Consumer learning, switching costs, and heterogeneity: A structural examination

Osborne, Matthew

doi:10.1007/s11129-010-9092-x

Cited by 79 publications

(51 citation statements)

References 35 publications

(52 reference statements)

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“…This sample size is similar to other research using dynamic models (Erdem, Keane, and Sun. 2008;Erdem, Keane, Oncu, and Strebel 2005;Osborne 2007) the one-year time frame used in the estimation. In our data, about 98% of customers cashed in at least once during the two-year period; 72% cashed in exactly once.…”

Section: Datamentioning

confidence: 99%

The Joint Sales Impact of Frequency Reward and Customer Tier Components of Loyalty Programs

et al. 2012

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We estimate the joint impact of the frequency reward and customer tier components of a loyalty program on customer behavior and resultant sales. We provide an integrated analysis of a loyalty program incorporating customers' purchase and cash-in decisions, points pressure and rewarded behavior effects, heterogeneity, and forward-looking behavior. We focus on four key research questions: (1) How important is it to combine both components in one model? (2) Does points pressure exist in the context of a two-component loyalty program? (3) How is the market segmented in its response to the combined program? (4) Do the programs complement each other in terms of the incremental sales they produce? Our most basic message is that the frequency reward and customer tier components of loyalty programs should be modeled jointly rather than in separate models. We find strong evidence for points pressure for both the customer tier and frequency reward components using both model-based and model-free evidence. We find a two-segment solution revealing a “service-oriented” segment that highly values cash-ins for room upgrades and staying in “luxury” hotels, and a “price-oriented” segment that is more price sensitive and highly values the frequency reward aspects of the loyalty program. Furthermore, we find that both components generate incremental sales. Also, there was slight synergy between the programs but not a huge amount. Overall, each component contributes to increased revenues and does not interfere with the other.

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Section: Datamentioning

confidence: 99%

The Joint Sales Impact of Frequency Reward and Customer Tier Components of Loyalty Programs

et al. 2012

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“…This effect has been found in markets for frequently purchased and utilized goods such as laundry detergent (Osborne 2011) and diapers and baby towels (Heilman, Bowman, and Wright 2000), for far less frequently purchased goods such as personal computers (Prince 2011), and for services purchased annually but less frequently utilized such as automobile insurance (Israel 2005a). The precise mechanisms underlying this positive relationship between inertia and experience differ across theoretical models and empirical contexts, but they rely on some combination of increasing switching costs and asymmetric learning.…”

mentioning

confidence: 99%

“…For example, List (2003) and Miravete (2003) provide evidence that learning through experience creates financial benefits to consumers and ameliorates certain types of consumer biases. Studies in a range of other contexts, however, show that as individuals gain experience with a good or service over time, they become less likely to switch to an alternative (Osborne 2011;Heilman, Bowman, and Wright 2000;Prince 2011;Israel 2005a).…”

mentioning

confidence: 99%

“…Such switching costs may arise from psychological factors or economic factors such as asymmetric learning. Asymmetric learning occurs as consumers gain knowledge of their status quo via their experiences but do not gain knowledge regarding the alternatives (Klemperer 1995;Osborne 2011;Prince 2011). 9, 10 Other related evidence, however, suggests that consumers' abilities to navigate through such new and growing information markets may themselves grow with experience (Miravete 2003;List 2003List , 2004List , and 2006and List and Millimet 2008).…”

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confidence: 99%

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Paying Attention or Paying Too Much in Medicare Part D

Ketcham

Lucarelli

Powers

2015

American Economic Review

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Across a range of contexts, researchers are reevaluating the long-held view that consumers benefit from being offered more options. A leading challenge to this view is the hypothesis that facing more options impairs decision making through a set of phenomena known as "choice overload" (Diehl and Poynor 2010; Iyengar and Lepper 2000), "status quo bias" (Samuelson and Zeckhauser 1988), "inertia" (Dube, Hitsch, and Rossi 2010) and "the paradox of choice" in which "more is less" (Schwartz 2004). Each of these terms carries somewhat different connotations and is ascribed to various underlying economic and psychological causes. Their common predictions, however, are that facing more options makes consumers less satisfied with their available options and with their chosen options and more likely to stay with their status quo, even if the status quo is making no purchase at all. Yet a recent experiment on the elderly population specifically (Besedeš et al. 2012) and a meta-analysis by yield conclusions antipodal to these other frequently cited studies, concluding instead that consumers benefit from additional options.

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“…In our random effects example, the acceptance probability of θ (t) i is around 15-25%, which is considered to be quite appropriate in the MCMC literature. If the acceptance rate is either too high or too low, then a different procedure such as the ones proposed by Osborne (2007) or Norets (2007) is recommended. that is, set θ (t+1) (2) = θ * (t) (2) with probability…”

Section: Block 3-modified M-h Algorithm For Drawing θ (2)mentioning

confidence: 99%

Bayesian Estimation of Dynamic Discrete Choice Models

2009

Econometrica

100

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This is the published version of the paper.This version of the publication may differ from the final published version. We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution-estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "curse of dimensionality." We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values. Permanent repository link

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Consumer learning, switching costs, and heterogeneity: A structural examination

Abstract: Consumer learning, Switching costs, Bayesian econometrics, Dynamic programming, Discrete choice, M31, C11, D12, D83, L41,

Cited by 79 publications

References 35 publications

The Joint Sales Impact of Frequency Reward and Customer Tier Components of Loyalty Programs

The Joint Sales Impact of Frequency Reward and Customer Tier Components of Loyalty Programs

Paying Attention or Paying Too Much in Medicare Part D

Bayesian Estimation of Dynamic Discrete Choice Models

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