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Measuring the Value of Recommendation Links on Product Demand

Author

Listed:
  • Anuj Kumar

    (Warrington College of Business, University of Florida, Gainesville, Florida 32611)

  • Kartik Hosanagar

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

Recommending substitute products on focal products’ pages on an e-commerce website can impact product sales in two ways. First, the visibility of a product as a recommendation on other products’ pages may increase its exposure and result in a greater number of its page views. Second, visibility of substitute products on the product’s page may cannibalize its own sales while resulting in greater exposure for the substitute products. The net impact of these opposing effects is unclear. We conduct a randomized experiment on a fashion apparel retailer’s website to answer the following questions: (1) what is the causal value of recommendation links from a product to its recommended products in terms of the additional sales for both the product and its recommended products, and (2) how does the value of a product’s recommendation links vary based on its network characteristics, such as its PageRank and the strength of its relationship with neighboring products? We find that as a result of a recommendation, on average, (1) the daily number of product page views increased by 7.5%, and (2) conditional on a product’s page view, its sales decreased by 1.9%, and the sales of its recommended substitutes increased by 9%. On average, recommendation links of a product result in an 11% gain in total sales of the product and its recommended substitutes. However, these gains are not evenly distributed among all products. We find that although the number of page views for a product is positively affected by the number and strength of its incoming links, its sales (its recommended products’ sales) conditional on its page view are negatively (positively) affected by the strength of its outgoing links. We conduct policy simulations to highlight how retailers and producers can apply this knowledge by engineering the recommendation network through sponsored links.

Suggested Citation

  • Anuj Kumar & Kartik Hosanagar, 2019. "Measuring the Value of Recommendation Links on Product Demand," Information Systems Research, INFORMS, vol. 30(3), pages 819-838, September.
    • Handle: RePEc:inm:orisre:v:30:y:2019:i:3:p:819-838
    DOI: 10.1287/isre.2018.0833
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    References listed on IDEAS

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    1. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    2. Gal Oestreicher-Singer & Arun Sundararajan, 2012. "The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets," Management Science, INFORMS, vol. 58(11), pages 1963-1981, November.
    3. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2010. "Technology Usage and Online Sales: An Empirical Study," Management Science, INFORMS, vol. 56(11), pages 1930-1945, November.
    4. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    5. Anuj Kumar & Yinliang (Ricky) Tan, 2015. "The Demand Effects of Joint Product Advertising in Online Videos," Management Science, INFORMS, vol. 61(8), pages 1921-1937, August.
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    Cited by:

    1. Xitong Li & Jörn Grahl & Oliver Hinz, 2022. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Information Systems Research, INFORMS, vol. 33(2), pages 620-637, June.
    2. Bo Zhou & Tianxin Zou, 2023. "Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces," Marketing Science, INFORMS, vol. 42(2), pages 360-376, March.
    3. Jing Peng, 2023. "Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis," Information Systems Research, INFORMS, vol. 34(1), pages 67-84, March.
    4. Marta Ballatore & Lise Arena & Agnès Festré, 2020. "The Use of Experimental Methods by IS Scholars: An Illustrated Typology," Post-Print halshs-02866756, HAL.
    5. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.

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