0.33 0.55 238.54 267.60

General willingness to return (GWR) 0.17* 0.10 266.84*** 70.45

Prior satisfaction 0.14 0.10

34.93 68.67Time absence before the survey 0.00 0.00 0.07 0.23

Time absence after the survey 0.50 0.31

GWR x Time absence after the survey 0.50* 0.24

Relationship duration before defection 0.00 0.00 0.02 0.06

Number of trial subscriptions 0.84*** 0.17 23.60 75.85

Number of regular subscriptions 0.33 0.19 54.49 108.64

Brand subscribed to before defection (categorical)

Several Brands 1.58* 0.78 32.95 280.93

Brand 1 0.44 0.38 255.86 159.65

Brand 2 0.19 0.43 59.52 176.04

Brand 3 0.57 0.43 113.51 179.89

Brand 4

0.84 0.47 845.71* 359.78

Brand 5 1.83* 0.72 113.28 208.61

Brand 6 1.48** 0.47 723.67* 329.21

N 538 292

*p


17/24

value perceptions and which have been shown to relate to

customersreturn decisions.

When comparing the models without attribution variables

(i.e., the baseline models) with the respective models with

attribution variables, we see a substantial improvement of

the explanatory power and model fit (see Table 3). This sug-

gests that consumerswillingness to reinitiate former relation-

ships is only partially explained by their perceptions of therelationships value (economic or social value) and, thus, em-

phasizes the value of integrating attribution theory proposi-

tions in research as requested by Homburg et al. (2007). By

summarizing the findings we derive from investigating attri-

bution theory in this context, we gather the novel insight that

the defectors rationalization for leaving and consideration to

return can be characterized as follows: Regardless of whose

fault it is, if the reasons for the relationship termination can

change or are preventable and the firm can control those

changes, then the defector has a higher general willingness

to return to the former relationship.

While attribution theory adds to our understanding of thewin-back process, we acknowledge that all of the theoret-

ical explanations help explain customers willingness to

return. However, the following is a logical question: does

one theory consistently explain the top factor(s) that have

an impact on the GWR? Our empirical analysis of custom-

er segments suggests that one dominant theoretical frame-

w o r k d o e s n o t e x p l a i n c u s t o m er s p e rc e p tio n s ,

rationalizations, and subsequent motivations for revitalizing

a relationship. However, if we were to focus on the most

important factor that affects reactivation, we find that

stability, a variable derived from attribution theory, has

the greatest influence (i.e., elasticity) on reactivation if (1)

the consumer has a low level of satisfaction or (2) if the

defection occurred recently (i.e., a short time absence be-

fore survey) (see Fig. 2). Thus, it is important to defected

customers who are considering a return to know that things

can change. For another subset of defectors (i.e., moderate

to high satisfaction and less recent defection), the most

influential factor is affective commitment, which reflects

consumers perception of the emotional value. This factor

is associated with the theory of social capital (Mathwick

et al. 2008). The importance of commitment is consistent

with prior research that finds that perceptions of emotional

value have a great impact (Sweeney and Soutar 2001).

The impact of time absence on gwr and second relation-

ship performance Time absence plays an important role in

relationship revival. For those who have had a recent defec-

tion (i.e., short time absence before the survey), their general

willingness to return is driven mostly by how the consumers

rationalize thepermanence(i.e., stability) of their reasons for

defection. This is not the case for those who have been

absent for a while. The contrast is seen by comparing

clusters 1 and 4 in Fig. 2. Both of these clusters report a

medium satisfaction level prior to defection. Yet, by tearing

apart the duration of their time absence we can see that the

top two drivers of GWR among the more recent defectors

(cluster 4) are first stability and second affective commit-

ment (elasticities are -0.437 for stability and 0.392 for com-

mitment). For the defected customers who have been absent

for a long time (cluster 1), the impact is the exact opposite(elasticities are 0.466 for commitment and -0.376 for stabil-

ity). Thus, over time, a defectors mindset shifts, leading to

different motives driving his/her willingness for relationship

revival. Hence, theory integration helps to better understand

the return propensities for defectors who have been absent

for varying lengths of time.

Another important and novel finding from our analysis is

the moderating effect of time absenceafterthe survey on the

relationship between GWR and the second relationship du-

ration. Such a moderating effect goes beyond direct effects

identified by attribution theory or any value perception the-

ory. Yet, one can conjecture that high GWR defectors mightestablish a longer second relationship if they return and that

the reverse is true for low GWR defectors, particularly if the

return happens shortly after the survey and possibly with

less contemplation. However, the fact that a low GWR de-

fector can behave similar to a high GWR defector if enough

time passes before the second relationship ensues is an in-

teresting new finding. It suggests that firms should carefully

consider the timing and how aggressively they pursue defec-

tors.Allowing time to Bheal a wound or for contemplation

can improve the quality of the second relationship in terms

of its duration.

Explanations of customerswillingness to reactivate a re-

lationship may not align with crm practices From the

firms perspective, CRM research has focused significantly

on customers economic value (see, e.g., Gupta et al.2006).

Monetizing customers and measuring their lifetime value

have been the guideposts of many of the recommendations

that firms have received (e.g., Blattberg et al. 2001). When

firms think about customers second lifetime value, they

generally consider incentives that they could offer the cus-

tomer to reactivate their relationship. From a companys per-

spective these incentives translate into costs while they add

to the perceived economic value of a relationship from the

customers perspective.

However, our research, which is undertaken from a cus-

tomers perspective, offers an interesting contradiction to

the firms thinking: on the whole, consumer perceptions

of economic value are less important for their willingness

to return than perceptions of emotional value (see Table4).

The only case in which this is not true, is in the low

satisfaction cluster. In this group (cluster 3), perceptions

of the economic value are the second most influential



18/24

factor, but a distant second behind the most influential fac-

tor of stability (elasticities for stability are -0.73, and -0.39

for perceived reactivation costs).

Based on these results, and the economic practices that

firms typically use to win customers back, firms could very

easily focus on the wrong activities, namely those that do

not drive the GWR. For example, a firm may focus on

devising promotions and economic incentives to recapturevaluable lost customers who have switched suppliers, when

the defected customers may actually be more concerned

with the bonds that they had with their former supplier, or

with special treatments that they previously received (Lam

et al. 2010; Tokman et al. 2007). A recent study supports

this rationale by finding that the efficacy of some marketing

activities such as advertising are quite low and even negative

in the long term (Ataman et al. 2010).

General willingness to return is a relevant and measurable

win-back metric Our focal measure, GWR, is new in the

CRM research domain. Prior research in this domainaccounted for demographics (e.g., the duration of the rela-

tionship prior to defection), emotion-based customer charac-

teristics such as customers overall satisfaction prior to their

defection (Homburg et al. 2007), and their intentions to re-

activate a (non-contractual) relationship given a specific

win-back offer (Tokman et al. 2007). To our knowledge,

prior research has not fully investigated the rationalization

and disposition of defected customers prior to any win-back

offer. While the GWR concept has similarities to the concept

of consumers affinity for a firm, product, or brand, we

emphasize that a key distinction between GWR and general

intention is that GWR measures the inclination to revise a

prior decision without any firm offered incentives. Thus

GWR is not a broad measure of repurchase intentions but

instead it is a specific measure of a customers disposition

toward relationship revival.

Managerial implications

Leveraging GWR and its antecedents to position win-back

communications Because the consumers defection reasons

can influence GWR and win-back performance, effective

marketing communications should address the consumers

concerns. The antecedents to GWR and how they vary

across customer groups can help CRM managers to position

their win-back communications. For example, if controlla-

bility is a key explanation for a specific customers GWR, a

firm could approach such a defected customer with a state-

ment that communicates, Bthis incident can be prevented.^

With such a statement, the firm is tapping into the cus-

tomers perception that it can change the situation for the

better. If stability drives the consumers GWR, the firm can

position its marketing communications so that the former

customer knows that Bthings have changed.^ Consumers

who emphasize that commitment to their relationship affects

their willingness to return could be approached with market-

ing communications that acknowledge that Bthe relationship

is important or Bis valued.^ This type of remark would

resonate with consumers who have an emotional connectionto the firm. In contrast, marketing communications directed

at defected customers who focus on factors that drive the

economic value of the relationship would require a differ-

ently positioned message. For example, customers who are

driven by reactivation costs would want a message from the

firm that communicates that Bcoming back can be easy^ or

Bis not expensive.^ To implement this, for example, in con-

tractual relationships, a firm can offer a pre-printed contract,

or a sufficient number of customer touchpoints for contract

renewal. These examples show that knowledge of the

drivers of the GWR can help a firm position its win-back

communications.Interestingly, one way to characterize these themed

talking points is based on whether they are grounded in

a rational appeal (i.e., a persuasive message that focuses

on facts and product/service attributes), or in an emotional

appeal (i.e., a persuasive message that taps into the con-

sumer sentiment and emotional or social value derived

from the exchange). Given the theories that we examine,

this is an appealing characterization, because it is consis-

tent with the emotional and subjective aspects of some of

our GWR drivers (e.g., affective commitment), as well as

with the more objective or fact-based aspect of our drivers

(e.g., reactivation costs).

We present a simplified demonstration that only focuses

on the top drivers of GWR in each cluster to apply the idea

of a rational communication appeal versus an emotional

communication appeal to our specific clusters. First, we note

that clusters 1 and 2 differ with respect to the intensity of

their effects, but are similar with respect to the relative im-

portance of the factors that affect GWR, i.e., affective com-

mitment and stability. Consequently, the main theme behind

marketing communications for these clusters would be the

same. Specifically, the theme should combine a strong emo-

tional appeal that perhaps emphasizes loyalty and commit-

ment. Settings that highlight families, parents and children,

or good friends interacting together are examples that may

be appropriate because they typically evoke a strong sense

of commitment. Within these contexts, a marketing commu-

nication may refer to statements such as: BWe value our

relationship and are committed to doing what it takes to

make it work.^ Through this type of statement, the firm

addresses the clusters emotional bond with the relationship



19/24

and their perception that the circumstances leading to their

defection can change.

In contrast to clusters 1 and 2, a marketing communica-

tion targeted at cluster 3 (Bdisillusioned^ customers) should

possibly comprise a far more rational appeal, because the

top two factors associated with this clusters GWR are (1)

stability and (2) perceived reactivation costs. The firm may

consider citing facts or statistics pertaining to the reasons forthe defection having been minimized or eliminated, and that

revitalizing the relationship will not be risky or costly. Thus,

a message theme that communicates Bthings have changed

and we can make it easy for you^ might motivate them to

consider returning to a relationship. A firm may try using

the familiar phrase Bunder new management for these types

of consumers. Using significant price promotions or finan-

cial incentives to stimulate return are also reasonable mar-

keting tactics that may resonate with this cluster.

Finally, cluster 4 (the Bremorseful customers, medium

satisfaction and short time absence) values the relationship,

but is most concerned with whether the factors that causedtheir defection have been addressed (i.e., stability). There-

fore, a rational appeal may be most persuasive and resonate

with these customers. Elaborating on the specific details of

changes is consistent with the idea of presenting a rational

appeal, and could strengthen the message. Examples of plau-

sible changes that the firm may want to communicate are: a

new web site, a new (re)ordering system, or a new distribu-

tion partner. Hence, a message theme that communicates

Bthings have changed and our relationship with you is

important may motivate these customers to reactivate a

relationship.

In summary, these examples show that by knowing and

understanding the key factors that drive the GWR, firms can

tailor specific messages that have a higher chance of resonat-

ing with defected customers. Notably, these examples can be

easily linked to the theoretical frameworks that were investi-

gated in this research. Further, the examples and frameworks

that we presented align easily with the concept of a rational

versus an emotional message appeal, which is a concept used

to guide firmsmarketing communications.

Clustering defected customers for win-backPractitioners

rely upon various methods and variables for segmenting

current customers. Adding to this practice, this research

demonstrates the value of clustering defected customers

based on combining metrics that capture their relationship

before and after their defection. Specifically, we show that

the time elapsed since the relationships termination and

before the survey along with a customers satisfaction lev-

el prior to the termination can delineate between defected

customers who vary in their general willingness to return

to prior relationships (see Table 2). The time elapsed can

easily be measured in a contract setting, as is the case

with this research. In non-contractual settings statistical

models can be used to predict lifetime durations and from

that estimates of time absence can be derived. Thus, time

absence is a practical clustering measure for defected

customers.

Customer satisfaction has repeatedly been linked to cus-tomer retention (e.g., Mittal and Kamakura2001), and numer-

ous firms frequently measure this at an individual level

(Reichheld 2003). Although many firms may presume that

all defectors are dissatisfied, we show that this is not neces-

sarily the case (see Table 2). Thus, we posit that satisfaction

prior to defection is also an appropriate clustering measure to

consider for defectors. In this research, the measure was taken

after the defection and thus did not require a survey of an

entire customer database.

Combining these two metrics to form clusters reveals an

interesting group of defected customers whom some man-

agers may have difficulty understanding or identifying. Forexample, if the firm were to only use satisfaction as a cluster-

ing variable, they would likely combine clusters 1 (Bmigrant

birds,^ long time absence) and 4 (Bremorseful, short time

absence) because they have the same level of satisfaction. This

would be detrimental from a managerial implementation per-

spective because, as our research shows, these two clusters are

motivated by very different factors.

The time since the last purchase, which is analogous to our

time absence before the survey variable, is a common metric

that database marketers use for targeting (Hughes 1996).If our

time absence variable were used without the satisfaction var-

iable, firms would find it hard to differentiate consumers in

cluster 3 (low satisfaction) and cluster 2 (high satisfaction).

Managerially, this could also cause significant problems when

implementing a win-back campaign because commitment is

the biggest driver of cluster 2s GWR, while commitment is

only the fifth most significant driver of cluster 3s GWR. In

general, the relative importance of the factors that drive GWR

differs greatly from the least satisfied to the most satisfied

clusters. Thus, the customer satisfaction level is an important

profiling factor for firms to consider when developing a win-

back strategy.

Proxy measurement Given the high level nature of many

of the constructs in our study, firms may try to use proxy

variables from their customer database that mirror those

constructs. By using such proxies, managers can avoid

conducting large scale surveys. For example, because they

also had relationships with other suppliers, one might de-

duce that customers with high degrees of variety seeking

behavior and switching experience are characterized by



20/24

short prior relationship durations. This is also reflected in

Table 1, where prior relationship duration is negatively

associated with variety seeking and switching experience.

Thus, prior relationship duration might serve as a good

pr ox y me as ur e fo r va ri et y se ek in g an d sw it ch in g

experience.

Relying solely on transactional measures might be insuf-

ficient when searching for proxy measures for e.g., attribu-tional variables. Nevertheless, with the availability of rich

data, managers might even be able to identify customer

characteristics or individual behavior, such as complaints,

interaction with frontline employees, or social media conver-

sations that strongly correlate with constructs such as prior

satisfaction, locus, stability, or controllability. The impor-

tance of measuring complaints to gain substantial insights

into the quality of the relationship has been highlighted in

several studies (e.g., van Oest and Knox 2011). Neverthe-

less, firms need to be aware that most customers do not

complain (e.g., Voorhees et al. 2006). Hence, customers

should be stimulated to raise their complaints (e.g., by ask-ing customers for their defection reasons during relationship

termination). Additionally, service employees often dispose

of in-depth knowledge of customer attitudes. Thus, their

feedback and evaluation of attributional variables might

serve as another proxy measure.

Firms may also find that they need to consider alterna-

tive variables for deriving clusters. Previous customers can

be segmented based on time absence and satisfaction prior

to defection to target them with specific win-back offers.

Both of these factors have been shown to be relevant for

customer reacquisition (Homburg et al. 2007; Thomas et al.

2004; Tokman et al. 2007). As we show in Fig. 2, in our

study only extremely low or high levels of these two fac-

tors had to be known to identify the four clusters. So even

if, for example, satisfaction is not known, proxies such as

product returns or customer complaints could suffice for the

purpose of segmentation. Our study also emphasizes the

benefits of collecting customer satisfaction scores or prox-

ies to be able to effectively segment defected customers for

win-back purposes.

Conclusion and limitations

Our research explores the perceptions and rationalizations

of defected customers to gain a deeper understanding of

their willingness to return to a contractual relationship

with a firm. While we are confident that our survey-

based research generates novel preliminary insights into

effects of attitudes and intentions on relationship revival,

we are aware of its incremental contribution to our field

and therefore encourage future research to conduct neuro-

scientific studies to validate or qualify our findings on the

outcomes of the internal mental workings of customers.

We further suggest conducting experimental studies to

broaden our knowledge on the impact of concrete win-

back offers (besides pricing issues) on customer return

behavior. Further, experimental studies might also help to

further enlighten the cognitive processes that a defectedcustomer goes through while forming her or his general

willingness to return.

Our theoretical framework and empirical findings can

provide managers with very specific guidance and sug-

gestions on how to segment defected customers and

engage them through specific communication messages.

Hence, additional research on proxy measures will be

helpful to translate the high level constructs studied in

this research into actionable insight. Future studies

might also consider the important revenue, cost, and

profit implications of new customer acquisition and re-

tention vis--vis win-back of former clients. Finally, fu-ture research might also reveal how characteristics of a

prior relationship other than prior satisfaction or affec-

tive commitment might influence customer responses to-

ward the firm.

While we are not aware of any win-back activities by

the firm before the survey, the possibility of this is a

potential limitation of this research. We also note that

the subscription context is another potential limitation to

the generalizability of this research. It would be fruitful

if the theoretical framework presented here were to be

examined in a non-subscription context and contexts

that have a higher risk associated with the purchase

(e.g., prepaid legal service or medical insurance) than

the one studied in this research (i.e., novels, media

content).

Another possible limitation of this research is the

ratio of women to men in the data sample. The study

participants were mostly women. It is possible that atti-

tudes could differ if the sample was more balanced. For

example, Melnyk et al. (2009) find that men are more

loyal to groups (e.g., companies), whereas women tend

to be more loyal to individuals, such as certain service

employees.

Despite the potential limitations and several ways to

extend this study, this research can be viewed as a cat-

alyst to expand our knowledge of customer reactivation.

Specifically, this research contributes to the literature by

expanding our theoretical knowledge about customer re-

vitalization and provides recommendations that are high-

ly relevant to practitioners responsible for managing

customer relationships.



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Appendix

Table 6 Measurement items (1= I do not agree at allto 7=I fully agree; r=reverse coded)

Constructs and items Loadings Composite reliabilities

Prior Satisfaction (Maxham III2001; Ping1993,1995):

As a whole, I am satisfied with the products and services offered by your publishing house. 0.82 0.93In general, I am pretty satisfied with my relationship with your publishing house. 0.91

Overall, you and your staff treated me fairly. 0.88

How satisfied are you with the quality of our performance? 0.87

Affective Commitment (adapted from Ganesh et al.2000):

The relationship I shared with your publishing house was very important to me. 0.90 0.89

Since the termination of my subscription, I still feel very committed to your publishing house. 0.90

Variety Seeking (Van Trijp et al. 1996):

I enjoy taking chances by trying out unfamiliar companies, products/contracts to provide variety to my life. 0.85 0.93

I like trying things out that I am not familiar with. 0.89

I always try something different. 0.86

I like to try something I am not very sure of. 0.80

I enjoy trying out new products. 0.83

Switching Experiences (Burnham et al.2003):

I occasionally try new subscriptions from competing publishing houses. 0.87 0.90

In the past, I often switched between different subscriptions. 0.83

I occasionally try other subscriptions. 0.89

Locus (Tsiros et al.2004):

Your publishing house is responsible for my decision to terminate the relationship. 0.87 0.92

Your staff is responsible for my decision to terminate the relationship. 0.90

The strategies and orientation of your publishing house are responsible for my decision to terminate the relationship. 0.89

Stability (Russell1982):

My reasons for terminating the relationship are

Permanent/temporary 0.86 0.86

Changeable/Unchanging (r) 0.76

Stable over time / variable over time 0.85Controllability (Hess et al.2003):

My reasons for terminating the relationship are

Controllable by your company. 0.91 0.91

Preventable by your company. 0.91

Reactivation costs (adapted from Ping1993):

I think the costs in time, money and effort to return to the publishing house would be high. 0.59 0.86

Overall, I could lose a lot if I return to your publishing house. 0.62

Returning to your company is too risky/insecure for me. 0.81

It is too complicated to renew my former subscription with you. 0.86

Returning to your publishing house is too cumbersome to me. 0.83

Attractiveness of alternatives (Ping1993):

Products and services available from alternative publishing houses offer exactly what I need. 0.83 0.89

Overall, alternative publishing houses would benefit me more than your publishing house.I am interested in many publishing houses.

0.82

Other publishing houses provide a bigger assortment of subscriptions than your publishing house. 0.83

Other publishing houses keep me better informed about attractive offers. 0.70

General willingness to return:

I am generally willing to return to you (publishing house) and to close a new contract. 0.91 0.92

I am generally willing to revise former decisions. 0.77

In the future I would like to close subscriptions with your publishing house again. 0.89

The renewal of my former relationship, e.g., a new subscription, is very probable/ not very probable. 0.86



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