Words Matter: How to Optimize the Content of a Tweet
140 characters means you need to choose your words wisely. Many articles discuss the importance of testing the placement of links in Tweets, best time of day to Tweet, the benefits of using hashtags, etc. But how do you really know which words are best to use in a Tweet?
Is this Tweet optimized for better engagement? Let’s find out.
This got me thinking, how can we possibly measure a keyword or hashtag’s value? In SEO you optimize a page for a target keyword. That keyword is chosen based on research, which compares several metrics, most importantly search volume. However, there is no search volume information provided by Twitter and the supposed “Twitter keyword research tools” out there are shit.
Well I came up with a Google Docs tool that will provide a better solution to this opportunity, but first let’s talk about why word choice in your Tweet is important.
Every Word Counts
I like to relate Tweets to a more intense version of a Meta description. In either case, you have minimal space to get a message across, optimize for target keywords, and entice viewers to click your link/title. The big difference between the two is with Tweets it’s much more challenging to accomplish those goals as you have slightly less space, your link is included in the 140 character limit, and your message has a small visibility window.
So just as you would optimize your meta data, you can do the exact same for a Tweet. To put it simply, the words you use can be the catalyst to huge increases in Twitter engagement.
These practices are the consensus on what increases engagement:
- Using action verbs and descriptors.
- Tweeting @ certain users.
- Time of day.
- Using specific hashtags and keywords.
So how do we measure this?
Yes. We Can Measure Everything. Well Almost Everything.
Much of the analysis that can be performed for Twitter engagement is done through trial and error. Tweeting out the same link or message a few times with different content and tracking how your audience responds and engages is the simplest form of this. After repeating the process several times, you should have a good idea of what works best. Tools like Bit.ly or SEOmoz’s social reporting for example, can help you accomplish this by providing ways to track and analyze past Tweets.
Bit.ly click analytics
With a little bit of work you can use these tools and others to:
- See trends in your Tweets.
- Test different phrasings and see which Tweets receive higher click through rates.
- Track engagement dependent on the time of day, or by link placement.
- Check if the people are engaging and Retweeting your message.
I won’t say it’ll be easy to complete the above, but with a detailed log of your tweets it can be accomplished. It is crucial to know how your audience engages with the content you Tweet and this can only be accomplished through testing and analysis. We can only expect that the available tools out there will continue to develop and provide easier solutions to this.
The Power of Google Docs
Taking your analysis a step further, Google Docs is a powerful tool to extract large data sets from Twitter and other sites or Social APIs. I strongly suggest learning how to use the tools Tom Critchlow provides in this Moz post about Tracking Your Social Media Strategy. Not to mention, the f-ing amazing guide to Import XML on Distilled’s blog.
These tools for example, can help you compare Twitter users by number of followers and allow you to make an educated decision on whom to Tweet new content at. You could even go H.A.M and calculate the true reach of a Tweet by extracting the number of followers from every user that Retweeted your message. This will not only give you actionable metrics to report to your boss, but your coworkers will think you’re the illest motherf**ker alive.
So I touched on how we can track and analyze data for the first three practices that increase engagement, but measuring the fourth practice is still unanswered.
My Solution: Tweet Frequency
As I mentioned before, I created a tool to provide some form of measurement in gauging which hashtags or words are best to use in a Tweet. Now the reason I so badly want to measure this is because of the growing usage of Twitter Search for the latest news or updates on a specific topic.
Search queries in Twitter pull up the latest Tweets that include the words in said query. In addition, there are tons of sites out there that grab feeds of Tweets based on a certain hashtag or phrase. So the value of getting this right is high, as you can reach a wider audience and gather more followers through these outlets that are dependent on Twitter Search.
Since we can’t get accurate search volumes for search queries in Twitter, we need to find another metric that can provide a similar relative measurement. What I am doing to supplement search volume is to use Tweet Frequency. Yes this is a term I coined myself and I’m not going to brag about it and say this is some revolutionary metric, but I will say I haven’t found anyone else calculating this so I thought it would be valuable to share.
Google Doc which pulls in a feed of Twitter search and calculates the frequency of tweets for the query
What I have been doing is using the importFeed function in Google Docs to import feeds of Twitter Search results given a specific query. You can use just keywords, hashtags, or usernames to get a feed of search results.
Right now I am pulling in the first 100 results that come up. The reason is that with how frequent people Tweet, 100 data points are enough to take a measurement of how frequent someone Tweets about a subject.
The feed provides a timestamp, which I then take and calculate the time variance between each Tweet. Once the variance is calculated, I have it calculate the average of those variances. This gives me an average of how often Tweets go out on the subject (the query or hashtag I entered).
Now we have our Tweet frequency for a given query and can collect that metric for several queries to compare! Exciting stuff right? Well in the likely case that it’s not, hopefully this Google doc tool will make up for it. Just make a copy of the doc and start testing it out.
Keep in mind that some queries will not provide a value for the Tweet Frequency metric. This is due to there being less than a 100 results for that search, so you can essentially rule that query out comparatively to ones that do provide a numeric result.
This is a pretty simple tool and metric, so don’t expect it to be a full proof way to do this. Consider it more of something to get you started with.Tweet