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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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    Cited by:

    1. Mohsen Jozani & Charles Zhechao Liu & Hongyi Zhu & Long Liu & Kim-Kwang Raymond Choo, 2025. "A Network Analysis of Mobile App Top Chart Appearance and Survival," Information Systems Frontiers, Springer, vol. 27(6), pages 2359-2381, December.
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    5. Hsing Kenneth Cheng & D. Daniel Sokol & Xinyu Zang, 2024. "The rise of empirical online platform research in the new millennium," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 416-451, March.
    6. 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.
    7. 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.
    8. Xue Chen & Bo Li & Weichun Chen & Shuang Wu, 2023. "Influences of information sharing and online recommendations in a supply chain: reselling versus agency selling," Annals of Operations Research, Springer, vol. 329(1), pages 717-756, October.
    9. 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.
    10. Marta Ballatore & Agnès Festré & Lise Arena, 2020. "The Use of Experimental Methods by IS Scholars: An Illustrated Typology," Working Papers halshs-03036837, HAL.
    11. Xiang (Shawn) Wan & Anuj Kumar & Xitong Li, 2024. "Retargeted vs. Generic Product Recommendations: When is it Valuable to Present Retargeted Recommendations?," Information Systems Research, INFORMS, vol. 35(3), pages 1403-1421, September.
    12. Tianshu Sun & Zhe Yuan & Chunxiao Li & Kaifu Zhang & Jun Xu, 2024. "The Value of Personal Data in Internet Commerce: A High-Stakes Field Experiment on Data Regulation Policy," Management Science, INFORMS, vol. 70(4), pages 2645-2660, April.
    13. Zhou, Chi & Li, He & Leng, Mingming & Yu, Jing, 2025. "To adopt an online recommender system? A manufacturer’s strategic choice in a dual-channel setting," International Journal of Production Economics, Elsevier, vol. 287(C).
    14. Yuyan Wang & Long Tao & Xian Xing Zhang, 2025. "Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach," Marketing Science, INFORMS, vol. 44(1), pages 1-29, January.
    15. 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.
    16. Chenshuo Sun, 2025. "How Does Prepopulating Search Bars with Keywords Affect Online Consumer Behavior? A Field Experiment," Marketing Science, INFORMS, vol. 44(6), pages 1217-1231, November.

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