论文标题

提高在线广告效率的统计建模

Statistical Modelling for Improving Efficiency of Online Advertising

论文作者

Scherbakova, Irina, Pepelyshev, Andrey, Staroselskiy, Yuri, Zhigljavsky, Anatoly, Guchenko, Roman

论文摘要

实时竞标改变了数字广告格局,允许公司在加载网页的时间内以毫秒的价格购买网站广告空间。加的夫大学和Crimtan之间的联合研究与大数据上的机器学习技术一起使用了统计建模,以开发计算机算法,这些算法可以选择应向广告显示的最合适的人。这些算法已被用来确定该特定广告的合适招标策略,以使整个过程对企业尽可能有利。 Crimtan对算法的使用使他们能够改善他们为客户提供的服务,省钱,带来巨大的效率提高并吸引新业务。这与客户本身产生了敲门效应,他们报告说,由于更有针对性,准确和知情的广告,转化率的提高。我们还使用了混合的泊松过程来建模用于分析在线客户的重复购买行为。为了进行数值比较,我们使用Crimtan收集的真实数据,用于运行最近的一些广告系列。

Real-time bidding has transformed the digital advertising landscape, allowing companies to buy website advertising space in a matter of milliseconds in the time it takes a webpage to load. Joint research between Cardiff University and Crimtan has employed statistical modelling in conjunction with machine-learning techniques on big data to develop computer algorithms that can select the most appropriate person to which an ad should be shown. These algorithms have been used to identify suitable bidding strategies for that particular advert in order to make the whole process as profitable as possible for businesses. Crimtan's use of the algorithms have enabled them to improve the service that they offer to clients, save money, make significant efficiency gains and attract new business. This has had a knock-on effect with the clients themselves, who have reported an increase in conversion rates as a result of more targeted, accurate and informed advertising. We have also used mixed Poisson processes for modelling for analysing repeat-buying behaviour of online customers. To make numerical comparisons, we use real data collected by Crimtan in the process of running several recent ad campaigns.

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