Ah, the USP. A short summary of the primary benefits your business offers and what you, as a brand, stand for. There’s no better feeling than standing out from the crowd. When your customers immediately recognize what it is you do and, more importantly, remember you over the competition you really feel like a champion […]
In today’s digital landscape, marketers are increasingly using video to connect with consumers. A whopping 93% of marketers are already using video and 70% of marketers plan to increase their video marketing budgets. With that in mind, it’s already time to think ahead of just using video in marketing efforts, it’s time to think about how to use video more effectively. Now that video creation has become accessible to everyone, through DIY video creation tools, more and more brands are flocking to video. Consumers alike prefer watching video to reading text and are more likely to purchase a product after seeing a demo video. What brands need to attempt to do now is increase video engagement levels.
If a company creates a video and simply puts it on their website and social channels without tracking and understanding how the video is being watched, it may end up being a wasted effort. Now that video is so widely available, analyzing analytics and consumer behaviour is vital in the video marketing arena. By doing this, brands can increase their video engagement levels, which in turn leads to more sales, return customers, and more brand interaction. This can all be done through machine learning.
What is Machine Learning
Machine learning is a hot buzzword that refers to an artificial intelligence (AI) that is similar to data mining in that the computer system searches through data to look for patterns. Where machine learning differs is that it takes these patterns to “learn” and adjust programs accordingly, without the need of human intervention. A great example of machine learning is Facebook’s News Feed. When a user Likes or Comments on a Friend’s wall, the program learns to show the user more of that friend’s content.
Machine learning can enhance video engagement levels because it can identify what audience responds to what type of content. In essence, machine learning can be used to identify which demographics engage with which type and which part of video content. It also tracks and analyzes video comments and shares, and in the process it learns what drives people to watch this video and which audiences have the highest engagement levels.
Furthermore, machine learning can identify which objects in the video that users respond to more and predict future video watching habits. What people watch can predict what they will watch in the future as well. A great example of this is when Netflix analyzes our viewing habits and offers suggestions on titles they predict we will enjoy.
What This Means for Brands
Machine learning is revolutionizing the way brands market through video. Video content creators and marketers alike can have a new way of understanding what the consumers watches and responds to in videos. They can know what consumers find more appealing and want to watch. Machine learning can identify patterns and create predictions for what consumers will want to watch, which can help marketers get ahead of video trends to capture their consumer’s attention.
Using this type of programmatic analysis is soon going to be vital to any video marketing strategy. It will not be enough that a brand simply uses video; they will be expected to analyze and predict user behavior, anticipating video trends for the future as well.