1987. Unraveling Information Traps: Consumer Learning in Assortment Planning with Online Reviews
Invited abstract in session MC-59: Pricing and learning 2, stream Pricing and Revenue Management.
Monday, 12:30-14:00Room: S08 (building: 101)
Authors (first author is the speaker)
| 1. | Denis Saure
|
| University of Chile |
Abstract
In this work, we study consumer learning in assortment planning, exploring the impact of online reviews on choice, convergence, and bias. In a utility-maximizing customer scenario, we analyze feedback mechanisms used by retailers and consumers to estimate product qualities.
When consumers are myopic, estimating mean qualities without considering selection bias, product quality estimates converge with an asymptotic bias. This extends findings in the literature for assortment planning settings.
Forward-looking consumers, anticipating and accounting for bias, prevent quality estimates from converging, hindering consumer learning. However, when a subset remains myopic, forward-looking consumers can disentangle bias, enabling convergence in quality estimates for both types.
Illustrating implications for assortment planning, we show how incorporating consumer feedback influences revenue and consumer surplus. In online commerce, real-time consumer information allows personalized assortment decisions based on browsing history.
Our findings illuminate the interplay between consumer learning, online reviews, and assortment planning, offering insights for navigating the e-commerce landscape.
Keywords
- Analytics and Data Science
- Revenue Management and Pricing
Status: accepted
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