How to Use Video Analytics to Create Effective Content

Did you know that viewers retain 95% of a message when they watch it on video, compared to just 10% when reading it in a text? 

But here’s the catch: Creating impactful video content requires more than just hitting the record button. 

To create content that truly resonates, you’ll need a deeper understanding of your audience’s behavior, preferences, and engagement patterns – insights that only video analytics can provide. 

In this blog, we’ll dive deeper into the type of video analytics to track, how to set up a solid video analytics framework, and finally, a closer look at one of the best tools to create and measure video success. 

What are video analytics?

Video analytics are data-driven insights that help content creators understand how viewers engage with their video content. 

This video data helps track and and measure various aspects of viewer behavior, from basic metrics like views and watch time to more complex data points such as audience retention patterns, interaction rates, and viewer demographics. 

By collecting and analyzing this data, creators can optimize their videos for better engagement and measure the return on investment of their video initiatives. Video analytics serve as a feedback loop, providing concrete evidence of what works, what doesn't, and where improvements can be made in video content strategy.

3 key types of video metrics to consider tracking

There are many different types of video analytics out there that you might consider tracking. 

Basic performance metrics like video views help quantify the reach of your content, whereas interaction metrics like click-through rates help you understand whether your content caused viewers to take action. 

All types of video metrics typically boil down to three categories – engagement metrics, interaction metrics, and performance metrics. 

Performance analytics tell you about your content’s reach, engagement metrics showcase how well you’re holding attention, and interaction metrics indicate how effectively your content is driving action. 

Below, we’ve broken down everything to know about the three main types of video analytics. 

Engagement metrics 

Engagement metrics measure how viewers interact with your video over time. This includes watch time, retention rates, and drop-off points. 

In other words, think of your engagement metrics as indicators of how “sticky” your content is – whether people stay to watch it or leave quickly. If you’re seeing a high drop-off rate within 10 seconds of your video, you’ll want to consider optimizing your intro for better engagement. 

Examples of engagment metrics: 

• Average view duration: The average amount of time viewers spend watching your video. For a 5-minute video, an average view duration of 3 minutes indicates solid engagement.
• Retention rate: The percentage of viewers who continue watching at any given moment. For example, a 70% retention rate at the 2-minute mark means 70% of initial viewers are still watching.
• Drop-off points: Specific timestamps where viewers stop watching. High drop-offs at 0:30 might indicate an ineffective introduction, for instance.
• Replay rate: How often viewers rewatch specific segments, indicating particularly engaging or complex content.

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Interaction metrics 

Just like its name suggests, your interaction metrics track viewer participation with your content. Not only does this include the likes, shares, and comments your video receives, but also participation in interactive video elements like polls or quizzes. 

Click-through rates (CTRs) are also considered an interaction metric. On YouTube, you might measure your CTR according to the percentage of people who clicked on your video compared to those who saw your video thumbnail. This metric helps YouTube creators determine how effective their videos are in encouraging viewers to watch it. 

Examples of interaction metrics: 

• Click-through rate (CTR): The percentage of viewers who click on calls-to-action or links within the video. A 5% CTR is typically considered good.
• Social shares: The number of times viewers share your video across platforms.
• Quiz participation rate: The percentage of viewers who engage with interactive elements embedded in your video. 
• Like to dislike ratio: Typically measured on YouTube, this is the ratio of positive to negative reactions, showing content reception. Aim for 90%+ positive.

Also read: How to Choose the Right Interactive Video Software

Performance metrics 

Think of your performance metrics as your top-level data that measures audience reach and audience characteristics. These performance metrics track total views, unique viewers, and even more detailed data like your traffic sources and audience demographics. 

Think of your performance metrics as a way to understand who’s watching your content and how they found it, giving you valuable insights into the reach of your content. 

Examples of performance metrics: 

• View count: Total number of times a video has been played, indicating reach.
• Cost per acquisition: Money spent to gain each new viewer or subscriber.
• Brand lift: The measurable increase in brand awareness or perception after deploying a video campaign.
• Conversion rate: The percentage of viewers who take desired actions after watching a video.

Setting up a solid video analytics framework

Video analytics are a powerful tool that can be used to bring your content to new heights. 

But in order to make the most of your video analytics system, you’ll want to have the right framework in place to help you easily assess what’s currently working, your goals for improvement, and the video analytics software tools you might need to get there. 

Here’s how we recommend getting started with a video analytics framework:

Establish baseline metrics 

In order to set realistic benchmarks for improving your video performance, it’s crucial to have a strong baseline understanding of how your audience is currently interacting with your content. 

Start by collecting 3-6 months of historical data across key metrics like average view duration, engagement rates, and conversion numbers. 

This baseline data becomes your foundation for measuring growth and identifying areas needing improvement, while accounting for variables like video length, content type, and target audience.

For new channels, you’ll want to research industry standards to assess what’s a realistic goal for your industry and content type. For instance, a 50-60% video completion rate is a good benchmark for educational content. 

Set measurable goals

Now that you have a baseline understanding of your typical video performance, you’ll want to set video analytics goals to meaningfully improve your content performance.

To ensure your goals are both relevant and measurable, they should follow the SMART framework: Specific, Measurable, Achievable, Relevant, and Time-bound. 

Instead of vague objectives like "increase engagement," set concrete targets such as "achieve 65% completion rate on training videos within three months" or "increase quiz participation rates by 25% this quarter." 

Your goals should align with broader business objectives – whether that's improving learning outcomes, boosting product awareness, or increasing conversions. Tracking this progress regularly will allow you to iterate strategies as needed. 

Choose the right video analytics software

Choosing the right video analytics platform requires you to balance your specific needs with available features and budget. 

Although platform-native analytics like YouTube Studio will provide you with basic metrics for free, third-party tools will offer far more comprehensive insights. 

That said, you want a video analytics solution that integrates neatly within your existing tech stack. Colossyan distinguishes itself by offering a video analytics suite and video creator all within the same platform, making it well-suited for creating effective training content.

Meet Colossyan’s comprehensive analytics dashboard

Colossyan is an AI video platform that helps teams create engaging and interactive videos, leveraging AI avatars and text-to-speech voiceovers to speed up the content production process. 

But not only can you create artificial intelligence videos in minutes with Colossyan, you can also track the performance of those videos with Colossyan’s real-time video analytics dashboard. 

This dashboard provides an overview of your video views, the average time spent watching the video, and – if your video includes interactive multiple-choice quizzes – the average video score. 

Colossyan’s AI-powered video analytics feature also provides a detailed breakdown of each viewer’s analytics, including how much time they spent watching the video and when they watched it. These views are organized by name for each Colossyan user who engaged with the content – providing additional visibility into team engagement.  

This video analytics data is available on all Colossyan enterprise plans, giving you easy access to these insights on the same platform you used to create your content. 

Interested in trying it out? Book a demo to get started. 

Using video analytics to optimize your content 

At the end of the day, the video analytics that are most worth tracking for your team depend on the metrics you care about most. This also determines how you approach optimizing your videos. 

For a video marketing campaign, measuring engagement metrics like reach and conversion rates might be top priorities for your team in order to properly measure the ROI of your campaign. 

Highly engaging thumbnails and proper distribution of your videos on sites like YouTube are key in helping with brand awareness and getting the word out about your campaign. A/B testing is another great strategy to see which small changes to your video title can have the largest impact on engagement rates and the like. 

However, if you’re creating training videos or other educational content, you’re likely looking more at how the material is resonating with audiences, such as through your interaction metrics capturing your average watch time, retention rate, or quiz scores. 

A low average watch time might mean that your viewers don’t feel properly immersed in the content. One way to combat this is by adding interactive branching scenarios into your videos to offer a choose-your-own-adventure type of viewing experience. 

By immersing your viewers in the scenario and allowing them to make decisions on what to do next, you can ensure they stay engaged for longer, instead of passively consuming the content. 

Embedding multiple-choice quizzes is another great way to make your videos more engaging for your viewers, as the results can also help capture audience understanding. If part of your video is garnering poor quiz results, then you may want to consider editing your video for clarity. 

Start tracking your workplace video analytics with Colossyan

Video analytics are just one of many features Colossyan offers to make your videos more interactive and engaging. 

With AI avatars, text-to-speech voiceovers, and the ability to automatically translate your videos to more than 80 languages, Colossyan offers a way to create high-quality, professional video content in a fraction of the time required by traditional video production. 

Here’s how Colossyan works: 

With Colossyan, you can spend more time on the things that matter, and less time worrying about hiring actors or refilming your footage when material changes. 

Instead, when using your video analytics to optimize your Colossyan content, all you need to do is update your video draft and click “regenerate.” Plus, swapping out your AI actors or AI voiceovers is extremely easy, giving you more opportunities for A/B testing to see what has the greatest impact on your video analytics. 

Try Colossyan for free today to create unique and interesting content with the power to improve your video analytics metrics. Get started for free – no credit card required.