The adventures of an analog engineer and digital storyteller who studies emerging networks and their impact on the great game of business.
Jun 12, 2014

Those who follow this blog know that I like all sorts of data. Whether it be tracking the tweets of a company dealing with a public relations issue, or analyzing the social media posts of a public utility company during a blackout, I love finding stories in data.

For the past three years, I’ve been collecting social media data for the top 13 contestants on the television show, American Idol. I wanted to know if social media data could predict who was going home every week.

I started by capturing both Facebook and Twitter data. It didn’t take too long to see a correlation between contestants’ Facebook numbers and those being sent home each week. Although the results weren’t perfect, the accuracy of these Facebook-derived predictions proved more accurate than simple chance. For example, in Season 11, when the television audience voted to chop the Top 24 to the Top 10, the data predicted 8 of the top 10. And during the course of the next 13 weeks, the data successfully predicted 18 of 25 bottom-three (bottom 2 in week # 13) candidates, for an accuracy of 72%.

By the end of the first season, I had come up with a theory that the correlation to America’s voting had less to do with the total number of Facebook fans and more to do with the number of fans gained the night immediately preceding an elimination show. This method worked in all three seasons with one major caveat: it’s accuracy dropped with the number of contestants. The larger the pool, the easier it was to choose both the bottom three and who was likely to go home. However, as the talent pool got smaller, the method faltered, as proven by the fact that it chose the wrong winners in two of the three seasons.

Now, with three individual seasons of data under my belt, I thought it would be fun (yeah, I know) to compare the Facebook data from the top three performers from each season, side by side. I created the interactive bubble chart above for you to play with.

Note: Because American Idol ran for different lengths of time each season, I needed to find a way to align the data between seasons. Since all three data sets had the “last 11 weeks of data,” that’s the information that I used. Once the chart was built, however, I could see a flaw that needs to be explained. In its default configuration, Facebook Fans are on the x-axis, “Talking about” on the y-axis, and a number (1901) on the slider. Unfortunately, the Google Motion Chart wants that bottom number in years, yet in my data is actually weeks (1 through 11). So, when you animate the graph, note that 1901= week 1 for all three seasons, 1902 = week 2 for all three seasons, and so on.

One of the things I did expect to see was a year-over-year reduction in the number of Facebook fans, since supposedly the show’s television ratings are falling. The past three years of Nielsen ratings verifies that story:

2012 (Season 11) Finale: 20.7 million
2013 (Season 12) Finale: 13.3 million
2014 (Season 13) Finale: 10.4 million

So, when we compare the top three from each season, it’s no surprise that the total number of their Facebook fans have dropped:

2012 (Season 11) Sum of the Top 3 FB Fans: 574,531
2013 (Season 12) Sum of the Top 3 FB Fans: 153,586
2014 (Season 13) Sum of the Top 3 FB fans: 114,567

Not only is the number of FB fans per contestant is dropping, but the season-over-season differences are significant. For example, Season 11’s second-place contestant, Jessica Sanchez, accumulated almost as many Facebook fans in her first week (43,799) as Candice Glover, Season 12’s winner gained over her entire season (45,219). And except for the socially-popular Angie Miller from Season 12, Season 11’s 3rd place contestant, Joshua Ledet, had more fans than all four finalists from Season’s 12 and 13 (Kree Harrison, Candice Glover, Jena Irene, and Caleb Johnson).

But, as often happens, the data did reveal something unexpected. Although the total number of Facebook fans fell from season to season, the overall engagement of those audiences rose. Season 13’s final three (Alex Preston, Jena Irene, and Caleb Johnson) lead all nine contestants of in “talk-to-fan” ratios (Facebook’s “Talking About” metric divided by the number of fans). The data suggests that today’s America Idol fans, although down in viewership, are talking bout their favorite contestants more.

Dwindling television viewership may not be the only reason why Facebook numbers are down. The social media landscape is different. For example, Instagram may have drawn some audience from Facebook since it came onto the scene in 2010. The chart above shows the number of Instagram followers for all nine contestants after Season 13 ended. Both Alex (Season 13’s #3) and Caleb (Season 13’s winner) had more Instagram followers than they did Facebook followers.

So, what did I conclude from this three year experiment?:

  1. The number of Facebook fans gained the night between the performance and elimination night is a good indicator of who is going home for the first five weeks. But as the pool of contestants dwindles, so does the accuracy of the method. The method is ineffective in choosing the winner on Finale night.
  2. Although the number of Facebook fans is down year-to-year, today’s fans are talking about their favorites more than yesterday’s.

Someone who’d been following my experiment asked if I was disappointed that social data could not help predict the outcome of the contest. Absolutely not! In an age of big data, it’s comforting to know that humans and freewill still rule.

Probably the most important knowledge that I gained during these past three seasons was the ability to use motion charts. If you want to play with the motion charts for each season, you can view them here:

Season 11 American Idol Contestants Motion Chart

Season 12 American Idol Contestants Motion Chart

Season 13 American Idol Contestants Motion Chart

Feb 9, 2014


On Saturday, February 1st, I launched Project Lizzie, a storytelling effort that’s built upon 99 postcards that were postmarked between 1904 and 1925. I first wrote about the project in a September 2012 post entitled Adding Measurable Value with Story. Since publishing that post, I’ve been working to uncover the life story of a woman whom I’ve never met, nor to the best of my knowledge, have any family connection to.

I’ve followed clues from both digital and analog artifacts–from online services like Google and–to snail-mailing city clerks to acquire death certificates. And I’ve learned something: for every mystery that I solve, new mysteries surface. The journey has lead me to old cemeteries and libraries where clues were etched into everything from stone to microfiche.

The Project Lizzie website is a platform that offers visitors multiple ways to interact with the story. If you want to simply follow the story, just read the it from the very beginning. If you want to spend some time flipping through all 99 of the postcards, visit the project’s Postcard Gallery. And, finally, if you want to take a more active role in the storytelling, you can either start your own research or accept some of the Special Assignments.

For me, the most satisfying part of the project’s launch were the reactions from visitors. For example, just twelve hours into the project, educators were tweeting about how the project could be used as a teaching tool for students. It just goes to show what can happen when smart people are allowed to use a platform for things that they are passionate about.

Please feel free to visit both the Project Lizzie website and the Project Lizzie Facebook Page.

Filed under: Social Media

Dec 26, 2013


Last year I wrote a post entitled Amazon KOLL Turns One, describing the success of the Kindle Owners Lending Library. Since another year has passed, I decided to revisit the subject. At the end of that article, I noted that Amazon had doubled its $700,000 monthly investment to $1.4 million for December 2012. Based on what appeared to be a goal to put $2.00 per borrow into the hands of authors, I anticipated that  700,000 books would be borrowed. Looking backward, Amazon crushed that number, lending 744,000 books (the highest number loaned in a one month period during the entire two years of the program).

  2013 2012 change
Funded $13,400,000 $7,300,000 83%
Borrowed 6,030,738 3,563,424 69%
Royalty $2.22 $2.05 8.5%

In its second year (Dec 2012- Nov 2013), Amazon has increased its investment significantly, adding another $13.4 million, an increase of 83% over the first year. The average royalty/book paid to authors also increased from $2.05 in the first year to $2.22 in the second.

The only potential hiccup in the “$2.00 per loan” assumption is Amazon’s December 2013 commitment of $1.1 million to the fund–21% lower than last December’s number. Holding the number flat in what has been historically a peak borrowing month offers us three scenarios:

  1. Amazon is expecting the number of borrowed books to drop from last year’s 744,000 to 550,000, thus holding the royalty per borrow at $2.00.
  2. Based on an estimated 25% growth rate, Amazon may be anticipating 940,000 books loaned in December, translating into authors’ royalties of $1.18 per book–26% lower than the lowest payout since the program started ($1.60).
  3. Perhaps a hybrid of the two. For example, if we see a 25% December over December increase to 940,000 books borrowed, a $1.87 million dollar investment would be required to hold royalties to about $2.00. Perhaps, Amazon is planning on covering the difference by adding another $780,000 retroactively?

Either way, the program and the business model seems strong. To date, Amazon has distributed $20.7 million to authors after lending 9.5 million of their books. It’ll be interesting to see what a third year brings.

Update: In Amazon’s January 2014 Newsletter, they mentioned that 591,000 books were loaned and that authors got $1.86 per borrow.  Therefore, it appears that the situation reflects scenario #1.


Filed under: Social Media