Organizations have generated unprecedented amounts of employee feedback through weekly or monthly pulse surveys, annual engagement surveys, and internal social networks and collaboration platforms. But many still struggle with how to efficiently comb through that mountain of information to identify actionable insights leaders can use to improve employee engagement and retention. Some companies are now turning to artificial intelligence (AI) tools to conduct sentiment analysis on employee feedback, gauge how employees feel and address their concerns. While text analysis of survey responses isn't new, the emergence of smarter algorithms enables faster and more precise search and categorization of unstructured data, such as open-ended comments, said Alan Lepofsky, vice president and principal analyst with Constellation Research, a technology research firm in Silicon Valley. Lepofsky, author of the recent report Why Artificial Intelligence Will Power the Future of Work, said vendors have made advances in sentiment analysis technology.
The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information -- R2=0.77. Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems.
Videos are a crucial component of your content marketing strategy. Once your videos are created, you need to distribute them on as many channels as possible. With more than 2.2 billion monthly active users, it's only logical for Facebook to be one of those distribution platforms. Overall, this should be a winning strategy for your business. According to research, 90% of consumers report that videos help them making purchasing decisions. And 64% of people say that watching a video increases their chances of buying something.
When you're running a YouTube campaign, direct conversions are an icing-on-the-cake scenario. Often, the people who have seen your YouTube ad for the first time are newcomers to your brand, and they are probably not going to convert right away. With these expectations in place, we need to look at engagement performance to evaluate the success of our YouTube campaigns. We have metrics like views, view rate, video played to, as well as my favorite: earned metrics. In this article, I'll go through what earned metrics are, why they're valuable to YouTube campaign performance, and how you can use them to improve your video campaigns.
Park, Jaram (Korea Advanced Institute of Science and Technology) | Cha, Meeyoung (Korea Advanced Institute of Science and Technology) | Kim, Hoh (Korea Advanced Institute of Science and Technology) | Jeong, Jaeseung (Korea Advanced Institute of Science and Technology)
Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its spread. We investigated the Domino’s Pizza crisis in 2009, where bad news spread rapidly through social media followed by an official apology from the company. Our work shows that bad news spreads faster than other types of information, such as an apology, and sparks a great degree of negative sentiments in the network. However, when users converse about bad news repeatedly, their negative sentiments are softened. We discuss various reactions of users towards the bad news in social media such as negative purchase intent.