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Recommendation engines influence the choices we make every day — what book to read next, which song to download, which person to date.
At their best, smart systems serve buyers and sellers alike: Consumers save the time and effort of wading through the vast possibilities of the digital marketplace, and businesses build loyalty and drive sales through differentiated experiences.
But, as with many other new technologies, digital recommendations are also a source of unintended consequences. Our research shows that recommendations do more than just reflect consumer preferences — they actually shape them. If this sounds like a subtle distinction, it is not. Recommendation systems have the potential to fuel biases and affect sales in unexpected ways. Our findings have important implications for recommendation engine design, not just in the music industry — the basis of our study — but in any setting where retailers use recommendation algorithms to improve customer experience and drive sales.
Consumer Choice in a Crowded Marketplace
E-commerce has dramatically affected consumer choice. Unconstrained by physical limitations of the brick-and-mortar model, businesses can offer virtually unlimited selections of products online, giving consumers access not only to popular items but to obscure, niche ones as well. There are both more needles and more hay. As consumers face a radically wider set of options, they must exercise greater care in evaluating potential products for purchase or consumption. Experience-based (or taste-based) goods such as music, books, and movies are particularly complex: Consumers must spend time experiencing them before they know if they like them. Even if the sticker prices for goods aren’t high, or the goods are included as part of subscription services, the time consumers must spend to evaluate each of them is valuable. Worse, the sunk cost of evaluation time is unrecoverable: Consumers can’t unlisten, unread, or unwatch goods that turn out to be a poor fit.
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In this context, sophisticated algorithms capable of making effective personalized recommendations provide sizable benefits. They reduce search and evaluation time, drive sales, and introduce new items to consumers.
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1.A. Sharma, J.M. Hofman, and D.J. Watts, “Estimating the Causal Impact of Recommendation Systems From Observational Data,” Proceedings of the Sixteenth ACM Conference on Economics and Computation (Portland, Oregon, June 15-19, 2015): 453-470.
2.C.A. Gomez-Uribe and N. Hunt, “The Netflix Recommender System: Algorithms, Business Value, and Innovation,” ACM Transactions on Management Information Systems 6, no. 4 (January 2016): 13.
3.E. Van Buskirk, “The Most Streamed Music From Spotify Discover Weekly,” July 7, 2016.