When is the best time to tweet?
… is the wrong question. At least as it’s currently being asked in social media, it’s not only the wrong question, it’s an overly simplified question that provides an answer that’s not likely to help you meet your goals. It’s a question that is presented to people who are looking for a too-simple, easy-money, push-button answer to their ailing and failing social media efforts. Luckily, I happen to know that you, as a reader of this blog, are looking for more than pat answers.
Let’s dig into this question of when to tweet a little bit, show how you can set up your own data collection, and look at different meanings of the question.
The most common measure of “when to tweet” is the retweet. It’s useful for judging how valuable an audience thinks your content is, and it does have some level of impact on your SEO. With that in mind, let’s see if anyone is retweeting you. Go to search.twitter.com and search for RT @yourusername. If no one is retweeting you, the question is moot. If people are retweeting you, then we should catch that data. Here’s how.
First copy the feed of your search:
Then subscribe to it in a Google Reader account:
Now that you’ve got the data feed, wait a week or two. The longer you can delay gratification while tweeting normally, the better the results will be. A week is the absolute minimum amount of time you need to wait for Google Reader to collect its data. A month is better.
Once you’ve finished your data collection period, it’s time to see what the data shows. Google Reader does a nice bit of data analysis for you – just click on the Show Details link in the upper right hand corner of your feed and you’ll be able to see reporting on the last 30 days, as well as the time of day and the day of week. Here’s when people retweet me, based on time of day:
Here’s when people retweet me, based on day of week:
Do you see how dangerous a pat answer like “the best time to tweet is Tuesday at 7 PM” is? If I were a social media newbie grasping for answers and I decided I should focus all my efforts on that overly-simple answer, I’d lose opportunities on Wednesday, Thursday, and Friday of every week.
Let’s take a step back. What if retweets isn’t what I’m looking for? Suppose I’m interested more in how engaged people are with my company, with me, with my brand? Suppose conversation matters more than mindless spamming of the retweet button by a zombie army? I’d want to focus on actual conversations, wouldn’t I? Here’s how to find them. Start by searching Twitter for your username minus RT and minus http (which is the prefix for a link). Example:
I will see more actual conversations and replies with this search. Let’s subscribe to it, get a week’s worth of data or more, and see what Google Reader says:
Apparently I’m antisocial on Saturdays:
Conversation and engagement are different measures than simple share of voice via retweet, and this search will gather that up for you. If you care about engagement, “when is the best time to tweet” answers based on retweets will not help you.
Suppose, however, I’m more of a numbers guy, and I’m more concerned about what social media can do for my business as opposed to mindshare or engagement, nice as those are. Suppose I want to see some dollars? Is there a best time to tweet? Why yes, yes there is. Assuming you use Google Analytics with goals and goal values set up, you can create a report that looks at sources of traffic (Twitter) and goal completions by hour of the day. There is, in fact, a best time to tweet for my audience for the purposes of completing transactions:
Obviously, I can slice this up by day of the week as well.
If I’m concerned about actual business volume being generated, this version of “when is the best time to tweet” is a lot more valuable.
One thing that none of these questions addresses, however, is causality. All of this is correlative and associative data, which can be incredibly dangerous to rely on. I recommend reading Tom Webster’s piece on this as it’s an excellent read.
What does this mean? If you’re making judgements about courses of action based on correlative data, you’re hoping the underlying cause (which you may or may not know) works to your benefit. For example, ice cream sales are correlated to drowning deaths. If you try to set inventories based on historical records of drowning deaths, you’ll probably do relatively well, even if you fail to understand that the underlying cause is summertime for both data sets.
However, by not understanding the cause, I’m at the mercy of another data set that isn’t the cause of my sales. For example, let’s say there was a tsunami near my place of business in December and there were thousands of deaths by drowning from it. If I rely on that data in future seasons, I’ll be buying a lot of wasted inventory in December because drowning deaths are not the cause of ice cream purchases.
When is the best time to tweet is heavily influenced by when I tweet, because Twitter is an attention-based, real-time medium. 5 AM Eastern Time will never be the best time to tweet according to this data because I’m not on Twitter then! It might actually be a great time for me to be tweeting, but I’m not awake.
This is the greatest fallacy of “when is the best time” questions: Looking at the existing data may be leading me astray because I fail to recognize underlying factors that are actual causes.
Let’s take a look at what I mean. Suppose I went to MyTweeple.com and extracted a list of my 2,500 most recent followers, then feed that data into a geo-mapping application like Google Maps and BatchGeo.com.
When is the best time to tweet? Well, suppose I tweet at 7 AM Eastern Time. If you look, there are a decent number of followers in Western Europe, where it is just around or after lunch. Without exploring data in great detail, I might assume that people are retweeting me because it’s early on the East Coast (where I live), but in fact, the retweets are coming from people at lunch in Europe.
Do you see now why “when is the best time to tweet” is a minefield fraught with wrong answers to misunderstood questions?
Do your own research. Test. Investigate. Be scientific in your approach. You might just uncover insights into your audience that no one else has!
You might also enjoy:
- Six Types of Marketing Demand Generation
- The Year of the Yin Metal Ox
- Four Requirements of Great Marketing Data Visualization
- Google Analytics 4 or Bust: Lessons from Google Marketing Live 2021
- iOS 14.5 and Marketing Analytics: How Concerned Should You Be?
Want to read more like this from Christopher Penn? Get updates here:
Get your copy of AI For Marketers