article

Empirical Analysis of the Impact of Recommender Systems on Sales

Authors:

Robert Garfinkel,

Rajkumar Venkatesan,

Fang YinAuthors Info & Claims

Journal of Management Information Systems, Volume 27, Issue 2

Pages 159 - 188

https://doi.org/10.2753/MIS0742-1222270205

Published: 01 October 2010 Publication History

Abstract

Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is how recommender systems affect sales. We take into consideration the interaction among recommendations, sales, and price. We then develop a robust empirical model that incorporates the indirect effect of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive effect on sales. Moreover, this effect is moderated by the recency effect, where more recently released recommended items positively affect the cross-selling efforts of sellers. We also show that recommender systems help to reinforce the long-tail phenomenon of electronic commerce, and obscure recommendations positively affect cross-selling. We also found a positive effect of recommendations on prices. These results suggest that recommendations not only improve sales but they also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher effect on sales than does consumer feedback. Our empirical results show that providing value-added services, such as digital word of mouth and recommendations, allows retailers to charge higher prices while at the same time increasing demand by providing more information regarding the quality and match of products.

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cover image Journal of Management Information Systems

Journal of Management Information Systems Volume 27, Issue 2

Number 2 / Fall 2010

330 pages

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Publisher

M. E. Sharpe, Inc.

United States

Publication History

Published: 01 October 2010

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Yu JLyu SChen P(2024)Deep Reinforcement Learning for Boosting Individual and Aggregate Diversity in Product Recommendation SystemsProceeding of the 2024 5th International Conference on Computer Science and Management Technology10.1145/3708036.3708043(39-48)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3708036.3708043
Chen YTsai HYuan CFarzan RLópez CCardoso Llach DQuercia DMustafa MNiu SWong-Villacrés M(2024)Exploring How Users Attribute Responsibilities Across Different Stakeholders in Human-AI InteractionCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing10.1145/3678884.3681852(202-208)Online publication date: 11-Nov-2024
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Jain KJindal R(2024)NLP-enabled Recommendation of Hashtags for Covid based Tweets using Hybrid BERT-LSTM ModelACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3640812Online publication date: 16-Jan-2024
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