Video content analytics is designed to help businesses gain brand insights by evaluating consumer sentiment from all types of video data. We are actually observing an increase in user-generated video content, as social media platforms like YouTube and TikTok encourage people to upload videos.
Brands are closely monitoring this trend and have increased the production of product and promotional video. In conclusion, video content analytics applications can offer organizations additional opportunities to analyze critical key performance indicators for video assets on social media channels. Let’s take a quick look at some of the most incredible ways that video content analytics is improving customer insights.
The training of machine models with industry-specific terminologies, including vernacular, is required for each industry. In reality, each industry should have its own domain-specific semantic clustering, which may include categories like competitor names, locations, collaborations, and material specifications. They are not a solution that is applicable to all situations. In this regard, video content analytics can help these organizations by extracting information from videos in their specific verticals.
Comments on videos like YouTube are not only valuable for understanding consumer sentiments regarding products or services, but they also offer insight into how people perceive the brand as a whole. This is essential for the brand’s reputation, as individuals are quick to identify when a company or its brand emissary is hypocritical in their actions and the values they advocate for. Without a doubt, video content analytics can help avoid issues like this in the future.
You are likely aware that video analytics software can help you conduct a search within your video repository similarly to how you would search for documents. The necessity to manually search for the necessary information has been eliminated. Ultimately, video content analytics allows you to focus on other critical aspects of your marketing function, while the machine learning models handle the laborious task of semantic organization and content discovery from your video catalogue.
In order to categorize, index, and organize your video content, you can also derive metadata from video content analytics. Additionally, content can be regulated and filtered according to its relevance. Nevertheless, video analysis automation is capable of achieving operational efficiencies and financial benefits that manual indexing is unable to achieve due to the high costs and human limitations. It should come as no revelation that the potential of video content analytics is impossible to understate.
The Relationship between Video Content Analytics and Customer Insights
