On SAGE Insight: Scheduling Content on Social Media: Theory, Evidence, and Application

From Journal of Marketing

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More than 1.8 billion users worldwide spent an average of 118 min a day on social media in 2016 and 77% of them actively engaged with social media content through likes, comments, shares, and link clicks. Content platforms (e.g., newspapers, magazines) post several stories daily on their dedicated social media pages and promote some of them using targeted content advertising (TCA). Posting stories enables content platforms to grow their social media audiences and generate digital advertising revenue from the impressions channeled through social media posts’ link clicks.

In this article researchers use data from a top 50 U.S. newspaper (referred to as the “content platform” hereinafter) that generates revenue through print subscriptions, print advertising, and digital advertising. The content platform has been a local monopoly for several decades. It has a daily circulation of ∼230,000 and weekend circulation of ∼336,000 and attracts ∼5.3 million monthly unique visitors to its website. The content platform reaches seven out of ten adults with annual household incomes of $100,000 or more in the two largest counties in its state. The data set comprises 5,706 individual posts from our content platform’s dedicated Facebook page between December 31, 2014, and December 31, 2015. The data are a snapshot of all posts and the corresponding engagement on the content platform’s Facebook page collected in June 2016. Therefore, all posts in the data set reach their maximum lifetime engagement.

Content platforms have experienced a dramatic decline in print advertising revenue and seek new practices to generate online advertising revenue. One such practice is to leverage social media channel to engage customers and direct traffic to websites. However, a formidable challenge is to design a systematic framework that enables social media managers to design profit-maximizing social media schedules. This need is urgent given practitioners’ call for effective scheduling strategies. This paper identifies 3 steps – First, building on circadian rhythms literature, the article provides novel insights into how content3 effectiveness varies by the time of day, which has typically been studied within the purview of behaviors such as variety-seeking decision quality and risk-taking behaviors. Moreover, researchers offer a coherent theoretical framework by theorizing how known drivers of social media engagement (i.e., TCA and content type) interact with the time-of-day effect to contribute to post performance. Second, authors develop, estimate, and validate a response model that simultaneously considers attribute-based social media schedules involving time of day, TCA, and content type using post-level data from a major content platform. Third, they build a decision-support tool to assist social media managers in profit-maximizing social media content scheduling, and show the profitability implications over a finite planning horizon.

Abstract

Content platforms (e.g., newspapers, magazines) post several stories daily on their dedicated social media pages and promote some of them using targeted content advertising (TCA). Posting stories enables content platforms to grow their social media audiences and generate digital advertising revenue from the impressions channeled through social media posts’ link clicks. However, optimal scheduling of social media posts and TCA is formidable, requiring content platforms to determine what to post; when to post; and whether, when, and how much to spend on TCA to maximize profits. Social media managers lament this complexity, and academic literature offers little guidance. Consequently, the authors draw from literature on circadian rhythms in information processing capabilities to build a novel theoretical framework on social media content scheduling and explain how scheduling attributes (i.e., time of day, content type, and TCA) affect the link clicks metric. They test their hypotheses using a model estimated on 366 days of Facebook post data from a top 50 U.S. newspaper. Subsequently, they build an algorithm that allows social media managers to optimally plan social media content schedules and maximize gross profits. Applying the algorithm to a holdout sample, the authors demonstrate a potential increase in gross profits by at least 8%.

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Article details
Scheduling Content on Social Media: Theory, Evidence, and Application
Vamsi K. Kanuri, Yixing Chen, Shrihari (Hari) Sridhar
First Published October 9, 2018 Research Article
DOI: 10.1177/0022242918805411
Journal of Marketing

     
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