So, you want to be a Twitter legend like Guy Kawasaki ? You want 250,000 followers. You want to make lots of money while you Tweet all day long. Well, the insights in this dashboard won’t turn you into Guy Kawasaki, but they will help you understand the four most important things that make Guy such a success on Twitter.
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Guy Tweets like a Firehose
Guy tweets about 3 times an hour, generating about 83 Tweets per day. Half of Guy’s Tweets are published between 9am and 6pm, Eastern time. Guy repeats his Tweets 3 times, 8 hours apart. He repeats Tweets because he knows that his repeats will deliver 75% more clicks than his initial Tweet alone. So do what Guy does and repeat your Tweets.
Guy Tweets to be ReTweeted
All of Guy’s Tweets have a link in them to his website, Alltop.com. Guy publishes lots of interesting content, and his 250,000 followers ReTweet Guy’s stuff about 1,500 times per day. By getting others to ReTweet his Tweets, Guy’s audience spans well beyond his 250,000 followers.
Guy’s optimal time to Tweet for ReTweets is 5pm Eastern. If you’re looking for ReTweets, try Tweeting when Guy does, and also read this. After you do that, make sure you pay attention to the next attribute.
Guy Tests and Tracks to refine his Twitter Strategy
Guy tested his Tweet repeat strategy before deciding on the 3 repeats, 8 hours apart. Why not go one step further and use Twitter data to predict how many ReTweets Guy’s post will get? I’ve constructed a model showing that that we can predict, based on the first 15 minutes of ReTweets, how many total ReTweets Guy will get from his initial Tweet in the following 24 hours. Guy could use this early indicator to alter his Tweeting strategy for the day, or to shuffle around advertising, or to change his repeat Tweet strategy on the fly. You should do the same.
Tweet great content
This is the most important thing of all. Tweet all you want, but if you don’t put out interesting stuff, who will want to follow or ReTweet you?
The data for this analysis were gathered using various APIs (YQL, BackTweet, Twitter Search, and longurlplease). SAS was used to gather and manipulate the data and JMP was used to build the predictive model. The data in this analysis span Guy’s Tweets from the first two weeks of June 2010. Weekend Tweets were excluded.
Not all of Guy’s tweets were used in this analysis. @replies were excluded, as were tweets which didn’t have a link to Alltop.com.
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