Market Positioning Using Cross-Reward Effects in a Coalition Loyalty Program

Stourm, Valeria; Bradlow, Eric T.; Fader, Peter S.

doi:10.2139/ssrn.3075129

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Loyalty programs have advanced dramatically in their design and management in response to technological advances that enable them to leverage big data. Modern programs allow points to be redeemed anytime and across select partners (Stourm et al 2017;Gardete and Lattin 2018), and some crypto-points can even be exchanged with other crypto-currencies, as well as between consumers (Fromhart and Therattil 2016). As big data fuels the expansion of LPs, LPs in exchange fuel the collection of big data through rewards.…”

Section: Resultsmentioning

confidence: 99%

Refocusing loyalty programs in the era of big data: a societal lens paradigm

et al. 2020

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Big data and technological change have enabled loyalty programs to become more prevalent and complex. How these developments influence society has been overlooked, both in academic research and in practice. We argue why this issue is important and propose a framework to refocus loyalty programs in the era of big data through a societal lens. We focus on three aspects of the societal lens-inequality, privacy, and sustainability. We discuss how loyalty programs in the big data era impact each of these societal factors, and then illustrate how, by adopting this societal lens paradigm, researchers and practitioners can generate insights and ideas that address the challenges and opportunities that arise from the interaction between loyalty programs and society. Our goal is to broaden the perspectives of researchers and managers so they can enhance loyalty programs to address evolving societal needs.

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Section: Resultsmentioning

confidence: 99%

Refocusing loyalty programs in the era of big data: a societal lens paradigm

et al. 2020

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“…Wang et al (2016) use a field experiment to study the impact of success in obtaining a reward or failing to get one on subsequent purchase behavior of customers and showed that this effect depends on the loyalty status of members. More recent descriptive research examines within-program competition where multiple companies participate in a common loyalty program through a single card (Danaher, Sajtos, and Danaher 2017; Evanschitzky et al 2012; Gardete and Lattin 2018; Pancras, Venkatesan, and Li 2015; Stourm, Bradlow, and Fader 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Consumer Dynamic Decision Making Under Competing Loyalty Programs

Liu¹,

Ansari²

2020

Journal of Marketing Research

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The authors develop an incentive-aligned experimental paradigm to study how consumer purchase dynamics are affected by the interplay between competing firms’ loyalty programs and their pricing and promotional strategies. In this experiment, participants made sequential choices between two competing airlines in a stylized frequent traveler task for which an optimal dynamic decision policy can be numerically computed. The authors find that, on average, participants are able to partially realize the long-term benefits from loyalty programs, though most are sensitive to price. They also find that participants’ preferences and levels of bounded rationality depend on the nature of the competitive environment, the particular state of each decision scenario, and the type of optimal action. Accordingly, the authors use an approximate dynamic programming model to incorporate boundedly rational decision making. The model classifies participants into five segments that exhibit variation in their performance and decision strategies. Importantly, they find that participants are able to adapt their decision strategies to the environment they face, and thus the overall market outcome and the performance of each firm are influenced by both the competitive environment and the assumption on the extent of consumer optimality.

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“…，購買数量・金額 (Dorotic et al, 2011;Kim et al, 2015;Lemon & Wangenheim, 2009;Villacé-Molinero et al, 2016;Wang et al, 2018), 提携企業での新規取引数量・率(トライアル) (Dorotic et al, 2021;Sharp & Sharp, 1997)，提携企業でのリピート購買数量・率 (Dorotic et al, 2021;Sharp & Sharp, 1997) が扱われている．またクロス購買の独立変数として，クロス購買の対象提携企業数 (Lemon & Wangenheim, 2009)，提携企業での購買回数 (Dorotic et al, 2021)，提携企業の取扱いカテゴリー (Stourm et al, 2017;Dorotic et al, 2021)，立地の距離 (Stourm et al, 2017)，PLP の提携企業間で共同で実施することを表すクロスプロモーション (Dorotic et al, 2011)，PLP の接触媒体 (Kim et al, 2015), 報酬の償還方法 (Wang et al, 2018) ントアプリ」…”

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Behavioral Traits of Trial Members Captured from Partnership Loyalty Program

TERAMOTO

WAKATSURU²,

Tsurumi

et al. 2022

Kodo Keiryogaku

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The authors clarify the behavioral traits of PLP (Partnership Loyalty Program) members before their trial purchase at the focal company. Specifically, they apply a zero-inflated negative binomial regression model using historical data of PLP members: purchase, marketing reaction, and reward program usage. There are two contributions to their research. First, they clarify the behavioral traits of PLP members at other coalitional companies before the trial purchase at the focal company, which cannot be captured by the loyalty program of a single company. Then, the behavioral traits of PLP members are captured from the three perspectives: purchasing behavior, marketing reaction, and reward acquisition/redemption behavior. These results induce discussions on the effectiveness of PLP from the perspective of sharing and utilizing behavioral information of PLP members among coalitional companies.

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Market Positioning Using Cross-Reward Effects in a Coalition Loyalty Program

Cited by 5 publications

References 31 publications

Refocusing loyalty programs in the era of big data: a societal lens paradigm

Refocusing loyalty programs in the era of big data: a societal lens paradigm

Understanding Consumer Dynamic Decision Making Under Competing Loyalty Programs

Behavioral Traits of Trial Members Captured from Partnership Loyalty Program

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