Tweet Much? Don’t Expect a High CTR. New data I’ve been working on seems to indicate that the more frequently you Tweet links, the fewer clicks you’ll get.
I’ve been working towards a statistical model of how an individual makes a decision to ReTweet a specific Tweet and in that process, I came across an interesting problem: before someone ReTweets something, they have to notice it. If you’re anything like me, you’re only able to actually read a small percentage of the total activity in your friend’s timeline, which means that very few of the Tweets I’m technically “exposed” to ever even have the chance of being ReTweeted.
As a measure of “attention,” I started looking into click-through data. The wonderful thing about bit.ly is that it has an API that allows anyone to view the stats on any bit.ly link. I grabbed as many of the bit.ly-containing Tweets of several of the most followed and link-heavy Twitter accounts as the Twitter API allows (it imposes a limit of 3,200 total Tweets accessible per user) and the number of clicks each link had gotten. For the time of each Tweet, I also pulled the number of followers that account had and calculated a followers-to-clicks conversion rate. I’ll call this rate CTR for simplicity’s sake. I was able to get this information for about 2000 Tweets. It is important to note that ReTweets of a bit.ly containing Tweet (if the ReTweeter does not change the link) also count toward the total number of clicks, so it is possible in some cases for a link to have a CTR of over 100%.
Digging into this data, I started to notice an interesting trend: the higher the number of links an account Tweets in a given timeframe, the lower the CTR on each individual link. If you want your Tweet to get noticed and ReTweeted, you should slow down your posting rate.
First, I looked at this data hourly, by graphing the CTR of Tweets over the number of other Tweets posted in the same hour. The first graph below shows individual lines for each account measured; the second graph shows an average for all those accounts.
Then I looked at the numbers by day. The CTR fall-off in these graphs seems to be slower than those above, but the trend is still prominent.
I’ve got a bunch more stats and analysis to run on this dataset to isolate some factors that lead to increased CTR, and therefore increased attention. I’d also love your feedback on data points you’d like to see.