Posts Tagged ‘video analytics’

Does Length Matter? It Does For Video: 2K12 Edition

Monday, May 7th, 2012


Does Length Matter? Initially, this question might evoke thoughts only appropriate for your spam inbox. Obviously, we aren’t going to write a long post here on that meaning of that question — but we do find ourselves uniquely poised to answer the very same question about business video, with tons of data from business videos of every type at our disposal. The graphs below summarize literally millions of data points from the last couple years (since our last post about this topic in 2009).


The above graph (let’s call it Exhibit A, because it’s fun to feel like a detective) is the most consolidated display of the data, with one data point for each video length range, on the x-axis, with the average % viewed for videos of that length on the y-axis. Basically, it’s representative of the video engagement number within Wistia (so you could compare your number with the number for that length on this graph to see how well you’re doing compared to the average!). This graph tells us that shorter videos are better for getting people to watch the whole thing. After all, most business video is created to serve up a pre-packaged message, so the longer the video, the less people will watch. It’s also noticeable that after a certain point the engagement average flattens out — so there’s not a major difference in engagement for a 4-minute versus a 10-minute video.


On to Exhibit B, where the x-axis represents the percentage of a video viewed (think of each line as the average engagement graph for a video of that length range in Wistia, one of the bars of Exhibit A over time, with each line representing the average video for that bin, with the lengths normalized), and the y-axis represents audience engagement. In this case, you could compare the engagement graph line of your own video to the appropriate line of this graph to compare yourself to the average.

A possible takeaway from this graph would be to organize the content of your videos journalistically, placing the most important, essential information first, then following with supporting details. For longer videos, notice that the dropoff at the beginning is extremely steep; it seems that most viewers decide quickly whether or not to watch, and once that decision is made, they tend to stick around until the end of the video, when they detect that the video is wrapping up and another drop off occurs. For this reason, if you’re using a post-roll call-to-action, you might want to consider a harder stop to your video, rather than a meandering wrap-up — this will ensure that more viewers stick around to see your CTA.

Exhibit C takes things one step deeper: this is the raw data that went into creating the above graphs. Each frame in this animated graph represents one of the time ranges from the above graph. Each of the faded lines is the engagement graph for an actual video, while the average line for that video length is in orange. Again, the x-axis represents percent viewed and the y-axis represents audience engagement.

The interesting thing to notice here is the wide variation even for videos of the same length. The variation tends to be more wide at the beginning, tightening toward the end (again hinting that people decide whether or not to watch pretty quickly). There are definitely outliers, but all in all, longer videos see a tighter overall distribution, where it’s safe to say that if you’re doing 30% versus 25% engagement on average, for example, you’re doing pretty well.

The main takeaways from our first “Does Length Matter?” post still hold true: overall, shorter videos are more engaging than longer videos. You should strive to make your content as concise as possible to achieve the highest engagement. If your message is more complex, feel free to give it the time it deserves, but understand that a major chunk your audience won’t make it to the end of the video and consider front-loading your video with the most important information at the beginning.

If you’re thirsty for more, we’re hosting a webinar on video length on Thursday, May 17!

Stats Snack: Considering Context

Tuesday, May 1st, 2012

We’re huge fans of measuring everything about your web video, but analytics are pointless unless you know how to read them to better understand how well your video is working and, more importantly, how to improve upon it. While we know you’re all (almost too) smart and capable of reading your analytics, we thought it might be nice to include periodic examples on our blog, adorably entitled Stats Snacks, of how we and others have personally used analytics to improve our video (or maybe even where we’ve messed up, even though we totally don’t mess up at all, ever).

In this particular morsel, which some of you might recognize from a recent webinar, we started with a customer testimonial from our friends at Litmus, an awesome email campaign testing and tracking application. In the first iteration of the video, we asked for them to provide the context of who they are and tell their story, then talk about how they use Wistia.

The analytics for that particular video are the browner portion of the graph below. You can see a pretty big drop off at the end of the video, when we were wrapping it up, showing our logo and pitching Wistia. Overall the engagement was okay for a 1:30 video, but on the lower side. We decided we wanted to mix it up; because we’d shot testimonials with different companies, we re-edited the video so that instead of being a testimonial from Litmus, it was interviews with four to five different companies, interviewing them about why they would pay for video hosting. The graphs look pretty similar — there’s still a drop off at the end, but it turned out the second video (featured lower in this post) was significantly more engaging than the first.

Instead of going back and re-editing the first testimonial, as we could have done, we wanted to take the opportunity to have the testimonial be more focused and work with other content we’d shot. We didn’t shoot any new content, just combined footage we already had.

The point of all of this? Whatever analytics you’re using, the numbers aren’t necessarily going to give you that final insight. You need to look in context of what you’re actually doing to see what caused the boost. In this case, we think the newer version was more engaging because more was packed into a smaller period of time to hold attention, hitting key points over and over (versus the first video that was a slower narrative video about Litmus). Additionally, the new video was placed right on the front page of the Wistia website. In that context, the question “Why Pay For Video Hosting” was probably a question on visitors’ minds — so the video aligned context to content, making the video more engaging.

By closing the circle with analytics and improving our own videos, we can apply our learnings to whatever we do in the future. If you have any stats stories that you think would be a good snack, send Alyce an email!

Wistia Webinars: Using Analytics to Make Great Web Video

Monday, April 23rd, 2012

On April 12, we held our second webinar, Using Analytics to Make Great Web Video! Compared to our first Intro to Video Marketing webinar, we went a bit less technical, this time using two cameras (one on a tripod and a GoPro in the background) and just one microphone. This was Brendan’s first webinar! We covered three examples of our own videos, using analytics and engagement graphs to show what we’ve learned from our videos. We hope those who tuned in found the webinar helpful!

You can catch up or re-watch with both the video and slides below.


Here are the links of interest from this webinar:

If you have good examples of your own, we’d love for you to share or to help you analyze your videos in a future webinar! Contact Ezra (ezra@wistia.com) if you’re interested.

Engagement graphs get a facelift

Monday, April 2nd, 2012

You might remember our old engagement graphs from the ancient days of Stats 1.0. They were great, but they could be confusing — people wondered why sometimes, areas of the graph exceeded 100%. It all made sense once we explained, but with Stats 2.0, we figured we’d remove the extra step in understanding and make these graphs more intuitive (as well as more informative).

This new engagement graph separates watches from rewatches (the former graphs were basically the top line of this graph). The first view by an individual of any part of the video is counted in the blue portion of the graph, while any subsequent views are counted in the orange (so take note: 16 views worth of orange could be one person watching 16 times, four people watching four times, or 16 individual rewatches — you can come to further conclusions in the individual heatmaps).

With the new engagement graphs, you can tell more than ever which parts of your videos are the most engaging, whether it’s because they’re the most interesting and most confusing, and continue to improve the videos you make in the future based on this information. Happy analyzing!

Say hello to IP Filtering and say goodbye to unclear stats!

Monday, March 19th, 2012

One of the feature requests we’ve received most often is for IP Filtering within our analytics. Alongside Stats 2.0, it totally made sense to include this feature: with new analytics this powerful, you don’t want your numbers muddied by views from within your company, or views from your mom, or views from whomever you might conclude isn’t a serious prospect. Now, you can filter out however many IPs you’d like to make sure your stats are as clean as possible. You can truly know what your audience is thinking, whether you’ve got 30 viewers or 30,000. The system filters when it’s turned on and stops once you turn it off, so you can decide when you’re filtering and when you’re not (for example, during heavy testing periods).

To access IP Filtering now, go to Account Dashboard > Embed Settings and enter the IPs you’d like to exclude!

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