This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modified logistic regression to predict the profit from each possible shaded bid price. The function form allows fast maximization at run-time, a key requirement for Real-Time Bidding (RTB) systems. We report production results from this method along with several other algorithms. We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost. Further, the particular approach described in this paper captures 7% more profit for advertisers, than do benchmark methods of just bidding the most probable winning price. We also report 4.3% higher surplus than an industry Sell-Side Platform shading service. Furthermore, we observed 3% - 7% lower eCPM, eCPC and eCPA when the algorithm was integrated with budget controllers. We attribute the gains above as being mainly due to the explicit maximization of the surplus function, and note that other algorithms can take advantage of this same approach.

中文翻译：

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通过胜率估计和盈余最大化的投标阴影

本文描述了一种新的基于赢率的投标着色算法 (WR)，该算法不依赖于来自卖方平台 (SSP) 的最小投标赢取反馈。该方法使用修改后的逻辑回归来预测每个可能的带阴影的投标价格的利润。函数形式允许在运行时快速最大化，这是实时出价 (RTB) 系统的关键要求。我们报告了这种方法以及其他几种算法的生产结果。我们发现，一般来说，出价阴影可以为广告商带来巨大的价值，将每次展示的价格降低到无阴影成本的 55% 左右。此外，与仅投标最有可能获胜的价格的基准方法相比，本文中描述的特定方法为广告商带来了 7% 的利润。我们还报告了 4。比行业卖方平台着色服务高 3% 的盈余。此外，当算法与预算控制器集成时，我们观察到 eCPM、eCPC 和 eCPA 降低了 3% - 7%。我们认为上述收益主要是由于剩余函数的显式最大化，并注意其他算法可以利用这种相同的方法。