Six of the top ten applications on Facebook are games as measured by Daily Active Users – here are the top six games and their daily active users from DeveloperAnaltics as of June 4th:
|Game||Developer||Daily Active Users (DAU)|
|Farm Town||Slashkey||3.15 million|
|Mafia Wars||Zynga||2.94 million|
|Pet Society||Playfish||2.77 million|
|Texas HoldEm Poker||Zynga||2.46 million|
|Restaurant City||Playfish||1.60 million|
|Game||Developer||Daily Active Users (DAU)||Facebook Lexicon Activity Pixel Index ™||FLAPI per Million DAU|
|Farm Town||Slashkey||3.15 million||131||41.6|
|Mafia Wars||Zynga||2.94 million||300||102.0|
|Pet Society||Playfish||2.77 million||68||24.5|
|Restaurant City||Playfish||1.60 million||50||31.3|
Note that I took the pixel distance using the LAST date where Facebook Lexicon presented data, which was May 31st. The resulting data looks a lot more interesting when you chart it like this:
Now you can see that when you take the FLAPI and adjust it for the audience, you get a great feel for which brands , relative to their audience, have users talking about their game in their Facebook status and wall pages. In the example above, if you took a 45 degree line as a baseline, both Zynga games (Mafia Wars and Yoville) would appear above the line and infer that they are better at getting their users engaged on Facebook than their competitors.
So while a game may wane based on game play, like Pet Society which clearly shows that it’s beginning to lose active users, it very well could be that the active engagement of the users with a game(and possibly the way that Zynga does it) could be actively extending the typical lifetime of a game application.
Interested in looking at how we can apply the FLAPI to other brands, including those that have only a Facebook Fan page and don’t have an application. And also how much the FLAPI is impacted based on these brands having multiple fan pages – today Facebook Lexicon does not appear to mine those pages (”Lexicon shows the number of users that posted each term per day on a profile, event or group Wall.”), but those pages could be driving user posts.
One of the things that struck me when I broke down three months of Zappos tweets is that while it was interesting to see how they used Twitter to engage users, by some measures less than 1% of followers seemed to interact.
Here’s the data point I was talking about – Zappos tweeting about an Ellen video on April 24th: “Fun video that a few employees put together to try to get Zappos.com on the Ellen show – http://bit.ly/zapposellen”. On that day, Zappos had 487,448 followers (based on Twitter Counter – see chart below) and the video has only 4,132 views to date. That would imply a 0.84% click thru or interaction rate.
But we know that denominator is probably high for two reasons:
- Followers typically don’t unfollow (unless you are tweeting bad content or too often), thus what percentage of the followers are really actively following? And
- how many followers are actually still on Twitter – the churn rate is reportedly high (churn is as much as 60% for Twitter, which is far greater than Facebook and MySpace).
So Twitter Count also shows that follower growth has been fairly consistent over the last three months, averaging 4,068 per day (no real hockey stick), which makes it reasonable to assume that the after churn, only about 40% of the Zappos Twitter users are retained, giving Zappos just under 195,000 “active” followers on April 24th. That takes us to about 2.11% of active followers engaging with a tweet.
Some more data points on company posted items via Twitter and the implied engagement:
|Tweeter||Date||Tweet||”Active” Followers||Views||Interaction Rate|
|@Zappos||5/3/09||Employees made a rap video about the Zappos golf cart. You know, just another day at Zappos offices – http://bit.ly/zgolf cart||215,600||2,607||1.21%|
|@Zappos||5/20/09||http://twitpic.com/5khqc – Headshaving day at Zappos! Employees shaving each others’ heads. I will be completely bald later!||249,108||4,805||1.93%|
|@Jet Blue||5/21/09||Boarding our inaugural flt to Montego Bay, Jamaica. Everyone’s excited to get to the white sand beaches! http://yfrog.com/15p1nj||217,597||30||0.01%|
|@SouthwestAir||6/2/09||Picking shirt designs for LGA launch! http://twitpic.com/6gulu||12,426||386||3.11%|
|@SouthwestAir||5/29/09||Yet another video shoot! This time no puppies or kitties! http://twitpic.com/66×74||12,008||314||2.61%|
|@ZyngaPoker||5/27/09|| Why do we love Zynga Poker!? Check out what some of us on the team have to say about it!
For the most part it looks like 1-3% of the “active” followers actually click-through to view the content (pictures or videos), but of course there are a lot of caveats (e.g. there may be other channels where the items are promoted, content of the 140 character Tweet may be more enticing than others, we are looking at videos and photos, not offers or deep links into things on the company’s website, we’ve applied an across-the-board churn rate but it is likely that it is different across different brands). That said, a couple things that standout though are the big 0% for Jet Blue and the near 17% for ZyngaPoker.
Zappos, SouthwestAir and JetBlue all have had their accounts for about two years but the posting frequency is a bit different: Since the accounts’ inception, Southwest posts about 100 times per month, Zappos 70 and JetBlue a scant 34 per month. Additionally, JetBlue’s tweets focus a lot on travel tips, some alerts, and occasional product mentions whereas the SouthwestAir has a bit more personality and peppy attitude coming through – they’re the ones you have more of a connection to and more likely to want to click through to see what they are doing.
On the other end of the spectrum is ZyngaPoker, which is the Twitter account for users addicted to Zynga’s Texas Holdem Application on Facebook, MySpace, iphone and other platforms. This account is brand-spanking new (there’s no history on either twittercounter or twitterholic) and has only had 15 tweets since coming online May 20th. So in this case I did NOT reduce the number of active users by the 60% churn (I consider them all active) and I also verified that there wasn’t co-promotion on the Zynga application Fan Page on Facebook (there wasn’t).
Interestingly, the quick jump to over 14,000 followers in a couple weeks might be a good proxy for understanding the Facebook and Twitter intersection. According to Developer Analytics, there are 2.46 million DAILY active users of the Zynga application and 1.46 million fans of the fan page. Determining the number of users who saw the promotion on Facebook requires a similar exercise: understand churn of those 1.46 million users (40% according to Nielsen) and determine what percentage opted in to receive fan page alerts within their news feed (let’s assume 90%) which brings us to a potential 788,400 people who might have seen the promotion of the Twitter account. With 14,516 followers, that suggests 1.84% of the fans who saw the message on Facebook had/established a Twitter account and began following Zynga. Again, lots of caveats here, but the range of engagement is similar to that coming from Tweets.
I realize this is a very small sampling and there are a lot of variables here, but I believe this provides a good starting point for benchmarking on-going active engagement metrics from social media campaigns. With these sort of benchmarks in place (ideally automated by social media campaign tools), marketers will be able to dive beyond gathering a number of followers or fans and start assessing, prioritizing and optimizing social media tactics to drive lifetime customer value.
If you’ve ever run forums around your product, you probably have a good head-start on managing social media –listening, responding, and managing forum posts is not so different from listening to broader media like Twitter, blogs and Facebook Fan Pages. While my team managed our forums well while I was at online multiplayer soccer game Power Soccer, it was difficult to get an overall sentiment of the community, identify hot buttons, and be able to benchmark how we were doing.
I’ve been playing with different social media listening/measurement/operational tools recently and came up with some interesting ways to get a better feel around your forum posts using the Radian6 dashboards. As an example, let’s look at two of the biggest social gaming providers, Zynga and Playfish. Zynga’s games include Texas HoldEm Poker, Word Challenge and Mafia Wars while Playfish scored with Who’s Got the Biggest Brain, Pet Society and just-launched Restaurant City. Below you can see the trending of some of these games’ popularity from Facebook Lexicon (MafiaWars is on quite the upward trajectory):
Conceptually, Radian6 (through BoardReader.com) can extract the number of posts and what is being talked about within the forums, primarily by the number of words used. Here is an example of the “Word Clouds” for just forum posts about Playfish and Zynga over the last 30 days:
Now how to make something out of those clouds is the question, and it’s not very straight forward. You have to weed out things that are part of standard replies (things like “forum”, “quote”, “originally”) and other things are very game specific references (with Zynga’s mafia wars you’d expect things like “hit” or “hitlist” or “attack” or “fight” whereas with Playfish’s Pet Society you’d see “cat” and “owl”). Ideally you’d like to filter out some of these known words to dive deeper into sentiment, but Radian6 doesn’t offer this capability at this time. [Similarly, it would be great to “stem” words, so that cheat, cheats, cheater, cheating would all be classified as “cheat” and you’d be able to see how big that sentiment is within your community. Today, Radian6 only looks at exact word matches.]
So what are the subjective terms you can find that would seem to infer positive or negative sentiment:
- Playfish: Sentiments are generally more positive than negative: fun, love, nice, thank, versus bad; some mentions of “bought”, “items”, “coins” imply they are talking the things they’ve bought for their characters.
- Zynga: Not a lot of sentiment-specific words on display here, just “good” and “lost” (diving in on that topic with Raidan6 you can jump to a “River of News” to see each post and infer what is being talked about – in this case it is lost chips or items which I’d classify as a negative customer experience); what DOES show up is that mentions of level(s), point(s), and stats points to a much more competitive user base which is completely logical given games like Poker and Mafia Wars.
In a very rudimentary analysis, it looks like the Playfish forum members are slightly more happy, but again this may be more about the audiences for the games in each community where Pet Society is more social and casual with a community posting 6x as much as the more competitive/cut-throat community on Zynga. While competitive comparisons of forums are interesting, I believe you are better served tracking and improving user sentiment within your own forum.
Word clouds are a poor way of determining overall community sentiment and should be used to identify hot buttons (like the example above where we dived in on the word “lost” and were able to highlight an issue where users were losing poker chips). Radian6 delivers a great work flow tool to do this: once you drill down from these clouds to the customer conversations around that word, you can mark that customer for some sort of action (either assigning the post for immediate response or flagging issues using special tags so you can begin prioritizing potential bug fixes or features required).
Workflow is an integral part of operations, and you can measure the number of tickets and responses you provide, but that doesn’t really ensure you are improving the customer experience. Only by measuring and tracking sentiment can you tell if you are making headway in improving how your customers feel about your brand.
To get a sentiment reading of the forums, Radian6 currently only allows you to hand-code each post and mark them as positive or negative – such a burdensome task that it’s really a non-starter for most marketers. Automating the sentiment categorization task is something Crimson Hexagon has made their point of differentiation (see why this is critical for marketers), while Radian6 noted that automated classification of post sentiment is scheduled for July of this year. So we wait, but relish the thought that the technology (ideally some combination of Radian6 tools and Crimson Hexagon methodology) is nearly here.