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Emergent Heterogeneity in Keyword Valuation in Sponsored Search Markets: A Closer-to-Practice Perspective

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Abstract

Reported literature in sponsored search advertising markets asserts that at equilibrium an advertiser has no incentive to swap her position with another advertiser and her bid on a keyword would be bound with the click value acting as an upper bound. We investigate a closer-to-practice case where advertisers do not have an ex-ante known value per click and her bid on a keyword is an outcome of simple cost-cap heuristics on a portfolio of keywords. Using simulations and an experimental setup containing advertisers that have the same upper-cap on cost, we show that the distribution of advertisers’ cost per click and bids are emergent in nature. Keywords exhibit ex post heterogeneity in observed valuation even when all advertisers bid under the same cost-cap constraint. We explore the dynamics of the market, such as temporal stability of advertiser’s bids, and advertiser’s rank based on the click-share along with the distribution of ex post valuation of keywords associated with this closer to practice setup. The results call for a richer understanding of these markets that can incorporate temporal interdependence between auctions of a keyword as well as boundedly rational behavior of advertisers working under imperfect information.

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Notes

  1. Data is part of sample data shared by an advertising agency (for a research project) and is suitably anonymized without changing the essential characteristics.

  2. Different types of bidding strategies like ‘greedy’ (Cary et al. 2007), ‘vindictive’ (Liang and Qi 2007), and ‘balanced’ bidding have been proposed in literature and not all of them have convergence properties, and cycles in bidding patterns have also been observed (Maillé et al. 2012).

  3. Advertisers do not have an individual value per click, rather, an aggregate value defined for the portfolio of keywords.

  4. It is commonly assumed that the click through rate decays monotonically and by same factor as we move down the ad slots (with slot 1 referring to the slot at the top of the page) for all advertisers (Gonen and Pavlov 2007).

  5. Target_CPC may be visualized as the maximum value that an advertiser gets from a click. Therefore, an advertiser would like to keep her cost below this value.

  6. Academic (Animesh et al. 2011) and independent research on the Internet (Tabeling 2014) suggests that click through rate decreases as an ad is placed lower down the page. This is also consistent with the heat-map studies conducted using eye tracking movements on the search engine results page (Sandberg 2012).

  7. The parameter was selected to ensure a sufficiently wide range of values while maintaining the exponential drop (Chaffey 2012) in CTR with position. The exponential drop in CTR was identified by empirically examining data obtained from an advertising agency and other independent analysis available on the Internet. While the advertising agency’s data gathered as a part of this research indicated that the value of the parameter varied between 0.1 and 0.4, independent research on the internet reported the value to be approximately 0.2.

  8. The reason we selected 20 advertisers was based on the value of the parameter which controls the decrease in probability as a function of location. The value of 0.17 selected for this simulation physically means that the probability of clicks on the last position (20 here) is only 0.64%. With 30 advertisers this probability decreases to 0.12%. Thus, there is only marginal effect of additional advertisers.

  9. See Levene (1960).

  10. The three categories comprise three equal divisions of the range of values observed. For example, the minimum average CPC was about 7.2 and the maximum about 9.7. We divided the range [7.2–9.7] into three equal categories. The standard deviation of the observed heterogeneity was about 0.42, therefore, the three categories roughly correspond to low = (mean \(-~2~*\) std. deviation, mean), medium = (mean, mean \(+~2~*\)  std. deviation), and high = (mean \(+~2~*\) std. deviation, maximum).

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Authors and Affiliations

  1. Management Information Systems Group, Indian Institute of Management Tiruchirappalli, Tiruchirappalli, India

    Agam Gupta

  2. Strategic Management Group, Indian Institute of Management Calcutta, Kolkata, India

    Biswatosh Saha

  3. Management Information Systems Group, Indian Institute of Management Calcutta, Kolkata, India

    Uttam K. Sarkar

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  1. Agam Gupta
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  2. Biswatosh Saha
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  3. Uttam K. Sarkar
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Correspondence to Agam Gupta.

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Gupta, A., Saha, B. & Sarkar, U.K. Emergent Heterogeneity in Keyword Valuation in Sponsored Search Markets: A Closer-to-Practice Perspective. Comput Econ 50, 687–710 (2017). https://doi.org/10.1007/s10614-016-9637-5

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