Prior Information and Consumer Search: Evidence from Eye-tracking

Ursu, Raluca; Zhang, Qianyun Poppy; Erdem, Tülin

doi:10.2139/ssrn.3717885

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…The expected choice utility, U ij(c) , which is accrued if product j is chosen, is modeled as a probability weighted sum of the part-utilities (μ i,m,l ) of the attribute levels: Zhang, Ursu and Erdem (2020) assume consumer i's prior belief about brand j's value is N α ij,0 , σ 2 ij,0 . The prior is updated with a noisy signal, η ijm,t ∼ N μ ijm , ω 2 ijm from an eye fixation on attribute m and brand j at time t, resulting in brand value N α ij,t , σ 2 ij,t , with:…”

Section: Economic Models Of Search and Choicementioning

confidence: 99%

Modeling Eye Movements During Decision Making: A Review

2022

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This article reviews recent advances in the psychometric and econometric modeling of eye-movements during decision making. Eye movements offer a unique window on unobserved perceptual, cognitive, and evaluative processes of people who are engaged in decision making tasks. They provide new insights into these processes, which are not easily available otherwise, allow for explanations of fundamental search and choice phenomena, and enable predictions of future decisions. We propose a theoretical framework of the search and choice tasks that people commonly engage in and of the underlying cognitive processes involved in those tasks. We discuss how these processes drive specific eye-movement patterns. Our framework emphasizes the central role of task and strategy switching for complex goal attainment. We place the extant literature within that framework, highlight recent advances in modeling eye-movement behaviors during search and choice, discuss limitations, challenges, and open problems. An agenda for further psychometric modeling of eye movements during decision making concludes the review.

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Section: Economic Models Of Search and Choicementioning

confidence: 99%

Modeling Eye Movements During Decision Making: A Review

2022

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“…The authors find different information design policies to have moderate effects on consumers, but a sizeable impact on seller profits. Zhang et al (2023) estimate a model of discovery and search based on Greminger (2021) using data from an apparel retailer's mobile app store. They then use their estimates to study the effects of counterfactual store changes.…”

Section: Store and Webpage Designmentioning

confidence: 99%

“…First, there is substantially more data on search than purchase behavior in many settings because some consumers search but do not make a purchase and some consumers search multiple products. For example, Ursu et al (2023c) document 9.5 more search than purchase incidences in their apparel data, and Zhang et al (2023) report 45 times more search than purchase incidences for shoes on a mobile app. Second, consumers typically search very few products.…”

Section: Introductionmentioning

confidence: 99%

“…We use the terms "consideration set" and "search set" interchangeably in this paper.2 Additional examples of consumers' typically small consideration set sizes are 2.4 for auto insurance (Honka 2014), 2.8 -6.4 for digital cameras(Bronnenberg et al 2016), 2.3 for online used cars (Gardete and Hunter 2020), 1.1 for new car purchases(Yavorsky et al 2021), and 1.9 for shoes(Zhang et al 2023). …”

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confidence: 99%

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Consumer search: What can we learn from pre-purchase data?

Honka,

Seiler,

Ursu

2024

Journal of Retailing

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The Importance of Price Beliefs in Consumer Search

Jindal¹,

Aribarg²

2021

Journal of Marketing Research

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A consumer’s decision to engage in search depends on the beliefs the consumer has about an unknown product characteristic such as price. Given beliefs are rarely observed, researchers typically assume that consumers have rational expectations or update beliefs consistent with Bayesian updating. These assumptions are not only restrictive, but additionally, do not afford the researcher, or the retailer, an opportunity to price discriminate among consumers based on heterogeneity in beliefs. We first show, through Monte Carlo experiments, how these assumptions impact estimates of search cost. Next, we design an incentive-aligned online study where subjects search over the price of a homogeneous good, and we elicit distributions of price beliefs before and after each search. Based on data collected from a nationally representative panel, we find substantial heterogeneity in prior price beliefs. We find that subjects update their beliefs in response to search outcomes, but they deviate from Bayesian updating in that they under-react to new information. Importantly, we show that (i) assuming Bayesian updating does not significantly bias search cost estimates at the aggregate level provided the researcher accounts for heterogeneous prior beliefs, (ii) eliciting heterogeneity in prior expected prices is much more important than eliciting heterogeneity in prior price uncertainty, and (iii) a retailer can increase profits through third-degree price discrimination by recognizing the heterogeneity in prior beliefs.

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Prior Information and Consumer Search: Evidence from Eye-tracking

Cited by 3 publications

References 32 publications

Modeling Eye Movements During Decision Making: A Review

Modeling Eye Movements During Decision Making: A Review

Consumer search: What can we learn from pre-purchase data?

The Importance of Price Beliefs in Consumer Search

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