Bid Landscape Evolution: Q1 2019

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As the mobile programmatic industry rapidly grows, so does the number of bidders, causing app marketers to pay more attention to auction dynamics. Bid optimization and bid landscape inferencing are increasingly becoming crucial parts of the bidder strategy.

Bid landscapes allow app marketers to estimate campaign performance under different bidding scenarios. We have described the three possible scenarios in the programmatic media buying landscape in one of our articles. This information can be used to adjust and optimize the bidding strategy.

We examined the bid landscape evolution for the first three fiscal quarters of 2018 here. To understand these trends, we continued investigating and now present bid evolution analyses for the first fiscal quarter of 2019. First, let’s review the definitions of the metrics:

  1. Bid/win price ratio. This metric indicates the bidder margin in a second-price auction. In a more competitive landscape, the ratio approaches 1.0, a first-price auction, in which the bidder pays the amount they bid.
  2. Bid price/floor ratio. This metric captures the trend in reserve prices. As the seller learns bidder strategies, they are able to set reserve prices more effectively, forcing this ratio towards 1.0. A high ratio indicates a “free market” in which the bidders entirely control the price of inventory.

We’ve computed these metrics within ten discrete bid tiers ($2 intervals) to normalize against any overall trend in our bid prices. Percent difference vs. Q2 2018 is shown in the tables below.

Table 1: Bid/win price ratio

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Table 2: Bid price/floor ratio

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From this data, we conclude that this year reserve prices overall are less aggressive than last year, but they became more aggressive since Q4 2018.

Keep an eye on our blog for insights on the evolving mobile advertising market to help optimize your bidder strategy accordingly.  

Topics: Machine Learning