Monthly Archives: July 2010

Old Spice Guy’s popularity on Twitter charted

Old Spice recently released about 14 ads with The Old Spice Guy (OSG) personally responding to Tweets from 14 celebrities. Some of the celebs are Hollywood types, others are Web Celebs like Guy Kawasaki, Biz Stone, Kevin Rose. You can see OSG’s video replies here. They are great.

I put together a chart showing the number of Tweets that mention the words ‘old’ and ‘spice’. The chart shows just how quickly the Twitterverse filled up with Tweets about the OSG. Before 9am on July 13th, there was hardly any mention of the OSG, but then, within 6 hours, there’s a spike of about 2,300 tweets per hour about Old Spice. Alas, nothing lasts forever, and after peaking at 4,500 Tweets per hour, the Twitterverse quieted down and settled at around 400 Tweets per hour about the OSG.

BTW, the OSG says he’s hung up his towel.

Chart of the Old Spice Guy's popularity on Twitter

OSG Trend

Watch as The Biz Intel Guru fixes a poorly designed WSJ graphic

A friend of mine pointed me to a story in today’s WSJ (no subscription needed) with a hard to understand graphic in it. I’ve pasted the graphic below.

The designer chose to use the entire background of the chart to represent the number of sudden cardiac deaths in a given year. They used squares of different sizes to represent the number of explained and unexplained deaths from cardiac arrest. In this case, I think the designer was trying to give the reader an easy way to compare the parts to the whole, but it doesn’t work. Also, there are over 100 words of annotation on this otherwise skimpy graphic, which makes me think they could have done away with the graphic and just used the words instead.

Here’s the WSJ graphic:

Poorly designed WSJ graphic

WSJ graphic

Here’s what I think the chart should look like:

What do you think? Is my graphic clearer than the WSJ’s? What would you do differently? I’d love to hear your comments.

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.

Infographic


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.