Using AI Powered Heat Map Technology Can Ensure the First Three Seconds of Your Videos Get the Attention they Deserve

A heat map (or heatmap) is a data visualization technique that shows the magnitude of a phenomenon as color in two dimensions. The variation in color may be by hue or intensity, giving obvious visual cues about how the phenomenon is being viewed. It is often used to graphically illustrate how people interact with a website. Where their cursor goes, hovers, clicks, etc. This technology has progressed to the point where it can now be used for video content.

The Value of Video Content is Established Within the First Three Seconds

The value of video content is established within the first three seconds and increases with duration: for example 65% of people that watch the first 3 seconds of a video on Facebook will watch it for at least 10 seconds. And 45% will watch the video for 30 seconds. Thus, if you can get your audience to watch the first 3 seconds of your video, you will have a 45% chance that they will watch it for 30 seconds – usually enough time for them to watch your entire video. So those first 3 seconds are super important.

Many of VideoFresh’s clients use their videos for paid media campaigns on platforms such as Google, Amazon, Facebook, IG, YouTube and Tiktok. And so for them, the more people that watch their videos can result in dramatic increases in their campaigns.

VideoFresh, a leading video production company for the eCommerce sector now offers its clients Attention Heatmap Technology. This new technology is powered by AI to determine the best visuals to use for the first few seconds of a video. Visuals that will lead to increased viewership then of the entire video.

Most of VideoFresh’s clients are long term and create multiple videos annually. And so VideoFresh is able to provide this service for every video that is used for videos campaigns that can generate more revenue with a strong first 3-seconds, that ultimately increases viewership of the of videos in total.   

Using Artificial intelligence (AI) to Optimize Video Content Can Result in Huge Increases in Sales and Conversions

ANN technology utilizes artificial intelligence (AI), and is able to forecast which elements of the video will draw the most attention from viewers. It generates heat maps and scores reflecting which areas of the video are likely to draw attention. The platform allows VideoFresh’s producers to analyze videos in a matter of minutes, without needing participants for obtaining results. Unheard of in the past, the AI technology is able to mimic traditional eye tracking with 95% accuracy which is pretty incredible. In the past the only way to measure video content in this manner was to use real people in a controlled test setting, a very expensive and time-consuming process.

VideoFresh is constantly looking for ways to create higher-converting ecommerce video content, and this new technology offering now enables VideoFresh to continue its reputation as being one of the leading eCommerce video production companies in the industry. The ability to optimize video content instantaneously and to understand what content gets the most attention in the first few seconds of a video, ensures that the videos that VideoFresh produces have an increased opportunity to provide a much higher ROI.

In the past it has been difficult at times to judge video creativity other than saying “it looks great”. Great looking concepts don’t necessarily sell a product. Having a scientific and data driven solution that serves as an independent check or point of reference that can validate that what is being produced will actually perform better is exciting for both VideoFresh as well as its clients.

The Secret Sauce? Artificial Neural Network (ANN) Technology

The attention predictions of the technology is generated by an Artificial Neural Network (ANN). An ANN is a collection of nodes, with each connection having a weight determining how much each node impacts the next. The nodes are aggregated into layers. ANN’s are trained using a large set of training data, millions of images & videos to learn to generate the correct attention heatmaps. It uses all this experience when generating the attention prediction results for each clients’ video. The attention prediction produced is generated using a so-called generative adversarial network, or GAN. The input is the RGB values of the video. The output is the attention prediction. On the input side a convolutional neural network, or ConvNet, is used that was pre-trained on over 14 million images to detect objects, and therefore it already holds a latent representation of object identity.

Wrap-up

Interested finding out more about this technology and how it can help your videos perform better? Contact us today.