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Journal of Retailing 83 (2, 2007) 223236

The effects of loyalty programs on customer lifetimeduration and share of wallet

Lars Meyer-Waarden

University Toulouse III Paul Sabatier (France), Department of Management and Cognition Sciences (LGC, EA 2043), France

Received 4 April 2005; accepted 18 April 2006

Abstract

In the retailing sector, consumers typically patronize multiple outlets, which confronts these outlets with an important issue: determininghow to gain a greater part of consumer expenditures. One potential avenue is to increase consumer lifetime duration and repeat purchases

through loyalty cards. This research, using BehaviorScan single-source panel data, examines the impact of loyalty programs on customer

lifetime duration in grocery stores. Thefindings suggest that loyalty schemes have positive effects on customer lifetimes and share of consumer

expenditures. However, multiple loyalty card memberships of geographically close retailers reduce lifetime duration. Furthermore, the higher

the share of consumer expenditures in a store, the longer the lifetime duration will be.

2007 New York University. Published by Elsevier Inc. All rights reserved.

Keywords: CRM; Loyalty programs; Loyalty; Customer lifetime duration; Survival analysis

Introduction

Manyretailers currently regard loyalty programs as funda-

mental. For example, the grocery retailer E. Leclerc in France

devotes approximately D18 million of its annual marketing

expenditures to managing its program. Other retailers, such

as Safeway, have decided to give up their loyalty schemes to

save $75 million. Considering these figures, the Marketing

Science Institute (20042006) raised the standing of cus-

tomer relationship management (CRM) and its associated

issues (e.g., the efficiency of loyalty programs and other

CRM tools) to its capital research priority for 20042006.

Moreover, the Journal of Retailing devoted a special issue

to customer loyalty to stimulate research on topics currentlyprominent in the minds of retailers, such as loyalty programs,

drivers of store loyalty, and so forth (Grewal et al. 2004).

Despite this strong interest, scarce empirical academic

work investigates the potential impacts of loyalty programs

Correspondence address: 98, rue Vestrepain, F-31100 Toulouse, France.

Tel.: +33 6 80 37 42 08.

E-mail address: [email protected].

URL: http://meyer-waarden.com.

on real buyers behaviors, and the research that does exist

provides mixed evidence (Nako 1997; Sharp and Sharp 1997;Bolton et al. 2000; Benavent et al. 2000; Leenheer et al. 2003;

Magi 2003; Yi and Jeon 2003; Lewis 2004; Taylor and Neslin

2005; Kivetz et al.2006). Theambiguity in theresults of these

studies likely reflects limitations in the data and methodology

that hinder the proper assessment of the effects of loyalty pro-

grams. To a large degree, the effects of loyalty programs are

difficult to measure because they act as dynamic incentive

schemes. Existing investigations employ either aggregated

panel data (Sharp and Sharp 1997; Nako 1997), which fail

to take into account customer heterogeneity, or internal store

data, which can make only limited use of competitive infor-

mation about purchasing behavior because clients frequentlybuy from different companies (Reinartz 1999; Benavent et

al. 2000; Bolton et al. 2000; Lewis 2004). An alternative

source has been declarative survey data, whose reliability

problems are well documented (Magi 2003; Yi and Jeon

2003). Finally, another contributing factor to this ambigu-

ity might be context-dependent effects that cause differences

in program success. For example, unlike most investigations,

Bolton et al. (2000) conduct their research in the banking

sector where exit barriers are relatively high.

0022-4359/$ see front matter 2007 New York University. Published by Elsevier Inc. All rights reserved.

doi:10.1016/j.jretai.2007.01.002
mailto:[email protected]://dx.doi.org/10.1016/j.jretai.2007.01.002http://dx.doi.org/10.1016/j.jretai.2007.01.002mailto:[email protected]


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224 L. Meyer-Waarden / Journal of Retailing 83 (2, 2007) 223236

To rectify some of these methodological issues, we use

marketwide scanner panel data about competitive purchas-

ing and store location to investigate to what extent loyalty

programs and the share of wallet (SOW) that a household

allocates to its focal grocery store influence lifetime duration.

We first provide a theoretical backgroundand then develop

our hypotheses. Subsequently, we describe our data andpresent the results. We conclude with a discussion and sug-

gestions for further research.

Theoretical background

Marketing theory and practice have become more and

more customer centered, and managers have increased their

emphasis on long-term client relationships because the length

of a customers tenure is assumed to be related to long-run

company revenues and profitability(Boltonetal.2002;Gupta

et al. 2004).

Customer relationship management is organized accord-

ing to the customer lifecycle because lifetime duration with

a firm generally is not perpetual. Consumers may be dis-

satisfied and find better value elsewhere (Oliver 1999) or

change their lifecycle in a way that causes them to lose the

need for theproduct. Companiestherefore search to influence

customers across their lifecycles through adequate acquisi-

tion and development strategies (e.g., delivering customized

products, cross-selling, up-selling) and use retention strate-

gies to enhance thetotal lifetime of thecustomerbase.If those

efforts focus effectively on the retention of valid customers, a

longer lifetime should lead to higher customer lifetime value

(CLV1), which is associated with lower operational costs insubsequent transactional flows and increased cross-buying,

as well as greater SOW (Dwyer 1989; Berger and Nasr 1998;

Gupta et al. 2004).

Customer SOW

Most grocery shoppers have a primary or focal store in

which they make a large share of their purchases, but the

extent to which other stores are used routinely, and conse-

quently the share devoted to the focal store, varies across

consumers (East et al. 2000; Magi 1999). In this context,

customer SOW corresponds to the share of category expen-ditures spent on purchases at a certain store, which integrates

both choice behavior and transaction values during a spe-

cific time period into a single measure of customer share. For

retailers, SOW is of great significance, because they need to

know how shoppers divide their purchases across competing

stores and how they can increase their share of total grocery

expenditures.

1 We define CLV as the net present value of all current and future trans-

actions with a customer.

Relationship among loyalty programs, SOW, and lifetime

duration

Loyalty programs, which represent tools for developing

relationships and SOW, offer integrated systems of market-

ing actions and economic, psychological, and sociological

rewards.Successful loyalty schemes increase customer reten-tion, lifetime duration, and customer SOW; their overall

objective is to modify customer repeat behavior by stimulat-

ing product or service usage and retain clients by increasing

switching costs.

Loyalty programs can create different types of switch-

ing barriers, including economical, in which case customers

lose advantages (e.g., points) if they change product or ser-

vice suppliers,and psychological,sociological, and relational

barriers that enhance customers commitment to and trust in

the organization (Morgan and Hunt 1994), which strength-

ens the loyalty program effects beyond those of the economic

aspects. Consumers may appreciate rewards make them feel

like preferred customers and thus will identify more stronglywith the company (Oliver 1999).2 In this scenario, an interac-

tive, high-quality, long-term relationship that leads to greater

trust, commitment, and loyalty becomes an emotional choice

factor and could lead to high and irreversible switching costs.

Asweshowin Table 1, limited and contradictory empirical

evidence challenges the efficacy of loyalty programs. Some

researchers express doubts about their benefits and suggest

that in a competitive market, good programs will be imitated,

which means that the end result will be a return to the ini-

tial situation but with increased marketing costs, a highly

inefficient situation. Some contend it is difficult to change

established behavioral patterns with the type of reward sys-tems that are prevalent today (Dowling and Uncles 1997;

Sharp and Sharp 1997; Benavent et al. 2000; Leenheer et

al. 2003; Magi 2003; Meyer-Waarden 2004; Meyer-Waarden

and Benavent 2006).

Nako (1997) and Bolton et al. (2000) report an impact on

customer purchasing and resistance to counter persuasion.3

Lewis (2004) indicates a positive impact for a specific online

grocery merchant loyalty program, and Taylor and Neslin

(2005) find that reward programs increase sales through

two mechanisms: points pressure and rewarded behav-

ior. The points pressure mechanism is the short-term impact,

whereby customers increase their purchase rate to earn

rewards, whereas the rewarded behavior mechanism is the

long-term impact, whereby clients increase their purchase

rate afterthey have receivedthe reward. Benaventet al. (2000)

and Kivetz et al. (2006) indicate that the illusion of progress

toward a reward goal induces purchase acceleration and find

that a strong tendency to accelerate toward the goal pre-

2 Thiscustomer identificationis especiallybeneficial in industries inwhich

consumers purchase frequently and the differentiation among suppliers is

low (Bhattacharya and Sen 2003).3 However, both studies should be interpreted with caution because exit

barriers in the industries they study are relatively high.


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dicts greater retention. Finally, Reinartz (1999) finds no link

between loyalty program memberships and lifetime duration.

Ubiquitous loyalty schemes in retailing and their frequent

connection to promotional devices even may have a nega-

tive effect on lifetime duration. However, the author tests a

proprietary credit card of a mail-order company, which differ

somewhatfrom the loyalty cardsFrench retailers use,becausethey offer added convenience and buying power to shoppers

and target only low-income customers.

Despite the contradictory empirical evidence and taking

into account the limitations of these studies, it seems intuitive

that members of theloyalty programs of their focal storescon-

centrate a larger share of expenditures in that outlet and are

less inclined to visit competitors because the loyalty cards

should provide a higher level of usefulness (i.e., due to finan-

cial advantages, added convenience, and identification). In

turn, there should be a positive association between loyalty

cardpossession, SOW, and lifetime duration in the focalstore.

We therefore hypothesize

H1. The possession of a loyalty card has a positive effect

on SOW in the focal store.

H2. The possession of a loyalty card has a positive effect

on customer lifetime duration in the focal store.

We also anticipate that shoppers who regularly use sev-

eral stores will be members of loyalty schemes offered

by all stores. On average, European and American con-

sumers possess threeretailingloyalty cards, whichmay create

cherry-picking behavior, through which consumers hop from

store to store, buying only promoted items on sale (Nielsen

2005). This suggestion corresponds with findings that showthat loyalty program members need not exhibit a high degree

of repurchase behavior (Benavent et al. 2000; Magi 2003;

Meyer-Waarden 2004). Thus, multiple loyalty cardholders

probably are less likely to stay loyal, because they obtain a

superior level of usefulness from different loyalty programs.

We hypothesize

H3. The simultaneous possession of competitors loyalty

cards relates negatively to lifetime duration with the focal

store.

Relationship between SOW and lifetime duration

Although it seems intuitive that a longer lifetime duration

will be associated with a greater degree of cross-buying and

higherSOW, in that thesharea householddesignatesto a store

depends on its attraction versus the attraction of competitors

(Reichheld 1996; Gupta et al. 2004), a theoretical problem

emerges regarding how program membership, lifetime dura-

tion,and SOW are interrelated. If loyalty scheme membership

is positively correlated with SOW (H1) and lifetime duration

(H2), does it follow that lifetime duration and SOW automat-

ically are positively correlated? We treat this problem and its

methodology more in detail in the methodology section.

Furthermore, we consider another theoretical problem

pertaining to the likely nature of the SOWlifetime dura-

tion relationship, specifically, differences in sectors. Jackson

(1985) and Dwyer (1989) differentiate two types of mar-

kets in this sense: (1) lost for good and (2) always a

share. In thefirst category, thecustomerenters into a contrac-

tual relationship with the company (e.g., telephone services,insurance) and has high switching costs. In this case, a longer

lifetime should be associated with increased cross-buying

and increased SOW at the expense of competitors (Bolton

1998; Allenby et al. 1999). However, most purchase pro-

cessesbelong to the always a share category (e.g., retailing,

packaged goods), so switching costs are low, and customers

typically buy from several competing companies simultane-

ously, depending on the situation, product availability, and

firm reputation; that is, they maintain a portfolio (Ehrenberg

1988). In this case, lifetime duration and SOW are not nec-

essarily associated (Reinartz 1999), because shoppers could

devote only a small portion of their purchases to a store but

continue to use that outlet indefinitely, or alternatively, heavyshoppers might defect frequently. However, at least in some

contexts, a longer lifetime should be associated with higher

SOW; in support of this claim, East and colleagues (1997,

2000) find an empirical relationship in a grocery context. We

therefore hypothesize that

H4. The SOW in the focal store is positively related to

lifetime duration.

Store location and lifetime duration

The location of a store and the distance the consumer musttravel to shop at it represent basic criteria in store choice deci-

sions and assessments of total shopping costs (e.g., Arnold et

al. 1983; Kahn and Schmittlein 1989; Bell et al. 1998), even

if consumers outlet choices are based on different criteria

depending on the nature of the trip. For example, shoppers

are unlikely to travel long distances for small basket, fill-in

trips. In addition, countries differ in their retail structures and

cultural traditions, and these variables also may affect store

choice behavior. Nevertheless, customers likely have longer

lifetime durations with closer stores, for convenience. We

thus hypothesize that

H5. Customers geographic proximity to the focal store is

related positively to lifetime duration.

Methodology

Data description

For a proper assessment of loyalty programs effec-

tiveness, we employ competitive information on individual

customer purchases in competitors stores by using the

single-source BehaviorScan panel based in Angers, a French


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L. Meyer-Waarden / Journal of Retailing 83 (2, 2007) 223236 227

Fig. 1. The outlet locations in Angers (S1= Store 1, S2= Store 2, etc. . . .).

town with approximately 100,000 inhabitants. Purchases

made by panel participants are recorded on a daily basis for

seven stores in the area (five hypermarkets, S1S5, with sur-

face areas of 5,0009,000 m2, situated at the town peripheries

and intersections of major highways; two supermarkets, S6

in the city center of 2,000m2 and S7 on the outskirts with

1,400 m2). These retail outlets represent 95 percent of the

fast-moving consumer goods sales in the area. Fig. 1 shows

the store locations.

S6 and S7, which represent smaller supermarkets, are

direct competitors due to their geographical proximity to big-

ger hypermarkets such as S1, S2, and S3. S5, which is on the

other side of the Maine River, is quite isolated from all other

competitors,with theexception of S4,whichis directly across

a bridge from it.

From this panel, we extracted a total of 397,000 purchase

acts by 2,476 consumers active over a 156-week period 4

4 Panelists are chosen randomly and replaced every 4 years. For their

participation, they receive purchase vouchers and sweepstakes as rewards.

(week 28/1998 to week 28/2001). We thereby smooth any

variations in stores recruitment and marketing strategies.

All large and small retails outlets except S6 offer a loy-

alty program. S1 and S2 belong to the same retailing chain

(RC1) and issue loyalty cards that are valid in both outlets.

S3 and S4 also belong to a single company (RC2) with a

joint loyalty scheme. Thus, we have information about four

loyalty programs memberships. In addition, we obtain loy-

alty card subscription dates for 266 customers of store S1,

which enables us to compare their activity before and after

their subscription to the program.

The features of all loyalty systems are similar, and all use

the loyalty cards for identification and registration. Typically,

theprogramsare free andprovide price discountson a varying

set of items. The point-saving feature provides points and

rewards that depend linearly on the amount shoppers spend.

Customers can also earn points if they buy certain promoted

products or brands and if they pass through the checkout

counter. Members must spend a considerable amount to reach

the minimum redemption threshold to exchange points for

gifts or purchase vouchers (the return/rebate corresponds to


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Table 2

Loyalty program membership duplication

Cardholder S1 and S2

(percent)

Cardholder S3 and S4

(percent)

Cardholder S5

(percent)

Cardholder S7

(percent)

Cardholder S1 and S2 100 39 6 46

Cardholder S3 and S4 13 100 12 38

Cardholder S5 6 37 100 33

Cardholder S7 15 39 11 100

0.11 percent of thepurchaseamount). Theschemesgivealso

other rewards such as lotteries, direct mailings, or member

Web pages. Receipts show the number of points the customer

has saved and the total discount earned.

Approximately 66 percent of the panel households are

members of at least one loyalty scheme, and the duplication

rate of program memberships is substantial: 27 percent of the

households have two or more loyalty cards, 6 percent have

three, and 1 percent have four. On average, a household holds

1.48 loyalty cards. In addition, 39 percent of the members of

RC1s program also hold a loyalty card of RC2 (see Table 2),6 percent also hold a card of S5, and 46 percent a card of S7.

For RC2s members, the duplication rate is highest for S7 (38

percent).

Measures

In grocery retailing, purchasing behavior is characterized

by high buying frequency, portfolio behavior,5 and basket

size variation (Kahn and Schmittlein 1989). We therefore use

different measures.

Our first indicator, the lifetime duration per store

(LT1LT7), corresponds to the difference between the date ofthe last and first purchase. This value is biased in two ways

because the panel has left- and right-censored data. First,

the panel does not necessarily contain the date of the first

purchase, which could have taken place before the observa-

tion period. However, insofar as all panelists reflect this left

censorship, this methodological problem should not repre-

sent a major concern. Second, shoppers might continue to

buy after the observation period (right-censorship). For these

customers, the lifespan thus indicates the difference between

the date of the first purchase and the end of the observation

period.

Our second variable measures customer defection to deter-

mine the point at which a store may consider a customer

to have defected. Peterson et al. 1989 consider customers

potentially active if their right-censored times are less than

or equal to 12 months. This value, which they derive from

the mail-order industry, is fixed in an arbitrary way and does

not necessarily fit grocery retailing, where purchase frequen-

5 Oneof theanonymousreviewers suggestedthe termportfolio behavior

to indicate that most households use several grocery stores concurrently. In

contrast, the term switching behavior implies that a shopper first uses one

product or service, then switches to another, discontinuing the use of the

first.

cies are higher. However, no empirical studies exist for this

topic. We therefore calculate the defection indicator by store

(Defect1Defect7) as follows: If the right-censored time in

a given store (Censor1Censor7), which corresponds to the

time between the last purchase in a given store and the end of

the observation period, is greater than four times the average

interpurchase time for that same point of sale (IP1IP7), the

consumer has defected (coded as 1). If the censor time in a

given store is less than four times the average interpurchase

time, the shopper is regarded as active (coded as 0).

The SOW by store is calculated as the average propor-tion of the households purchases in the outlet compared

with its total category purchases. Because SOW could vary

significantly over the 3-year period, we calculate the store

SOW at two different times: year 1 (beginning of the

observation period) and years 2/3 (middle to end of the obser-

vation period). Thereby, we can determine whether program

members changetheir SOW over the study period. Some con-

sumers may have changed their behavior before the start of

the data collection, but unless they provide 100 percent SOW

to thefocalstore, theoretically, they still mayshop there more.

Finally, we take geographical location into account

through seven proxy variables (Dist1Dist7). We computethe variable for distance as the number of kilometers between

the household and the store, as measured from the centroid

of the stores zip code to the centroid of the households zip

code (Bell et al. 1998). In the BehaviorScan test market, the

effect of location is expected to differ. If competing stores are

located close to one another (S2, S3, S6, S7), location should

have less of an effect than for outlets that are farther apart

(S1, S4, S5).

During the 3-year observation period, the households

used, on average, 2.2 stores (standard deviation [SD] 0.93).

Only 1 percent of households limited their purchases to one

store, and, respectively, 40 percent, 36 percent, 12 percent,

6 percent, 4 percent, and 1 percent visited 2, 3, 4, 5, 6, and

7 outlets. This breakdown implies portfolio behaviors in the

households store choice decisions. The mean and total bas-

ket amounts (S1S7) over the 3 years were, respectively, D60

(SD 9.8) and D9420 (SD 397.6). The mean number of store

visits during the 3-year period was 157 (SD 60.3), which

means that households shop once a week on average. The

average SOW was 35.6 percent in RC1, 37.2 percent in RC2,

11 percent in S5, 9.4 percent in S6, and 6.9 percent in S7

(median 20 percent, MAD 12). No significant differences

between men and women were found. Moreover, temporal

variations during the 3 years were weak. The mean lifetime


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was 609 days (SD 157.3). Finally, the defection rates after 1

year (3 years) were approximately 13 percent (27 percent) for

cardholders and 27 percent (49 percent) for customers with-

out a program membership. These rates correspond to those

found by East and colleagues (2000) and may indicate that

shoppers are always a share consumers with weak switch-

ing costs and purchase from several stores without being lostforever (Jackson 1985).

Modeling defection and lifetime duration

Customer lifetime duration and CLV have been mainstay

concepts in marketing for many years. Several approaches

exist, and the topic has been addressed methodologically

by aggregate-level Pareto/NBD models (Schmittlein and

Peterson 1994; Reinartz 1999), Markov models (Pfeifer and

Carraway 2000), individual models of discounted cash flows

(Berger and Nasr 1998), recencyfrequencymonetary value

models (Colombo and Jiang 1999), logit or multivariate pro-

bit models (Donkers et al. 2004), and topdown financialmodels (Gupta et al. 2004).

However, despite the sophisticated, mathematical mod-

els that have been developed, there is little, if any, detailed

discussion in literature of the actual applied calculations

that approximate retention patterns (Berger et al. 2003).

DuWors and Haines (1990), Helsen and Schmittlein (1993),

and Bolton (1998), among others, show that these standard

modeling approaches can break down because of the pecu-

liarities inherent in durations. They suggest that event history

models (also known as hazard models) handle duration and

purchase timing events more effectively in noncontractual

settings in terms of the stability and face validity of the esti-mates and predictive accuracy. These models with greater

flexibility, if they incorporate the proper censoring (preva-

lent in duration time data), seem promising alternatives to

the regression, logit,and discriminant analyses that marketers

typically use to analyze duration and interpurchase times.

We apply the proportional hazard model (Cox 1972) at

the individual customer level. The hazard approach provides

estimates of the residual lifetime duration of a customer, as

well as information about those customers who are at risk.

Our target is to test, for each of the seven stores (store-level),

the impact of loyalty cards, geographical distance, and SOW

on survival probability.

We briefly introduce the core of the analysis methodology

here. The semiparametric model examines the hazard that a

defection will occur at a certain moment. It also describes the

time distribution of that event and estimates quantitatively the

impact of various independent variables, called covariates, on

this distribution. The model works with right-censored data,

that is, those who have stopped buying in an outlet before the

endof theobservation (and thus whose lifetime is known) and

those who have not defected by the end of the observation

period (whose lifetime is unknown).

Two basic variables are introduced into the model: a posi-

tive random variable Tthat represents the lifetime (LT1LT7)

of a randomly selected customer and a binary variable for

whether the defection event will occur (Defect1Defect7). At

every moment of a clients lifetime, there is a certain proba-

bility for the defection event. If the event occurs within the

observation period t, the variable is coded as 1, and the life-

time duration is the difference between the defection date and

the date of the first purchase in the store. In the opposite case,the observations are right censored and take a value of 0. The

lifetime duration is thus the difference between the censor

date and the date of the first purchase.

S(t), the survivor function, is the cumulative survival prob-

ability and represents the likelihood that thecustomerwill not

to have left a given store by time t:

S(t) = Pr(T = t) = 1 F(t) = 1 Pr(T < t). (1)

F(t) is the cumulative distribution function of the variable

T and corresponds to the cumulative likelihood of defection

in a given store by time t(between 0 and t):

F(t) = Pr(T < t). (2)

f(t), the probabilitydensity function, represents the likelihood

that a customer will defectat moment tandis calculated as the

product of the survivor function S(t) and the hazard function

h(t). In

f(t) = lim[Pr(t < T < t+ dt)] = h(t) S(t), (3)

h(t) is the hazard function and corresponds to the conditional

likelihood that defection occurs at duration time t, given that

it has not occurred in the duration interval [0, t]. It also rep-

resents the ratio between f(t) and S(t). If h(t) is high, the

defection rate is important:

h(t) = Pr

t T t+ dt

T > t

=

f(t)

[1 F(t)]=

f(t)

S(t). (4)

The semiparametric estimation for hazard model parame-

ters is based on a partial likelihood regression procedure (Cox

1972), in which explanatory covariates xis are introduced for

each unit. To take the prevalence of multiple card holders into

account, we use dummy variables for cards (Card1Card4)

for focal storesand for competingchains (0 = no membership,

1 = membership). We also consider the distance of the house-

hold from the stores (Dist1Dist7) and the SOW1SOW7 for

each store as the focal store (measured at year 1 and years2/3). The dependent variable is the lifetime (LT1LT7) for

one of the seven stores:

S(t) = [S0(t)]p, where p = ebx. (5)

The estimated survival function S(t) is considered the

survival probability, and as it approaches 0, purchase proba-

bilities become less important. Negative estimated regression

coefficients b of the covariate are assumed to increase the

likelihood of survival, whereas positive coefficients should

reduce the likelihood. We thus test: b = 0 (no significant

impact of the covariate on the survival probability) against


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b < 0 (significant impact of the covariate on the survival prob-

ability.

Because we want to test whether cardholders stay longer, it

would be dangerous to consider only covariates that are fixed

over time andinclude just onevariable, that is,whether people

subscribe to the loyalty program. In this situation, the propor-

tional hazard assumption of the Cox regression model maynot hold because hazard ratios change across time (i.e., SOW

values probably differ at different time points). Therefore, we

use an extended Cox regression model, which enables us to

specify time-dependent covariates whose values are subject

to change with time, aswellas to model the effects of subjects

transferring from one group to another. Moreover, loyalty

program membership is not systematically related to time, so

we must define a segmented time-dependent covariate. We

thus create a variable that gives us the time until and after

S1s6 loyalty program adoption at every moment t from the

beginning to the end of the process (Anderson and Gill 1982).

These values are3,2, and1 and 0, +1, +2, and +3 quar-

ters after card adoption. We then measure SOW and lifetimeduring the seven quarters, update behavior at each quarter,

and compare it with the chosen time frame using a variable T

that relates to the process time and the covariate in question.

Thus, at each point in time, actual process time is added to the

time value at thebeginning, and if the result lies between t 3

and t1 quarters, the dummy variable for card possession

takes the value 0. For t0 to t+ 3, the variable has a value of 1.

Thus, different time-dependent covariates, T COV , are cre-

ated and included in the Cox regression model. The models

supported by SPSS belong to the following class of uni-

variate proportional hazards models with a single response

time:

h(t, x(t), z(t)) = h0j(t) exp(x(t)b + z(t)u), (6)

where h(t) is the hazard function of an individual (instanta-

neous risk that the event will occur at time t, given it has not

already occurred), depending on time t, a vector of (possibly)

time-dependent covariatesx(t) with corresponding parameter

vector b, and a vector of (possibly) time-dependent random

covariates z(t) with corresponding parameter vector u.

For a detailed description of the methodology of survival

analysis, see Cox (1972), Kalbfleisch and Prentice (2002),

and Klein and Moeschberger (1997).

Modeling customer shares

To test whether SOW in the focal store is positively related

to loyalty card possession and store distance, we apply a gen-

eral linear model (GLM). In this case, SOW1SOW7 are the

dependant variables that we explain by loyalty card member-

ships, the households store distance, and their interaction.7

6 We do thisonly for S1, because we obtainedindividual information about

the 266 customers loyalty card subscription dates only from S1.7 We perform all analyses for the outlets at the store level, even though S1

and S2/S3 and S4 belong to RC1 and RC2, respectively, because we need

To analyze whether S1 loyalty cards affect SOW after

subscription, we analyze variations in repeated data measure-

ments. We formulate a linear model (ANOVA with repeated

measures) for the sample with S1s 266 loyalty scheme mem-

bers to indicate their SOW developments on the basis of

observed indicators three quarters prior to loyalty program

membership and four quarters after. H0 posits that the cardhas no effect and variations in purchase behavior are system-

atic, which would mean variations are linked to systematic

evolutions rather than loyalty cards. In contrast, H01 argues

that variations in purchase behavior are not systematic over

time and are observed for cardholders, possibly driven by

loyalty scheme membership.

To test the SOWlifetime relationship, we compute the

Pearson r12 and the partial correlation r12.3 for lifetime

durations and SOW, while controlling for possession of a

loyalty card from the focal store simultaneously (Blalock

1961; Davis 1985, pp. 3844)8. If there is no difference

(r12 = r12.3) between the controlled and the original corre-

lation, the loyalty card of the focal store has no effect, andthe SOWlifetime relationship is not automatic. If the partial

correlation approaches 0 (r12.3 = 0), the original correlation

is spurious, and there is no direct causal link between lifetime

and SOW, because the control variable is either a common

antecedent or an intervening variable.

In Table 9, we find no difference between the partial and

original correlations (r12 = r12.3). Thus, the control variable

loyalty card of the focal store has no effect, and the lifetime

durationSOW relationship is not automatic.

Results

The impact of loyalty programs on SOW

In Table 3, we show the results of the GLM. For all stores, the

respective loyalty program memberships are positively linked to

the focal stores SOW (p < 0.01 or p < 0.05). The distance variables

have the expected negative signs and, in most cases, are significant

(p < 0.01; p < 0.05). The farther a household is from the focal store,

the more its SOW decreases. With regard to the interaction of the

loyalty programs and store distances, we find that loyalty schemes

moderate the negative distance effect on SOW, but this impact is

strongest when the distance is small ( 0.1). For S5,

the geographically isolated location, the loyalty program influences

to include the location effect (i.e., S1 and S2 and S3 and S4 do not have the

same zip codes).8 Partial correlation is common when there is only one control variablebut

is sometimes used with two or three. For large models, researchers use path

analysis or structural equation modelingwhen data are near or at theinterval

level or use log-linear modeling for lower-level data. Newer versions of

structural equation modeling software allow variables of any type on either

side of the equation.


9/14


Table 3

Regression coefficients b GLM

b (SOW1) Sig b (SOW2) Sig b (SOW3) Sig b (SOW4) Sig b (SOW5) Sig b (SOW7) Sig

Constant 0.49 ** 0.17 0.24 ** 0.31 ** 0.002 ns 0.02 ns

Loyalty card focal store 0.29 ** 0.02 * 0.47 ** 1.5 ** 0.44 ** 0.15 **

Distance 4 km 0.44 ** 0.14 ns 0.19 ** 0.23 ** 0.31 ns 0.09 ns

Loyalty card distance 4 km 0.29 ** 0.18 * 0.22 ** 0.20 * 0.28 ns 0.29 ns

R2 (adj. R2) 0.17 (0.16) 0.4 (0.39) 0.45 (0.44) 0.36 (0.35) 0.49 (0.48) 0.41 (0.39)

ns: non-significant.* p < 0.05.

** p < 0.01.

Table 4

SOW before (t3 to t1)/after (t0 to t+ 3) loyalty card subscription S1

Quarter Before card subscription After card subscription

t3 t2 t 1 t0 t+ 1 t+ 2 t+ 3

SOW card holder (percent) 32 41 36 44 46 45 45

F 5.6 ns 11.5* 17.4** 2.3 ns 2.7 ns 1.2 ns


** p < 0.01.

SOW5 only when a store is nearS5 ( 0.1). For all other out-

lets (S3, S4, S5, S7), the focal store cards reduce the relative

risk of defection (p 0.1), possibly because the outlet

is geographically isolated from all competitors. Finally, if focal

store S6 shoppers, the only outlet without a loyalty program, hold

one of the four loyalty cards, the risk of defection is not affected

(p > 0.1).

Tables 7 and 8 show the regression coefficients b of the extended

Cox regression model with segmented time-dependent covariates

three quarters before and four quarters after card subscription.

From quarter t0 to t+ 3 (Table 8), theRC1 loyalty scheme signif-

icantly (p < 0.01) reduces focal store S1s relative risk of defection

by 59 percent, 63 percent, 46 percent, and 27 percent, respectively.

The impact seems relatively short-term. Over all quarters, if shop-

pers are simultaneously members of RC2s program, the defection

risk rises (p < 0.05), though its impact seems to decrease with time

(t+ 2, t+3).

These results clearly support H2: Loyalty cardholders display

longer lifetimes. However, the simultaneous possession of com-

petitive loyalty cards of geographically close retailers decreases

lifetime duration and makes customers more vulnerable. We thus

find support for H3.


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Table 5

Regression coefficients b Cox model S1S4

Focal store S1 Focal store S2 Focal store S3 Focal store S4

b Wald Sig b Wald Sig b Wald Sig b Wald Sig

Card S1 and S2 (RC1) 0.31 14.77 ** 0.23 4.51 * 0.00 4.51 ns 0.08 0.69 ns

Card S3 and S4 (RC2) 0.06 0.62 ns 0.00 0.00 ns 0.41 8.51 ** 0.23 9.32 **

Card S5 0.08 0.45 ns 0.13 1.76 ns 0.18 1.76 ns 0.36 8.59**

Card S7 0.17 1.49 ns 0.24 11.34 ** 0.13 11.34 * 0.17 5.52 *

Distance S1 0.40 4.39 * 0.10 0.48 ns 0.09 0.48 ns 0.05 0.07 ns

Distance S2 0.06 0.56 ns 0.08 4.71 * 0.04 1.77 ns 0.12 1.55 ns

Distance S3 0.04 0.20 ns 0.06 0.78 ns 0.29 0.78 ns 0.04 0.18 ns

Distance S4 0.18 2.00 ns 0.30 7.68 ** 0.07 7.68 ns 0.06 0.25 ns

Distance S5 0.24 3.45 ns 0.00 0.00 ns 0.16 0.00 ns 0.03 0.06 ns

Distance S6 0.70 5.37 * 0.40 2.47 ns 0.16 2.47 ns 0.15 0.25 ns

Distance S7 0.45 4.03 * 0.25 5.22 * 0.08 1.68 ns 0.06 0.07 ns

SOW focal store year 1 0.07 230.23 ** 0.06 154.24 ** 0.07 154.24 * 0.06 352.68 **

SOW focal store year 2+ 3 0.085 289.34 ** 0.07 181.56 ** 0.08 177.37 * 0.07 394.23 **

Change 2 729.9 883.3 900.1 1073

2 initial log likelihood 13903 17158 18327 15977

2 final log likelihood 13173 16275 17427 14904


** p < 0.01.

Table 6

Regression coefficients b Cox model S5S7

Focal store S5 Focal store S6 Focal store S7

b Wald Sig b Wald Sig b Wald Sig

Card S1 and S2 0.02 0.068 ns 0.00 0.003 ns 0.03 0.166 ns

Card S3 and S4 0.07 1.572 ns 0.02 0.087 ns 0.02 0.115 ns

Card S5 0.25 6.12 * 0.07 0.661 ns 0.05 0.469 ns

Card S7 0.11 3.58 ns 0.1 3.128 ns 0.1 5.837 *

Distance S1 0.05 0.065 ns 0.1 0.752 ns 0.3 6.269 *

Distance S2 0.0 0.002 ns 0.0 0.137 ns 0.0 0.034 ns

Distance S3 0.1 1.512 ns 0.0 0.041 ns 0.0 0.007 nsDistance S4 0.04 0.169 ns 0.0 0.008 ns 0.1 2.949 ns

Distance S5 0.18 3.228 ns 0.0 0.221 ns 0.1 1.972 ns

Distance S6 0.12 0.157 ns 0.16 10.46 * 0.1 0.072 ns

Distance S7 0.13 0.423 ns 0.2 2.06 ns 0.48 9.355 **

SOW focal store year 1 0.09 166.1 ** 0.1 232.1 ** 0.1 108.9 **

SOW focal store year 2+ 3 0.1 181.3 ** 0.12 256.3 ** 0.11 139.2 **

Change 2 934.8 973.7 584

2 initial log likelihood 25377 26752 31820

2 final log likelihood 24442 25779 31236


** p < 0.01.

The impact of SOW on lifetime duration

Our different event history models indicate a SOWlifetime rela-

tionship. In Tables 58, SOW in all focal stores has the expected

negative sign and is significant (p < 0.01 or p < 0.05), which means

that the more a household spends in the outlet proportionally, the

lower its defection risk becomes. Tables 5 and 6 also demonstrate

that the negative impact of SOW on the relative risk of defection

in the focal store increases with time (from year 1 to year 2/3).

For example, S1s SOW reduces the risk of defection by 7 per-

cent [(1 exp(0.07)=1 0.93= 0.07] in the first year and by

8 percent [(1 exp(0.085)=1 0.92 = 0.08] in the second/third

year (p < 0.01). In the Cox regression model with segmented time-

dependent covariates (Tables 7 and 8), the negative impact of SOW

on the risk of defection in the focal store rises with time(t 3 to t0).

As we discussed previously, Table 9 shows no difference between

the Pearson and the partial correlations (in italics and brackets) for all

stores (r12 = r12.3). Thus, there is a direct causal link between life-

time and SOW. Correlations between SOW and lifetime durations

in focal stores are strongly positive (p < 0.01), but the relationships

are mostly negative between SOW of competing stores and lifetime

durations (p < 0.01; p < 0.05).10 We thus confirm H4 regarding the

SOWlifetime relationship.

10 Noticing the positive .083, which is significant for the relationship

between S2/SOW1 but not between S1/SOW2, the competition between S1


11/14


Table 7

Regression coefficients b extended Cox model before (t3 to t1) program subscription S1

Quarter t3 t 2 t1

b Wald Sig b Wald Sig b Wald Sig

Card RC1

Card RC2 0.079 4.29 * 0.062 4.10 * 0.038 3.9 *

Card S5 0.081 0.41 ns 0.087 0.43 ns 0.071 0.42 nsCard S7 0.71 0.36 ns 0.7 0.34 ns 0.8 0.42 ns

Distance focal store S1 0.42 4.2 * 0.40 4.0 * 0.45 4.3 *

SOW focal store S1 0.07 32.2 ** 0.08 24 ** 0.1 56 **

Change 2 757 873 999

2 initial log likelihood 13802 12038 13164

2 final log likelihood 13045 11165 12165


** p < 0.01.

Table 8

Regression coefficients b extended Cox model after (t0 to t+ 3) program subscription S1

Quarter t0 t+ 1 t+ 2 t+ 3

b Wald Sig b Wald Sig b Wald Sig b Wald Sig

Card RC1 0.88 22.7 ** 0.99 35.4 ** 0.61 18.4 ** 0.31 14.76 **

1 exp(b) 59 percent 63 percent 46 percent 27 percent

Card RC2 0.085 4.48 * 0.016 3.45 * 0.06 4.66 * 0.05 3.45 *

Card S5 0.11 0.56 ns 0.14 0.70 ns 0.13 0.68 ns 0.12 0.62 ns

Card S7 0.86 0.075 ns 0.75 0.04 ns 0.72 0.02 ns 0.69 0.01 ns

Distance focal store S1 0.40 4.2 * 0.39 3.8 * 0.33 4.0 * 0.34 4.1 *

SOW focal store S1 0.08 42.2 ** 0.08 38.2 ** 0.09 49 ** 0.06 32.4

Change 2 741 669 439 663

2 initial log likelihood 13264 14222 13118 13652

2 final log likelihood 12523 13553 12679 12989


** p < 0.01.

Table 9

Pearson and partial correlations (in brackets) SOW/lifetime

SOP1 SOP2 SOP3 SOP4 SOP5 SOP6 SOP7

Lifetime S1 0.806** (0.81) 0.186** 0.181** 0.022 ns 0.236** 0.128** 0.093*

Lifetime S2 0.083** 0.787** (0.78) 0.021 ns 0.237** 0.232** 0.043 0.104**

Lifetime S3 0.094** 0.048* 0.835** (0.82) 0.133** 0.009 ns 0.237** 0.239**

Lifetime S4 0.021 ns 0.299** 0.228** 0.829** (0.83) 0.062* 0.252** 0.225**

Lifetime S5 0.155** 0.141** 0.057* 0.041 ns 0.871** (0.87) 0.178** 0.221**

Lifetime S6 0.086** 0.154** 0.167** 0.248** 0.226** 0.860** 0.036 ns

Lifetime S7 0.156** 0.076** 0.175** 0.204** 0.185** 0.090** 0.863** (0.86)

ns: non-significant.*

p < 0.05.** p < 0.01.

The impact of store distance on lifetime duration

Finally, according to the results of the survival analyses in

Tables 58, the distance variables to focal stores have the expected

positive signs and, in most cases (with the exception of S4 and

and S2 seems asymmetric. That is, within the same retailing chain, longer

lifetime in the smaller store S2 influences SOW in the bigger outlet S1

positively. S1 appears to be used by S2 shoppers for major shopping trips,

whereas the opposite is not the case.

S5), are significant (p < 0.01; p < 0.05), which means that the far-

ther a household is from the focal store, the more its defection risk

increases. The distance regression scores for the focal store S1 are

stable over time (Tables 7 and 8). In contrast, the distance variables

to (geographically close) competitive outlets have expectednegative

signs and, in most cases, are significant (p < 0.01; p < 0.05), indicat-

ing that the farther a household is from the competitive stores, the

more itsdefection risk decreases. Therefore, we find support for H5;

the geographical proximity to a given store is related positively to

its lifetime duration.


12/14


Discussion

In line with our expectations, as well as Magi (2003)

and Dowling and Uncles (1997) work, the results of this

study provide support for the positive effects of loyalty pro-

grams on lifetime duration and customer SOW at the store

level. In contrast, taking into consideration the large num-ber of multiple-card holders, the effects of competing loyalty

schemes by geographically close retailers may cancel one

another out as a greater degree of imitation than innovation

emerges.

We find a positive relationship between SOW and lifetime

duration, which indicates that the more customers purchase

proportionally in a store, the longer they will remain with that

retailer. Furthermore, the impact of SOW on lifetime duration

increases with time (from year 1 to year 2/3). These results

are in line with those of East and colleagues (1997, 2000) but

contrast with those ofReinartz (1999). Different explanations

are possible due to consumer heterogeneity. For example,

SOW and lifetime may not be related when shoppers lackinterest in stores and have a lifestyle thatemphasizesactivities

unrelated to shopping; in these circumstances, people try to

simplify their shopping problems by limiting the range of

stores they use and continuing to use the same store for long

periods of time. Increased SOW also occurs when people

ignoredeals and simplify theirshopping by consistently using

the same stores (East et al. 1997).

An important issue for loyalty schemes is the causal direc-

tion of effects; Dowling and Uncles (1997) doubt if loyalty

programs modify purchase behavior or if heavier cus-

tomers are simply more loyal. Our study gives an answer

to this question. After the program subscription, the loyaltycard reduces significantly the relative risk of defection and

increases SOW in the focal store. Our results indicate that

the loyalty programs tend to change the shopping behavior

of some consumer segments after they join the program, even

if some already loyal buyers were being rewarded for their

established shopping patterns. The loyalty scheme probably

prevents loyalty card holders from changing their behavioral

patterns,such as shopping more at competitors stores, or cre-

ates a purchase concentration effect for the focal outlet. This

suggestion might offer an explanation for why many multi-

loyal shoppers, who already use several chains on a regular

basis, join all available programs to take advantage of their

benefits (Ehrenberg 1988).

Overall, consumer segments likely reactdifferentlyto loy-

alty programs, as occurs with sales promotions (Mela et al.

1996). Consumercharacteristics(e.g., variety-seeking behav-

ior, shopping orientations, sensitivity to sales promotions)

influence the strength and direction of the impact of loyalty

programs on repurchase behavior. Consequently, a more thor-

ough analysis of loyalty cards effects at the individual level

and of its determinants is required. Such segmentation would

enable a better measurement of consumers sensitivity to

loyalty-developing actions and an assessment of customers

potentialvalue. Loyalty schemes seembe fullyprofitableonly

when applied to a small number of customers (Benavent et

al. 2000); many existing grocery loyalty programs therefore

may fail because they lack precise customer segmentation

and targeting.

Our findings have important implications for managing

customer portfolios and lifetime value. First, they suggest

great possibilities for the extent to which customer shareand lifetime duration can be created or fostered through loy-

alty schemes. This insight is important when retailers design

and evaluate the outcomes of programs aimed at changing

customer behavior. Second, measurable factors can predict

retention, given that store defections in the grocery industry

are inevitable. Retailers can gather shoppers information,

such as SOW, lifetime duration, and loyalty card portfolios,

as well as store distance, then use these data to segment

according to customer vulnerabilities, defection risks, deal

proneness, price sensitivities, or lifetime values. With this

information, retailers can undertake tailored strategies and

incentives to appeal to different segments and restore their

patronage. Loyalty schemes thus may become strategic toolsto manage customer heterogeneity by selecting, identifying

and segmenting consumers, whichimprovesand personalizes

the focus of marketing resources.

Our investigation also suggests loyalty programs should

go beyond just rewarding usage and reward customers

accordingto future-oriented measures suchas estimatedCLV.

Kumar and Shah (2004) similarly suggest that companies can

build and sustain behavioraland attitudinalloyalty simultane-

ous with profitability. According to their two-tiered system,

customer loyalty should be managed at the first level by treat-

ing all shoppers equally and rewarding them in proportion to

their total expenses to encourage more spending. At the sec-ond level, customer data indicate customer-level differences,

so the retailer can determine whether a particular customer

qualifies for additionalrewards. Through the careful selection

of appropriate customers, companies can selectively build

loyalty for their most valuable customers (measured as the

CLV metric) with more qualitative second-level rewards (e.g.,

personalized relationships, privileged services).

Limitations and directions for further research

Studies of loyalty programs remain rare and incomplete,

because the majority of cases have not been empirically mea-

sured. Thus, many questions remain that provide options for

developing this work further.

One restriction of our investigation is the difficulty of get-

ting the mixed data on which our analysis is based (store

intern scanner data and single-source panel data). Thus,

applying our approach to other sectors (e.g., airlines, restau-

rants) is difficult, because single-source panel data usually

exist onlyfor fast-moving consumer goods. More replications

in other sectors are needed to enhance the generalizibility

of our findings from the retail sector to other domains. Our

study also does not integrate financial data, though the suc-


13/14


cess of a loyalty program should be measured by its financial

contribution (Kopalle and Neslin 2003).

The question of how program membership, lifetime dura-

tion, and SOW interrelate also should be expanded. Do

customers engage in long-term relationships with retailers

because their expenses are high, or do they spend money in

stores because they have high lifetime durations? Does SOWmediate an effect of membership on duration,or areall effects

independent?

The relationships between loyalty programs and behav-

ioral outcomes are probably more complex than has

been assumed. How consumer characteristics (e.g., variety-

seeking behavior, shopping orientations) moderate the

relationship between schemes and repurchase behavior likely

is contingent on the product category. The level of price com-

petition in grocery retailing also means that a significant

segment of consumers shop around for specials and there-

fore choose different stores during different shopping trips.

Another interesting area of study might be to expand the indi-

vidual modeling to shopping basket content, because loyaltyprograms likely work better for certain products (e.g., baby

products; Dreze et al. 1994).11

Finally, experimental approaches analyzing how rewards

influence purchase behavior are highly recommended (Kivetz

and Simonson 2003; Roehm et al. 2002; Yi and Jeon

2003; Keh and Lee 2006; Meyer-Waarden 2006; Meyer-

Waarden and Benavent 2007). These questions are only

partially solved, and additional research therefore should

contribute to better theoretical and empirical knowledge

about the way rewards influence value perceptions of loyalty

schemes, because rewards determine program adoption and

use.

Acknowledgements

The author expresses his warmest thanks to Market-

ingScan and an anonymous retailer for kindly providing data.

He also is grateful to the anonymous reviewers.

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