As a runner, I spend a lot of time keeping in shape by using the elliptical, and since I have the attention span of an eight year old, I opt toward watching YouTube videos on my iPad instead of full blown TV shows or movies. I have a lot of regular channels I subscribe to, which includes one of my favorites, The Game Theorists.
Just like the title of the channel sounds, the channel and its host Matthew Patrick (commonly known as MatPat) create regular video game theories ranging anywhere from why Link is dead in Zelda: Majora's Mask to why Smash Bros creator Masahiro Sakurai is secretly sick of creating the Smash Bros series.
They're all excellent and well produced videos, but what some people might not realize is that MatPat is a consultant for other channels on YouTube on helping them optimize their content to gain the most views. He's even posted videos about it a few times, and I would definitely encourage you to watch the video below.
To summarize that video, MatPat theorizes that popular YouTube gamer PewDiePie is not really wrong that YouTube is "broken" since the algorithm for returning content in people's results seemed to be screwy at a certain point. One of the most telling points in MatPat's video is that he, in consulting with other YouTube channels, suggested a channel slash half the content they produce in order to double their views.
Yup, you read that right. Putting content out on a more frequent basis is actually more harmful than helpful, at least on YouTube.
This is definitely piqued my curiosity about other social platforms, so I see this post as a first in the series of analytics-focused posts. For now, I am curious to run experiments on my own social profiles to see if sharing content in different ways produces different results, so I'll focus on two of them: my LinkedIn profile and my Twitter profile.
Let's get into it!
My LinkedIn Profile
As I shared in my 50th post, I purposefully try to post content about my blogs only one time per week (per blog) and do it on a very regular Monday (Engedi Artistry) and Thursday (LYEATT) schedule. Fortunately, my consistency of this posting style has helped show consistent trends over time, and one particular result was interesting.
Much like my consistency with posting, my LinkedIn posts follow a very consistent pattern:
- A short blurb about the posts I wrote in the past week
- A URL link to the blog
- A corresponding image related to one of the recent blog posts
As a free user, LinkedIn only provides to me a number count of how many times my share was viewed. I can only guess at what that truly means, but given that the view ratio certainly doesn't match my blog view ratio, my guess is that LinkedIn's "Share" number basically means "Your share was placed in somebody's feed X number of times, and they may or may not have breezed right past it." Again, I don't get the specific analytics on how many times it was actually engaged, but given that LinkedIn's Share count is way higher than my blog's views, I think I'm right about this.
Looking at some posts from the past few weeks, I notice a relative pattern on my share numbers: they generally get somewhere between 120-150 views per post. And just to show you I'm keeping honest, here are a couple screenshots that indicate that.
Here is where things get interesting. One week, I got super busy and didn't produce much content, so instead of the traditional "blurb, URL, image" post I normally do, I threw up a post with only the blurb and URL, no image. Look at how many views that one got.
86 views. That's significantly less than the normal range! Now, we can theorize as to why this is, so I'll quickly address the top 3 theories I can think of.
- LinkedIn is throttling my content: It would be possible, but the numbers bounced back in subsequent weeks. I'm tossing this one out.
- Less people used LinkedIn that week: Again, entirely possible, but given that that week didn't have any special holidays or anything, I highly doubt this is the case, especially because I think the theory below is the real culprit.
- I didn't use an image that week, so LinkedIn's post sharing algorithm didn't return my post as much: And given how much more often I get posts with images in my own LinkedIn feed, this one certainly makes the most sense.
It's interesting to say the least. Instead of dwelling on this one more now, we'll jump on over to Twitter. Perhaps we'll revisit LinkedIn analytics in a future post.
My Twitter Profile
This one is much more difficult to gauge for me since I don't post with the same consistency at all that I do on LinkedIn. I post much more frequently to Twitter and at varying time, and the content of those posts vary on whether or not I choose to use things like images, URLs, or hashtags.
In any case, I decided to run a little experiment using one piece of content that got a fair amount of engagement: my review of Lady Gaga's new album, Joanne, over at my other blog. (And if you haven't checked it out yet, here's my shameless plug to do so now! Click here to read that.)
In my original tweet sharing that review, the tweet was comprised of the following pieces of content:
- A blurb
- A URL link
- An image
- Relevant hashtags
The screenshot below provides the analytics Twitter provided me after roughly 72 hours of the post being live.
Being interested in how that tweet would perform under different circumstances, I chose to post a new tweet again sharing the same review, but instead, here is the content I chose to share:
- A blurb
- A URL link
- No image
- No hashtags at all
Trying to level the playing field some, I set a reminder on my phone to check the engagement analytics of that tweet roughly 72 hours after its posting: the same timeframe I allotted the original tweet with all the bells and whistles. 72 hours later, here's what Twitter shared with me.
This one has me scratching my head a little more than the LinkedIn one, and you can probably understand why. The numbers are almost the exact same! There are a lot more variables to contend with here because of the inconsistency, and I have one main theory why the second tweet performed about the same.
The second tweet was posted in the middle of the night, 3:07am to be precise, where as the original tweet was posted in the morning at 9:27am. I honestly believe this made all the difference, and I'll explain why. You're going to have to take my word at it since I don't have photo proof, but out of sheer curiosity, I checked the analytics of the second tweet after 6 hours of the post having been live, and the "Impressions" number was already in the 50s.
To reiterate, I think timing made all the difference there. This is also coupled with the fact that the first tweet came roughly 2 days after Gaga's album debut whereas the second tweet came in 5 days later, so its wholly possible my first tweet didn't fare so well against all the other tweets rolling in about Gaga's album.
We'll go ahead and wrap this post up here for now. Being totally new to this world of analytics and algorithmic sorting, this is definitely an idea I'll be exploring more in the future. If you have suggestions to help me learn more about this field, please reach out! I am eager to hear what you have to say.
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