数据驱动的网页设计和个性化实验

Rasika Irpenwar, Nikhil Gupta, Rahul Ignatius, M. Ramachandran
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引用次数: 0

摘要

在当今世界,我们几乎在生活的各个方面都使用在线媒体。公司进行受控的网络实验,以做出数据驱动的决策,提供直观的在线体验。我们看到在线客户行为与设计和个人待遇之间存在很大的相关性,这可以用来创造更好的客户参与度。在本文中,我们研究了设计元素对聊天邀请的影响*,通过在小人群中运行实验,使用机器学习算法。在此基础上,我们确定重要元素,并在邀请上构建最合适的个性化信息。统计结果表明,更多的访问者在网站上接受个性化和优化设计的聊天邀请。[24]7,我们在用户界面设计和基于旅程的个性化方面进行了广泛的实验,这对我们的年收入产生了积极的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Driven Web Experimentation on Design and Personalization
In today's world for we use online medium for virtually every aspect of our lives. Companies run controlled web experiments to make data driven decisions, to provide an intuitive online experience. We see a big correlation between online customer behaviors and designs and personal treatment, which could be used to create better customer engagement. In this paper we have studied the impact of design elements on chat invites*, by running experiments on a small population, using machine learning algorithms. Based on this we identify significant elements and build the most opportune personalized messages on invites. Statistical results show that, more visitors on the website accept chat invites which are personalized and optimized for the design. At [24]7, we have experimented extensively on user interface designs and journey based personalization which resulted in positive impact on our annual revenue.
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