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5 Biggest Video Content Mistakes and What You Can Do About Them

March 13, 2019

If you can read an article, you can watch a video.

Visual information does a LOT for people. In fact, most information transmitted via perception is visual. The brain processes visual information faster than it does text, and we respond to it better, too.

What’s more, the impact of visual content on your marketing goals can be quantified. In order to notice when the opportunity arises to do more of what works, content marketers need to understand how their videos are performing, and why. Just as social media content performance can be analyzed, so can visual content. 

How is Your Content Performing?

If you want to be successful with your video content, then it is a mistake to not be creative. Whether you happen to capture a special moment in the office, make use of animation, film the CEO hard at work, or experiment with creative ways to use your product, you are telling your company’s story. Remember, only you are the expert. Take a look at how Hubspot tells a story in this video while also generating brand awareness:

 

 

By showing off the product, we can see exactly how it works before we decide to buy it. This is especially important for understanding something like software. 

Don’t shy away from social trends like Harlem Shake or The Mannequin Challenge. They are entertaining and can provide valuable opportunities for marketers. The video will be shared widely across a variety of channels, and it will be inexpensive to produce.

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There’s nothing like a personalized video from the senior VP thanking you for your attendance at a seminar. It stands out, it's cost-effective, and it can be made with a smartphone. The “Oddly satisfying” trend on Snapchat is a great example of internet traffic flooding mostly toward videos.

Remember the days where Facebook consisted of only statuses and photos? As per the old model of video marketing, content marketers would create a video and upload it to YouTube. Then they promoted the video by embedding it into a webpage or linking to it in an email or social media post. But there is no way to measure performance this way! When it comes to the effort that was put into the creation and production of the video, it’s like throwing away all your past work.

It's harder for consumers to subscribe or buy your product when they are learning about it on a platform with the main goal to keep users on their site. It’s a mistake to rely too much on Youtube, and a mistake to not take advantage of the interactive component that is a feature of e-commerce. Users can immediately subscribe, sign-up, or make a purchase during or after watching - but only if you monetize your video using machine learning.

Metrics such as engagement rate, play rate, shares, clicks, and conversions keep you informed about the performance of your visual content from the moment you publish it. Do your videos look the same? Are they immediately distinguishable from others? Videos are no longer just for humans to watch and learn from, but now for machines as well. Big data and machine learning enable you to understand the holes that are created by both your video content and that of your competitors.

For example, The Journal of Video Education located in Cambridge, MA took advantage of the opportunity to show others how to perform experiments rather than writing about them in dense, hard to read articles. This is a wonderful idea coming into the digital age.

At the same time, many creatives are reluctant to incorporate user-generated content. Machine learning takes so much weight off creative's shoulders. As well as helping with the optimization of captions, titles, and descriptions for videos, gaining insights into the correct context and timing of posts has also proven to be of extreme use to marketers. Where should you put your video for maximum impact? At which point is the viewer dropping off? This is where you need to focus your attention.

Capitalize on the fact that videos are more likely to be shared than images or text. You can even edit the video after it has been published in a way that maintains your viewer's interest. By utilizing visual content intelligence to guide the production, release, and promotion of your video, you're ensuring that the right kind of content is reaching the target audience.

Videos provide a great opportunity to clear up any lapses in communication that may exist between you and the customer. Things like FAQ videos and company story videos can be instrumental to converting users into long-term customers. 

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One last mistake is failing to tailor your content to each individual user. With the information gained from machine learning, you can decide what types of videos would be the most engaging and useful to the user. If the video is intended for multiple audiences, it might just end up engaging no one, and this is a risk you cannot afford to take. Machine learning is essential in dividing up the audience into individual types of users.

The old model of video marketing is inefficient. Content marketers have proven to be over-reliant on YouTube; success is not measured by the amount of views your video gets. What is most important is whether the right people are watching your video for the right reasons, and resonating powerfully with what they are seeing and feeling. Successful marketing serves as a catalyst: this is how you convert a viewer into a buyer.

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About the Writer: Meghan Carron is a Content Marketing Intern at Cortex, as well as Data Associate for Amazon. With a degree in Philosophy, Psychology, and Cognitive Science, she contributes what she knows about the human mind to the field of machine learning.

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