The effects of loyalty programs on customer lifetime duration and share
of wallet
Lars Meyer- Waarden*
*University Toulouse III Paul Sabatier (France), EA 2043, Department of Management and Cognition Sciences (LGC) and Customer Research Marketing Group.
Corresponding Address: Lars Meyer-Waarden 98, rue Vestrepain, F-31200 Toulouse
Tel.: +33/(0)6.80.37.42.08
E-mail address: [email protected]
Acknowledgements: The author expresses his warmest thanks to MarketingScan and an
anonymous retailer for kindly providing data. He also is grateful to the anonymous reviewers. First submission date: 4 April 2005
The effects of loyalty programs on customer lifetime duration and
share-of-wallet
Abstract:
In the retailing sector, consumers typically patronize multiple outlets, which confronts these outlets with an important issue: determining how 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 BehaviorScansingle-source panel data, examines the impact of loyalty programs on customer lifetime duration in grocery stores. The findings 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.
Introduction
Many retailers currently regard loyalty programs as fundamental. For example, the grocery retailer E. Leclerc in France devotes approximately €18 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 raised the standing of customer relationship management (CRM) and its associated issues (e.g., the efficiency of loyalty programs and other CRM tools) to its capital research priority for 2004–2006. Moreover, the Journal of Retailing devoted a special issue to customer loyalty to stimulate research on topics currently prominent in the minds of retailers, such as loyalty programs, drivers of store loyalty, and so forth (Grewal, Levy, and Lehmann 2004). Despite this strong interest, scarce empirical academic work investigates the potential impacts of loyalty programs on real buyers’ behaviors, and the research that does exist provides mixed evidence (Nako 1997; Sharp and Sharp 1997; Bolton, Kannan, and Bramlett 2000; Benavent, Crié and Meyer-Waarden 2000; Leenheer, Bijmolt, van Heerde, and Smidts 2003; Mägi 2003; Yi and Jeon 2003; Lewis 2004; Taylor and Neslin 2005; Kivetz, Urminsky, and Zheng 2006). The ambiguity in the results of these studies likely reflects limitations in the data and methodology that hinder the proper assessment of the effects of loyalty programs. 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 information about purchasing behavior because clients
Lewis 2004). An alternative source has been declarative survey data, whose reliability problems are well documented (Mägi 2003; Yi and Jeon 2003). Finally, another contributing factor to this ambiguity 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.
To rectify some of these methodological issues, we use marketwide scanner panel data about competitive purchasing 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 background and then develop our hypotheses. Subsequently, we describe our data and present the results. We conclude with a discussion and suggestions 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 customer’s tenure is assumed to be related to long-run company revenues and profitability (Bolton, Lemon, and Verhoef 2002; Gupta, Lehmann, and Stuart 2004).
Customer relationship management is organized according to the customer lifecycle because lifetime duration with a firm generally is not perpetual. Consumers may be dissatisfied and find better value elsewhere (Oliver 1999) or change their lifecycle in a way that causes them to lose the need for the product. Companies therefore search to influence customers across their lifecycles through adequate acquisition and development strategies (e.g., delivering customized
products, cross-selling, up-selling) and use retention strategies to enhance the total lifetime of the customer base. 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 in subsequent 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 consequently the share devoted to the focal store, varies across consumers (East, Harris, Willson, Lomax, and Hammond 2000; Mägi 1999). In this context, customer SOW corresponds to the share of category expenditures spent on purchases at a certain store, which integrates both choice behavior and transaction values during a specific 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.
Relationship among loyalty programs, SOW, and lifetime duration
Loyalty programs, which represent tools for developing relationships and SOW, offer integrated systems of marketing actions and economic, psychological, and sociological rewards. Successful loyalty schemes increase customer retention, lifetime duration, and customer SOW; their overall objective is to modify customer repeat behavior by stimulating product or service usage and
Loyalty programs can create different types of switching barriers, including economical, in which case customers lose advantages (e.g., points) if they change product or service suppliers, and psychological, sociological, and relational barriers that enhance customers’ commitment to and trust in the organization (Morgan and Hunt 1994), which strengthens 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 strongly with the company (Oliver 1999).2In this scenario, an interactive, 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.
As we show in 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 initial 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 systems that are prevalent today (Dowling and Uncles 1997; Sharp and Sharp 1997; Benavent et al. 2000; Leenheer et al. 2003; Mägi 2003; Meyer-Waarden 2004).
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 behavior.” 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
2This customer identification is especially beneficial in industries in which consumers purchase frequently and the
differentiation among suppliers is low (Bhattacharya and Sen 2003).
3However, both studies should be interpreted with caution because exit barriers in the industries they study are relatively
increase their purchase rate after they have received the reward. Benavent et al. (2000) and Kivetz, Urminsky, and Zheng (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” predicts 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 negative effect on lifetime duration. However, the author tests a proprietary credit card of a mail-order company, which differ somewhat from the loyalty cards French retailers use, because they offer added convenience and buying power to shoppers and target only low-income customers.
Insert table 1 here
Despite the contradictory empirical evidence and taking into account the limitations of these studies, it seems intuitive that members of the loyalty programs of their focal stores concentrate 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 financial advantages, added convenience, and identification). In turn, there should be a positive association between loyalty card possession, SOW, and lifetime duration in the focal store. 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 several stores will be members of loyalty schemes offered by all stores. On average, European and American consumers possess three retailing loyalty cards, which may create cherry-picking behavior, through which consumers hop
This suggestion corresponds with findings that show that loyalty program members need not exhibit a high degree of repurchase behavior (Benavent et al. 2000; Mägi 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 higher SOW, in that the share a household designates to 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 duration, 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 automatically are positively correlated? We treat this problem and its methodology more in detail in the methodology section (p.18).
Furthermore, we consider another theoretical problem pertaining to the likely nature of the SOW–lifetime duration relationship, specifically, differences in sectors. Jackson (1985) and Dwyer (1989) differentiate two types of markets in this sense: (1) “lost for good” and (2) “always a share.” In the first category, the customer enters into a contractual 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, Leone, and Jen 1999). However, most purchase processes belong to the “always a share” category (e.g., retailing, packaged goods), so switching
costs are low, and customers typically buy from several competing companies simultaneously, 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 necessarily 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, heavy shoppers 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 must travel to shop at it represent basic criteria in store choice decisions and assessments of total shopping costs (e.g., Arnold, Oum, and Tigert 1983; Kahn and Schmittlein 1989; Bell, Ho, and Tang 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’ effectiveness, we employ competitive information on individual customer purchases in competitors’ stores by using the single-sourceBehaviorScan panel based in Angers, a French 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, S1–S5, with surface areas of 5,000–9,000 m², situated at the town peripheries and intersections of major highways; two supermarkets, S6 in the city center of 2,000 m2and S7 on the outskirts with 1,400 m²). These retail outlets represent 95% of the fast-moving consumer goods sales in the area. Figure 1 shows the store locations.
Insert figure 1
S6 and S7, which represent smaller supermarkets, are direct competitors due to their geographical proximity to bigger 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 the exception of S4, which is 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 period4(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 loyalty 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
4Panelists are chosen randomly and replaced every four years. For their participation, they receive purchase vouchers and
four loyalty programs’ memberships. In addition, we obtain loyalty 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, the programs are free and provide price discounts on 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 0.1–1% of the purchase amount). The schemes give also 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% of the panel households are members of at least one loyalty scheme, and the duplication rate of program memberships is substantial: 27% of the households have two or more loyalty cards, 6% have three, and 1% have four. On average, a household holds 1.48 loyalty cards. In addition, 39% of the members of RC1’s program also hold a loyalty card of RC2 (see Table 2), 6% also hold a card of S5, and 46% a card of S7. For RC2’s members, the duplication rate is highest for S7 (38%).
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 (LT1–LT7), corresponds to the difference between the date of the 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 observation period. However, insofar as all panelists reflect this left censorship, this methodological problem should not represent 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 determine the point at which a store may consider a customer to have defected. Peterson, Albaum, and Ridgway (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 frequencies are higher. However, no empirical studies exist for this topic. We therefore calculate the defection indicator by store (Defect1–Defect7) as follows: If the right-censored time in a given store (Censor1–Censor7), 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
5One of the anonymous reviewers suggested the term “portfolio 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.
of sale (IP1–IP7), 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 proportion of the household’s purchases in the outlet compared with its total category purchases. Because SOW could vary significantly over the three-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 observation period). Thereby, we can determine whether program members change their SOW over the study period. Some consumers may have changed their behavior before the start of the data collection, but unless they provide 100% SOW to the focal store, theoretically, they still may shop there more.
Finally, we take geographical location into account through seven proxy variables (Dist1–Dist7). We compute the variable for distance as the number of kilometers between the household and the store, as measured from the centroid of the store’s zip code to the centroid of the household’s 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 three-year observation period, the households used, on average, 2.2 stores (standard deviation [SD] 0.93). Only 1% of households limited their purchases to one store, and, respectively, 40%, 36%, 12%, 6%, 4%, and 1% visited 2, 3, 4, 5, 6, and 7 outlets. This breakdown implies portfolio behaviors in the households’ store choice decisions. The mean and total basket amounts (S1–S7) over the three years were, respectively, €60 (SD 9.8) and €9420 (SD 397.6). The mean number of store visits during the three-year period was 157 (SD 60.3),
RC1, 37.2% in RC2, 11% in S5, 9.4% in S6, and 6.9% in S7 (median 20%, MAD 12). No significant differences between men and women were found. Moreover, temporal variations during the three years were weak. The mean lifetime was 609 days (SD 157.3). Finally, the defection rates after one year (three years) were approximately 13% (27%) for cardholders and 27% (49%) for customers without 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 switching costs and purchase from several stores without being lost forever (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), recency-frequency-monetary value models (Colombo and Jiang 1999), logit or multivariate probit models (Donkers, Verhoef, and De Jong 2004), and top-down financial models (Gupta et al. 2004).
However, despite the sophisticated, mathematical models that have been developed, there is little, if any, detailed discussion in literature of the actual applied calculations that approximate retention patterns (Berger, Weinberg, and Hanna 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 peculiarities 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
estimates and predictive accuracy. These models with greater flexibility, if they incorporate the proper censoring (prevalent 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 end of the observation (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 positive random variableTthat represents the lifetime (LT1–LT7) of a randomly selected customer and a binary variable for whether the defection event will occur (Defect1–Defect7). At every moment of a client’s lifetime, there is a certain probability for the defection event. If the event occurs within the observation periodt, the variable is coded as 1, and the lifetime 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 probability and represents the likelihood that the customer will not to have left a given store by timet:
1. S(t) = Pr(T≥t) = 1 – F(t) = 1 – Pr(T < t).
F(t) is the cumulative distribution function of the variable Tand corresponds to the cumulative likelihood of defection in a given store by timet(between0andt):
2. F(t) = Pr(T < t).
f(t), the probability density function, represents the likelihood that a customer will defect at moment t and is calculated as the product of the survivor functionS(t) and the hazard function h(t). In
3. f(t) =lim[Pr(t < T < t+dt)] =h(t) * S(t),
h(t)is the hazard function and corresponds to the conditional likelihood that defection occurs at duration timet, given that it has not occurred in the duration interval [0,t]. It also represents the ratio betweenf(t)andS(t). Ifh(t)is high, the defection rate is important:
4. h(t) = Pr (t≤T≤t + dt / T>t) = f(t) / [1-F(t)] = f(t) / S(t).
The semiparametric estimation for hazard model parameters 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 (Card1–Card4) for focal stores and for competing chains (0 = no membership, 1 = membership). We also consider the distance of the household from the stores (Dist1–Dist7) and
the SOW1–SOW7 for each store as the focal store (measured at year 1 and years 2/3). The dependent variable is the lifetime (LT1–LT7) for one of the seven stores:
5. S(t) = [S0(t)]p
,
wherep = ebx.The estimated survival functionS(t) is considered the survival probability, and as it approaches 0, purchase probabilities become less important. Negative estimated regression coefficientsbof 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 H01: b < 0 (significant impact of the covariate on the survival probability.
Because we want to test whether cardholders stay longer, it would be dangerous to consider only covariates that are fixed over time and include just one variable, that is, whether people subscribe to the loyalty program. In this situation, the proportional hazard assumption of the Cox regression model may not 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, as well as 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 S1’s6 loyalty program adoption at every moment t from the beginning to the end of the
process (Anderson and Gill 1982). These values are -3, -2, and -1 and 0, +1, +2, and +3 quarters after card adoption. We then measure SOW and lifetime during the seven quarters, update
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 the beginning, and if the result lies between t-3 and t–1 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 created and included in the Cox regression model. The models supported by SPSS belong to the following class of univariate proportional hazards models with a single response time:
6. h(t,x(t),z(t)) h0j(t)exp(x( t)bz(t)u),
where h(t) is the hazard function of an individual (instantaneous risk that the event will occur at time t, given it has not already occurred), depending on time t, a vector of (possibly) dependent covariates x(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 general linear model (GLM). In this case, SOW1–SOW7 are the dependant variables that we explain by loyalty card memberships, the households’ store distance, and their interaction.7
To analyze whether S1 loyalty cards affect SOW after subscription, we analyze variations in repeated data measurements. We formulate a linear model (ANOVA with repeated measures) for the sample with S1’s 266 loyalty scheme members to indicate their SOW developments on the
7We 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 include the location effect (i.e., S1 and S2 and S3 and S4 do not have the same zip codes).
basis of observed indicators three quarters prior to loyalty program membership and four quarters after. H0 posits that the card has no effect and variations in purchase behavior are systematic, 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 SOW–lifetime relationship, we compute the Pearson r12 and the partial correlation r12.3for lifetime durations and SOW, while controlling for possession of a loyalty card from the focal store simultaneously (Blalock 1961; Davis 1985, pp.38-44)8. If there is no difference (r12 =
r12.3) between the controlled and the original correlation, the loyalty card of the focal store has no effect, and the SOW–lifetime 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 duration–SOW 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 < .01 or p < .05). The distance
variables have the expected negative signs and, in most cases, are significant (p < .01;p < .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 (<1 km), as indicated by the change of sign, which signifies that program memberships are even more positively linked to stores’ SOW when the distance is less. The values are mostly significant (p< .01; p< .05) for S1, S2, S3, and S4, but they are mostly insignificant for S5 and S7 (p> .1). For S5, the geographically isolated location, the loyalty program influences SOW5 only when a store is near S5 (<2 km). In addition, because S7 is a smaller, filler-trip supermarket, its close geographical location plays a major role, and a loyalty card cannot create an attraction effect for households that are situated farther away.
Insert Table 3
With Table 4, we provide S1’s SOW three quarters before (t–3, t-2, t-1) and four quarters after (t0, t+1, t+2, t+3) the card subscription, according to the ANOVA with repeated measures. A stepwise, more or less one-time adjustment in behavior occurs rather than a continuous increase. Between t-2 and t-1, SOW decreases significantly (p < .05), after which point the loyalty program’s impact on SOW increases by 8% and is highly significant from t-1 to t0 (p< .01), or the moment of card subscription. From t+1 to t+3, SOW is stable over time. S1’s loyalty program influences customer behavior, at least in the short term, and appears to protect S1 from losing customer share. We thus find support for H1; loyalty program memberships are positively linked to the focal stores’ SOW.
The impact of loyalty programs on lifetime duration
The event history analyses provide significant results, which appear in Tables 5–8. Globally, the covariates improve the –2 log likelihood in comparison with the base model (p < .01). All b coefficients for the respective loyalty programs in the six analyses are negative for the six stores (S1, S2, S3, S4, S5, S7).9
As Table 5 shows, for example, holding the RC1 loyalty card reduces the relative risk of defection in focal store S1 by 27% (p< .01) and in focal store S2 by 21% (p < .05). Conversely, if focal store S1 shoppers are simultaneously members of RC2’s, S5’s, or filler-trip S7’s competing loyalty programs, the risk rises by 6%, 8%, and 19%, respectively, though the values are not significant (p> .1). For all other outlets (S3, S4, S5, S7), the focal store cards reduce the relative risk of defection (p< .01 orp< .05); conversely, the defection risk for loyalty programs of close competing stores rise in most cases (p< .01 orp< .05). For S5, the values for the other loyalty programs are not significant (p > .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> .1).
Insert Table 5 Insert Table 6
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.
Insert Table 8
From quarter t0 to t+3 (Table 8), the RC1 loyalty scheme significantly (p < .01) reduces focal store S1’s relative risk of defection by 59%, 63%, 46%, and 27%, respectively. The impact seems relatively short-term. Over all quarters, if shoppers are simultaneously members of RC2’s program, the defection risk rises (p< .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 competitive loyalty cards of geographically close retailers decreases lifetime duration and makes customers more vulnerable. We thus find support for H3.
The impact of SOW on lifetime duration
Our different event history models indicate a SOW–lifetime relationship. In Tables 5–8, SOW in all focal stores has the expected negative sign and is significant (p< .01 orp< .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, S1’s SOW reduces the risk of defection by 7% [(1 – exp (-0.07) = 1-0.93 = 0.07] in the first year and by 8% [(1 – exp (-0.085) = 1-0.92 = 0.08] in the second/third year (p < .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 lifetime and SOW. Correlations between SOW and lifetime durations in focal stores are strongly positive (p< .01), but the relationships are mostly negative between SOW of competing
stores and lifetime durations (p < .01; p < .05).10
We thus confirm H4 regarding the SOW– lifetime relationship.
Insert Table 9
The impact of store distance on lifetime duration
Finally, according to the results of the survival analyses in Tables 5–8, the distance variables to focal stores have the expected positive signs and, in most cases (with the exception of S4 and S5), are significant (p< .01;p< .05), which means that the farther 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 expected negative signs and, in most cases, are significant (p< .01;p < .05), indicating that the farther a household is from the competitive stores, the more its defection risk decreases. Therefore, we find support for H5; the geographical proximity to a given store is related positively to its lifetime duration.
Discussion
In line with our expectations, as well as Mägi’s (2003) and Dowling and Uncles’s (1997) work, the results of this study provide support for the positive effects of loyalty programs on lifetime duration and customer SOW at the store level. In contrast, taking into consideration the large number 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 of Reinartz (1999). Different explanations are possible due to consumer heterogeneity. For example, SOW and lifetime may not be related when shoppers lack interest in stores and have a lifestyle that emphasizes activities 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 ignore deals and simplify their shopping by consistently using the same stores (East et al. 1997).
An important issue for loyalty schemes is the causal direction of effects; Dowling and Uncles (1997) doubt if loyalty programs modify purchase behavior or if “heavier” customers are simply more loyal. Our study gives an answer to this question. After the program subscription, the loyalty card 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 creates a purchase concentration effect for the focal outlet. This suggestion might offer an explanation for why many multiloyal 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 react differently to loyalty programs, as occurs with sales promotions (Mela, Gupta, and Lehmann 1996). Consumer characteristics (e.g., variety-seeking behavior, shopping orientations, sensitivity to sales promotions) influence the strength and direction of the impact of loyalty programs on repurchase behavior. Consequently, a more thorough 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’ potential value. Loyalty schemes seem be fully profitable only 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 share and lifetime duration can be created or fostered through loyalty 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 tools to manage customer heterogeneity by selecting, identifying and segmenting consumers, which improves and personalizes the focus of marketing resources.
Our investigation also suggests loyalty programs should go beyond just rewarding usage and reward customers according to future-oriented measures such as estimated CLV. Kumar and Shah (2004) similarly suggest that companies can build and sustain behavioral and attitudinal loyalty simultaneous with profitability. According to their two-tiered system, customer loyalty should be managed at the first level by treating all shoppers equally and rewarding them in proportion to their total expenses to encourage more spending. At the second level, customer data indicate customer-level differences, so the retailer can determine whether a particular customer qualifies for additional rewards. 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 measured. Thus, many questions remain that provide options for developing this work further.
One restriction of our investigation is the difficulty of getting 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, restaurants) is difficult, because single-source panel data usually exist only for 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 success of a loyalty program should be measured by its financial contribution (Kopalle and Neslin 2003).
The question of how program membership, lifetime duration, 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 SOW mediate an effect of membership on duration, or are all effects independent?
The relationships between loyalty programs and behavioral 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 competition in grocery retailing also means that a significant segment of consumers shop around for specials and therefore choose different stores during different shopping trips. Another interesting area of study might be to expand the individual modeling to shopping basket content, because loyalty programs likely work better for certain products (e.g., baby products; Drèze, Hoch, and Purk 1994).11
Finally, experimental approaches analyzing how rewards influence purchase behavior are highly recommended (Kivetz & Simonson 2002; Roehm, Bolman Pullins and Roehm Jr. 2002; Yi and Jeon 2003; Keh and Lee 2006). 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.
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Table.1
Comparison investigation
Authors Sector, Country Dependant Variables Research Design, Sample, Method
Results
Sharp & Sharp (1997)
Grocery retailing, Australia
Market share, sole buyer, repeat purchase& frequency
Self-reported panel survey (N=745), Dirichlet model
Little to no impact on all Dirchlet indicators.
Nako (1997) Airlines, USA Market share, basket value, sensitiveness competitors’ offers
Declarative panel (N=650), MNL
Stronger increase airline’s utility by loyalty program than by brand image, number destinations, on-board service. Correlation program member-ship & length of flight route, frequent flyers less price sensitive.
Benavent et al. (2000) Grocery retailing, France Turnover, margin, traffic, purchase volume & value, interpurchase time
POS scanner data (150,000 purchasing acts), OLS
regression
+4.8% turnover & +3.5% traffic; negative impact on margin if massive card distribution; no impact purchase volume& value. Bolton et al. (2000) Credit cards, Europe Retention, purchase frequency, purchase value, dissatisfaction
Credit card usage & self-reported data (N=405), logistic & Tobit regression
-10% attrition,+300% frequency, +200% purchase value;no impact on number of transactions even if temporarily dissatisfaction.
Reinartz (1999) Charge card mail-order firm, USA
Lifetime duration Purchase database (N= 9.167, 2 years), Pareto/NBD model
No impact on lifetime; promotion creates opposite effect. Meyer-Waarden (2004) Grocery retailing, France
Market share, repeat purchase rate, basket, frequency, sole buyer, interpurchase time, switching behavior
GfK scanner panel data combined with POS data (N=5.476; 3 years), Dirichlet model, ANOVA
Little impact on all Dirchlet indicators. Before/after card subscription comparison: No or weak short-term impact on purchasing behavio r. Leenheer et al. (2003) Grocery retailing, Nether-lands
Share-of-wallet GfK panel data (1.926 individuals; 2.5 years), Tobit-II model
3/7 programs not effective; 4/7 programs give too much value. Effectiveness increases with value given but diminishes with higher price discounts.
Mägi (2003) Grocery retailing, Sweden
Share-of-purchase &share- of-visits in the focal store
Self-reported survey (N=643, 4 weeks), OLS regression
Mixed support impact of loyalty cards on customer behavior. Yi& Jeon
(2003)
Perfumery & restaurant USA
Loyalty to program & brand
Experimental design (N=262), SEM
Impact of program’s perceived value on program&brand loyalty. Lewis (2004) Online grocery
retailing,USA Basket, customer purchase incidence rate, revenues, number of orders Online purchase data, (N=1.058,1 year), discrete choice programming
Impact loyalty program on basket, purchase incidence rate, revenues, number of orders.
Taylor&Neslin (2005) Grocery retailing,USA Basket, purchase incidence rate, revenues, nb. orders Purchase data (N=776, 2 years ).
Increase sales “points pressure” (short term impact) & “rewarded behavior” (long term impact). Kivetz, Urminsky & Zheng (2006) Coffee& Music on Internet, USA
Interpurchase time Experimental design, (N=952). Tobit &logit models.
Progress toward goal induces purchase acceleration. Tenden cy to accelerate toward goal induces greater retention.
Table.2
Loyalty program membership duplication
Cardholder S1&2 Cardholder S3&4 Cardholder S5 Cardholder S7
Cardholder S1&2 100% 39% 6% 46% Cardholder S3&4 13% 100% 12% 38% Cardholder S5 6% 37% 100% 33% Cardholder S7 15% 39% 11% 100% Table.3 Regression coefficientsbGLM
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 < 1 km -0.23 * -0.10 ** -0.03 * -0.02 ns -0.03 ns -0.05 ns
Distance focal store 1-2 km -0.33 ** -0.13 ns -0.10 ns -0.02 ns -0.06 ns -0.06 ns Distance focal store 2-4 km -0.43 ** -0.14 ns -0.18 ** -0.19 ** -0.10 ns -0.07 ns Distance focal store >4 km -0.44 ** -0.14 ns -0.19 ** -0.23 ** -0.31 ns -0.09 ns Loyalty card *distance < 1 km 0.33 ** 0.38 ** 0.50 ** 0.41 ** 0.68 ** 0.03 ns
Loyalty card *distance 1-2 km -0.14 * -0.12 * 0.02 * 0.20 ** 0.45 * -0.04 ns
Loyalty card *distance 2-4 km -0.21 * -0.14 * -0.16 ** -0.16 * 0.29 ns -0.11 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)
** p<.01; * p<.05; ns: non- significant
Table.4
SOW before (t-3 to t-1)/ after (t0 to t+3) loyalty card subscription S1
Quarter t-3 t-2 t-1 t0 t+1 t+2 t+3
Before card subscription After card subscription
SOW card holder 32% 41% 36% 44% 46% 45% 45%
F 5.6ns 11.5* 17.4** 2.3ns 2.7ns 1.2ns
** p<.01; * p<.05; ns: non- significant
Table.5
Regression coefficientsbCox model S1-S4
Focal store S1 Focal store S2 Focal store S3 Focal store S4
b Wald Sig b Wald Sig b Wald Sig b Wald Sig
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 729.9 883.3 900.1 1073
-2 initial log likelihood 13903 17158 18327 15977
-2 final log likelihood 13173 16275 17427 14904
** p<.01; * p<.05; ns: non- significant
Table.6
Regression coefficientsbCox model S5-S7
Focal store S5 Focal store S6 Focal store S7
b Wald Sig b Wald Sig b Wald Sig
Card S1 & S2 0.02 0.068 ns 0.00 0.003 ns 0.03 0.166 ns Card S3 & 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 ns Distance 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 934.8 973.7 584
-2 initial log likelihood 25377 26752 31820
-2 final log likelihood 24442 25779 31236
** p<.01; * p<.05; ns: non- significant
Table.7
Regression coefficientsbextended Cox model before (t-3 to t-1) program subscription S1
Quarter t-3 t-2 t-1
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 ns
Card 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 757 873 999
-2 final log likelihood 13045 11165 12165
** p<.01; * p<.05; ns: non- significant
Table.8
Regression coefficientsbextended 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% 63% 46% 27%
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 741 669 439 663
-2 initial log likelihood 13264 14222 13118 13652
-2 final log likelihood 12523 13553 12679 12989
** p<.01; * p<.05; ns: non- significant
Table.9
Pearson & partial correlations (in brackets) SOW/Lifetime
SOP1 SOP2 SOP3 SOP4 SOP5 SOP6 SOP7
Lifetime S1 .806**(.81) -.186** -.181** .022 ns -.236** -.128** -.093* Lifetime S2 .083** .787**(.78) -.021ns -.237** -.232** -.043 -.104** Lifetime S3 -.094** -.048* .835**(.82) -.133** -.009 ns -.237** -.239** Lifetime S4 .021 ns -.299** -.228** .829**(.83) -.062* -.252** -.225** Lifetime S5 -.155** -.141** -.057* -.041 ns .871**(.87) -.178** -.221** Lifetime S6 -.086** -.154** -.167** -.248** -.226** .860** -.036 ns Lifetime S7 -.156** -.076** -.175** -.204** -.185** -.090** .863**(.86) ** p<.01; * p<.05; ns: non- significant Figure.1
