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Archive for March, 2011

The 2011 State of Community Management

 

My friends over at The Community Roundtable, Rachel Happe and Jim Storer, have just published their 2011 State of Community Management Report. It chronicles best practices and discussions related to the of field community management. The Community Roundtable, a leading resource for community management practitioners, and is based off of conversations with representatives from over 60 different companies within the TheCR Network held over the span of the past year.

TheCR has been an invaluable resource to me in my work with online communities, especially their Community Maturity Model Framework (seen below).

thecr_maturity_model

I’d advise anyone who plays a role in an online community effort to download the report for detailed reading. It’s packed full of insight and information on the current state of community management within businesses both big and small. The key findings fall into these four themes:

  • Social Business Has Become A Strategic Imperative
  • Interest in Community Management Continues to Increase
  • The Community Management Discipline is Evolving
  • A Lot of Confusion Remains in the Market around Social Business

That said, I’m just scratching the surface of the knowledge and tidbits in the report. Get a copy and you’ll see for yourself!

Facebook Demographics Revisited – 2011 Statistics

Approximately a year ago, I published "Dispelling the Youth Myth – Five Useful Facebook Demographic Statistics" on this blog. It’s been one of the most visited posts ever since, so clearly there is a lot of interest in understanding the demographics of the Facebook user population. A year later, Facebook is bigger than ever, now the most visited site on the internet. So, I’ve updated the statistics below, and included some new ones, so that we can all be informed, and dispel any myths about Facebook user demographics. Like the original post, I’m writing this one to help avoid the need for us to explain over and over again, who uses Facebook, and instead direct people to this post. Here is the most recent data on Facebook that you can use to enlighten yourself and others on just who uses Facebook and where they come from.

1) Facebook.com average user figures and facts:

  • Average user has 130 friends on the site
  • Average user sends 8 friend requests per month
  • Average user spends an average 15 hours and 33 minutes on Facebook per month
  • Average user visits the site 40 times per month
  • Average user spends an 23 minutes (23:20 to be precise) on each visit
  • Average user is connected to 80 community pages, groups and events
  • Average user creates 90 pieces of content each month
  • 200 million people access Facebook via a mobile device each day
  • More than 30 billion pieces of content are shared each day
  • Users that access Facebook on mobile devices are twice as active on Facebook compared to non-mobile users
  • Facebook generates a staggering 770 billion page views per month
Source: facebook.com, pingdom.com

 

2) Breakdown by country: More than 70% of Facebook users come from outside the United States

Global User Population: 629,982,480

Image

Sources: checkfacebook.com and facebook.com

 

3) Global User Demographics: The global breakdown of users on Facebook by gender and age

Image(1)

Sources: insidefacebook.com

4) Breakdown of US users (gender and age):

Image(2)

 

 

 

 

 

 

 

 

 

 

 

As the chart above illustrates, the total US Facebook population is made up of millions of people across a range of ages groups. While young adults (18-25) lead the way with a combined ~50 million users (almost double the size from a year ago), the 26-34 group is now well behind with ~29 million users. According to the data from Facebook there a combined ~28 million people over the age of 45 active on Facebook. These are impressive user numbers from an older demographic that continue to grow. It’s important to note that the 55-64 age group is almost the size of the 13-17 group, further evidence that Facebook isn’t limited to "young" people.

A visual look at the US users by age (using data from above):

Image(3)

Sources: facebook.com

 

5) Facebook.com – a top destination site for the majority of online Americans, but some states more so than others.

image

Source: Socialbakers

The most important takeaway from the list above is probably the degree of penetration Facebook has relative to the population of each state. Over 50% for many!

3 Tips to Improve Your Online Influencer Analysis

Identifying influencers is an important part of social marketing, and becoming big business for some companies like Klout . Yet, influencer identification and analysis isn’t well understood, or easy to optimize. That’s because no two influence analysis efforts are the same. Like fingerprints or snowflakes, an influence analysis program is unique, specific to the goals and priorities of the organization executing it.

To date, automating influence analysis has relied heavily on quantitative measures (example: like followers/friends, RTs, mentions/replies). While these metrics can be valuable inputs into determining whether or not someone is influential, they aren’t worth much at all without their counterpart, qualitative metrics. If influence is a coin, then the 2 sides are quantitative and qualitative. Focusing purely on quantitative metrics tells you if someone is popular, not influential.

A recent and fitting example of this is the news of celebrity Charlie Sheen joining Twitter. Sheen amassed 1 million followers in approximately 24 hours. An impressive feat, no? Is he popular? Definitely. Is he influential? Well, according to Klout he is. Sheen currently has a Klout score of 88 (higher than most people, including industry thought leaders like Chris Brogan (81), Jason Falls (72), and Brian Clark a.k.a Copy Blogger (73). Does this mean Sheen is more influential than any of those 3? It depends on the topic.

Enter relative influence. Relevance brings the qualitative perspective necessary to complete the influence picture. Without it, you cannot be sure that you have an actionable list of influencers. The 3 individuals mentioned above are respected, trusted voices on topics such as marketing, social media and blogging. If you were to take a simple quantitative approach, and rely on Klout’s score, you could make the argument that Sheen is the more valuable influencer to focus on. Would you take his advice on any of those topics over Brogan, Falls, Clark? Absolutely not! Relative influence should be the focus on influencer analysis. You’re looking for individuals with deep, strong ties to the community on specific topics, not individuals with weak ties to many communities. Don’t confuse popularity with influence, they aren’t the same.

In addition to using automated tools to improve the effectiveness of your quantitative influencer analysis, here are some tips to gain a more robust qualitative view and gain a balanced perspective.

  • Define relevancy first - Before you begin, think hard about the topics, types of conversation or actions that are relevant to your business goals. Create a relevancy list to act as a compass, guiding your analysis away from anything that doesn’t meet those criteria. You want relative influence, not popularity (Example: Nike recently targeted absolute influence, not relevancy, including David Armano in a basketball outreach program, details here). Also, think critically about what qualitative metrics are relevant to you. Not all may matter, especially if you’re focusing on a single channel. As an example, if targetting Twitter, then exclude Facebook and/or LinkedIn data from your quantitative analysis.
  • Use Influencer Tools are a starting point – Don’t throw out the baby with the bathwater. Tools like Klout may not be where we want them to be, yet. However, they can be used as a starting point to help you find the general direction to move forward. A more comprehensive list of influencer analysis tools, can be found here at Jason Falls blog.
  • Integrate with other data to gain clarity – Social data is a great start but represents only a small slice of the digital data spectrum. Combine social data with other digital inputs to validate your influencer analysis. Two good types of data are search and web analytics. Pull web analytics data for the website/blog of a suspected influencer. Are there many inbound links? Traffic? Search volume? etc… The point is, you should find a corresponding level of activity in these other data types to support the social data findings and make the strongest possible case for them as an influencer.

  • Finally, if you’re interested in a rich, in-depth perspective on influencer analysis, take the time to read this eBook, authored by @chuckhemann and @radian6. It will enlighten you on the finer points of influencer analysis, including specific methods and metrics to consider in yours.

    Radian6_JAN2011_Book

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