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Sponsored Search Marketing: Dynamic Pricing and Advertising for an Online Retailer

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  • Shengqi Ye

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Goker Aydin

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Shanshan Hu

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

Abstract

Consider a retailer using sponsored search marketing together with dynamic pricing. The retailer's bid on a search keyword affects the retailer's rank among the search results. The higher the rank, the higher the customer traffic and the customers' willingness to pay will be. Thus, the question arises: When a retailer bids higher to attract more customers, should the accompanying price also decrease (to strengthen the bid's effect on demand) or increase (to take advantage of higher willingness to pay)? We find that the answer depends on how fast the retailer increases its bid. In particular, as the end of the season approaches, the optimal bid exhibits smooth increases followed by big jumps. The optimal price increases only when the optimal bid increases sharply, including the instances where the bid jumps up. Such big jumps arise, for example, when the customer traffic is an S-shaped function of the retailer's bid. This paper was accepted by Serguei Netessine, operations management.

Suggested Citation

  • Shengqi Ye & Goker Aydin & Shanshan Hu, 2015. "Sponsored Search Marketing: Dynamic Pricing and Advertising for an Online Retailer," Management Science, INFORMS, vol. 61(6), pages 1255-1274, June.
    • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:6:p:1255-1274
    DOI: 10.1287/mnsc.2014.1915
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    References listed on IDEAS

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    2. Schulte, Benedikt & Sachs, Anna-Lena, 2020. "The price-setting newsvendor with Poisson demand," European Journal of Operational Research, Elsevier, vol. 283(1), pages 125-137.
    3. Subodha Kumar & Xiaowei Mei & Liangfei Qiu & Lai Wei, 2020. "Watching Ads for Free Mobile Data: A Game-Theoretic Analysis of Sponsored Data with Reward Task," Working Papers 20-08, NET Institute.
    4. Yiangos Papanastasiou & Kostas Bimpikis & Nicos Savva, 2018. "Crowdsourcing Exploration," Management Science, INFORMS, vol. 64(4), pages 1727-1746, April.
    5. Tunuguntla, Vaishnavi & Rakshit, Krishanu & Basu, Preetam, 2023. "Bidding for an optimal portfolio of keywords in sponsored search advertising: From generic to branded keywords," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1424-1440.
    6. Tunuguntla, Vaishnavi & Basu, Preetam & Rakshit, Krishanu & Ghosh, Debabrata, 2019. "Sponsored search advertising and dynamic pricing for perishable products under inventory-linked customer willingness to pay," European Journal of Operational Research, Elsevier, vol. 276(1), pages 119-132.
    7. Tong Wang & Cheng He & Fujie Jin & Yu Jeffrey Hu, 2022. "Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model," Information Systems Research, INFORMS, vol. 33(2), pages 659-677, June.
    8. Chen Jin & Luyi Yang & Kartik Hosanagar, 2019. "To Brush or Not to Brush: Product Rankings, Customer Search, and Fake Orders," Working Papers 19-02, NET Institute.

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