Michael Wu, Ph.D. is Lithium's Principal Scientist of Analytics, digging into the complex dynamics of social interaction and group behavior in online communities and social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics and its application to Social CRM.He's a regular blogger on the Lithosphere's Building Community blog and previously wrote in the Analytic Science blog. You can follow him on Twitter at mich8elwu.
Since our annual Lithium Network Conference (LiNC 2011) is this week, I’m going to take a little detour from our gamification journey. Rather than trying wrap up my mini-series on gamification, I am going to show you some exciting data from a little secret project (code name Project Atlas) that I’ve been working on. The details of this project will be revealed at LiNC, so stay tuned. And if you get the chance, come join us at LiNC.
A few months ago, I was posed an interesting question: “Can we quantify the level engagement on a Facebook fan page that is a little deeper than just the number of fans or likes?” My default answer was, “Probably... give me some data and some time to play with it.” Subsequently, I got a small data set from our Social Media Monitoring (SMM) platform.
This dataset consisted of ~39 million participation records from 11+ million unique fans on 3,050 fan pages spanning from Feb-2009 to May-2011. This may sound like a lot of data, but really it is a tiny data set compare to the data volume processed by our SMM platform. Today, I will show you some of the results from playing with this small data set.
A Facebook Fan Page is Structurally a Community
Before I go into the data, I want to take you back to a mini-series I wrote on Cyber-Anthropology. It examines social media from a relational perspective and describes the complementary roles of social networks and communities in the development of interpersonal relationships throughout human history. In that mini-series, I characterized the structural and functional differences between social networks and communities. If you missed those posts, I will summarize the key point here (feel free to skip ahead to the next section if you are familiar with the structural and functional differences):
Held together by pre-established interpersonal relationships between individuals
People know everyone that is in their social network (i.e. direct connections)
Each person has one social network. But a person can have many different social graphs depending on which relationship we want to focus on (see Social Network Analysis 101)
The primary anthropological function of social networks is to maintain people’s pre-existing relationships
Social networks have a network structure
Within each person’s social networks there are sub-communities with different interests
Held together by the common interests of a group of people
Pre-existing relationships may exist, but are not required, so new members generally do not know any or most of the people in the community
Any one person may be part of many communities at a given time
The primary anthropological function of communities is to develop people’s weak ties into strong relationships
Communities have overlapping and nested structures
Within each communities, social networks develop naturally as people build their tie strength
From this perspective, we can see that although Facebook is definitely a social network, a fan page is structurally more like a community. It is held together by the common interest (e.g. around a brand), and most of the fans don’t know each other when they join. Moreover, people can be part of many fan pages at any given time. So a fan page is really a community within the Facebook social network.
The Depth of Engagement on Your Fan Page
If we are treating a fan page as a community, how can we measure the engagement of fans on that fan page? Well, to start with, clearly you need to have fans! People realized this and many have used fan count as a way to measure engagement, but fan count is in reality, like the total registration or membership of your fan page. When someone liked your fan page, they merely joined your fan page as a community member. However, as I described in an earlier post (i.e. No Game, No Gain: Realizing the ROI of Your Facebook Fans), the true value of your fans cannot be realized until they take actions to interact with you and with others. Therefore fan count is only the most superficial characterization of engagement, because it says nothing about the fans’ subsequent action and their interactions.
I consider fan count the level 0 engagement metric. Figure 0 shows the distribution of fan counts in my data set. We see a wide variety of pages with fans counts spanning over 7 orders of magnitude (from tens to 39 millions) with a median level around 3,400. Note: Most of the distributions we deal with are power-law distributed and must be displayed on a logarithmic scale.
I am going to describe several deeper engagement metrics for your fan page and show you some data. I will focus on the most visible action (i.e. posting a message or comment) for now, and describe other actions, such as likes, in subsequent articles. For the rest of this article, I’ve used fan pages that have 1,000 posts or more.
If fan count is level 0, then level 1 is should focus on the active fans of your fan page (i.e. those who posted something). With all things being equal, it is clear that a fan who posts something is probably more engaged than a fan who doesn’t post. Figure 1a shows the distribution of active fans across all the fan pages I examined. The median number of active fans is around 2,900. However, the number of active fans may be biased by the age of the fan page. Younger fan pages that haven’t been around long enough may not have the time needed to develop a large active-fan base. Figure 1b shows the distribution of age normalized active fans. After normalization, the median level for the number of active fans per day is only about 19, but the most active pages can still have up to 2,200 active fans per day.
As we have learned from observing the 90-9-1 rule, the majority of your fans are inactive at any given time and only a small fraction of your fans are actively participating. Figure 1c shows that a conservative estimate of the fractional active fans (i.e. active fans divided by the total fans). This distribution has a median value of 3.45%. So on average, only 3.45% of your fans are actively engaging (i.e. posting). This is slightly less than what the 90-9-1 rule would have predicted, but the distribution definitely covers the expected 10% active fans with a wide margin.
If your fans are sufficiently engaged to post messages on your fan page, then the next level of engagement (level 2) digs deeper and looks at what fraction of the posts are interactive. That is, what fraction of posts have a comment? Figure 2 shows the distribution of interactive posts. The data shows that a significant portion of the posts on fan pages are not interactive. The median level for the fraction of interactive posts is about 66.8% (with a pretty large standard deviation). That means on average, over 1/3 of the posts on any fan page will never get a response before disappearing down the stream and off the wall.
Alright, that is probably enough data for you today. The full spectrum of engagement can go very deep and we need to quantify each level of engagement to get a full picture of how well your fans engage. Unfortunately, fan count is merely the shallowest (level 0) of all engagement metrics and doesn’t tell you very much. We can go much, much deeper.
In this post I covered the first two levels, but there are actually eight levels of engagement, and each level goes deeper than the previous. I will cover the deeper level engagement metrics in the subsequent posts. After I introduce all the components, I will show you how to combine these different engagement levels into a single score that quantifies the overall engagement of your fans. But for now:
Level 0: Total fan counts
Level 1: Active fans
Level 2: Interactivity through comments
I must say that one of the best things that Lithium did for me, as a scientist, is acquiring Scout Labs (now Lithium SMM). Through our SMM platform, I basically get an unlimited supply of social and behavior data. To me, that’s data heaven! Project Atlas is just the beginning of my intellectual playground. I certainly look forward to sharing more data and deeper insights in the future.
By the way, after LiNC, I will be traveling in Europe starting next week for about 3 weeks. I will be participating in several speaking engagements, interviews, launch events, and a little bit of vacation between them. So I apologize in advance if I am unusually slow in responding. Coming up next week are: