Category Archives: Headline

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, 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

200+ things you need to know about unemployment in the US, all presented on one insightful dashboard

There are 208 charts on the dashboard below. Each one is loaded with information from the Bureau of Labor statistics. Check it out, you’re bound to learn something you didn’t know before you came here.

The unemployment insight dashboard is now updated with May’s unemployment figures from the BLS. The unemployment rate dropped from 9.9% to 9.7%, in part due to the fact that approximately 200,000 people stopped looking for work and stopped being counted by the BLS as unemployed.

The long-term unemployment population, those out of work for 6 months or more, grew by an additional 47,000 people and account for 46% of all unemployed. That’s the equivalent to all the people (men, women, and children) in the entire state of Washington.

Note: click the picture below to bring up a large version. Then click again to get a crystal clear look at the dashboard.

Dashboard of Joblessness in the U.S.-May 2010

Pie Charts and faulty analytics in the NYTimes? Watch as the Biz Intel Guru fixes a seriously flawed blog post.

“Is Amazon Working Backward?” That’s the title of NYTimes blogger Nick Bilton post on Dec 24, 2009. Mr. Bilton is writing about Amazon’s product, the Kindle. Regarding the Kindle, he writes, “customers aren’t getting any happier about the end product.”

The day Mr. Bilton posted his story, best-selling author Seth Godin poked holes in it. Mr. Godin’s post is titled, “Learning from bad graphs and weak analysis.” Below is a brief listing of the serious flaws in Mr. Bilton’s approach. The listing is a mashup of Mr. Godin’s thoughts and mine.

1. Bilton should know better than to use pie charts because it’s really hard to determine the percentages when we’re looking at parts of a circle. Bar charts would’ve been much better. Stephen Few has stressed this for years. If you’re posting a chart in the NYTimes, you’d better have read your Stephen Few and Edward Tufte.
2. When your charts are the main support for your story, you’d better get them right. Mr. Bilton did get the table of numbers to the left of the pie charts correct. Perhaps he’d be better served by relying on them over the pie charts to make his point.
3. When you’re analyzing something, you shouldn’t compare opposite populations while ignoring their differences.

Mr. Godin cited 4 specific problems with the piece, ranging from the graphs being wrong (later corrected) to Bilton misunderstanding the nature of early adopters. In addition, Mr. Godin writes, “Many of the reviews are from people who don’t own the device.” Obviously, it’s hard to take a review of a Kindle seriously if the reviewer doesn’t own a Kindle. These are the different populations I’m talking about in item #3 above. I’ll address some of Mr. Godin’s concerns with Bilton’s post now and fill in some of the gaps that Godin left to be filled.

Mr. Bilton tried to make the case that each new version of the Kindle is worse than the one before it. His argument is based almost exclusively on the pie charts below, specifically, the gold slices of each pie. The gold slices are the percentage of one star reviews (lowest possible) each Kindle receives.

Here are the original 3 pies that Mr. Bilton showed in his post.

Despite difficulties in estimating the size of each slice in a pie chart, it is apparent that the 7% slice in the first pie chart is much larger than 7%. His corrected version is here.

Another problem Godin has with Bilton’s piece goes to the nature of early adopters. “The people who buy the first generation of a product are more likely to be enthusiasts,” writes Godin. The first ins are more forgiving than the last ins. I can’t really argue with that insight. My brother, an avid tech geek, is an early adopter of lots of tech gadgets. He was the first person I knew to buy an Apple Newton. I don’t recall a single complaint from him about the Newton, despite it not being able to recognize handwriting, which was its main selling point.

Mr. Godin’s claim that many of the reviewers don’t own a Kindle intrigued me the most. If I could quantify the number of one star reviewers who don’t own a Kindle then I could show the difference in one star ratings between the two groups, owners and non-owners.

I recreated the dataset that Mr. Bilton used for his analysis, 18,587 reviews in all. I also read up on how Amazon determines if a reviewer is an “Amazon Verified Purchaser.” Basically, Amazon says that if the reviewer purchased the product from Amazon, they’ll be flagged with the Amazon Verified Purchase stamp. So let’s see, do the one star ratings vary between the Amazon Verified Purchaser reviews compared to the non-Amazon Verified Purchaser reviews? Why yes, they do!

Amazon Kindle one Star reviews

Amazon Kindle 1 Star reviews

It’s clear from these charts that the reviewers who didn’t purchase a Kindle are much more likely to give a one star rating compared to the reviewers who Amazon verified as purchasing the Kindle. With each Kindle release, the non-verified Kindle owners were consistently four times more likely to give a one star review than the Amazon Verified Reviewers—the ones who actually purchased a Kindle. What’s up with that?

Let’s look at the reviews from the verified purchasers. The percentage of one star ratings each new Kindle edition receives doubles from 2% with Kindle 1, to 4% with Kindle 2, and then moves up to 5% with KindleDX. However, this evidence provides very weak support for Bilton’s claim that Kindle owners are getting progressively less happy.

What about the reviewers who are happy to very happy with the Kindle, the four and five star reviewers? Once again, the non-verified Kindle reviewers provide consistently lower ratings than the reviewers who actually own a Kindle. And once again we see the trend of the non-verified reviewers liking each new version of the Kindle less than the previous one. The four and five star ratings for actual owners of the Kindle jibe with Mr. Godin’s claim that the early adopters are more likely to be enthusiasts than those late to the game.

4 & 5 star Amazon Kindle Reviews

Four & five star Amazon Kindle Reviews

So there you have it, Mr. Godin’s hunches are correct!

What’s most interesting to me, though, is the fact that 75% of reviews of the Kindle aren’t made by people who own a Kindle. On my next post on this subject we’ll hear from a good friend of mine, and text mining expert, Marc Harfeld. We’ll mine the text of the 15,000 customer reviews looking for differences in the words used between the verified and non-verified Kindle owners. Perhaps that will shed light on this mystery. We’re also going to weight the reviews by the number of people who told Amazon that they found the review helpful. You’d think that a review that was helpful to 1 out of 3 people is different than a review that was found helpful by 18,203 out of 19,111 people, like this one.

Lastly, we’d love to hear suggestions from you on other next steps we might take with this analysis.

Thanks for reading.

The Best Insights into U.S. unemployment, revealed in this Dashboard

At precisely 8:30am, on the first Friday of each month, the Bureau of Labor Statistics releases its Employment Situation report, the most anticipated report for stock, bond, and currency traders in the world. The report is analyzed by a wide variety of sources like CNN, WSJ, Bloomberg, NYTimes,, AP, and MSNBC.

The Economic Situation report is critical because it covers the single most important factor in the world’s economy, employment in the U.S. Put simply, if U.S. consumers are losing their jobs, spending will decrease. And since household spending accounts for more than two-thirds of the U.S.’s economy, any change in spending will have an impact on the rest of the world’s economy.

The Economic Situation report is important for another reason. According to Bernard Baumohl, author of the book, The Secrets of Economic Indicators, “Experts have a difficult time trying to predict the unemployment figures because so little other information is out yet for that month.”

With so much riding on this one report, the Business Intelligence Guru thought it the perfect area to apply his information visualization and analytical skills. After all, the data released by the Bureau of Labor Statistics are pretty lifeless–just a bunch of numbers in twenty different data tables. Trying to identify trends in such raw form data is difficult and time consuming. When high quality info viz is properly applied to such data, however, the fog lifts and insights come shining through.

The BLS tables contain different looks at employment and unemployment like:

  • Employment status by sex and age
  • Employment status by race, sex, and age
  • Employment status by education level
  • Unemployment by reason for unemployment
  • Unemployment by duration of unemployment
  • Average weekly hours of work
  • Average earnings (hourly/weekly) by type of industry
  • Monthly changes in employment

The challenge and opportunity here is to provide a clear, consolidated, and insightful view of related and relevant data from the BLS. The Economic Situation report for July 2009 contains nearly 1,000 words. The data tables in the report add approximately 300 data points to the document. But neither the text nor web version of the report on BLS’ website contain a single graph. It doesn’t take a Business Intelligence Guru to know that this is a ripe opportunity for a well-designed dashboard to shed light on. And so, The Business Intelligence Guru presents you with the “Insights into Unemployment in the United States” dashboard for July 2009.

The Busines Intelligence Guru's Dashboard of U.S. Unemployment

The Business Intelligence Guru's Dashboard of U.S. Unemployment

I intend to update this dashboard the first Friday of each month, shortly after the BLS releases the report, so check back then for timely updates.

Lastly, I’m always on the lookout for ways to improve my work, so feel free to leave suggestions and criticism.


Reblog this post [with Zemanta]