This paper introduces the concept of traffic-fingerprints, i.e., normalized 24-dimensional vectors representing a distribution of daily traffic on a web page. Using k-means clustering we show that similarity of traffic-fingerprints is related to the similarity of profitability time patterns for ads shown on these pages. In other words, these fingerprints are correlated with the conversions rates, thus allowing us to argue about conversion rates on pages with negligible traffic. By blocking or unblocking whole clusters of pages we were able to increase the revenue of online campaigns by more than 50%.
翻译:本文介绍了交通指纹的概念,即代表每天流量在网页上的分布的24维向量的正常化。使用 k- means 群集,我们发现交通指纹的相似性与这些页面上广告的盈利时间模式相似有关。换句话说,这些指纹与转换率相关,从而使我们能够在交通量微不足道的页面上争论转换率。通过封锁或打开整组网页,我们得以将在线运动收入增加50%以上。