Customer satisfaction is critical to your company’s success. No matter how innovative your product or how competitive your pricing is, if your customers are ultimately dissatisfied, they will leave. Researchers investigated the role and economic impact of recommender systems, as well as how they influence consumer decisions.
Auto-generated recommendations can help you find exactly what you’re looking for among extensive offerings, whether you’re scrolling through Amazon looking for the perfect product or flipping through titles on Netflix looking for a movie to fit your mood.
These recommender systems are used in a variety of industries, including retail, entertainment, and social networking. Two researchers from The University of Texas at Dallas investigated the informative role of these systems as well as the economic impacts on competing sellers and consumers in a recently published study.
“Recommender systems have become ubiquitous in e-commerce platforms and are touted as sales-support tools that help consumers find their preferred or desired product among the vast variety of products,” said Dr. Jianqing Chen, information systems professor at the Naveen Jindal School of Management. “Until now, the majority of research has been focused on the technical side of recommender systems, with little research on the economic implications for sellers.”
Recommender systems have become ubiquitous in e-commerce platforms and are touted as sales-support tools that help consumers find their preferred or desired product among the vast variety of products. Until now, the majority of research has been focused on the technical side of recommender systems, with little research on the economic implications for sellers.
Dr. Jianqing Chen
Chen and Dr. Srinivasan Raghunathan, the Ashbel Smith Professor of information systems, developed an analytical model in which sellers sell their products through a common electronic marketplace in the study, which will be published in the December 2020 issue of MIS Quarterly.
The paper focuses on the recommender system’s informative role: how it influences consumer decisions by informing them about products about which they would otherwise be unaware. Sellers like recommender systems because they don’t have to pay the marketplace to receive recommendations, whereas traditional advertising is expensive.
According to the researchers, recommender systems have been shown to increase sales on these marketplaces: Recommendations account for more than 35% of what customers buy on Amazon and more than 60% of what they watch on Netflix. The systems predict a user’s preferences and recommend the product the consumer is most likely to buy based on information such as purchase history, search behavior, demographics, and product ratings.
While recommender systems introduce consumers to new products and increase market size, which benefits sellers, free exposure is not always profitable, according to Chen.
The researchers discovered that the advertising effect causes sellers to advertise less on their own, while the competition effect causes them to lower their prices. Sellers are also more likely to benefit from the recommender system if it is highly precise.
“This means that sellers are likely to benefit from the recommender system only if the recommendations are effective and the products recommended are indeed the preferred products of consumers,” Chen explained.
The researchers determined that whether sellers use targeted or uniform advertising, the results are the same. Although the exposure is desirable for sellers, the negative effects on profitability may outweigh the benefits. If sellers are unable to accurately target customers, they should carefully choose their advertising approach and use uniform advertising, according to Chen.
“It turns out that free exposure isn’t really free,” he said. “To counteract such a negative impact, sellers should strive to assist the marketplace in making effective recommendations. Sellers, for example, should provide accurate product descriptions, which can aid recommender systems in better matching products and consumers.”
Consumers, on the other hand, benefit from recommender systems both directly and indirectly, according to Raghunathan. They may be introduced to a new product, for example, or benefit from price competition among sellers.
In turn, they may end up paying more than the value of such recommendations in the form of increased prices, according to Raghunathan.
“Consumers should accept recommender systems,” he says. “However, sharing additional information with the platform, such as their preference for online reviews, is a double-edged sword. While it can help recommender systems find a product that a consumer might like more effectively, the additional information can be used to increase recommendation precision, which can reduce competition pressure on sellers and be bad for consumers.”
According to the researchers, while significant efforts are being made to develop more sophisticated recommender systems, the economic implications of these systems are poorly understood.
“The business and societal value of recommender systems cannot be properly assessed unless the economic issues surrounding them are investigated,” Chen explained. He and Raghunathan intend to conduct additional research on this subject.
