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Using Tweets and ImpactWatch Tools to Predict American Idol

Once again, the Internet is abuzz with predictions and theories about who is going to win American Idol.  In the early days of the competition (back when Kelly Clarkson was still a nobody singing karaoke and we only hypothesized that we hated the British without actually knowing it through Simon Cowell), there was much less web traffic about the show.  This season and the previous one, however, it's all the Internet can talk about.

This leads people in finding numerous ways to predict who of the now-6 remaining contestants will be voted off each week.  After all, this is a show that purports that the American public gets to decide who is going to stay and who is going to go.  Polls, blogs, and fansites may all play an important role in deciding the overall victor, much like a modern day political campaign. 

TV Squad, a popular television site, uses polls from various sources as well as their own intuition to predict the next bootee.  Most of the polls incorrectly predicted Syesha Mercado's demise, while the real loser was Kristy Lee Cook.  Obviously, this is not an accurate way to predict the contestant with the lowest votes.  The polls are simply too specific in the sense that only those Internet snoopers that come across them will actually get a chance to vote in the poll.  This does not represent an accurate view of the American public.

DialIdol.com has found a more inventive way to predict the successful contestants.  Their software measures the busy signal of each phone line to determine who is getting the most votes.  They started the program during the previous season, but achieved only moderate success in the predictions.  The company also sells software to enable one person to vote many times for a contestant.  Many sites have reported that the software is now known by the American Idol producers and rarely works anymore.

Tivo also found a creative way to measure the votes.  The company claims that they can predict who is going to be voted off by which minutes of the recorded programs are re-watched.  The theory is that Idol favorites will have their performances re-watched by their adoring public, while soon-to-be eliminees will have fewer views.  Unfortunately, the system seems to not be altogether accurate, since Tivo has incorrectly predicted Mercado two weeks in a row.

Another social media company, BuzzLogic, uses their "influencer blog" ratings to follow the entire competition via their blog.  I was impressed by the fledgling company's efforts at first glance, but upon closer inspection realized that few, if any, of their predictions have been true.  In addition, BuzzLogic gives very little explanation when they are incorrect.  This does, however, bolster my recent opinion that Katie Paine's connection between online activity and offline activity is flawed.  Many ‘influential' bloggers may be writing about certain candidates for American Idol, but that does not necessarily mean that they are voting for them, or voting at all.

I decided to tackle the task of predicting American Idol, ImpactWatch style.  Instead of using news articles, I used Tweet Scan to analyze 90 tweets per remaining contestant, using two separate searches for each.  I searched for each contestant's full name as well as their first name and the phrase "American Idol".  I read and ranked each tweet post as positive, negative, or neutral.

Castro Tweet Example

There are two reasons why I believe this method to be more valid than the other ways that were described above.  First, tweets represent impulses and first impressions, which I assume mirrors the mindset of actual voters.  Secondly, this is the only method that ascribes a positive or negative take on the information.  Polls just rank the favorite, while the Tivo system lacks any real information about why certain parts of the show are re-watched.  BuzzLogic's system has merit, but suffers from the need of personal input by its bloggers to explain anomalies in the amounts of influencer blogs.

Using my ImpactWatch inspired protocol, I found that David Cook and Jason Castro have the highest amount of positive tweets.  Sure enough, after doing some extended research, I found that the two received much praise for their performances last week.  All three females had an identical number of negative tweets (45), but Mercado has the lowest amount of positive tweets at a scant 30.  This is preliminary, but on Wednesday morning, I will post an updated tweet analysis (since Tuesday is when the contestants will perform their new songs).  Voters will most likely be tweeting away while they are waiting to vote.  Let's see if I can accurately predict which Idol will fall.

My current results are summarized below, using a graph created using ImpactWatch.

American Idol Tweets Bar Graph

Facebook Applications Analysis – Part 4

This is the final part of my four-part analysis of Facebook applications.  (For the preceding part, click here.)  In this section, I will attempt to make some conclusions and predictions from all of the data that I collected.  For a complete list of every single one of the Facebook pages that I analyzed, check at the bottom of the page for an Excel spreadsheet link.  The names of the users have been deleted, but originally I used them to avoid accidental repetition during my research.

One of the most notable aspects when you take a look at the graphs (a PDF of all the graphs from the previous posts is included at the bottom of the post) is that not a single user had recently deleted an application.  After looking at many users, I decided to check a few extended histories, but alas, I still found no deletions.  Personally, I have deleted applications in the past, so I am aware that it happens.  My theory is that users have begun to recognize when they want to add an application or not, and as such, are becoming more ‘picky' when they are presented with a new one.  This would explain why there are still several additions present within the data.  With so many applications now available, newer ones have to be worthwhile in order to garner interest from users.  This is still possible, as Bumper Sticker proves, being a fairly recent application itself and already in the Top Ten.

Speaking of the Top Ten, my inner predictions were accurate.  According to Adonomics.com, approximately 5%-10% of users have each of the individual applications installed, so if I am ranking ten of them, my statistics professor from college would be thrilled to know that I realized about half of the total users would have at least one of them.

It is also interesting to note that users that only have 1 to 2 applications typically had one of the Top Ten as that lone application.  This makes perfect sense, since many of these are Hug Applications.  Any user wanting to receive these pokes and hugs from other users must have the application installed; so many users probably have it simply to receive and not to give.  It's total Christmas Stocking Syndrome.

I was pleased to find that a clear majority of users (of those who actually had applications) have 5 or less applications in their profiles.  When I began this research study, I had a gut feeling that I would find more 9+ entries than any other kind of profile.  Perhaps it is that those profiles simply stand out more.  In my personal opinion, given that some of the user-created applications are fun, and dare I say, ‘useful,' it is perfectly reasonable to have five or fewer.

I was also not surprised to find that the majority of typical usage was for Extended Use.  Some of the notable Extended Use applications–other than the ones already explicitly mentioned in the study–were ones that allowed users to post bigger pictures and give extra information about themselves.  It's somewhat of an old Internet cliché: people do not want to be limited in anything that they are doing, no matter what it is.  I was a tad surprised that Online Games were the least used category, but then again, users of Facebook can find free online games in other avenues.  Why use Facebook when there are better games out there?

As I was researching prior to the study, I saw many web postings comparing Facebook to its main rival, MySpace.  One of the main advantages to Facebook, according to those writings, was that it was not cluttered like MySpace profiles.  I find it ironic that people add applications when this is the popular opinion.  Many of the applications take up much space on a profile, adding a cluttered feeling to the overall page.  Forget Christmas Stocking Syndrome, Facebook users suffer from wanting to have their cake and eat it too.

Excel Spreadsheet of Facebook Data Collected

All Pie Charts PDF

Facebook Applications Analysis – Part 3

[This post is cross posted at our ImpactWatch site

Continuing the study (see the preceding part of the analysis here), I analyzed if there had been recent activity by users regarding the addition of new applications.  Facebook applications can be added or deleted from profiles at any time, and there is a specific tab on the left-hand side of user profiles designated to the addition or removal of applications.

appeditbar.gif 

I used the mini-feeds (which show recent user input) to analyze if there had been recent application-related activity.  35 users had made recent additions, while not a single user had recently deleted an application.  An overwhelming majority of users had done neither in the last week.  Below is a graph showing this data, made using ImpactWatch features.

recentactivity.jpg

The final area of study concerned the ‘Top Ten' applications as elected by Adonomics.com.  These are Super Wall, Top Friends, Hug Me, Super Poke, Bumper Sticker, iLike, Graffiti, Zombies, Scrabulous, and Quizzes.  These were the top ten applications at the time of the research.  With the addition and removal of applications, the top ten applications could change periodically.  More information on these applications can be found in the background post about the study.

Of the 300 users, 146 had at least one of the Top Ten applications, while 154 did not.  Below is the graphical representation of this data, made using ImpactWatch features.

toptenapps.jpg

The final part of the study will be posted soon.  It will include an Excel spreadsheet of all the data, as well as some conclusions drawn from the data.

More on Comcast and Tweets

[This post is cross-posted at the ImpactWatch Blog

To follow up on a recent post concerning Comcast’s effort to answer consumer complaints via Twitter, I used Tweet Scan to search specifically for Comcast posts and research exactly with what we are dealing. A basic one-word search found well over 1000 tweets about Comcast within just the last couple of hours, so I narrowed my focus down to the most recent 300. I read each of them, and categorized them in three different ways.

The first specification was whether the tweet was positive, negative, or neutral, overall. The results are as follows: 26 of the tweets were positive, 86 were neutral, and a majority of 188 were negative. It is a pretty negative environment for Comcast on Twitter right now.

positive1.jpg

The second category dealt with what category of complaint or praise under which the tweet fell. There were four distinctions: Not Working, Slow, Prices, and Company. “Not Working” and “Slow” deal with complaints about the Internet and cable service. “Prices” concern any complaints or praise about cost or billing issues. “Company” refers to any mention of the company that does not fall into one of those categories, or short tweets with little information (i.e. “grrr…Comcast”). 178 were about the company itself, 66 were problems with the Internet or cable completely not working, 33 were about slowdown, and 22 were about pricing concerns. It is interesting that on Twitter there is a lot of general venting about Comcast (bad for the brand), and less specific complaints.

tpics1.jpg

The final category is whether or not the tweet contains cursing of any sort. From a quick skim of the 300 tweets, it seems like this is a good indicator of the level of frustration by the writer of the tweet. 35 contained curse words, and 265 did not.

curses.jpg

Found below are some examples of Comcast-related tweets, as well as a document containing all the graphs above. This post is similar to the kind of analysis we perform through out service ImpactWatch. Interesting to note is that several of the tweets among the 300 were by the same user, who claims to be a representative from Comcast. Also, many of the tweets contained links to articles referencing the recent customer service use of Twitter by Comcast. Unfortunately, the representative could only handle one consumer problem at a time, so the use of tweets was just as effective as phone consumer services. The links below represent the tweet-by-tweet written data, some examples of Comcast-related tweets, and analytics.

All Data Collected

Example Tweets

Graphs Made in IW

What are the best days and times to send bulk email?

My post yesterday about the use of images in email got me thinking about what the best days or times are for doing bulk email sends. There really isn’t one answer, as each list is unique and finding the best times is a matter of trial and error. But I’ll take a shot at providing some general guidelines.

Based on my own experiences and everything I’ve read, for business to business emails (or any list that consists mostly of work addresses) the best days are Tuesday, Wednesday and Thursday.

The rationale for this is pretty simple. Most people don’t check their business email accounts on Saturdays or Sundays. On Mondays people tend to be in heavy delete mode, as they try to clear all the email that has accumulated over the weekend. On Fridays, people tend to be less focused, as they look forward to the weekend. Friday is also an extremely popular day of the week to take off, meaning your email could end up not being seen until Monday.

In terms of time for business-focused email, it is best to send email out during normal business hours. If you send your email before or after work hours, there is a good chance folks won’t see the email as they try to clear their email queue first thing in the morning and go into delete mode. Assuming you are trying to reach a US audience, I think between 12:00 and 4:00 EST is the best time, as you’ll catch people at work in all time zones.

For business to consumer emails (or any list made up mostly of personal email accounts), the situation is more fluid. Some studies show that these audiences tend to be more responsive to emails sent after work hours and on Fridays and weekends, as this is when people tend to check personal email accounts.

It makes sense – lots of people with desk jobs might not check their personal email accounts during the day and others don’t even really use email in their jobs. Just as importantly, business to consumer email tends to be about action. You want users to buy something or enter your contest or whatever. Most people are more comfortable performing those type of activities on their personal computers during their off hours.

My sense though is that there is more room for error for business to consumer emails in terms of time/date. In my experience, volume to personal accounts is lower and spam tends to be less of a problem. So I don’t think people get as delete happy as they do with their corporate email accounts.

Ultimately though, every list is different. Email Labs suggests looking at when people register for your list and trying to send during the days/time you get the most sign ups. But the best way to figure out when to send is through trial and error. Try sending out messages on different days at different times and see when you get the best open and click through rates. This will allow you to figure out your own best practices for your list.