Affiliation:
1. University of Minnesota, Minneapolis, MN, USA
Abstract
Bulk email is often used in organizations to communicate "important-to-organization'' messages such as policy changes, organizational plans, and administrative updates. However, normal employees may prefer messages more relevant to their jobs or interests. Organizations face the challenge of balancing prioritizing the messages they prefer employees to know (tactical goals) while maintaining employees' positive experiences with these bulk emails, then they continue to read these emails in the future (strategic goals).
Could personalization help organizations achieve these tactical and strategic goals? In an 8-week field experiment with a university newsletter, we implemented a 4x5x5 factorial design on personalizing subject lines, top news, and message order based on both the employees' and the organization's preferences. We measured these designs' influences on the open/interest/recognition/read-in-detail rate of the whole newsletter and the single messages within it.
We found that ''important-to-organization'' messages only got higher recognition rates when being put on subject lines / top news (tactical goal). Mixing them with employee-preferred messages in top news did not bring further improvement to their own recognition rates but could improve the whole newsletter's recognition rate. Only when the top news solely contained the employee-preferred messages were the employees slightly more interested in the newsletter (strategic goal). We further analyze on which topics the employees and the organization's preferences conflicted. Finally, we discuss the design suggestions for organizational bulk email.
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)
Reference58 articles.
1. A multi-industry, longitudinal analysis of the email marketing habits of the largest United States franchise chains
2. Evaluating User Actions as a Proxy for Email Significance
3. DJ Barr , R Levy , C Scheepers , and HJ Tily . 2013 . Random effects . In Annual Conference of the Cognitive Science Society. 197--202 . DJ Barr, R Levy, C Scheepers, and HJ Tily. 2013. Random effects. In Annual Conference of the Cognitive Science Society. 197--202.
4. Dale J Barr . 2021. Learning statistical models through simulation in R: An interactive textbook. Retrieved from https://psyteachr.github.io/stat-models-v1 , Vol. Version 1 .0.0 ( 2021 ). https://psyteachr.github.io/ug3-stats/linear-mixed-effects-models-with-one-random-factor.html Dale J Barr. 2021. Learning statistical models through simulation in R: An interactive textbook. Retrieved from https://psyteachr.github.io/stat-models-v1 , Vol. Version 1.0.0 (2021). https://psyteachr.github.io/ug3-stats/linear-mixed-effects-models-with-one-random-factor.html
5. Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets
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