Data is a small piece of the analytics puzzle
Breaking down the psychology behind Twitter analytics
As the news editor of my campus publication last semester, delving into our analytics was a huge portion of my job.
I was not necessarily required to do this, but looking at our newspaper analytics was an excellent way for me to be able to understand which types of stories readers were drawn to. Another major aspect of looking into our data was learning about the different social platforms people used to reach our content.
As a marketing student and journalist, this was vital information.
I always worked hard to promote our stories on Facebook and Twitter, as our analytics showed this drove most of our traffic. Without looking at the data and adapting our strategy, our readership would certainly be lower than it is.
As I began to work with Twitter for my mobile storytelling class, I used to analyze aspects of my account without any fancy algorithm doing the work for me.
I would go onto Twitter some mornings and think, “Hey, this tweet about pop culture did really well.”
So, I would engage more frequently with pop culture news.
I even took notice when my tweets about finance didn’t generate many responses. I chose to water down some of the language I used in order to make it easier for my non-finance industry followers to relate to it.
Everyone has this kind of intuitive mindset to some degree, but it’s extremely helpful to be able to see raw data in front to adapt your platform around.
This week, I discovered Twitter Analytics, which showed a lot of information that surprised me.
I have been slightly out of commission for almost two weeks, as I was very sick and did not have the brainpower to hop on Twitter every day and engage.
However, the data on my page is significantly trending upward. This is most likely due to the fact that I am definitely using Twitter now more than ever in the past, even though I still haven’t been tweeting as much as I could.
Another major data point that surprised me is that my top tweet for the month of June so far is actually finance-related, despite the fact that most of my tweets on this topic do not show great engagement.
I believe this tweet did exceptionally well because I spoke about my success story with a stock that was universally believed to fail. I think my followers most likely engaged with the exciting aspect of making money in the stock market, rather than the topic of stocks itself.
This information is very helpful for me to understand that my jargon can sometimes be too much for my followers, and I can talk about something I’m passionate about without using pretentious words normal people don’t use or understand.
My most significant takeaway from working with my analytics is realizing that my some of my finance tweets may not have had a lot of engagement, but that doesn’t mean they were not good tweets.
My Twitter following consists mostly of high school peers and journalists that I have interacted with throughout college.
There are very few people following me who really have a serious desire to engage with the things that I tweet.
I came to the conclusion that my tweets were not bad — my audience just wasn’t right.
I can respond to industry professionals and tweet at them all I want. But at the end of the day, my following is not interested in what I have to say.
My current goal for the future is to tweet more about things my current followers might appreciate, but also try and gain a follower base of people who are interested in economics and finance.
Looking at analytics this week has been extremely eye-opening. Picking apart data is not a holistic way to understand if your content is good or not. There is a real psychological aspect to understanding what makes a tweet exciting to engage with, and those who are diving into their analytics need to recognize this before concluding anything about their data.
