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Article

How Do Algorithmic Recommendations Lead Consumers to Make Online Purchases?

Data Science
Published on:
Updated on:
June 22nd, 2022

E-commerce and web services such as Amazon, YouTube, and Netflix, but also online advertisers, use recommender systems. Recommender systems are algorithms that, based on data sets, recommend to users contents and products that match their preferences. In this interview, Xitong Li of HEC Paris, Associate Professor of Information Systems and a Hi! PARIS center’s research fellowship holder, reveals new research, a joint work with two GErman researchers, and explains how recommender systems induce consumers to buy.

e-commerce - cover

Photo Credit: NaMaKuKi on Adobe Stock

What do you study?

We examine how algorithmic recommenders could induce online consumers to buy products. For example, product recommendations nowadays are widely used by online retailers, so we would like to see how showing recommendations to consumers on the online retailers’ websites would influence consumers’ consideration set, which in turns affects their purchases. By consideration set, we refer to the assortment of alternative products that consumers consider before actually making a purchase.

What recommender systems did you study?

Different types of recommender systems exist, with algorithms based on content, on collaborative filtering, or a mix of both. Collaborative filtering recommender systems are the most common in current business practices. In our study, we employed the recently developed causal mediation approach to examine the causal paths through which the use of recommender systems eventually leads to consumer purchases.

We conducted a randomized controlled field experiment on the website of an online book retailer in Europe. In the field experiment, some visitors on the online retailer’s website were randomly assigned in the treatment group to see personalized recommendations, whereas the other visitors could not see any recommendations.

Can you explain what effects of the use of personalized recommendations you observed?

Unsurprisingly, the results show that the presence of personalized recommendations increases consumers’ propensity to buy by 12.4% and basket value by 1.7%.


The presence of personalized recommendations increases consumers’ propensity to buy by 12.4% and basket value by 1.7%.


But more importantly, we find that these positive economic effects are largely mediated through influencing consumers’ consideration sets.

As explained before, a consideration set is the set of alternative products that consumers consider before making a purchase. We distinguish two aspects of a consideration set: its breadth and its depth. The breadth (also called “size”) of a consideration set refers to the number of different choices in the set, whereas the depth refers to how intensively a consumer is engaged with the choices. That engagement is measured by the number of page views per item or the average session duration.

We find that the presence of personalized recommendations increases both the size and depth of consumers’ consideration set. It is these two changes that increase the consumers’ propensity to buy and the basket value.

Furthermore, we find that the effect mediated via the size of consumers’ consideration set is much stronger and significant than the effect mediated via the depth. In other words, the more choices you have taken into consideration, the more likely you will buy, and the larger quantity you will buy.

Your findings suggest important managerial implications for professionals and practitioners. How should online retailers use recommender systems?

Now we know that consideration sets play an important role in mediating the positive effects of recommender systems on consumer purchases, online retailers need to consider the practical strategies that facilitate their formation. For example, to reduce consumers’ search costs and cognitive efforts, online retailers can display the recommended products in a descending order according to the predicted closeness of consumers’ preferences. Given such a placement arrangement, consumers can quickly screen the recommended products and add the most relevant alternatives to their consideration sets, which should facilitate the consumers’ shopping process and increase shopping satisfaction.

 

To reduce consumers’ search costs and cognitive efforts, online retailers can display the recommended products in a descending order according to the predicted closeness of consumers’ preferences.

 

As recommender systems induce consumers to take more choices into consideration, it could be difficult for them to manage many choices. In other words, the more choices there are, the more difficult it is for the consumer to choose. To facilitate consumers’ shopping process, online retailers need to consider strategies and web tools that help consumers manage the choices in a better-organized manner and facilitate their comparison.

Are your findings generalizable to other contexts or business domains?

Although the field experiment in the study was conducted on the website of an online book retailer, we believe the findings are generalizable to a broader context of online retailing that uses recommender systems (Kumar and Hosanagar 2019). For example, the results can easily be applied to online retailers like Amazon and Cdiscount, as well as merchants’ online stores (e.g., Darty.com).
 

Interview with Xitong Li, based on his paper, "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment", co-authored by Jörn Grahl (Head of Data Science at PHOENIX and former Professor of Information Systems, Digital Transformation and Analytics at the University of Cologne) and Oliver Hinz of the Goethe University Frankfurt, and forthcoming in Information Systems Research. Xitong Li won the Reviewer of the Year (2021) Award of MIS Quarterly and serves as an Associate Editor on the editorial board of Information Systems Research since January 2022. He also served as a Program co-Chair for International Conference on Information Systems (ICIS) 2021. Professor Xitong Li and his co-authors are very open to collaborate with practitioners and help them develop innovative online retailing strategies based on big data and business analytics.

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