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As exemplary instruments of relationship marketing, loyalty programs are being implemented and studied at an unprecedented rate. Yet real-world efforts often fail—or at least do not live up to expectations—and despite the growing richness of loyalty program literature, the field remains fragmented. Thus, a comprehensive perspective is required. To guide further research and suggest ways that managers might improve loyalty program effectiveness, this article synthesizes insights on loyalty programs from empirical research and underlying psychological theories. The proposed conceptual model of loyalty program effectiveness consists of psychological, design, and operational elements; in turn, it suggests a set of 12 propositions that account for differential effects across customer acquisition, onboarding, expansion, and retention stages. With an evolving theory of loyalty programs across relationship dynamics, this propositional inventory parsimoniously delineates the trade-offs associated with relationship stage–based management of these programs. The proposed comprehensive foundation can guide loyalty program practice and research.

Despite firms’ extensive usage of loyalty programs (LPs) and decades-long academic research on their effectiveness, LPs’ effects on customer loyalty are still heavily debated. We perform a comprehensive meta-analysis of loyalty programs across various LP designs and industries and spanning different performance metrics to identify moderators of LP effectiveness. Based on a data set with 429 effect sizes, published or available between 1990 and 2020, we find strong evidence that LPs enhance customer loyalty. However, while LPs particularly enhance behavioral loyalty, shifting consumers’ attitudinal loyalty is more challenging. Further, LP effectiveness differs systematically depending on LP design characteristics (LP structure, reward content and delivery) and industry characteristics. These effects are enabled by both cognitive and affective drivers, acting sequentially, as underlying mechanisms. Despite a wide range of methodologies investigating LPs’ effectiveness, methodological choices have little impact on the substantive results. We develop a comprehensive research agenda and managerial implications.

Loyalty programs are a ubiquitous marketing tactic, yet many of them perform poorly and the reasons for loyalty program failure remain unclear to both marketing managers and researchers. This article presents three studies—two experiments and one survey—in support of the notion that a greater understanding of loyalty program performance demands an expanded theoretical framework. Specifically, researchers and managers must account for loyalty programs’ effects on both target and bystander customers in the firm’s portfolio, the simultaneous effects of three performance-relevant mediating mechanisms (gratitude, status, unfairness), and the contingent effects of program delivery (rule clarity, reward exclusivity, reward visibility) on specific mediating linkages. The results provide insights into why and when loyalty programs fail and into the complex trade-offs managers face. Loyalty programs have opposing effects on target and bystander customers’ loyalty and sales. While rule clarity suppresses both negative bystander as well as positive target effects, reward visibility enhances both types of effects. Exclusive rewards offer a means to alleviate negative bystander effects without affecting targets. The article both conceptually and empirically establishes a comprehensive analysis framework that can help marketing managers and researchers evaluate and improve loyalty program effectiveness.

Building on the meta-analytic model suggested by Palmatier et al. Journal of Marketing, 70, 136–153, ( 2006 ), this study extends the relationship marketing framework to the domain of online retailing to identify what strategies help build relationships with online customers. Specifically, this meta-analytic study identifies key antecedents and consequences of relationship marketing in online retailing. The study also examines the relationship between the four mediators—trust, commitment, relationship quality, and relationship satisfaction— and the antecedents and consequences of relationship marketing. Similarity and seller expertise were found to have the strongest impact on relational mediators, and word of mouth was the most critical outcome of relationship marketing efforts. The model proffered in this study will motivate hypotheses to be examined by future researchers. The model also helps managers to identify the key drivers of relationship marketing in online retailing.

Gaining customer loyalty is an important goal of marketing, and loyalty programs are intended to help in reaching it. Research on loyalty programs suggests that customers differentiate between loyalty to a company and loyalty to a loyalty program, yet little is known about the consequences of these two types of loyalty. Therefore, our study intends to make two main contributions: (1) improving our understanding of the constructs “program loyalty” and “company loyalty”, (2) investigating the relative impact of the two types of loyalty on preference, intention, and purchase behavior for the case of a multi-firm loyalty program. Results indicate that company loyalty influences a customer’s choice to visit a particular provider and to prefer it over competitors, but it is not a strong predictor of purchase behavior. Conversely, program loyalty is a far more important driver of purchase behavior. This implies that company loyalty primarily attracts customers to a particular provider and program loyalty ensures that once inside the store, more money is spent.

Loyalty programs (LPs) are marketing investments designed to foster behavioral loyalty among a firm’s best customers and, ultimately, increase firm performance. Surprisingly, the effectiveness of introducing LPs on firm performance in the short and long term has not been thoroughly evaluated. This research examines the extent to which introducing an LP can increase both firm sales and gross profits. Leveraging data from 322 publicly-traded firms that introduced an LP between 2000 and 2015, the authors demonstrate that introducing an LP can increase sales and gross profits in the short term (within the first year), and these positive effects are sustained long term (for at least three years). However, the effects on gross profits do not become significant until the second quarter after LP introduction, and their overall impact on performance lags substantially behind sales. Complementing these primary findings, the results reveal that offering an LP with tiers or earning mechanisms can provide firms with significant increases in sales and gross profits. Taken together, this research demonstrates that introducing strategically designed LPs can dramatically increase firm performance in both the short and long term.

Customers can belong to multiple competing loyalty programs each with multiple reward levels. We extend loyalty program theories by proposing five mechanisms that capture the competitive effects in multi-firm, multi-level loyalty programs. We empirically test our hypotheses using data from a loyalty program management app where customers manage points independently across competing firms. We utilize goal shielding theory to show how a customer’s purchase at the focal firm is affected by the customer’s purchases and redemptions across competing firms. Specifically, we find that a customer’s purchase probability at the focal firm decreases after they qualify for a reward independent of redemption at a competing firm (competitive mere reward qualification) and after they redeem a reward at a competing firm (competitive rewarded behavior). Further, we find that the customer’s purchase probability at the focal firm increases if the customer is far from both the qualified and higher-level rewards at the competing firm (competitive stuck-in-the-middle), and if the customer accelerated their purchase frequency to qualify for or redeem a reward at the competing firm (competitive effort balancing post qualification and redemption). Four lab experiments supplement our empirical findings with causal evidence. Our research shows that customer progress toward a goal in a loyalty program is influenced by competing loyalty programs.

Commonly used discrete choice model analyses (e.g., probit, logit and multinomial logit models) draw on the estimation of importance weights that apply to different attribute levels. But directly estimating the importance weights of the attribute as a whole, rather than of distinct attribute levels, is challenging. This article substantiates the usefulness of partial least squares structural equation modeling (PLS-SEM) for the analysis of stated preference data generated through choice experiments in discrete choice modeling. This ability of PLS-SEM to directly estimate the importance weights for attributes as a whole, rather than for the attribute's levels, and to compute determinant respondent-specific latent variable scores applicable to attributes, can more effectively model and distinguish between rational (i.e., optimizing) decisions and pragmatic (i.e., heuristic) ones, when parameter estimations for attributes as a whole are crucial to understanding choice decisions.

This research investigates whether there are enduring effects of goal achievement and failure within customer loyalty promotion programs. We collaborated with a major hotel chain to launch a large scale field experiment involving 95,532 existing loyalty customers. We observed customers’ hotel stays for eight months before the experiment, eight months during the experiment, and eight months after the experiment. Customers in the treatment group were asked to increase their hotel nights during the 8-month promotion by a set percentage relative to their baseline to receive a reward. Overall, the promotion led to increased purchasing in the post-promotion period. However, only 20% of customers successfully reached the goal whereas 80% missed the goal. We use a propensity score analysis to examine the distinct effects of goal achievement versus goal failure. Results show that goal attainment significantly increased post-promotion purchasing whereas goal failure significantly reduced post-promotion purchasing. Additionally, we use econometric methods to empirically test a behavioral theory of relationship-based reciprocity. We find that customers in a high status tier relationship, with the most invested in the firm, are most affected by goal failure whereas customers in a low status tier relationship, with the least invested in the firm, are most affected by goal success. Because the type of loyalty program described in this paper is widely used in a variety of industries the findings suggest that marketers should set reachable goals within loyalty promotion programs. Firms should be particularly cautious about the impact of goal failures for the firm’s most loyal customers. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0966 .

Loyalty programs offer unique opportunities for firms to personalize their marketing communications to consumers. However, limited attention has been paid to how consumers’ actions within a loyalty program can be leveraged to design effective personalized messages. Addressing this gap, the current research investigates how tier level and goal distance in hierarchical loyalty programs can dictate varying responses to cognitive vs. affective message designs. Through two lab experiments and one field study involving real email campaigns from an international frequent flyer program, we show that the two components work jointly through a Boolean-like mechanism to determine a consumer’s mental construal level. A lower construal level results from simultaneous low tier and distance, which leads to a more favorable response to affective message appeals. In comparison, when either or both of tier level and goal distance are of high magnitude, the resulting higher construal level favors more cognitive message appeals.