Cost per click is a common metric to judge digital advertising campaign performance. In this paper we discuss an approach that generates a feature targeting recommendation to optimise cost per click. We also discuss a technique to assign bid prices to features without compromising on the number of features recommended. Our approach utilises impression and click stream data sets corresponding to real time auctions that we have won. The data contains information about device type, website, RTB Exchange ID. We leverage data across all campaigns that we have access to while ensuring that recommendations are sensitive to both individual campaign level features and globally well performing features as well. We model Bid recommendation around the hypothesis that a click is a Bernoulli trial and click stream follows Binomial distribution which is then updated based on live performance ensuring week over week improvement. This approach has been live tested over 10 weeks across 5 campaigns. We see Cost per click gains of 16-60% and click through rate improvement of 42-137%. At the same time, the campaign delivery was competitive.
翻译:每个点击成本是判断数字广告运动绩效的通用衡量标准。 在本文中, 我们讨论一种方法, 产生一个针对目标的功能建议, 优化每个点击的成本。 我们还讨论一种在不损及推荐的功能数量的情况下, 将标价指定为特色的技术。 我们的方法使用与我们赢得的实时拍卖相对应的印象和点击流数据集。 数据包含设备类型、 网站、 RTB 交换身份等信息。 我们利用了所有我们能够访问到的运动中的数据, 同时确保建议既敏感于单个运动级别的特点,也敏感地关注全球业绩良好的特点。 我们围绕一个假设, 即点击是Bernoulli 的试验, 点击流是Binomial 分布的模型, 然后再根据保证周内改进的现场表现更新。 这个方法在5个运动中经过10周的现场测试。 我们看到每点击16- 60%的成本收益, 并通过42- 137%的速率改进点击。 与此同时, 运动的交付是竞争性的。