Email Volume Optimization at LinkedIn

Rupesh Gupta, Guanfeng Liang, Hsiao-Ping Tseng, Ravi Kiran Holur Vijay, Xiaoyu Chen, Romer Rosales
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
Online social networking services distribute various types of messages to their members. Common types of messages include news, connection requests, membership notifications, promotions and event notifications. Such communication, if used judiciously, can provide an enormous value to members thereby keeping them engaged. However sending a message for every instance of news, connection request, or the like can result in an overwhelming number of messages in a member's mailbox. This may result in
more » ... reduced effectiveness of communication if the messages are not sufficiently relevant to the member's interests. It may also result in a poor brand perception of the networking service. In this paper we discuss our strategy and experience with regard to the problem of email volume optimization at LinkedIn. In particular, we present a cost-benefit analysis of sending emails, the key factors to administer an effective volume optimization, our algorithm for volume optimization, the architecture of the supporting system and experimental results from online A/B tests.
doi:10.1145/2939672.2939692 dblp:conf/kdd/GuptaLTVCR16 fatcat:fo672we46jdevjxgxnl6dsewf4