Video content analytics is designed to help organizations extract brand insights by evaluating consumer sentiment from all types of video data. Actually, we are witnessing an increase in user-generated video content as social platforms like YouTube and TikTok incentivize people for uploading videos.
Brands are keeping a close eye on this trend and have increased product and promotion videos as well. In everything, video content analytics applications can offer organizations greater avenues for analyzing important key performance indicators for video assets on social media channels.
Let’s take a quick look into some of the most remarkable ways in which video content analytics is improving customer insights.
Need for an Industry-Specific Machine Model
Machine models should be trained for each industry with industry-specific terminologies, including jargons. Actually, different industries should have their own domain-specific semantic clustering that can also contain categories like competitor names, locations, collaborations, material specifications, to mention a few. They are not a one-size-fits-all solution. In this regard, video content analytics can help these organizations by extracting information from videos in their specific verticals.
Analyzing Posts, Chats and Comments in Videos
Videos like YouTube have comments that are valuable for not just understanding customer sentiments pertaining to products or services, but also how people perceive the brand as a whole. This is quite important for brand reputation since people are quick to point out when a company or its brand ambassador is being hypocritical in what they promote compared to what they practice. Video content analytics can surely help contain issues like this proactively.
Video Search and Retrieval
You probably already know that video analytics application can help you search inside your video repository the same way you search documents. No longer do you have to manually search for the necessary information. After all, video content analytics allows you to focus on other important areas of your marketing function while the machine learning models handle the hard work of semantic organization and content discovery from your video catalogue.
With video content analytics, you can also extract metadata that can be used to tag, index, and organize your video content. You can also control and filter content as per its relevancy. That said, video analysis automation allows for operational efficiencies and financial benefits that manual index cannot due to high costs and human limitations. No wonder you can never downplay the power of video content analytics.
