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Thin Markets, Strategic Moves: Pricing Dynamics in Google’s Sponsored Search
Albert, Michael; Popescu, Dana; Feldman, Pnina; Chinnaswamy, Trent Case QA-0974 / Published August 13, 2024 / 7 pages. Collection: Darden School of Business
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Product Overview

Based on public sources and written to include the fictionalized thoughts of Tomás Claro, a mid-level product manager at Alphabet Inc. (Google), this case offers a launching point into the discussion of Google’s sponsored search auctions in early 2017. This case examines advertising pricing analytics from the publisher’s perspective with and without strategic buyers using Google's sponsored search advertising market. Students create strategies to maximize Google’s advertisement revenue for three types of advertisers: myopic, random, and strategic. Supplemental spreadsheets and a handout are provided for a three-part game demonstrating the unique dynamics of each advertiser category. This case has been used at the University of Virginia Darden School of Business in a second-year quantitative analysis elective on pricing analytics. The case supports class discussion on price analytics concepts such as thin auctions, reserve prices, and online advertising auctions.



Learning Objectives

- To understand the perspective of an advertising publisher in price-setting - To understand the necessity of employing different strategies depending on advertiser behavior (myopic versus strategic) - To recognize the challenges and opportunities of tactical pricing in repeated settings - To practice creating an optimal pricing strategy using Solver and Microsoft Excel


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  • Overview

    Based on public sources and written to include the fictionalized thoughts of Tomás Claro, a mid-level product manager at Alphabet Inc. (Google), this case offers a launching point into the discussion of Google’s sponsored search auctions in early 2017. This case examines advertising pricing analytics from the publisher’s perspective with and without strategic buyers using Google's sponsored search advertising market. Students create strategies to maximize Google’s advertisement revenue for three types of advertisers: myopic, random, and strategic. Supplemental spreadsheets and a handout are provided for a three-part game demonstrating the unique dynamics of each advertiser category. This case has been used at the University of Virginia Darden School of Business in a second-year quantitative analysis elective on pricing analytics. The case supports class discussion on price analytics concepts such as thin auctions, reserve prices, and online advertising auctions.

  • Learning Objectives

    Learning Objectives

    - To understand the perspective of an advertising publisher in price-setting - To understand the necessity of employing different strategies depending on advertiser behavior (myopic versus strategic) - To recognize the challenges and opportunities of tactical pricing in repeated settings - To practice creating an optimal pricing strategy using Solver and Microsoft Excel