Can Non-tiered Customer Loyalty Programs Be Profitable?

Gopalakrishnan, Arun; Jiang, Zhenling; Nevskaya, Yulia; Thomadsen, Raphael

doi:10.1287/mksc.2020.1268

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…Given these benefits, use cases of HMMs in management science are widespread (Netzer et al 2017). Examples include customer relationship management (Ascarza and Hardie 2013, Ascarza et al 2018, Danaher et al 2020, Gopalakrishnan et al 2021, Kumar et al 2011, Luo and Kumar 2013, Netzer et al 2008, cross-selling analyses (Li et al 2011, Moon et al 2007, marketing resource allocations (Kappe et al 2018, Montoya et al 2010, service portfolios (Schweidel et al 2011), and clickstream analytics (Ding et al 2015, Montgomery et al 2004, Park and Park 2016, Shi and Zhang 2014.…”

Section: Hidden Markov Models In Marketingmentioning

confidence: 99%

“…In marketing, Gopalakrishnan et al (2021) recently proposed a duration-dependent hidden Markov model for predicting customer visit frequency. However, this model is distinctly different from our DD-HMM.…”

Section: Hidden Markov Models In Marketingmentioning

confidence: 99%

“…To this end, one must incorporate duration dependence into the latent state duration and, thus, into the transition probabilities. This allows us to reveal latent user behavior that depends on the duration, as opposed to Gopalakrishnan et al (2021) who model observed user behavior that depends on the duration. These two approaches are, hence, orthogonal and are concerned with fundamentally different modeling questions.…”

Section: Hidden Markov Models In Marketingmentioning

confidence: 99%

“…Our work contributes a novel, duration-dependent HMM. In doing so, we connect to a growing body of HMMs in marketing literature (e. g., Abhishek et al 2012, Ascarza and Hardie 2013, Ascarza et al 2018, Ding et al 2015, Gopalakrishnan et al 2021, Li et al 2011, Montgomery et al 2004, Montoya et al 2010, Netzer et al 2008, Schwartz et al 2014, Schweidel et al 2011. HMMs are typically based on the Markov property, according to which a latent state can only be dependent on the previous latent state but not the previous duration in a latent state.…”

Section: Implications For Researchmentioning

confidence: 99%

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Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model

Hatt¹,

Feuerriegel²

2022

Preprint

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In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hidden Markov models (HMMs) due to their ability of modeling latent shopping phases with different user intents. In this work, we develop a duration-dependent hidden Markov model. In contrast to traditional HMMs, it explicitly models the duration of latent states and thereby allows states to become "sticky". The proposed model is superior to prior HMMs in detecting user exits: out of 100 user exits without purchase, it correctly identifies an additional 18. This helps marketers in better managing the online behavior of e-commerce customers. The reason for the superior performance of our model is the duration dependence, which allows our model to recover latent states that are characterized by a distorted sense of time. We finally provide a theoretical explanation for this, which builds upon the concept of "flow".

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Section: Hidden Markov Models In Marketingmentioning

confidence: 99%

Section: Hidden Markov Models In Marketingmentioning

confidence: 99%

Section: Hidden Markov Models In Marketingmentioning

confidence: 99%

Section: Implications For Researchmentioning

confidence: 99%

See 2 more Smart Citations

Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model

Hatt¹,

Feuerriegel²

2022

Preprint

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“…The key difference between these two types of programs lies in the up-front payment. Some studies find that loyalty programs can increase customer lifetime value and share of wallet (e.g., Gopalakrishnan et al 2021; Kopalle et al 2012; Lal and Bell 2003; Liu 2007). Others find no or weak evidence that loyalty programs are effective (e.g., Hartmann and Viard 2008).…”

mentioning

confidence: 99%

The Impact of Subscription Programs on Customer Purchases

Iyengar¹,

Park²,

Yu³

2022

Journal of Marketing Research

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Subscription programs have become increasingly popular among a wide variety of retailers and marketplace platforms. Subscription programs give members access to a set of exclusive benefits for a fixed fee upfront. In this paper, we examine the causal effect of a subscription program on customer behavior. To account for self-selection and identify the individual-level treatment effects, we combine a difference-in-differences approach with a generalized random forests procedure that matches each member of the subscription program with comparable non-members. We find subscription leads to a large increase in customer purchases. The effect of subscription is economically significant, persistent over time, and heterogeneous across customers. Interestingly, only one third of the effect on customer purchases is due to the economic benefits of the subscription program and the remaining two thirds is attributed to the non-economic effect. We provide evidence that members experience a sunk cost fallacy due to the upfront payment that subscription programs entail. Finally, we illustrate how firms can calculate the profitability of the subscription program and discuss the implications of our findings for customer retention and subscription programs.

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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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Can Non-tiered Customer Loyalty Programs Be Profitable?

Abstract: We show that a simple, nontiered loyalty program can substantially increase customer lifetime value and that most of this benefit comes from increasing customer retention.

Cited by 19 publications

References 26 publications

Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model

Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model

The Impact of Subscription Programs on Customer Purchases

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

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