Youtube Analytics Data

How To Read Your YouTube Analytics Data to Create Better Content

The data tells a story. In Youtube analytics, there is a very helpful section that showcases key moments for audience retention. By looking at the dips, spikes, and continuous segments we can determine what is working and what is not, and most importantly how to improve. I’m going to break down one of my more recent videos to help you learn how to read your Youtube analytics data to help you create better content.

You don’t need thousands of views either to start reading your data, my most recent video “flopped” and got only 32 views but it revealed some big insights.

Setting the Stage

My last video was on 5 things I wish I knew before started building my personal brand. I’ve built roughly 1,000 subscribers of people interested in becoming better public speakers and building their personal brand. 

Typically I try to create videos that are based on keyword research using the tool Tubebuddy but this time I made the content that I wanted to make. This partially explains why it “flopped” but we learn more from our failures than our successes.

The video

Thumbnails, Intros and Drop Off

There is always a drop-off in the first few sections of someone clicking on your video. The key though is minimizing that drop-off. In one of my other videos that is starting to catch in the Youtube algorithm and ranking for keywords, my drop off in the first 15 seconds is only 25%.

Youtube analytics data good intro drop off

Comparing that to my most recent video, my thumbnail was a bit clickbait and there was a disconnect between it and the content of the video. This caused a 40% drop-off in the first 4 seconds.

Reading into Youtube Analytics data, moving forward I have to spend more time thinking about the thumbnail and creating something that is inciting/get’s clicks but also is related to the video. 

Youtube analytics data bad intro drop off

Youtube Analytics Data’s Holy Grail – Continuous Segments

Continuous segments are times throughout the video that I maintained audience retention. Ideally, we want our entire video to be one long continuous segment! This is the most important piece to look at as it’s a single you are doing something correctly.

I had 1 continuous segment start at timecode 5:20 to 7:12 almost 2 full minutes!

analytics continuous segment

I’m going to be 100% honest, I’m not sure why this section resonated so much with my audience compared to the rest of the video and I’d love the internets feedback. If I had to take a guess, it is the most relevant section to my audience as I specifically call out public speakers.

The Almost Continuous Segment

From timecode 3:42 to 5:15 I had really good consistency in retention time. In this section, I had 3 value-packed images that break down a standard marketing funnel, what content to create, and what metrics to track. I feel those images helped keep retention time as they conveyed a lot of quality information quickly without lingering on the images.

continuous segment #2

The dip that stopped me from achieving a continuous segment was a 5-second pitch to opt-in to my email list. I could remove future pitches in my videos to help me achieve an overall higher retention time while I’m a smaller creator. On the other hand, as a marketer, it seems silly to me to not have one pitch. A couple of seconds of a pitch really shouldn’t affect me too much overall.

But this small dip illustrates how the Youtube analytics data tells a story. It’s clear as day from the analytics that people don’t like the pitch.

The Last Section of Consistent Retention Time

Starting at timecode 2:06 I had a solid 30 seconds of continuous watch time. A small dip happened from 2:37 to 2:42 but quickly recovered. A second dip happened at 2:51 but a climb, later on, brought my retention time back up again to the exact same spot. This section is where we can learn a lot from the Youtube analytics data.

Youtube analytics reading the data

My takeaway is that during the second dip at 2:51 I went into a second example for the point I was trying to make. If I removed that second example and continued with the video, I wouldn’t have had that drop and would have had a 2nd continuous segment. Being concise is important.

The Big Spike

I had a big spike that started at 3:18 and went to 3:27 going from a retention percentage of 25 to 42. What a massive boost! Something I did at that time grabbed people’s attention and made them want to pay attention.

Reviewing the footage, I don’t think what I was saying at that time was particularly interesting or unique that would cause a spike. I didn’t showcase a graphic that had information that you would want to reread etc. A little further digging reveals what I believe caused the spike.

Youtube analytics spike

If you scrub through the video I start point #3 shortly after 3:18. You can see me hold up 3 fingers in the preview at timecode 3:23. I think the audience sees this and clicks back to 3:18 a 5-second difference but enough that it caused a spike. 

Shortly after I say point 3, I start to see a drop-off again. This leads me to believe that people hear the points and click away as they assume that they know what I’m about to say.

What Causes Those Little Spikes?

Unlike some of my previous videos which have a slow drop off as time goes forward, this video had a number of small spikes throughout the video. It almost looked like a heart rate monitor. This singles that either a) people are going back and rewatching a section or b) more likely people are scrubbing through the video and stopping at certain points.

Youtube analytics small spikes

Taking a look at when these points happen at 18 seconds, 58 seconds, 1:07, 1:30. 1:48, 5:38

During my editing process at those times I added stock videos, graphics, or motion graphics. My assumption is this is people stopping at those sections to hear what I’m saying and pay attention for a brief moment. 

To increase retention time I need to increase my use of motion graphics and video footage. This will add time to my editing process but with services like Motion Array, it won’t add too much extra effort.

In Conclusion

I hope seeing my Youtube analytics data of my latest video helped you understand how to improve your own content. It takes a bit of time and energy to review your old work but it’s one of the most effective ways of learning how to make better content.

Let me know if it was helpful in the comments.