How to Optimize Your Twitter Schedule

There are over 67 million active Twitter users in the US every month, according to Business of Apps. This can make it complicated when trying to create engagement with your specific target audience due to the huge flow of tweets that come across each user’s screen. To help marketers better understand their followers and Twitter’s general audience, there are analytics tools that give insights to different demographics, lifestyles, and consumer behavior trends that users have. If utilized properly, the analytics can be a great tool to grow your audience’s engagement and track the results.

I began working on the social media accounts for one of our retail brands a few months ago. This brand publishes a wide variety of articles ranging from new products and convenience store openings, to technological innovations and environmentally friendly efforts. I wanted to find out if the posting schedule I was following was in line with when my viewers were seeing each post. To find the answer, I looked at Twitter and Google Analytics to gain an idea of where my followers were located demographically. I found that the largest segment of my followers and my organic audience resided in the Central, Mountain, or Pacific US time zones. I looked at the companies I posted articles about and researched the locations of their stores throughout the US. For the convenience store industry, the top companies are mostly convenience and retails chains, so I went to the websites of the biggest chains and compiled a list of all their US locations. This showed me that nearly 75% of all the stores are in the Central, Mountain, or Pacific time zones, just as most of my audience is. This led me to believe that the cause behind the stagnant metrics were due to the posting schedule being set for the wrong time zone because the tweets were based off my own Eastern time zone.

In order to start optimizing your posting schedule, try looking at tools like Twitter and Google Analytics to gain a general idea of where your audience is located. Then think about your current strategy for selecting times for your posts. Do these times align? Or are you in the wrong time zone just as I had found? If you find there is a gap, try altering your posting schedule accordingly and taking note of the analytics to test the impact.

For my test, the results were insightful. I found that the number of overall impressions rose, but the percentage of engaged users from those impressions had dropped from .5% to .4%; meaning that taking account for the time zone difference succeeded in reaching more users, but it did not help my goal of increasing engagement. I didn’t account for the diversity in the type of posts I was creating. The “strongest” tweet for that day could be something like a new use for artificial intelligence in data software, or it could even be about newly implemented dog parks at travel stops. Promoting content that regularly is about entirely different topics means that some of the “stronger” tweets will only appeal to certain users and not others. My new question – what times are most optimal for each type of tweet?

I found that like Twitters general audience, nearly all the retail brands tweets can be broken down into found categories. SproutSocial creates an annual social media report that lists the most optimal times to reach twitters audience for those categories. Comparing this data with that of my own account, I was able to create a formula to assist me with cyphering through the different types of tweets and placing them into ranked time slots for each of the four categories. Compiling all the optimal times into one single chart seems complicated at first, but I found that I could simplify the process by filling in all the highest priority times first for all four categories and then moving onto the next ranking time slots. The chart slowly began filling itself in until I had a full-week posting schedule that accounted for more than enough time slots to choose from. If your account is less diverse or more specific than the convenience store industry, then just focus on the categories that apply to you.

I implemented my formula for one month as a test. On Twitter Analytics, I found that the engagement metric had risen to near 3%, much better than what it had been averaging for nearly a year at .5%. This may not sound like a lot, but when talking about thousands of impressions each day, it is a huge difference. Try comparing the success of a certain type of tweet with your previous schedule vs a similar tweet on the new schedule to see results of your own.