Last time I talked about what got me interested in social analytics and what is the big community topic that is currently taking up most of my brain cycles. This time, let me give you a bit more detail about my current projects at Lithium Lab.

My research at Lithium focuses on a couple of key areas. First, since a community is all about the people, the first area of research focuses on understanding user behaviors. The goal of this research is to understand the complex interplay between different groups of users through social network analysis (see figure below) and discover the dynamics that drives a healthy and successful community.

Currently I am particularly interested in two groups of users

1. the superusers,
2. and the lurkers.

Superusers are obviously interesting because they contribute so much and bring so much value to the community. But why do they contribute? What is their incentive? No one comes to the community as a superuser. Yet, in every community, we observe the rapid emergence of influential superusers. Can we accurately predict who will become a superuser soon after they join the community?

Lurkers are interesting in their ownright because there are so many of them. The majority of the audience - up to 90% of the users - could be lurking. What keeps them engaged even though they don't participate? Can we incent lurkers to change their behavior and start to participate and move up the rank ladder, maybe ultimately becoming a superuser?  That is surely a holy grail for community managers.

Another area of focus is research which aims to derive predictive models for business value. The goal of this research is to discover all the mechanisms where the Lithium platform can bring value and then quantify the actual value they bring to the business. There are many mechanisms that our community platform and services can bring value to our client. Just to name a few, for example: call deflections, word of mouth (WOM), collaborative innovation, crowd sourcing, even lurking can bring certain values to our client. Some of these mechanisms, such as call deflection, are well understood and their ROI are readily quantifiable. But the value of WOM, and lurking are less tangible.

Currently I am working a model that quantifies the value of WOM in a community. This is along the road to quantifying the value of a superuser. Superusers actually come in many flavors (product experts, advocates, brand evangelists, opinion leaders, etc) and each type of superusers brings value through different mechanisms. More importantly, different community needs a different mix of superusers. For example, a support community probably needs a lot of product experts and some opinion leaders; where as a marketing community would need more advocates and brand evangelists. What is the optimal mixture of superusers for any given community?

With all that said, I hope you are excited? I certainly am. I am hoping this will give you a little more context for the live-chat at the Social CRM Virtual Summit. I look forward to seeing you there and chatting with you on November 11th. Remember if you haven't registered for the Virtual Summit, I highly recommend it - and you can sign up here.

Lithium is hosting the Social CRM Virtual Summit on Nov 11, 2009 (you can sign up here), and I was asked to hold a live-chat with the audience at the summit. This will be a great opportunity for me to talk to practitioners and get a sense of what kinds of analytics people want from their communities. To get the conversation started, let me tell you a little bit about what got me into social analytics and what I am working on now.

As some of you know, I was a computational neuroscientist (my bio is here). So what got me interested in social analytics? Honestly, it's all about the data! As a SaaS company, Lithium has recorded a huge data set over the 10 years of its business operation. The data at Lithium is very rich and diverse. Besides the 200+ metrics that Lithium records, there are also loads of conversation data between real people. This is what got me excited about social analytics.

You may ask why I didn't go to some place like Google or Facebook then? Certainly they have also collected a lot of social network data, probably a lot more than Lithium if we are talking about sheer storage volume. But as a statistician, we care about sample size. Facebook may have the biggest social network of 300 millions users, but it is only one network. Lithium has hundreds and the number is growing! This enables benchmarking and cross sectional studies that are not possible anywhere else. It is almost as if you can play god and start the network over and over again hundreds of times with different initial conditions. In statistics terms, this is what gives statistical powers to any inferences we make about the community.

Because Lithium has such a rich set of conversation data, we can also glean much insight from understanding these conversations using advance text analysis tools from machine learning. Because the conversations in a community are highly relevant to the sponsoring company, we do not need to worry about information retrieval and deal with the tradeoff between precision and recall. So we can focus our computing power on understanding the content of the conversation. Personally, I believe this will revolutionize the CRM industry, and this is the topic that I am most excited about.

By listening and comprehending the conversation of their customers, companies can understand customer needs and serve them better. On the flip side, customers can truly make their voices heard! CRM would be much more than an automation system of business processes on top of a database of customers' name, contact, when, what, and where they bought in the past. CRM system would know, for example, is a customer satisfied about the product? Do they like all the features? Which feature didn't they like? What problem did they have when using the product? Are they considering switching to a different brand? Are they considering your brand because of the bad experience with another brand? These are the kinds of insight we can reveal by understanding the conversation within the community.

So now that you know what got me into social analytics and what's in my mind, next week let's can get a little more detail about my research at Lithium Lab and what I am currently working on. Stay tuned at mich8elwu.

After more than two months, we have finally reached the 6th and final blog on the current and future development of the community health index (CHI) and its health factors. I want to thank all of you readers out there for all the feedback; the analytics team at Lithium takes them seriously in prioritizing what we deliver. I guess this mini-series is getting too long and you are probably a little tired of it by now. So, let's nail it this time! Alright! On with the final health factor: Responsiveness. If you missed any of the previous blog in this series, they are: Traffic, Content, Members, Liveliness, and Interaction.

The Responsiveness Health Factor

In this era of information, there is no shortage of quality information, and because this information lives on the internet, it is nearly free to the end users. However, if this information is not delivered in a timely manner, it will lose its value. Therefore, time is a critical resource that is going to influence a user's experience when he/she is engaging with your community. A community that responds promptly will give the visitors a much better user experience than one that takes a long time to respond.

The responsiveness health factor is a measure of the average elapsed time between responses. It is very similar to the traditional time-to-response metric, which is generally defined as the elapsed time between the first message in a thread and the first response to that message. However, this traditional metric does not take into account the elapsed time between subsequent responses. So, you can think of this health factor as a more accurate version of the time-to-response metric.

Responsiveness also has a significant impact on user retention because visitors will abandon an irresponsive community altogether and spend their valuable time elsewhere. Therefore, this health factor can be interpreted as the perceived quality of engagement for potential visitors.

The Current and Future of Responsiveness

The current formula for computing responsiveness ignores threads that have no response, and the fraction of responded threads (or the response ratio) has no effect on this health factor. The response ratio was originally part of the responsiveness calculation, but it was removed when I found that it is highly correlated to the interaction health factor for the community. Since threads that have no responses will have zero interactions, these unresponsive threads are already penalized by their interaction health factor. However, people seem to inherently expect the response ratio to affect the community's responsiveness. To make CHI and the health factors accessible to a wider audience, I may need to formulate the responsiveness calculation in the future. Besides this minor point, we have not received much troubling feedback concerning this health factor.

Finally, I like to draw your attention to an important point about the three predictive health factors: Liveliness, Interaction, and Responsiveness. Recall that these health factors are predictive because they describe the intrinsic social dynamics within the community, and they tend to be leading indicators of community health. In practice, this means the predictive factors can often serve as an early warning sign to imminent problems. By analyzing these predictive health factors, you can take corrective actions against potential problems before they occur. So the predictive factors are also called actionable analytics.

Well, this concludes our coverage on all the health factors of CHI. Next time we will talk a bit more about how these health factors are combined. Stay tuned at mich8elwu.