Herding, Learning, and Incentives for Online Reviews

47 Pages Posted: 3 Dec 2020

See all articles by Rajeev Kohli

Rajeev Kohli

Columbia University - Columbia Business School, Marketing

Xiao Lei

HKU Business School, The University of Hong Kong

Yeqing Zhou

Eindhoven University of Technology (TUE) - Department of Industrial Engineering and Innovation Sciences

Date Written: October 11, 2020

Abstract

Incentives for online reviews have become a prevalent practice in e-commerce. However, despite empirical experiments, optimal strategies to incentivize customers remain underexplored. Our study investigates the impact of consumer herding and learning on the design of incentives for online customer reviews. Herding refers to the phenomenon where consumers are attracted to a product that seems popular due to a large number of reviews. Learning, on the other hand, refers to consumers inferring product quality from reviews. We introduce a novel generalized Polya urn process to model the evolution of reviews for a single seller. The expected value of the resulting aggregate demand takes the form of the Gompertz function. We then evaluate and compare three incentive policies: pre-purchase incentive, post-purchase incentive, and conditional incentives exclusively for positive (potentially fabricated) reviews. We determine conditions under which each type of incentive is profitable and preferred by a seller over other incentive strategies for reviews. Our results suggest that sellers should tailor their incentive policies based on a product's quality and profit margin. A pre-purchase incentive proves most profitable when both product quality and profit margin are high; a post-purchase incentive is most profitable when product quality is high and profit margin is low; and an incentive for only positive reviews is most profitable when both product quality and profit margin are low. Our findings indicate that sellers should customize their incentive policies in accordance with product quality and profit margin: a pre-purchase incentive is most lucrative when both are high; a post-purchase incentive is best when product quality is high but profit margin is low; an incentive for only positive reviews is optimal when both are low. Our study offers valuable insights for online sellers to efficiently incentivize customer reviews. Furthermore, our results imply that e-commerce platforms that host online sellers could more effectively deter fake reviews by permitting sellers to implement either pre-purchase or post-purchase incentives. A case study calibrated with real data further substantiates our insights in a practical setting.

Keywords: Customer reviews; incentive design; online platforms; e-commerce; herding; learning; diffusion; stochastic processes; Polya urn.

Suggested Citation

Kohli, Rajeev and Lei, Xiao and Zhou, Yeqing, Herding, Learning, and Incentives for Online Reviews (October 11, 2020). Columbia Business School Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=3709486 or http://dx.doi.org/10.2139/ssrn.3709486

Rajeev Kohli

Columbia University - Columbia Business School, Marketing ( email )

New York, NY 10027
United States

Xiao Lei (Contact Author)

HKU Business School, The University of Hong Kong ( email )

Hong Kong
China

HOME PAGE: http://www.xiao-lei.org

Yeqing Zhou

Eindhoven University of Technology (TUE) - Department of Industrial Engineering and Innovation Sciences ( email )

Eindhoven
Netherlands

HOME PAGE: http://research.tue.nl/en/persons/yeqing-zhou

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
501
Abstract Views
2,324
Rank
108,308
PlumX Metrics