The Reinforcing Effects of Loyalty Program Partnerships and Core Service Usage: A Longitudinal Analysis. Katherine N. Lemon. Florian v.

Transcription

1 The Reinforcing Effects of Loyalty Program Partnerships and Core Service Usage: A Longitudinal Analysis Katherine N. Lemon Florian v. Wangenheim * * Katherine N. Lemon is Professor and Holder of the Accenture Professorship at the Carroll School of Management, Boston College. 140 Commonwealth Avenue, Chestnut Hill, MA Voice: FAX: kay.lemon@bc.edu. Florian v. Wangenheim is Professor of Service and Technology Marketing at TUM Business School, Technische Universitaet München, Arcisstr. 21, Voice: +49.(0) FAX: +49.(0) florian.wangenheim@wi.tum.de The authors are listed in alphabetical order and contributed equally to the manuscript. Please send any correspondence to Katherine N. Lemon. 1

2 The Reinforcing Effects of Loyalty Program Partnerships and Core Service Usage: A Longitudinal Analysis ABSTRACT In this research, we develop a dynamic model of cross-buying across loyalty program partnerships. We test the model using data from a European airline. We identify a reinforcing mechanism that operates when loyalty program partnerships are operating effectively. The results suggest that customer usage of (and satisfaction with) the core service influences customer cross-buying from loyalty program partners. The cross-buying behavior then reinforces the customer s relationship with the core service, as cross-buying positively influences future purchases of the core service. Further, we find that these reinforcing effects are influenced by the type of cross-buying service (partner) being considered. This dynamic reinforcement mechanism has not been shown in prior research. Our findings have implications for our understanding and management of loyalty programs and brand partnerships, valuating return on investments in improvements in product/service quality and customer satisfaction and, more broadly, the dynamics of customer purchase behavior. Key Words: Loyalty Programs, Brand Partnerships, Customer Management, Cross-buying, Dynamic Models 2

3 Introduction Firms offer loyalty programs in the hope that they will engender loyalty and improve the overall profitability of each customer (Bolton, Kannan and Bramlett 2000). Loyalty programs represent approximately a $30 billion industry in the United States (Zhao 2006), with over $10 billion spent in the financial services industry in 2006 (Feig 2006). A recent study found that, in the United States alone, loyalty program membership grew 35.5 percent from 2000 to 2006, with a total of 1.3 billion individual memberships in loyalty programs in 2006 (Ferguson and Hlavinka 2007). In a typical loyalty program, customers earn points (or some type of exchangeable currency) in return for purchases of what we call the firm s core service offering (e.g., airline miles for an airline, hotel points for a hotel, free cups of coffee for a coffee shop). In addition to earning points through purchases of the core service, loyalty program partnerships abound. Customers can earn additional points through a firm s loyalty program by making purchases from loyalty program partners. For example, a recent search of American Airlines Advantage Program shows that its program partners include: 22 airlines, 6 charities, 10 credit cards, 71 hotels, thousands of restaurants, 12 financial services/mortgage firms, 5 phone and internet providers, 8 rental car firms, 25 retailers, 3 vacation/cruise providers, and several other firms that provide members the opportunity to earn miles with purchases ( Loyalty program partnerships and the resulting cross-buying behavior offer a rich context in which to study consumer behavior, providing a laboratory in which we can observe an intriguing interplay between customer usage of the core service offered by the firm and customer usage of (potentially unrelated) services offered by program partners. We believe it is important to understand how these loyalty program partnerships may be influencing customer behavior. For example, if a customer uses the core service more, does this influence the customer s cross- 3

4 buying activity from program partners? More importantly, if a customer engages in cross-buying activity (i.e., purchases products or services from a loyalty program partner), does this increase the customer s usage of the core service and thus their overall value to the firm? Finally, could customer satisfaction with the core service influence customer partnership cross-buying behavior? Accurate answers to these questions have major implications for customer management and for loyalty program partner selection. Consider a typical loyalty program. A customer signs up for the loyalty program (for example, an airline program). The customer then earns points through purchases of the core service (e.g., purchasing tickets from the airline). In addition, the customer may earn additional points by purchasing products and services from partner organizations (e.g., credit card, rental cars, hotels, flowers). It is not yet well understood how customer use of loyalty program partners might influence subsequent customer behavior, or what factors may influence cross-buying of partner products and services. Firms typically create loyalty programs to strengthen relationships with customers. Loyalty program partnerships can serve to strengthen these relationships because customers who purchase multiple products and services within the program may perceive higher commitment and higher switching costs (Bolton, Lemon and Verhoef 2004). In addition, firms may receive compensation from loyalty program partners, thereby increasing the lifetime value of a firm s customers. Current models that evaluate the effectiveness of loyalty programs may be incomplete because they are not measuring or incorporating the potential reinforcing effects from these partnerships. We develop and test a reinforcing model of loyalty program partnerships. We find strong support for the hypothesized relationships in the model. Theoretically, this research enhances our understanding of customer behavior over time, providing new insights into how customers 4

5 usage of a firm s core service (e.g., an airline) may influence their purchases of other services from other firms through loyalty program partnerships (e.g., credit cards, gifts, hotel stays), and, in turn, how purchases of these other services influence core service purchases. The remainder of the paper is organized as follows. First, we develop a conceptual model of the reinforcing effects of cross-buying across loyalty program partnerships. Data description, results of the analysis, and additional tests to examine the robustness of the model follow. Finally, we provide a discussion of the implications of the research for marketing theory and practice and suggest directions for future research. Theoretical Framework To understand the relationship between customer experiences and usage of a company s core offering, and customer cross-buying from loyalty program partners associated with that core offering, we develop and test a model of customer behavior over time. First, we examine the influence of customer experience with the core service (in terms of usage and satisfaction) on customer cross-buying in a loyalty program context over a specific time period. Then, in the following time period, we examine the effects of cross-buying on future usage of the core service. This dynamic approach enables us to understand how the customer s experience of the service influences cross-buying and how cross-buying then influences subsequent usage of the core service. We adopt the view of many recent researchers that it is helpful to consider customers as intangible assets (e.g., Srivastava, Shervani and Fahey 1998; Gupta and Lehmann 2002; Hogan et al. 2002). In this context, Bolton et al. (2004) develop the CUSAMS (CUStomer Asset Management of Services) framework which gives a general overview of the drivers of customers relationships with firms and the resulting customer equity. According to the 5

6 CUSAMS framework (as well as other theoretical and conceptual work on customer equity and lifetime value, e.g., Blattberg Getz and Thomas 2001, Rust, Zeithaml and Lemon 2000), one important driver of customer lifetime value is the breadth of the relationship. Bolton et al. (2004, p.7) define breadth as the number of additional (different) products or services purchased from a company over time. Bolton et al. (2004) offer a number of (so far widely untested) propositions regarding the determinants and consequences of cross-buying. More recently, Gupta and Zeithaml (2006) provide a framework for customer metrics, and identify a need for research to understand the links between relationship breadth and customer retention, and a need for additional research incorporating perceptual constructs into behavioral outcome models. In particular, Gupta and Zeithaml (2006, p.734) note that Many firms believe that cross-selling improves customer retention. In other words, customers who buy multiple products from a firm are likely to be more loyal. This may indeed be true. However, the evidence to date is generally correlational. Causality may, in fact, go in the opposite direction, i.e., customers who are more loyal to a firm may tend to buy multiple products, instead of the other way around. If cross-selling does indeed enhance customer loyalty, then it also has strong implications for pricing of subsequent products sold to a customer. Almost no research has been done in this area. These two frameworks (Bolton et al. 2004, Gupta and Zeithaml 2006) serve as a basis for our hypothesis development. In H1-H4 below, we first consider how the customer s experience of the core service may influence cross-buying. In H5-H8, we focus on how cross-buying activity and other aspects of the customer s experience may influence future core service usage. Experience with the core service influences cross-buying activity Usage of Core Service We define core service usage as purchases and usage of the primary service offered by a firm. For example, the core service of an airline is airline travel; the core service of a hotel is lodging. In a loyalty program this is a critical distinction because firms encourage customers to 6

7 earn points through use of this core service (which leads to direct revenue for the firm) and through use of partner services (which lead only to indirect revenue for the firm). To our knowledge, no research results about a potential relationship of core service usage and crossbuying are known. It may be argued that core service usage reflects loyalty, of which crossbuying is another dimension. Bolton and Lemon (1999) find that prior (core) service usage is positively related to future (core) service usage. Further, heavy usage of the core service from one provider may increase perceived switching costs even for purchasing additional services because of the commitment to the service provider (Bolton et al. 2004, Sheth and Parvatiyar 1995). Kumar, George and Pancras (2008) find a positive relationship between the size of the average past purchase and cross-buying additional product categories in a retailing context, but in this case there is no single core product that customers purchase from the firm. They also find a significant (inverted-u) relationship between interpurchase time and cross-buying. Verhoef, Franses and Hoekstra (2001) find that cross-buying of insurance is negatively influenced by the number of services held in the prior period, but prior ownership of a financial product has a positive effect. They suggest this result is due to a ceiling effect in the context they examine. However, Verhoef et al. (2001) do not identify a core service in their research. Kivetz, Urminsky and Zheng (2006) find that customers purchase more when they are close to achieving a loyalty program award, suggesting that higher usage (overall) may lead to higher cross-buying activity. Thus, prior research suggests that higher past usage may be linked to higher crossbuying; however, prior research has not investigated the extent to which purchases of the core service associated with a loyalty program may influence cross-buying of other services. Hence, we hypothesize: H1: Cross-buying activity increases as usage of the core service increases. 7

8 Duration of Customer Relationship and Cross-Buying The literature is somewhat mixed about the effect of customer relationship length or duration on profitability. Reichheld and Sasser (1990) have suggested that long-term customer relationships are more profitable than short-term relationships because, among other things, long-term customers will be more likely to engage in cross-buying. However, the claim of a general increase of customer profitability as relationship length increases has been challenged and partially rejected by other researchers (Dowling and Uncles 1997; Reinartz and Kumar 2000). The few empirical studies on cross-buying do not research cross-buying activities over a long enough time frame to allow for a directional statement regarding the relationship between customer relationship duration and cross-buying. In our research context, customers may use additional (partner) services multiple times in each period. It may be argued that as relationship length increases, trust and commitment to the service provider will also increase, making it more likely that additional services will be purchased (Morgan and Hunt 1994, Kumar et al. 2008). One could also argue that relationship length (duration) and breadth (cross-buying) should be positively related because both variables represent one behavioral manifestation of customer loyalty. Verhoef, Franses and Hoekstra (2002) find that relationship age is positively related to the number of financial services purchased. Bolton et al. (2004) propose (but do not test) an inverted U-shaped response, in which cross-buying increases with duration to a certain point and then declines suggesting a possible ceiling effect. Based upon prior research, we expect to find an overall positive relationship between customer duration and cross-buying, but recognize that an inverted U-shaped response may also be expected. Thus, we hypothesize: H2: Cross-buying activity will increase with the duration of the customer relationship. 8

9 Customer Satisfaction and Cross-Buying Prior research suggests that there may be a relationship between customer satisfaction and cross-buying. For example, if a firm performs a service reliably, dependably and accurately, customers will be more satisfied and may be more willing to purchase additional services from the provider (cf. Anderson, Fornell, and Rust 1997; Berry, Parasuraman, and Zeithaml 1994; Zeithaml et al. 1996). Loveman (1998) finds that customer satisfaction has a strong positive influence on the number of services used by a customer. In contrast, Verhoef et al. (2001) find that satisfied customers do not engage in more cross-buying than unsatisfied customers, but that as relationship length increases, the effect of satisfaction on cross-buying increases. In the case of cross buying in a loyalty program, prior research has not investigated whether (and how) satisfaction with the core service may influence or transfer to other products and services associated with the loyalty program. Following much research in the area of services supporting the effects of satisfaction on additional purchases, we expect satisfaction with the core service to have a positive effect on cross-buying from loyalty program partners, i.e., we expect customers to transfer their perception of the core service to cross-buying opportunities such that: H3: Higher customer satisfaction with the core service in time t-1 leads to more crossbuying in time t. Prior research in the area of brand partnerships and brand extensions suggests that the fit between the core product and the partner product may influence the success of the brand partnership (Tauber 1988, Aaker and Keller 1990, Simonin and Ruth 1998). Simonin and Ruth (1998, p.33) define consumer perception of product fit as the extent to which consumers perceive the two product categories to be compatible. They investigate the spillover effects of brand alliances in a loyalty program context (Northwest Airlines and Visa credit card) and find that the alliance has a significant effect on attitudes toward both partner brands, even after 9

10 controlling for prior brand attitudes. However, they do not examine the effects of these spillover effects on customer behavior. In addition, Lafferty, Goldsmith and Hult (2004) find that attitudes toward both the core brand and the brand partner (in this case, a charitable organization) both benefit from the alliance if perceptions of the alliance are favorable. Washburn, Till and Priluck (2004) find that the mere act of pairing with another brand increases consumer evaluation of a brand, especially for high-equity partners. Bolton et al. (2004, p.277) note, However, if customers perceive a company s service offerings to be similar (in terms of attributes or benefits), their cumulative satisfaction should positively affect cross-buying. Similarity among offerings is analogous to the notion of fit in the brand extension literature (Aaker and Keller 1990). Overall, we propose that customers should be more likely to transfer their quality perception from the core service to the additional service when services are more similar, i.e., have a closer fit. The influence of loyalty program brand partners on customer behavior has not been addressed in prior research. We examine the role of loyalty partner fit and its influence on customer behavior. We expect the fit between the core service and the loyalty program partner to affect the relationship between customer satisfaction with the core service and cross-buying. Specifically, we expect that a stronger fit of the loyalty program partner with the core service will result in a stronger reinforcing effect of customer satisfaction. Thus, we hypothesize: H4: The fit of the loyalty partner will moderate the relationship between customer satisfaction with the core service usage and cross-buying, such that the increase in crossbuying activity will be stronger for those loyalty partners that exhibit a strong fit with the core service. 10

11 The influence of cross-buying on future service usage Cross-buying and Core Service Usage Little research has investigated the effects of cross-buying on future purchases. Bolton et al. (2004) suggest that cross-buying broadens the customer relationship and may create switching costs. Gupta and Zeithaml (2006) call for additional research in this area. In a recent study, Kumar et al. (2008) find that higher cross-buying (from several product categories of a retailer) is associated with a higher number of future purchases from that retailer. Prior research also suggests that cross-buying may be an indicator of loyalty to a provider, suggesting a positive link between cross-buying and future patronage (Blattberg et al. 2001, Venkatesan and Kumar 2004, Reinartz and Kumar 2000). Verhoef (2003) finds that membership in a loyalty program has a positive effect on customer share development. However, no prior research has examined the effects of cross-buying from loyalty program partners on future usage of the core service provided by the firm. If this type of cross-buying does increase customer future purchases, then this suggests that loyalty programs may be more effective in increasing customer revenue than previously thought. Further, it may be that customer experience with loyalty program partner services may increase the trust and commitment the customer perceives toward the core service provider (as the customer has earned loyalty points from these experiences). This increased customer commitment may fuel future purchases as suggested by Kumar et al. (2008) and is consistent with Kivetz et al. s (2006) findings that consumers purchase more as they approach rewards. Thus, building upon this prior research, we propose: H5: Higher cross-buying (in time t) leads to higher core service usage (in t+1). However, we do not expect all cross-buying to influence future core service usage equally. Following the discussion above for H4, research in the area of brand partnerships 11

12 suggests that the fit of the brand partner will have a strong influence on the success of the partnership (Aaker and Keller 1990, Lafferty et al.2004, Simonin and Ruth 1998, Tauber 1988, Washburn et al. 2004). However, the influence of loyalty program brand partners on customer behavior has not been addressed in prior research. We propose that cross-buying from partners with a strong fit will have a stronger influence on future usage than cross-buying from weak fit partners. Thus, we hypothesize: H6: The fit of the loyalty partner will moderate the relationship between cross-buying (t) on core usage (t+1), such that the increase in future usage of the core service will be stronger for those loyalty partners that exhibit a strong fit with the core service. Customer Duration and Core Service Usage Similar to our discussion above for H2, the duration of the customer relationship may also influence customer future usage of the core service as relationship length increases, commitment to the service provider will also increase, making it more likely that the customer purchases additional services (Morgan and Hunt 1994, Kumar et al. 2008). Verhoef et al. (2002) find that relationship age has a positive effect on the total number of services purchased from a financial services provider. Surprisingly, we could not find prior research that investigated the relationship between customer duration and overall service usage. Based upon prior research in related areas, we expect to find an overall positive relationship between customer duration and future service usage (consistent with Bolton and Lemon 1999, Bolton et al. 2004), but recognize that an inverted U-shaped response may also be expected. Thus, we hypothesize: H7: Core service usage in t+1 will increase with the duration of the customer relationship. Customer Satisfaction and Core Service Usage For completeness, we also incorporate the effects of customer satisfaction into the model of future core service usage. Much research has found a significant relationship between 12

13 customer satisfaction and customer behaviors such as repurchase and share of wallet (see Oliver 1996 for a review). Directly related to this research, Bolton and Lemon (1999) find that customer satisfaction in the current time period has a positive effect on service usage in the subsequent time period. Bolton et al. (2000) also show that customer satisfaction has a significant influence on customer future purchases of financial services. Building upon this research, we hypothesize that customer satisfaction will have a positive influence on future core service usage. In particular, we propose: H8: Higher customer satisfaction (in time t) will lead to higher core service usage (in time t+1). Model Summary An overview of the model appears in Figure 1. Specifically, we hypothesize that crossbuying in time t will be influenced by customer usage of the core service in time t (H1), duration of the customer s relationship with the core service (H2), customer satisfaction in time t-1 (H3), and prior cross-buying in time t-1. In addition, the fit between the core service and the crossbuying partner will influence the effect of satisfaction on cross-buying (H4). Subsequent usage of the core service in time t+1 will be influenced by cross-buying in time t (H5), and this effect will be moderated by partner fit (H6). Subsequent core service usage will also be influenced by customer relationship duration in time t (H7), and satisfaction in time t (H8). Prior usage of the core service in time t and share of wallet are included as covariates to account for heterogeneity as prior research suggests that membership in a loyalty program is related to customer usage and share of wallet (Leenheer et al. 2007, Liu 2007, Wirtz, Mattila and Lwin (2007). Data We investigate loyalty program partnership cross-buying in the airline industry using time-series data from more than 2,000 airline customers. In particular, we research purchases of 13

14 three additional services offered by the airline s program partners to its customers who have signed up for the airline s frequent flyer program (hotel booking, car rental, and credit card usage) and use both longitudinal customer transaction and survey data to develop and test a model that investigates the relationships between core service usage, customer satisfaction, and cross-buying over time. We use data from the loyalty program of a European airline. We observe customer transactions in three consecutive years. The first year of the data represents time t-1, year 2 represents time t, and year 3 represents time t+1. Core service usage (i.e., flying behavior) and customer relationship duration are readily available from the firm s database. Duration of the customer relationship can easily be calculated as the beginning of the relationship is recorded in the customer database as the date the individual joined the frequent flyer (loyalty) program. For the cross-buying variables (which we denote as CBUY it ), we obtain data on (A) car rental, (B) hotel booking, and (C) credit card usage in the respective year. For hotel booking and car rental, this means that the respective service was booked via a distribution channel that is managed by the loyalty program. For credit card usage, it means that the customer has used the credit card offered by the airline to purchase a product or a service. All customers in the sample have the loyalty program partner credit card, and all customers flew the airline (e.g., purchased the core service) and engaged in cross-buying behavior during the time periods examined in the model. 1 Data specifically measuring fit was not available in the database collected from the airline. Thus, to assess the similarities between the three partner services, we conducted a small survey with a random sample of consumers in the northeastern United States. We identified the car rental and hotel services as stronger fits with the airline, and the credit card as a weaker fit with the airline. Specifically, we asked respondents to rate the overall similarity of air travel 14

15 to the three cross-buying services hotel, car rental and credit cards (7-point scale, 1 = not at all similar, 7 = very similar (following Keller and Aaker 1992)). Air travel and car rental are rated most similar (mean=4.20), followed by air travel and hotel (mean= 3.56). Air travel and credit cards are rated least similar (mean=2.48). The differences in similarity are significant (p<0.01), suggesting that car rental and hotel are a stronger fit with air travel than credit cards. For both core service buying and cross-buying, data on both the number of transactions conducted in the respective time period and the miles collected for the activity in the frequent flier program are available. As one would expect, intercorrelations between number of transactions and miles collected in a given time period is generally high (r =.75 and.78 for core services in t and t+1, respectively and r =.89 and.96 for cross-buying car rental,.95 to.97 for cross-buying hotel,and.68 to.71 for cross-buying credit card for t-1, and t, respectively). We use the miles collected rather than the number of transactions as variables to be analyzed because the miles collected through cross-buying activity are directly related to the additional gain in customer profitability, as the airline receives a certain monetary amount for each mile collected through partnership services. 2 For this airline, customers obtain a fixed number of miles for booking one night in a hotel or for booking a one day car rental. For the credit card, customers accrue miles equal to the euro amount on the bill. The similarities across the number of miles earned through each of the cross-buying partnership opportunities suggest that the incentive structure of the loyalty program is not driving the results. In addition, all loyalty program members (regardless of level of membership in the program) accrue miles at the same rate. Survey data, as is usual in the airline industry, is regularly obtained by conducting onboard surveys. Measurement of customer satisfaction was performed using a one-item relational measure of Overall, how satisfied are you with airline X (six-point rating scale from 1 very 15

16 dissatisfied to 6 very satisfied. ) Single-item measurement of overall customer satisfaction has been applied in quite a number of studies (e.g., Bolton 1998, Bolton and Lemon 1999; Ganesh, Arnold and Reynolds 2000; Mittal, Ross and Baldasare 1998). Since the surveys were completed by customers while on board the aircraft, it was necessary to use a limited number of questions and response formats to encourage response and to aid respondent comprehension. Survey data can be linked to transaction data since customers were asked to identify themselves via their frequent flier number on the survey. Our sample consists of customers for whom we have a completed onboard survey in both time t-1 and time t, where the minimum time difference between measurement point one and two was set to eight months. Ultimately, we obtained a sample of 2354 customers for which such data were available. Thus, the model is estimated with multiple waves of survey and transaction data from the same customers, making causal inferences possible. Model As described, we have three distinct cross-buying partners in the data, resulting in three equations for cross-buying in time t. In addition, we have one equation for core service usage in time t+1. In addition to our hypotheses, we added the respective usage patterns of cross-buying and core service buying from the previous period, and also added gender and customer selfreported share of wallet to our models, both as control measures and in order to account for unobserved (past behavior) and observed (gender) heterogeneity. 3 Thus, the resulting set of functions, reflecting our hypotheses, is as follows: CBUY(A) it = f(core it, DUR it, SAT it-1, CBUY(A) it-1, GEN i, SOW i ) (1) CBUY(B) it = f(core it, DUR it, SAT it-1, CBUY(B) it-1, GEN i, SOW i ) (2) CBUY(C) it = f(core it, DUR it, SAT it-1, CBUY(C) it-1, GEN i, SOW i ) (3) 16

17 CORE it+1 = f(cbuy(a) it, CBUY(B) it, CBUY(C) it, SAT it, CORE it, DUR it, GEN i, SOW i ) (4) where: CBUY(A) it = Cross-buying (in miles) for customer i in time t of partner service (A) CBUY(B) it = Cross-buying (in miles) for customer i in time t of partner service (B) CBUY(C) it = Cross-buying (in miles) for customer i in time t of partner service (C) CORE it = Core service usage (in miles) for customer i in time t DUR it = Duration of the customer s relationship with the core service at t SAT it-1 = Customer satisfaction with the core service at t-1 4 SAT it = Customer satisfaction with the core service at time t CORE it+1 = Core service usage (in miles) for customer i in time t+1 GEN i = Gender of customer i SOW i = Share of wallet of customer i (self reported) in time t-1 Table 1 contains key descriptive information regarding the variables in the model TABLE 1 ABOUT HERE Estimation Since some of the regressors in equation (4) are predetermined (i.e., the three crossbuying outcomes are independent variables in the core service usage equation), the error terms of equations (1) to (4) are likely to be correlated and thus limited information methods such as OLS are both inefficient and inconsistent (Wooldridge 2002). Instead, full information instrument variable methods are recommended for estimating the four equations simultaneously (Greene 2003). Full Information Maximum Likelihood (FIML), three stage least squares (3SLS) and the generalized method of moments (GMM) method (suggested by Greene 2003) are available for such applications, FIML estimates are efficient and consistent only when applied to normally 17

18 distributed data (Wooldridge 2002). The present data, however, are unlikely to be normally distributed given the fair amount of zero outcomes in the data and the large variability across customers for some variables displayed in Table SLS is robust to violations of the normality assumption, but not efficient in case of heteroscedasticity. In such cases, a GMM estimator is preferred which is robust to both non-normality and heteroscedasticity (Greene 2003). Breusch- Pagan tests were run to test for heteroscedasticity. For all four equations, the chi-square statistic is highly significant (p <.001), indicating the presence of heteroscedasticity. This is actually not surprising, since estimation errors are likely to increase strongly as values on the dependent variable become more extreme. Consequently, a GMM estimator was applied. In each equation, the exogenous variables function as their own instruments, as recommended by Greene (2003). Results Overall, our results support our hypotheses (see Table 2). We find that core service usage in time t has a significant, positive effect on cross-buying for all three services, supporting H1. Customer relationship duration has a positive effect on cross-buying for car rental and hotel cross-buying but is not significant for credit card, thus offering limited support for H2. For car rental and hotel cross-buying, we also find that the quadratic effect for duration is negative and significant, offering support for the ceiling effect (inverted-u response) proposed by Bolton et al. (2004). Customer satisfaction with the core service has a positive effect on cross-buying for the two strong-fit cross-buying partners (rental car and hotel), but not for the weak-fit partner (credit card), supporting H3 and H4. Further, as hypothesized, cross-buying in time t has a positive effect on core service usage in time t+1, and is significant for the two strong-fit services car rental and hotel, but is not significant for the weak-fit service, credit card, thus supporting H5 and H6. In addition, duration of customer relationship has a significant effect on core service 18

19 buying, supporting H7, and the quadratic effect is also significant. Finally, we find that satisfaction with the core service in time t affects core service usage in time t+1, supporting H8, and the quadratic effect is also significant. In addition, the R 2 -values ranging from.47 to.59 indicate a good approximation of the data through the model. 6 Model Robustness Checks TABLES 2 AND 3 ABOUT HERE It is clear that the customer cohort for which we obtain attitudinal data on at least two occasions by using this procedure is not a random sample. First, customers who fly more often than others are also more likely to be asked to complete the survey. Second, frequent fliers may expect benefits from revealing their frequent flier number since they are aware of the fact that they are valuable customers. Hence, our sample consists of many more high-value customers than we could expect to find in a random sample. While finding the reported relationships in this special group may be particularly interesting for the airline, we believe that it is important to test the robustness of our findings. Therefore, we ran similar models using data from other customers. First, we conducted the same analysis (as described above), with a group of customers who only completed one customer satisfaction survey (in t-1). Thus, for this set of customers, we included the measure SAT it-1 in the cross-buying equations. We also replaced SAT it with SAT it-1 as a predictor of core service usage in t+1. The measure for duration (DUR it ) and other measures were the same as in the original model. The results of this analysis can be seen in Table 3. Second, we conducted an additional analysis for a random sample of customers from the firm s customer base (still members of the loyalty program), but for whom no measures of customer satisfaction were available. The results from these models are consistent with the 19

20 results presented here and support the hypotheses (to the extent they can given the missing data i.e., we cannot test the satisfaction hypotheses for the sample with no satisfaction data). As a further robustness and heterogeneity check, we tested whether substantially different results emerge if we conduct separate analyses for different levels in the loyalty program of the airline (e.g., American AAdvantage, Gold, Platinum, or Executive Platinum customers). That seems reasonable since other research in the airline industry (Wangenheim and Bayón 2007) shows that there exist differences between high and low value customers with regard to their reactions towards negative events (i.e., downgrading and denied boarding) and positive events (i.e., upgrading). Similarly, Lacey, Suh, and Morgan (2007) find that levels of preferential treatment influence relationship commitment and intended future purchases in the context of an upscale department store. Although the likelihood ratio statistics of the models we estimated including loyalty program level suggest that a significant improvement in terms of model fit is achieved when estimating three separate models as compared to estimating one model, there are no differences with regard to the substance of the results. In particular, the results of our hypotheses tests are replicated for each segment. 7 Discussion Implications for Marketing Theory and Practice Theoretically, the key contribution of this research is uncovering the reinforcing mechanism of cross-buying in loyalty programs. This dynamic reinforcement, in which usage of the core service increases cross-buying and, subsequently, cross-buying increases future usage of the core service has not been shown in prior research. What is new in our research is the finding that cross-buying from a loyalty program partner (which is not part of the core service firm) has a positive transfer effect on the amount of purchases from (and usage of) the core service. It is useful to note that this is not a mere co- 20

21 occurrence effect, i.e., merely that customers who travel more are more likely to use the travelrelated services than customers who travel less. We have addressed this issue by accounting for past behavior in our model, so that we are solely considering the reinforcing effects of the core service (on cross-buying partners) and the reinforcing effects of cross-buying partners on the core service after accounting for this potential correlation in usage by including past customer behavior. In addition, the mere co-occurrence argument does not explain the reinforcing effect of satisfaction with the core service on cross-buying of close fit services (hotel and car rental) but not on the less similar credit card service. This research is the first to examine the notion of fit with respect to loyalty program partnerships. Partners that offer a strong fit with the core service appear to reinforce the customer s relationship and behavior more than partners that have a weaker fit. The notion of fit that we have identified suggests that the findings from the brand partnership research also hold in the context of loyalty programs. We believe we have just scratched the surface in this area, and that more research is needed to understand what truly makes a great loyalty program partner. Third, we believe this research is the first to find non-linear effects of customer relationship duration on cross-buying. It has been proposed in prior research, but it is heartening to observe this relationship empirically. We do find support for the inverted U-shape response for the effect of duration on cross-buying for two of the three cross-buying services (hotel and car rental). For these services, cross-buying increases initially with duration and then reaches a ceiling effect. We find the same result for duration on future usage of the core service. Hence, customers appear to increase their usage over time until a certain saturation level has been reached, regardless of the category being the core or the cross-selling service. 21

22 Fourth, the effects of satisfaction on this reinforcing mechanism offer a new contribution to our understanding of the role of customer satisfaction in cross-buying. The findings from prior research on this topic have been mixed. We believe that this may be because: (a) customer satisfaction influences both usage of the core service and cross-buying and (b) the effect of customer satisfaction on cross-buying depends upon the fit of the cross-buying opportunity with the core service. Overall, we find that satisfaction with the core service also influences this reinforcing mechanism. The fit between the core service and the cross-buying opportunity appears to influence the extent to which satisfaction influences cross-buying behavior, as satisfaction is related to cross-buying for those services with a strong fit but not for the service with a weak fit. We also find evidence for the ceiling effect proposed by Bolton et al. (2004). Finally, satisfaction with the core service is positively associated with future core service usage. It appears that the effect of satisfaction on cross-buying may be both direct and indirect as it influences cross-buying directly and also indirectly through core service usage. Implications for Marketing Practice The results of our research suggest that the central goals of loyalty programs can be achieved if customer behavior is well understood and if loyalty program partners are chosen carefully. If the goal of a loyalty program is to increase the customer s usage of the firm s core products and services, our research suggests specific strategies to improve a loyalty program.. First, firms should choose loyalty program partners that offer a good fit with the core product or service. Given that program partners pay to purchase points or miles from the core service, this will increase the value of a customer (e.g., CLV) in two ways first, by the additional revenue gained from the purchases of points/miles and, second, by the additional revenue gained through increases in purchases of the core service. Firms that are seeking 22

23 partners for their loyalty program or those that are considering being a partner should be aware that such partnerships are particularly beneficial when there is a strong fit between the core services and the cross-buying offer. Second, the results suggest that marketing strategies that seek to increase customer satisfaction may have a stronger effect than originally thought. Improvements in customer satisfaction are typically evaluated by the extent to which they increase customer retention and usage. In addition to this direct effect of improvements in customer satisfaction, the research suggests that, for some cross-buying partners, customer satisfaction also increases the likelihood of cross-buying, which in turn increases the likelihood of using the core service (subject to some potential ceiling effects). Thus, an investment in customer satisfaction may pay off twice once for the direct effect on core service usage, and once for the indirect effect through cross-buying. This also has implications for return on customer satisfaction or service quality considerations (e.g., Rust, Zahorik and Keiningham 1995). Firms that have set up a loyalty program with many good-fitting partners may be able to amortize high investments in service quality through the returns not only from repurchases, but also from partner cross-buying. Limitations and Directions for Future Research While the study is to our knowledge the first to use longitudinal data to study crossbuying in loyalty programs, limitations should be noted that offer the opportunity for further work in the area. First, the limitation to one industry and one company should be resolved in future research. Further, while multiple time measurement points for customer satisfaction exist, most of the variance comes from cross-sectional differences. Hence, the role of satisfaction may be understated in this study and, ideally, even more measurement points (and multiple scale items) should exist for understanding the true effect of satisfaction changes. Ideally, it would 23

24 also be useful to measure satisfaction with the cross-buying partners, to understand if satisfaction with loyalty partners moderates the reinforcing effects found here, as well as measures of additional (potential) intervening constructs, such as trust and commitment to the core service provider. As is common in longitudinal data sets, our results exhibit a general pattern and our interpretation may be complemented or challenged by competing explanations. Although the model tested here is based upon a rich, longitudinal dataset with multiple measures of satisfaction and customer transaction data, such a dataset creates its own limitations. For example, we cannot compare our results with those customers who are not in the loyalty program, as these data are not collected by the airline. In addition, we do not have data on trips customers took on other airlines, or purchases made with non-loyalty program partners. That said, the general reinforcing mechanism of core service usage and cross-buying seems to be a robust finding in this research, as well as the finding that the mechanism appears to be influenced by the fit of cross-buying service offered. There are many opportunities for future research in this area. For example, it would be useful to understand customer motivations for cross-buying from loyalty program partners. In addition, it would be helpful to understand the specific conditions under which satisfaction with the core service influences cross-buying, and gain a deeper understanding of the structure of customer segments in loyalty programs. It may be interesting to understand whether some customers are more satisfaction-prone than others. Further research could also examine the effect of the firm s choice of a specific brand partner on cross-buying and future core service usage for example, does the brand equity of the loyalty program partner influence the reinforcing mechanism in addition to the fit? Finally, our model only includes partners that do not compete directly with the core service. As these competitive partners would be substitutive 24

25 to the core service (rather than complementary), future research could examine how competitive partners (i.e., other partner airlines) might influence this reinforcing mechanism. Conclusions In this research, we have developed and tested a model of cross-buying in loyalty programs. We have identified a reinforcing mechanism that operates under certain conditions when loyalty programs are operating effectively. We find that customer usage of the core service (and satisfaction with the core service) influences customer cross-buying from loyalty program partners. This cross-buying behavior then reinforces the customer s relationship with the core service, as cross-buying positively influences future purchases of the core service when the fit of the loyalty partner with the core service is strong. Overall, we hope that this research has begun to address the gap in our understanding of the antecedents and consequences of cross-buying over time. By utilizing longitudinal data, customer perceptions and customer behavior, we are addressing a call for research to understand how cross-buying activities may develop over time and how customer perceptions may influence and relate to cross-buying (Bolton et al. 2004; Bowman and Narayandas 2001; Gupta and Zeithaml 2006; Verhoef 2003). The results of this research contribute to our understanding of the dynamics of customer behavior, as we uncover a new process through which customer evaluations and decisions at one point in time influence and reinforce customer decisions at another point in time. 25

26 Table 1 Descriptive Statistics for Key Variables in Equations Estimation Sample (n=2354) Standard Variable * Description Mean Deviation CORE t-1 Core service usage (miles)for customer i in time t-1 145, , CORE t CORE t+1 CBUY(A) t-1 Core service usage (miles)for customer i in time t Core service usage (miles)for customer i in time t+1 147, , , , Cross-buying (miles) for customer i in time t-1 of car rental (partner service A) CBUY(A) t CBUY(B) t-1 CBUY(B) t CBUY(C) t-1 CBUY(C) t Cross-buying (miles) for customer i in time t of car rental (partner service A) Cross-buying (miles) for customer i in time t-1 of hotel booking (partner service B) Cross-buying (miles) for customer i in time t of hotel booking (partner service B) Cross-buying (miles) for customer i in time t-1 of credit card (partner service C) Cross-buying (miles) for customer i in time t of credit card (partner service C) Satisfaction t-1 Share of Wallet t Satisfaction t ΔSatisfaction Customer satisfaction with the core service at t-1 Self Reported share of wallet at t Customer satisfaction with the core service at t Difference between customer satisfaction at time t-1 and time t * Satisfaction was measured on a six-point rating scale (1 very dissatisfied to 6 very satisfied. ). 26

27 Table 2 GMM-Estimation Results for Cross-Buying in t and Core Services Buying in t+1 Dependent Variables (Standard errors in parentheses) Hyp. Independent Variables Constant CBUY(A)t CBUY(B)t CBUY(C)t COREt (88.94) (59.75)** (299.55)* ( ) H1 Core Service Usage t (0.000)** (0.000)**.010 (0.020).710 (0.011)** H2/H7 Duration t-1/t (16.04)* (8.10)** (51.71) (333.08)* Duration t-1/t Squared (36.17)* (18.25)** (116.62) (75.79)* H3/H8 Satisfaction t-1/t (31.25)** (21.77)** (109.99) (77.36)** Satisfaction t-1/t Squared (9.77)** (36.58)** (35.54) (96.59)** H5 CBUY (A) t CBUY (B) t CBUY (C) t (0.48)** 1.69 (0.52)** 0.27 (0.80) Control CBUY (A) t (0.04)** CBUY (B) t (0.05)** CBUY (C) t (0.09)** -- Gender (58.90)** (47.82) (261.51) ( )** Share of Wallet (77.28)** (53.62)** (207.64) ( )** Adj. R ** Statistically significant at the.01 level * Statistically significant at the.05 level 27