Optimizing Email Volume For Sitewide Engagement

Rupesh Gupta, Guanfeng Liang, Rómer Rosales
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引用次数: 18

Abstract

In this paper we focus on the problem of optimizing email volume for maximizing sitewide engagement of an online social networking service. Email volume optimization approaches published in the past have proposed optimization of email volume for maximization of engagement metrics which are impacted exclusively by email; for example, the number of sessions that begin with clicks on links within emails. The impact of email on such downstream engagement metrics can be estimated easily because of the ease of attribution of such an engagement event to an email. However, this framework is limited in its view of the ecosystem of the networking service which comprises of several tools and utilities that contribute towards delivering value to members; with email being just one such utility. Thus, in this paper we depart from previous approaches by exploring and optimizing the contribution of email to this ecosystem. In particular, we present and contrast the differential impact of email on sitewide engagement metrics for various types of users. We propose a new email volume optimization approach which maximizes sitewide engagement metrics, such as the total number of active users. This is in sharp contrast to the previous approaches whose objective has been maximization of downstream engagement metrics. We present details of our prediction function for predicting the impact of emails on a user's activeness on the mobile or web application. We describe how certain approximations to this prediction function can be made for solving the volume optimization problem, and present results from online A/B tests.
优化电子邮件量为全站参与
在本文中,我们专注于优化电子邮件量的问题,以最大限度地提高在线社交网络服务的网站参与度。过去发布的电子邮件数量优化方法建议通过优化电子邮件数量来最大化受电子邮件影响的用户粘性指标;例如,从点击电子邮件中的链接开始的会话数量。电子邮件对下游用户粘性指标的影响可以很容易地估计出来,因为这种用户粘性事件很容易归因于电子邮件。然而,这个框架在其对网络服务生态系统的看法上是有限的,网络服务生态系统由几个工具和实用程序组成,这些工具和实用程序有助于向成员提供价值;电子邮件只是其中之一。因此,在本文中,我们通过探索和优化电子邮件对这个生态系统的贡献,从以前的方法出发。特别是,我们呈现并对比了电子邮件对不同类型用户的网站参与度指标的不同影响。我们提出了一种新的电子邮件量优化方法,该方法可以最大限度地提高整个网站的参与度指标,如活跃用户总数。这与之前的方法形成鲜明对比,后者的目标是最大化下游用户粘性指标。我们详细介绍了我们的预测函数,用于预测电子邮件对移动或web应用程序上用户活跃度的影响。我们描述了这个预测函数的某些近似可以用来解决容量优化问题,并给出了在线A/B测试的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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