Tag Archives: JMP

How to build a Twitter Empire like Guy Kawasaki–4 simple steps–Infographic

Infographic is at the bottom of this post.

Photo of GuySo, you want to be a Twitter legend like Guy Kawasaki ? You want 250,000 followers. You want to make lots of money and Tweet all day long. Well, the insights in this dashboard won’t turn you into Guy Kawasaki, but they will help you understand the 4 most important things that make Guy such a success on Twitter.

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 because he knows that his repeat Tweets will bring in about 75% of his total clicks. So do what Guy does and repeat your Tweets.

Guy Tweets to be ReTweeted
Just about all of Guy’s Tweets have a link 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. While you’re doing that, make sure you pay attention to Guy’s 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.

Guy Tweets 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.


Single click image for full screen version.
Download a high-resolution pdf of this infographic here.

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.

Do you know the simplest, yet most overlooked lesson of Business Intelligence?

Below is a data set with 4 groupings of data and 2 columns for each grouping. The summary statistics–mean, variance, correlation, sum of squares, r², and linear regression line are the same for all 4 groupings of X and Y values. If we stopped our analysis here we could move forward confidently knowing that the 4 groups of data are the same. And we’d be dead wrong.

anscombes quartet

visualize these data

In my 15 years in analytics I’ve seen good analysts, time and again, stop their analytical efforts when their data summaries don’t tell a compelling story. I’ve sat through hours of meetings, going through page after page of data related to critical financial forecasts, looking at historical trends going back years, without seeing a single graph to show a trend. For whatever reason, data exploration for many analysts starts and ends with a table of summary statistics describing the data. What a shame. In relying on summary statistics we give short thrift to one of our most powerful assets–our eyes.

To see what I mean, click here.

For years Edward Tufte and Stephen Few have been telling the BI community to, “above all else, show the data”. Make your intelligence visible. Go beyond the summary look of your data and show it, warts and all. In fact, the Business Intelligence Guru recommends looking at graphic representations of your data before you even look at summary statistics. There are tools available today that make looking at graphic distributions of data easier than ever. I have years of experience using JMP (link will take you to a fully functional 30 day free trial), from SAS, which has a distribution engine that makes it a snap to look at distributions. Even SAS graph, with its new statistical graph (sg) procedures in version 9.2 make it a snap to view your data up close and personal.

Lastly, I didn’t invent the data that I’m using to make my point. I came across two references last week that made me think that I should write about it. I watched an info viz legendJeff Heer, tell a story making the case for info viz. I didn’t realize it then, but that story he told actually dated back to 1973 and also appeared on the first page of Chapter 1 in Edward Tufte’s book, “The Visual Display of Quantitative Information” published in 2001. The story goes to the heart of why we need to show the data.

The credit for this eye-opening example goes to F.J. Anscombe, a statistician who created this data set in 1973 to make the case for graphing data before analyzing data. He was a man ahead of his time.