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Customer Complaints, Defection and Lifetime Value

George Knox and Rutger van Oest*

This Version: July 1, 2010 * George Knox and Rutger van Oest are Assistant Professors of Marketing at Tilburg University, Warandelaan 2, 5000LE Tilburg, The Netherlands, (email) [email protected] (tel) +(31) 13 466 8232 (fax) +(31) 13 466 8354, and (email) [email protected] (tel) +(31) 13 466 2057 (fax) +(31) 13 466 8354. The authors thank Bart Bronnenberg, Marnik Dekimpe, Josh Eliashberg, Els Gijsbrechts, Rik Pieters and participants of the 2007 Tilburg Summer Research Camp, 2007 Marketing Dynamics Conference, 2008 Marketing Science Conference, 2008 Direct/Interactive Marketing Research Summit, the Zaragoza Logistics Center, and 2009 Marketing Science Conference for their detailed and useful comments, and Sander Beckers and Inge Vening for their help recording data.

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Customer Complaints, Defection and Lifetime Value

Abstract

Complaint management is an essential component of defensive marketing; yet the impact

of customer complaints and firm responses to those complaints on subsequent customer

purchasing remains unexplored. Using a unique database that tracks actual purchases,

complaints, and recoveries for 20,000 customers over 2.5 years, we identify eight complaint and

six recovery types and investigate their effects on customer defection and lifetime value (CLV),

while accounting for the customer’s history with the firm. Previous complaints (purchases)

intensify (dampen) the negative impact of current complaints, but the effect of past complaints

fades over time more quickly than that of past purchases. Complaints about out-of-stock items

most often result in defection, while firm responses involving merchandise certificates (i.e., store

credit) often lead to sustained recovery and are more effective (in terms of CLV) than refunds.

Although providing a constructive recovery tends to be financially rewarding, extra

compensation usually does not appear worth the extra resources.

KEY WORDS: Complaints, Complaint Management, Defection, Customer Lifetime Value, Failure, Recovery

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INTRODUCTION

Product and service failures are an unavoidable reality of business and may represent

critical turning points in the customer-firm relationship. Complaints give voice to the resulting

dissatisfaction and offer firms an opportunity to save the customer by choosing an effective

recovery (Fornell and Wernerfelt 1987). Firms can also monitor complaints to anticipate failures

before they escalate (Berry and Parasuraman 1997; Hart, Heskett and Sasser 1990). Previous

studies have examined both recovery effectiveness and failure severity in terms of customer

perceptions and intentions, yet research that uses behavioral and financial outcomes such as

customer retention or lifetime value is still rare (Zhu, Sivakumar and Parasuraman 2004). This

has led to a call from both academics (Parasuraman 2006; Rust and Chung 2006) and managers

(Goodman 2006) to make complaint management more financially accountable.

To that end, we develop and estimate a model that links two important behavioral metrics

– customer defection and lifetime value (CLV) – to an actionable set of complaint and firm

recovery types obtained from company records, while accounting for the customer’s history with

the firm. Since our setting is non-contractual, firms cannot directly observe customer defection;

instead they must infer it from the timing of customer-firm interactions (e.g., Schmittlein,

Morrison and Colombo 1987; Fader, Hardie and Lee 2005). Our data come from a major U.S.

internet and catalog retailer and track purchases and complaints for 20,000 customers over 2.5

years. Such a panel database, that describes actual customer behavior over time, has not yet been

used to study complaint management (Rust and Chung 2006, p. 563).

Although most dissatisfied customers do not complain, complaints can be managerially

useful because they are observed indicators of latent dissatisfaction and potential defection

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(Blattberg, Kim and Neslin 2008). Studying their effects on actual subsequent purchasing offers

several advantages over the more common survey and experimental approaches. First, complaint

records are typically stored in firm data warehouses, and, when combined with transaction data,

can provide a low-cost but meaningful way to trade off competing recovery options on the basis

of financial impact (i.e., CLV). Second, actual complaints represent both extreme and “typical”

incidents (rather than the mostly extreme memory-based responses from surveys), and trigger

“real-life” rather than experimentally-induced reactions (Weiner 2000).

In the next sections, we present the conceptual framework underlying our empirical

analysis, followed by a description of our data set and the model. Next, we discuss the empirical

findings and illustrate the impact of several firm recoveries on CLV. The final section offers the

implications of the findings for information systems, failure prevention and recovery strategies.

CONCEPTUAL FRAMEWORK

CLV and modeling framework

CLV is the sum of all discounted contribution margins over the customer’s lifetime

(Berger and Nasr 1998; Blattberg, Kim and Neslin 2008). Typically, it is computed on a time

period basis (e.g., annual time blocks), with the two drivers being the customer’s lifetime (or

retention) and the contribution margin per period, discounted by the exogenous discount rate

(Kumar and Reinartz 2006). However, similar to Rust, Lemon and Zeithaml (2004), we consider

a non-contractual setting in which the data are defined on an event basis (i.e. purchases and

complaints). Correspondingly, CLV results from (1) potential defection after purchases and

complaints, (2) the timing of these events, and (3) the dollar amount that is spent on each

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purchase occasion (multiplied by the margin rate and discounted by the discount rate). While

Rust, Lemon and Zeithaml (2004) assume that a firm can always have a fraction of a customer’s

business (“always a share”), our model assumes that customer defection is an absorbing state

(“lost for good”), i.e., customers are “active” until they irreversibly “defect”, an assumption

shared by many models of non-contractual relationships (e.g., Fader, Hardie and Lee 2005;

Schmittlein, Morrison and Colombo 1987). As defection is not observed, its likelihood is

unknown and needs to be inferred.

[Figure 1 About Here]

The flow chart in Figure 1 describes the consumer's sequence of steps, with equation

numbers for later reference. Customers start in an “active” state and may purchase or complain

whenever they want to, as long as they stay active. If the event is a purchase, the customer

spends a certain amount and may accompany the purchase by a complaint on the same day (e.g.,

not qualifying for free shipping). Any complaint, either separate from, or on the same day of a

purchase, is of a specific type and triggers a response by the firm. At the end of the event, the

customer decides whether to stay with the firm (and remain in the active state for at least one

more round) or defect (and move to the absorbing inactive state). Though our model contains

several steps, all of these are needed in order to forecast customer behavior into the future and

calculate CLV. Below, we discuss the potential indicators of customer defection, i.e., the specific

complaint types, recovery types and the role of customer history. We do so with the

understanding that underlying dissatisfaction is the final driver of defection: though complaints

are observable indicators of dissatisfaction, they do not cause defection themselves.

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Complaint types

Based on the services and complaint literatures, we identify eight mutually exclusive

complaint types. They are (1) perceived price issues, (2) order placement and billing issues, (3)

out-of-stock, (4) late or no delivery, (5) wrong package contents, (6) wrong information, (7)

defective product, and (8) product-not-as-expected. Table 1 provides the full set of complaint

types along with their frequency of occurrence in our data, the dimensions along which they

differ, and their expected effects on defection. Complaint type 1 is the perceived price attribute

in the e-service quality framework of Zeithaml, Parasuraman and Malhotra (2000) and includes

issues such as expired promotional offers or not qualifying for free shipping. Complaint types

(2)-(6) are the attributes captured in the reliability/fulfillment dimension of these authors.

Complaint types (7)-(8) are taken from Kelley, Hoffman and Davis (1993). They go beyond e-

service quality and are product-related. The three super-categories, i.e. price, fulfillment and

product, are provided in the second column of Table 1.

[Table 1 About Here]

The services marketing literature has identified several dimensions along which failures

and subsequent complaints can be classified in our context. An outcome failure refers to what a

customer has received – the core service – whereas a process failure concerns how the service is

received (Smith, Bolton and Wagner 1999). Recall-based surveys indicate that incidents related

to the outcome are more negatively experienced than incidents related to the process (Bitner,

Booms and Tetreault 1990), and are more often mentioned as a reason for abandoning the firm

(Keaveney 1995). Perceived prices issues (type 1) and order placement and billing issues (type

2) are related to the process; hence their predicted effects on defection are modest. Wrong

information failures (type 6) involve customers blaming the firm for having informed them

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incorrectly (for example about product size or color) and therefore may contain both process and

outcome elements. The other five failure types are related to the outcome, as shown in Table 1.

Attribution theory suggests characterization of failures by their underlying causes as

inferred by the complainant. The causal dimensions are locus, controllability and stability

(Weiner 1980). Customers react more negatively if the failure is attributable to the firm rather

than the consumer, if the firm could have prevented the failure from occurring, and if the failure

is likely to happen again (Bitner 1990; Folkes 1984). While the stability dimension is hard to

judge in our database, we can infer locus and controllability. Product-not-as-expected complaints

(type 8) imply that the customer takes at least some responsibility for the mistake; hence its

predicted impact on defection is modest. All other complaint types can substantially be attributed

to the firm (rather than to the customer) and are to a large extent controllable.1

These are

therefore predicted to have a large impact on the likelihood of defection. In particular, wrong

information (type 6) implies that the firm is explicitly accused by the customer, since it is

strongly attributed to and potentially controlled by the firm. Locus and controllability are

summarized in Table 1, together with the overall predicted effects of the eight complaint types

on customer defection.

Recovery types

Service recovery refers to the actions that a firm takes in response to service failures

(Grönroos 1988). Here, we examine recovery in the context of our data, using the terms recovery

and response interchangeably. We identify six mutually exclusive types of recovery: (1) reship,

(2) non-reship correction, (3) refund, (4) certificates, (5) extra compensation, and (6) no

1 One potential exception is late or no delivery (type 4), a failure which may be at least partially attributed to the delivery company instead of the retailer.

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recovery. Recovery types 1 and 2 are corrective recoveries (i.e., they fix the problem without

providing compensation), and types 3 and 4 are compensatory (Hoffman and Chung 1999).

These first four types describe basic recoveries; types 5 and 6 capture extraordinary positive and

negative firm responses, respectively. Our response categories are similar to those used by Tax,

Brown and Chandrashekaran (1998); additionally they are identifiable in complaint databases,

actionable, and involve clear cost-benefit tradeoffs. Table 2 presents these six recovery types,

their frequency of occurrence, the dimensions along which they differ, and their expected

effectiveness in preventing defection.


Distributive, procedural and interactional justice aspects influence customer satisfaction,

trust and commitment (Smith, Bolton and Wagner 1999; Tax, Brown and Chandrashekaran

1998). Social exchange and equity theories predict that refusing recovery (type 6) leads to low

perceived fairness and repurchase intentions (Bitner, Booms and Tetreault 1990; Blodgett, Hill

and Tax 1997). Similarly, merchandise certificates and other non-monetary compensation like

free shipping on the next order (type 4, henceforth “certificates”) may lead to perceived injustice,

because customers are “forced” to return to the retailer at which they experienced the failure.

According to self-reported retention rates, certificates are “unacceptable” (Kelley, Hoffman and

Davis 1993). At the same time, it is this “forced” return aspect that could make certificates very

effective when considering observed behavior (Conlon and Murray 1996). Furthermore, whether

the customer’s return marks a one-time affair in order to redeem the certificate or the resumption

of the relationship is a critical issue we subsequently test in our data.

It is not yet clear which of the recovery types reship (type 1), non-reship correction (type

2), refund (type 3) or extra compensation (type 5) should be the most effective, because the

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literature contains several conflicting results (Davidow 2003). Hoffman and Chung (1999) find

that compensatory responses are more effective than corrective responses in two typical services

settings, but Kelley, Hoffman and Davis (1993) find the opposite in a general merchandise,

brick-and-mortar setting. Also the impact of extra compensation (type 5) has not been fully

explored (Davidow 2003). In typical services settings, excellent recoveries can result in more

loyal customers than in the hassle-free case (Hart, Heskett and Sasser 1990; Smith and Bolton

1998). At the same time, consistent with the equity principle, compensation may only work up to

a certain level (Davidow 2003; Smith, Bolton and Wagner 1999). Table 2 summarizes our

predictions: though no recovery (type 6) is expected to be least effective, there is no unequivocal

evidence that certificates (type 3) are more or less effective in preventing defection than the other

four recovery types.

Customer history

Customer history may play two roles. First, it may directly signal the customer's

propensity to defect, even if the focal event does not involve a complaint (Bolton 1998). Second,

it may indirectly affect defection via the customer’s reaction to a complaint incident (Smith and

Bolton 1998). Furthermore, we expect that recent past experiences may get more weight than

earlier ones since they are more salient (Bolton, Lemon and Bramlett 2006).

For the direct effect of past purchases and complaints, a lengthy and extensive purchase

history indicates a stable and satisfactory customer-firm relationship (Morgan and Hunt 1994);

hence we predict a lower likelihood of defection (Israel 2005). Similarly, past complaints may

signal a lower level of overall satisfaction (Kowalski 1996) and a higher likelihood of defection.

Furthermore, even a satisfied customer may drop out if saturation is reached and the firm’s

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products do not need to be bought anymore, especially if the products are easily stockpiled

(Reinartz and Kumar 2003); this implies that large purchase amounts increase the risk of

defection.

For the indirect effect of past purchases and complaints, the literature has found that

overall (prior) satisfaction affects the impact of a complaint incident on trust and commitment, as

more satisfied customers react less intensely (Tax, Brown and Chandrashekaran 1998).

Customers with a long purchase history assign more weight to past experiences and less weight

to new events (Bolton 1998); hence we predict that they are more forgiving toward complaint

incidents. Similarly, we predict that previous complaints will make customers less forgiving

(Smith and Bolton 1998). Lastly, we do not expect any indirect effect of past purchase amounts.

DATA

Our transaction and complaint data are from a major U.S. internet and catalog retailer; it

is ranked in the Top 50 largest direct marketers by Catalog Age and Internet Retailer and it sells

toys, novelties, and party supplies. It has a proactive 100% satisfaction policy, as stated on its

website, and can be contacted using a toll-free phone number and by email.

The data include 20,000 new customers who made their first purchase in one of the first

10 weeks of 2001 and are observed until June 30, 2003, i.e., at most 2.5 years since their initial

purchase. Customers made on average 1.3 repeat purchases (ranging from 0 to 25) and spent $57

on each order. The novel feature of our data is the complaint log file, which identifies the

customer, date, and a description of the voiced complaint as well as the firm’s response. There

are 922 complaints recorded from 805 customers (4% of total customers). Most complaints are

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voiced soon after a purchase: 18% of all complaints occur on the same day as a purchase and

80% occur within three weeks. We use content analysis to classify complaint incident

descriptions into the eight complaint types from Table 1 and six recovery types from Table 2.

We hired and trained two M.Phil. students to independently code the 922 complaint events. Their

mutual hit rate (Cohen’s kappa) was .85 (.82) for the complaint types and .76 (.70) for the firm

recoveries. Discrepancies were resolved by a third judge (one of the authors).

In order to identify separate complaint and recovery effects, there needs to be sufficient

variation in complaint-recovery pairs observed in the data. A strict firm protocol that always

assigns the same response to a specific complaint would make such parameter identification

difficult. To measure this in our data, we use the entropy index (Vermunt and Magidson 2005, p.

47), where 0 implies seemingly random firm responses (no protocol), and 1 means that every

complaint type is always addressed by one specific response (strict protocol). The actual value,

.25, confirms there is substantial variation in complaint-recovery pairs. We estimate a main

effects model and are cautious in interpreting only frequently observed complaint-recovery

combinations in our CLV calculations.

Our estimates may also suffer from endogeneity bias if the company’s willingness to

offer a favorable response to a complaint depends on customer history. We tested for this by

estimating an ordered logit model and found that a favorable outcome (low = no recovery,

medium = any of the four basic recoveries, high = extra compensation) is less likely if the

customer has complained before, while purchase history is not used in either direction. That is,

the firm does not treat new customers (who may be less valuable but more responsive, as in

Bolton 1998) and customers with a long purchase history (more valuable but less responsive)

differently. Hence, endogeneity is not expected to be a major issue in our empirical application.

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MODEL FOR PURCHASING, COMPLAINING AND DEFECTING

This section translates the conceptual framework into the empirical model (see Figure 1

for the connection between the steps and the equations below). The model describes when

customers purchase and complain, the amounts purchased, the types of complaint voiced, and the

likelihood of defection after any of the events. As indicated earlier, these components (with

margin and discount rates) are necessary to calculate CLV.

Step 1 in Figure 1 concerns event type (i.e. purchase or complaint) and event timing. The

joint type-timing density for repeat event j of active customer i is

(1) ( )( ) ( )( ) ( )( ) ( ), ,0 0

, , ,, 1 , if Z 1i j i jI y I y

i j p p i j c c i jf t f tπ α β π α β> =

− =

where ,Z 1i j = indicates that customer i has remained active after the previous event, a necessary

condition for event j to occur ( ,Zi j is updated in the defection component, discussed later on),

1, , ,( | , ) exp( [ / ] )i j i j i jf t t tα α αα β αβ β− −= − denotes the Weibull density of inter-event time ,t i j

with shape parameter 0α > and scale parameter 0β > , π is the probability that the event is a

purchase rather than a complaint, and ,i jy is the dollar amount spent: , 0i jy > indicates a

purchase, while , 0i jy = implies a complaint. In line with the customer base and purchase timing

literatures, we model the inter-event time as a continuous time process (measured in days). The

joint density in (1) is able to capture a crucial feature of complaints: they tend to occur

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infrequently (large π ) and with short interevent-times (with a corresponding Weibull density

mode close to zero).2

If a purchase occurs, the customer moves to step 2 and decides how much to spend (in

dollars) and whether to accompany the purchase with a complaint on the same day. We use a

gamma density to model purchase amounts since it is positive and right-skewed (Fader, Hardie

and Lee 2005). We use a two-component gamma mixture density because it provided markedly

better fit to the data than a single component. The dollar amount has density:

(2) ),~,~|()1(),|()0|( 21,21,,. λλπλλπ jigjigjiji yGyGYyg −+=>

where )(/)/exp(),|( 12,1

,221,11 λλλλλ λλ Γ−= −−

jijiji yyyG denotes the gamma density with shape

parameter 01 >λ and scale parameter 02 >λ , and gπ is the mixture weight. We model the

likelihood of a same-day complaint separately, since continuous-time models assign zero

probability to joint events and, as we mentioned earlier, purchases with same-day complaints

occur frequently. We model the probability of the purchase being accompanied by a same-day

complaint via a logistic function as

(3) ,)exp(1

1)0|0Pr( ,, κ−+=>> jiji YC

where parameter κ determines the frequency of joint occurrence. 0, =jiC implies that the event

does not involve a complaint, while 0, >jiC implies the presence of a complaint.

2 The joint density is the marginal probability of the event-type multiplied by the conditional density of timing. Alternatively, one could first formulate the marginal density of timing as a mixture of two Weibulls and multiply it by the conditional event-type probability, obtained by Bayes’ rule. This order results in the same joint density.

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If the customer complains, the customer moves to step 3 in Figure 1, which identifies the

complaint type }~,,1{ Cc ∈ . The probability of each type is defined by the multinomial logistic

specification

(4) .))ln()ln(exp(

))ln()ln(exp()0|Pr( ~

1~

2,~,2,~,1~,0

2,,2,,1,0

,,

∑=

++

++=>=

C

cjicjicc

jicjiccjiji

tt

ttCcC

γγγ

γγγ

The formulation in (4) is a flexible, non-monotonic function that allows the specific type of

complaint to depend on the number of days elapsed . For identification, the three parameters

1,0γ , 1,1γ and 1,2γ corresponding to complaint type 1 are set equal to zero.

Lastly, after any event, the customer moves to step 4 in Figure 1 and decides whether to

move to the permanent inactive state, i.e., defect, or stay with the firm for another round. The

defection probability is described by the logistic function

(5) ()

, 1 ,

, 1 , 2 , 3 ,

, , , ,1 1

Pr( 0 | 1)

LOGIST ( 0) PURCH_HIST COMPL_HIST AMOUNT_HIST

( 0) ( ) ( ) ,

i j i j

i j i j i j i j

C Ri j i j c i j r i jc r

Z Z

I c

I c I c c I r r

µ θ θ θ

ξ δ η

+

= =

= =

= + = + +

+ > = + = ∑ ∑� �

where )]exp(1/[)exp()LOGIST( ⋅+⋅=⋅ , and }~,,1{, Rr ji ∈ is the firm's actual response to a

complaint voiced by customer i in event j. For identification, the coefficient of firm response

type R~ (no recovery) is set at zero. The three customer history variables are measured as in other

studies (Ansari, Mela and Neslin 2008; Van Diepen, Donkers and Franses 2009):

ji,PURCH_HIST is a weighted count of past purchases, ji,COMPL_HIST is a weighted count of

past complaints, and ji,TAMOUNT_HIS is a weighted average of past purchase amounts. The

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weights of past events are empirically determined, depend on the number of days since

occurrence, and allow us to assess how long the impact of a previous event lasts. The

specification in (5) captures both the direct impact of customer history (i.e., if 0, =jic ) and the

indirect impact of how the customer reacts to a complaint incident (i.e., if 0, >jic ), where

recovery types are included as main effects. The multiplier3

(6) )TAMOUNT_HISCOMPL_HISTPURCH_HISTexp( ,3,2,1, jijijiji ωωωξ ++=

takes on positive values and uses the same three history variables as (5). It allows us to test

whether the likelihood of defection after a complaint is dampened (if 10 , << jiξ ), or magnified

(if 1, >jiξ ) by customer history. As indicated earlier, we expect that complainants react less

intensely if they have a longer purchase history ( 1 0ω < ) and more intensely if they have a

complaint history ( 2 0ω > ).

We extend the base model described by (1) to (6) with latent classes in order to account

for unobserved heterogeneity (Wedel and Kamakura 2000), where class },,1{ Ss ∈ has size

sψ such that 1

1Sss

ψ=

=∑ . We make all coefficients in (1) to (3) and the intercept in (5)

heterogeneous (Netzer, Lattin and Srinivasan 2008), and estimate all model parameters using

Maximum Likelihood4

(see Web Appendix A).

3 We have also checked whether a second multiplier should be included for firm response, but this did not result in an empirically better model specification. We therefore use the more parsimonious specification (5) as the focal model. 4 We assume homogeneity in (4), as there is no reason to expect significant differences in the complaint timing-type relationship across segments and for model parsimony reasons.

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RESULTS

Based on the BIC criterion, three segments are sufficient to capture unobserved

heterogeneity in purchasing, complaining and defecting. Web Appendix B provides the full set

of segment-specific parameter estimates (equations (1) to (3) and the intercept in (5)) and Web

Appendix C provides the estimates for the complaint type equation (4). Though the timing, event

type and amount-spent distributions in (1) to (4) are necessary to calculate CLV, our substantive

interest centers on the defection equation (5). Likelihood Ratio (LR) tests allow us to assess the

predictive importance of our complaint and firm recovery types in the defection equation.

Omitting both complaint and recovery components leads to a significantly worse fit than the full

model (LR=55.00, df=15, p<.001), as does removing the complaint component (LR=50.61,

df=10, p<.001) or the recovery component (LR=35.63, df=5, p<.001). Hence, complaint and

recovery types significantly enhance model fit. We next discuss the parameter estimates, and

illustrate how our framework can be used to assess the consequences of complaints and

recoveries for defection and CLV.

Which complaints lead to defection most?

Table 3 provides estimates for the defection equation (5), where positive coefficients

mean increases in the likelihood of defection, along with our predictions developed earlier. The

top part of the table shows that all complaint type coefficients are significant at 5%, while seven

out of eight are significant at 1%. The results are consistent with our predictions. Complaints

about perceived price issues ( 1δ = .87), order placement and billing issues ( 2δ = 1.34) and

product-not-as-expected ( 8δ = 2.14) have a modest impact on defection, while late or no delivery

16

( 4δ = 3.54), wrong package contents ( 5δ = 2.84), wrong information ( 6δ = 3.29) and defective

product ( 7δ = 3.34) have larger effects. Out-of-stock ( 3δ = 4.08) has the largest effect of all

types, confirming survey-based and experimental findings that stock outs lead to diminished

loyalty (Goodman 2006) and likelihood of return (Fitzsimons 2000), but casting doubt on the

results of recall-based surveys (e.g., Kelley, Hoffman and Davis 1993). Taken together, our

complaint type estimates show that outcome failures have a larger impact than process failures

and that a failure attributable to the firm (e.g., defective product) has a larger impact than a

failure at least partially attributable to the customer (product-not-as-expected). The top part of

Figure 2 translates these effects into defection probabilities, while averaging over customer

history and firm response variables. The defection probabilities vary from .28 for perceived price

issues (type 1) to .46 for out-of-stock (type 3), whereas the defection probability after a purchase

without a complaint is .16.

[Table 3 and Figure 2 About Here]

Which recoveries are most effective?

The coefficients of the recovery types in the middle of Table 3 describe main effects and

are all negative (the base case is no recovery, type 6). They suggest that good recoveries matter

(all coefficients are significant at 1%). As predicted, no recovery is less effective than reship

( 1η = -1.21), non-reship correction ( 2η = -.91), refund ( 3η = -1.11) and extra compensation ( 5η = -

.96); this latter type is no more effective than the other three constructive recovery types. We

could not provide a strong prediction for merchandise certificates (type 4), but we reasoned that

they could encourage the recipient to “try” the company once more (consistent with the

estimated coefficient). Strikingly, such certificates have the most negative coefficient ( 4η = -

17

1.46); hence, they are the most effective recovery type considered here. But does this lead to a

sustained recovery or does the customer return one more time to cash in the certificate and

defect? To address this question, we added a dummy variable to the model indicating whether

certificates were provided during the previous event. The dummy coefficient was economically

small (d = .17) and statistically insignificant (p = .70), indicating that defection in the first

occasion after voicing a complaint and receiving certificates is not significantly different from

other purchase occasions. This amounts to a sustained recovery. The bottom of Figure 2

translates these recovery effects into defection probabilities, where customer history variables are

kept at their average values and complaint types have been averaged out. The defection

probabilities vary from .27 for certificates (type 4) to .60 for no recovery (type 6) and illustrate

the wide range of effectiveness.

How does customer history influence defection?

The smoothing coefficients of the three customer history variables are .998, .982 and

.982, respectively (Table 3), indicating that the recent past gets more weight (Bolton, Lemon and

Bramlett 2006). The impact of past purchases decays slowly, with a halving time of almost a

year ( =)998.0ln(/)5.0ln( 285 days), whereas past complaints are “forgotten” relatively quickly,

with a halving time of 38 days. The weights of past purchase amounts are also halved in 38 days.

Even if the current event does not involve a complaint, the customer's history directly

influences the probability of dropping out (see Table 3). As predicted, more past purchases

decrease the probability of defection ( 1θ = -1.04), whereas past complaints ( 2θ = .59) and average

purchase size ( 3θ = .0018) increase the probability of defection. Unless the customer decided to

defect after the previous complaint or a second failure occurs, the relationship returns quickly to

18

business-as-usual. The customer's history also indirectly influences defection if a complaint

occurs. Consistent with the conceptual framework, a long purchase history makes the customer

more forgiving and less likely to defect ( 1ω =-.89), while a second complaint intensifies the

customer’s determination to abandon the retailer ( 2ω = .77), especially if it occurs soon after the

first one. Both effects are significant at 1%. The effect of (weighted) average purchase amount

from previous transactions is not significant ( 3ω = -.00078). We did not have a prediction for this

coefficient.

The impact of recovery on CLV: two scenarios

The previous discussion suggests that customer complaints and firm recoveries can have

far-reaching consequences for CLV. We investigate this further by considering two failure-

recovery scenarios in more detail. The first corresponds to a complaint about late or no delivery,

which has the highest frequency in our data set (Table 1), is likely a representative failure for a

catalog and online retailer, has a large effect on defection (Table 3 and Figure 2) and is variously

dealt with by the firm. The top part of Table 4 provides three possible firm responses (no

recovery, refund and certificates) as well as a regular purchase as a benchmark. This allows us to

assess (1) the financial consequences of refusing to make any recovery, and (2) the effectiveness

of giving certificates rather than the other compensatory response, i.e. refund. We examine the

role of customer history by considering three possible complaint histories (no previous

complaints, recent and distant past complaint) as well as two possible purchase histories (new

and experienced customer).


19

Columns 1 and 2 of Table 4 show the defection probabilities in the various scenarios. The

smallest probability is .03 for an experienced customer (i.e. 5 repeat purchases) without past

complaints who makes a regular purchase. The largest probability is .94 for a new customer (0

repeat purchases) who complained 7 days before the current complaint and for whom no

recovery effort is made. Consistent with earlier results, experienced customers are more

forgiving than new customers after a complaint incident. Columns 3 and 4 display simulated

CLV estimates, i.e. the net present value of the margins generated by the customer's purchases

after the complaint incident, using an annual discount rate of 12% and accounting for the average

margin rate in our data. If the firm provides no recovery, CLV is reduced from $90 to $50 for an

experienced customer, and is virtually wiped out for a new one. In all cases – except for a new

customer who has complained recently – certificates or a refund help to avoid a large drop in

CLV. Columns 5 and 6 give the difference in CLV between these recoveries and no recovery.

The monetary benefits of recovery are substantial for experienced customers and new customers

who have not complained before: they total about $22 for refund and $28 for certificates. Given

that the marginal benefit of certificates is non-trivial and firm costs are likely to be similar, this

calculation supports providing certificates over refunds.

As a second scenario, we consider a complaint involving a perceived price issue (e.g.,

expired promotional offer), which has the smallest effect on defection and is usually addressed

by the firm with a non-reship correction, with the second most frequent response being no

recovery. The bottom part of Table 4 shows the consequences for defection and CLV of these

two recovery types. The defection probability reveals a pattern similar to late or no delivery, but

with smaller values. Correcting even small failures matters: the drop in CLV due to a perceived

price issue is mitigated by about $20 if a correction is made rather than no recovery.

20

DISCUSSION AND CONCLUSION

In this study, we developed and tested a model for the effects of customer complaints

about product and service failures and firm recovery responses on defection and CLV. Our

results have implications for information systems, failure prevention and recovery strategies.

Complaint database management

Firms invest heavily in CRM systems that track customer-firm interactions over time

(Bolton, Lemon and Bramlett 2006). This implies that both transaction and complaint data are

typically available. As we have demonstrated them, combining these two sources can provide a

low-cost and powerful method to study the bottom-line effects of failure and recovery. In our

study, we relied on content analysis of verbal complaint descriptions. Retailers could make such

coding more efficient by electronic text-mining (Kanaracus 2008) or training customer service

representatives to use a consistent pre-set categorization system of complaint types and firm

responses. By encompassing the content analysis step, it may become feasible to build an

automated decision support system for financially accountable complaint management.

Failure prevention strategies

We find that failures related to stock-outs have the largest impact on defection. An

implication for retailers is to take pre-emptive measures to monitor and maintain adequate

inventory. In a similar vein, Blattberg and Deighton (1996) start their influential customer equity

paper by discussing an online and catalog retailer (Lands’ End) keeping “unusually” high

inventory levels in order to avoid stock-outs. Such a strategy may indeed be rewarding.

21

Furthermore, even the mildest failure type, perceived price issues, can lead to substantially lower

CLV than a regular purchase without such a complaint (usually occurring on the same day). For

instance, perceived price issues include promotional offers that have expired. The retailer could

both save resources and avoid the negative consequences for CLV by automatically granting a

small extension to the offer deadline, while not directly informing customers about it. Similarly,

a relatively modest investment in the retailer's website may be sufficient to mitigate order and

billing issues, a failure which has a defection probability roughly twice that of a hassle-free

purchase. Insights such as these are possible only with complaint types that reveal the specific

source of the failure, and linking them to defection and CLV.

Recovery strategies

Survey-based research classifies merchandise certificates as a bad recovery, based on

self-reported retention. Our results, based on tracking actual behavior, indicate that certificates

are on average the most effective recovery strategy since they reduce the likelihood of defection

most. Moreover, this effect leads to a sustained recovery rather than simply prolonging defection

for an extra purchase. Coupled with the result that the “memory” for past complaints fades

quickly, one implication for managers is that offering merchandise certificates to reset the

customer relationship is a surprisingly effective recovery strategy. At a minimum, if a retailer is

considering a compensatory recovery to handle a complaint, our results favor offering a

merchandise certificate over a refund. In general, the CLV calculations indicate that it is worth to

devote substantial resources to a constructive recovery. For instance, not correcting a complaint

about perceived price issues costs about $20 extra (reducing CLV by an extra 25%, see Table 4).

Since the cost of recovery by granting a deadline extension or free shipping is only a few dollars,

22

making such corrections is clearly rewarding. Similarly, not recovering a late delivery implies an

extra drop in CLV by about $25 (Table 4). Again, we expect the cost of a constructive recovery

to be substantially less. On the other hand, our results also suggest that it is usually not necessary

to provide extra compensation for the customer's hardship.

Further research

Our basic model is a first step toward financially accountable, data-based complaint

management. There are several avenues for further research. Most importantly, the sparsity of

complaint incidents in our data set limits the potential to test more complex models, such as

those that include interactions between complaint types and firm recoveries (Smith, Bolton and

Wagner 1999), structural models that capture customer expectations of response, or more

sophisticated psychological mechanisms that may underlie the consumer’s response.

Substantially expanding the data to include more customers or time periods may increase

statistical power and allow for testing such models. A second avenue would be to collect self-

reported perceptual measures, such as customer satisfaction, and merge them with longitudinal

purchase and complaint data (Gupta and Zeithaml 2006). Surveys combined with observed

behavior may comprise the backbone of any firm’s service-quality information system (Berry

and Parasuraman 1997), and allow for a more detailed assessment, especially distinguishing

recoveries that are effective at preventing defection from increasing satisfaction. Third, it would

be useful to develop an approach that accounts for word-of-mouth and other network effects

across customers (Gupta and Zeithaml 2006).

23

Conclusion

This study has shown how historical transaction data can be integrated with complaint

log files, by linking specific complaint types and firm recoveries to post-complaint purchasing

behavior and CLV. As such, it provides an initial but important step toward more financially

accountable complaint management in which the benefits of both failure prevention and recovery

are measured by their impact on the bottom line. The required data are typically available within

companies, and the approach has the potential to end up in decision support systems.

24

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28

Table 1

Classification of Complaint Types

Category Super1 Freq.2 Process /

Outcome Locus Control Predicted

Impact 1. Perceived price issues

PRI 14% Process Firm High Low

2. Order placement and billing issues

FUL 7% Process Firm High Low

3. Out-of-stock FUL 4% Outcome Firm High High

4. Late or no delivery FUL 21% Outcome Firm or third party

In between High

5. Wrong package contents FUL 18% Outcome Firm High High

6. Wrong information FUL 2% In between Firm Very high High

7. Defective product PRO 15% Outcome Firm High High

8. Product-not-as-expected PRO 18% Outcome Customer Low Low

1 Super-category: PRI = price, FUL = fulfillment, PRO = product. 2 Frequency of occurrence in the data, N = 922.

29

Table 2

Classification of Firm Recovery Types

Category Super1 Freq.2 Justice Return Trigger

Predicted Effectiveness

1. Reship COR 19% High Low

Moderate

2. Non-reship correction COR 27% High Low

Moderate

3. Refund (cash) COM 30% High Low

Moderate

4. Certificates (non-cash) COM 11% Low High

?

5. Extra compensation EXT 8% High Low

Moderate

6. No recovery EXT 4% Low Low

Low

1 Super-category: COR = corrective, COM = compensatory, EXT = extraordinary. 2 Frequency of occurrence in the data, N = 922.

30

Table 3

Defection Model Equation Results (eq. 5)

DV: Likelihood of Defection Parameter Estimate

Predicted Effect

Complaint Type1

δ1, Complaint Type 1: Perceived price issues 0.87* Low

δ 2, Complaint Type 2: Order placement and billing issues 1.34** Low

δ 3, Complaint Type 3: Out-of-stock 4.08*** High

δ 4, Complaint Type 4: Late or no delivery 3.54*** High

δ 5, Complaint Type 5: Wrong package contents 2.84*** High

δ 6, Complaint Type 6: Wrong information 3.29** High

δ 7, Complaint Type 7: Defective product 3.34*** High

δ 8, Complaint Type 8: Product-not-as-expected 2.14*** Low

Firm Recovery Type2

η1, Firm Response 1: Reship -1.21*** Moderate

η2, Firm Response 2: Non-reship correction -0.91*** Moderate

η3, Firm Response 3: Refund -1.11*** Moderate

η4, Firm Response 4: Certificates -1.46*** ?

η5, Firm Response 5: Extra compensation -0.96** Moderate

η6, Firm Response 6: No Recovery 0.003 Low

Customer History (Direct Effect) 4

θ1, Purchase history (smoothed) -1.04*** –

θ2, Complaint history (smoothed) 0.59* +

θ3, Amount history (smoothed) x 10-2 0.18*** +

Customer History (Indirect Effect) 4

ω1, Purchase history (smoothed) -0.89*** –

ω2, Complaint history (smoothed) 0.77** +

ω3, Amount history (smoothed) x 10-3 -0.78 ?

Model Fit Log-likelihood = -88,453 *** p < .001; ** p < .01; * p < .05. 1 Segment specific intercepts suppressed to save space. 2 Base case is no recovery, prediction concerns absolute value of negative coefficient. 3 Fixed at 0 for identification. 4 Corresponding smoothing coefficients are .998, .982 and .982, respectively. .

31

Table 4

Likelihood of Defection and CLV after Late or No Delivery and after Perceived Price

Issues, as a Function of Firm Recovery and Purchase-Complaint History

Likelihood of Defection Customer Lifetime Value (CLV)

Difference CLV due to recovery

Purchase History Purchase History Purchase History Current Event

Type Complaint History

(1) New1

(2) Experienced

(3) New

(4) Experienced

(5) New

(6) Experienced

Purchase only (No Complaint)

None2 0.26 0.03 71.37 89.34 -- -- Distant 0.29 0.04 68.60 89.13 -- -- Recent 0.37 0.05 61.79 87.99 -- --

Late or No Delivery

- No Recovery None 0.78 0.49 23.25 51.07 -- -- Distant 0.83 0.50 17.61 50.18 -- -- Recent 0.94 0.53 6.70 46.91 -- --

- Refund None 0.54 0.24 46.58 72.60 23.33 21.53 Distant 0.62 0.25 38.42 72.10 20.82 21.92 Recent 0.83 0.28 17.70 69.75 10.99 22.85

- Certificate None 0.45 0.18 54.64 77.40 31.39 26.33 Distant 0.54 0.18 46.51 77.06 28.90 26.88 Recent 0.78 0.20 23.31 75.14 16.61 28.23

Perceived Price None 0.53 0.45 47.29 54.34 -- --

- No Recovery Distant 0.55 0.45 45.23 54.24 -- -- Recent 0.62 0.46 38.97 53.41 -- --

- Non-Reship Correction

None 0.31 0.25 66.69 71.82 19.40 17.46 Distant 0.33 0.25 64.98 71.80 19.75 17.58 Recent 0.40 0.26 59.41 71.20 20.44 17.79

1 New = 0 repeat purchases; Experienced = 5 repeat purchases. 2 None = no past complaints; Distant = one complaint 70 days ago; Recent = one complaint 7 days ago.

32

Figure 1

Steps between the Customer’s Unobserved Active and Inactive States

stay

defect

no

yes

complaint purchase

Step 1 Time & Purchase

or complaint? [eq. 1]

Active state

Step 2 Purchase amount &

Immediate complaint? [eq. 2 - 3]

Step 3 Complaint type?

[eq. 4]

Step 4 Next state? [eq. 5 - 6]

Inactive state

33

Figure 2

The Likelihood of Defection for Complaint Types and Firm Recoveries

34

WEB APPENDIX

Web Appendix A: Derivation of Likelihood Function This appendix provides the log-likelihood function which is maximized in order to find

the parameter estimates. We discuss the model without heterogeneity. The extension with

heterogeneity is straightforward, and we refer to Wedel and Kamakura (2000) for the details. For

a given customer i, the likelihood function can be decomposed into three components, i.e. the

initial event, all repeat events, and the silent period after the last event until the end of the

observation period.

Initial event

The initial event 0=j involves a purchase by definition, and occurs at time 0. Therefore

the likelihood contribution of this event only considers the amount spent and a possible

immediate complaint of a given type. It is given by

( )

.)0|Pr(

)0|0Pr(1)0|0Pr()0|()(C~

1 ,,

)0(,,

)0(,,,.,initial

,

,,

ccI

c jiji

cIjiji

cIjijijijii

ji

jiji

CcC

YCYCYygL=

=

=>

∏ >=×

>>−>>>=

Repeat events

Repeat event },,1{ iNj ∈ involves the timing and type of this event as well as the

amount spent or the possible complaint type. Furthermore, event j can only occur if the customer

has not dropped out after event 1−j . The corresponding likelihood contribution

becomes

35

( ) ( ) ),,(,|()1(,|())1|0Pr(1( ,,)0(

,)0(

,1,,,repeat,,,

jijiyI

ccjiyI

ppjijijiji cyhtftfZZL jiji =>− −==−= βαπβαπ

where

( )( ).)0|Pr(

)0|0Pr(1)0|0Pr()0|(),()(C~

1 ,,

)0()0(,,

)0(,,,.,,

,

,,,

ccI

c jiji

yIcIjiji

cIjijijijijiji

ji

jijiji

CcC

YCYCYygcyh=

=

>=>

∏ >=×

>>−>>>=

Silent period after last event

The likelihood that no event has occurred in between the last observed event iN (with

timing ∑ =

iN

j jit1 , ) and the end of the observation period iT~ consists of two components: the

customer has either dropped out after event iN or has remained active with the next event

occurring beyond iT~ . This gives the likelihood contribution

( ) ,~))1|0Pr(1()1|0Pr(1 ,,1,,1,,silent ∑ =++ −==−+=== i

iiii

N

j jiiNiNiNiNii tTSZZZZL

with function S denoting the mixture-Weibull survival function counterpart of (1).

Overall likelihood

The overall likelihood for customer i is given by

,,silent1

,repeat,,initial i

N

jjiii LLLL

i

×

×= ∏

=

where by convention the empty product (running from 1=j to 0) has value one. After taking

logarithms and combining all M customers in the data set, the overall log-likelihood is given by

∑=

=M

iiLLL

1).ln(

36

Web Appendix B: Segment Specific Parameter Estimates Segment 1 Segment 2 Segment 3

Defection Equation (5)

µ , Intercept 0.007 -0.471*** -0.419***

Event Timing and Type (1) 1

pα , Shape parameter, 1st mixture component 0.905*** 0.959*** 0.844***

pβ , Scale parameter, 1st mixture component 2.722*** 2.665*** 2.029***

cα , Shape parameter, 2nd mixture component 0.643*** 2.548*** 0.923***

cβ , Shape parameter, 2nd mixture component 0.856*** 0.122*** 0.219***

π , Mixture weight 0.985*** 0.980*** 0.955***

Purchase Amount (2) 2

1λ , Shape parameter, 1st mixture component 3.509*** 62.359*** 2.117***

2λ , Scale parameter, 1st mixture component 0.069*** 0.010*** 0.443***

1λ , Shape parameter, 2nd mixture component 2.051*** 2.834*** 1.085***

2λ , Scale parameter, 2nd mixture component 0.317*** 0.187*** 2.274***

gπ , Mixture weight 0.861*** 0.058*** 0.842***

Complaint on Same Day as Purchase (3)

κ , Intercept -5.943*** -5.667*** -4.046***

Segment Weights

sψ , Segment size 0.441 0.382 0.177 *** p < .001; ** p < .01; * p < .05. 1 Timing is measured as number of days divided by 100. 2 Purchase amount is measured as dollars divided by 100.

37

Web Appendix C: Complaint Type Parameter Estimates From (4)

DV: Likelihood of Voicing a Specific Complaint Parameter Estimate

Complaint Type 2: Order placement and billing issues1

γ0,2, Intercept -1.094***

γ1,2, Coefficient of log time since last purchase 0.519*

γ2,2, Coefficient of log time squared since last purchase -0.063

Complaint Type 3: Out-of-stock


γ1,3, Coefficient of log time since last purchase 1.407***

γ2,3, Coefficient of log time squared since last purchase -0.220***

Complaint Type 4: Late or no delivery




Complaint Type 5: Wrong package contents




Complaint Type 6: Wrong information




Complaint Type 7: Defective product




Complaint Type 8: Product-not-as-expected



γ2,8, Coefficient of log time squared since last purchase -0.209*** *** p < .001; ** p < .01; * p < .05. 1 Parameter estimates for complaint type 1 fixed at zero for identification.

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