The Social CRM Virtual Summit is almost upon us - and I am getting ready to take part in two expert chat sessions tomorrow on the Science of Social Analytics, and How you can build Brand Equity through community. This last topic is particularly relevant as last week we published a whitepaper on the value of using your customer network for word-of-mouth (WOM) marketing. This is joint research I conducted with renowned professors in the field of marketing science, including:

1. Barak Libai, MIT Sloan School of Management and the Recanati School of Business in Tel Aviv
2. Eitan Muller, NYU Stern School of Business and the Recanati School
3. Renana Peres, The Wharton School, UPenn and Hebrew University of Jerusalem

This whitepaper is a particularly hot topic, so I will be joining our VP of Product Marketing, Phil Soffer, and chatting on the topic of WOM at 6:15am and 11:15am PST - I look forward to talking with you.

That leads me into my topic for today's post. As I've alluded in previous blog posts, the value of WOM is not particularly tangible. Estimating the ROI on WOM is nontrivial and it is still a research topic for academics.

In a network of hundreds of thousands of customers, the value of WOM really comes down to how we estimate a customer's lifetime value with the effect of WOM and without it. Let's consider the Lithosphere community as an example.

If I told PaulGi about Lithium's mobile community product and he subsequently buys it, then a small fraction of the value that Lithium gains from PaulGi's purchase should be attributed to my WOM interaction. However, if I didn't tell PaulGi about the product, maybe ScottD could have told him about it. Then that small fraction of WOM value should now be attributed to ScottD, instead of me. To complicate things, maybe we both told him about it, and who's to say that people I've spoken with listen to me instead of Scott. So the conundrum is, how should we estimate my WOM value to Lithium?

Working with academics, we use a simulation methodology, which I will refer to as "impact upon removal." In essence, my WOM value to Lithium is the value that Lithium would have lost if I did not tell anyone about their product. So a user's WOM value is the value lost if we blocked all of his interaction with other community members, essentially remove him from the social network. It's like the saying that "You don't really know the value of someone until you lose him/her." In a nutshell, this technique is what allowed us to study the effect of WOM in a customer network.

Now that you know some of insights to our research result, I hope it will encourage you to find out more. If you are intellectually inclined, you can get the details from our whitepaper. Better yet, come by and ask me question during the Virtual Summit tomorrow. You can still register at http://www.bit.ly/vscrmreg. I hope to see you 'virtually' and talk to you tomorrow!

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.

This is the fifth blog in the miniseries on the Health Factors of CHI. Other blogs already in this miniseries are:

1. Traffic
2. Content
3. Members
4. Liveliness

Last time we talked about the most troublesome predictive health factor: Liveliness. This time we will discuss Interaction.

The Interaction Health Factor

When you are able to create a lively community, the hard work is half done because by definition a lively community has solved a difficult conundrum of participatory media: How do you get people to participate? However, in a healthy community, it is not enough just to participate. There must be interaction with other users. Otherwise, where is the social of social media?

There are two important dimensions to interaction:

1. The amount of conversation you have with a particular user.
2. The number of different users you've communicated with.

Clearly, the more you talk to a user, the more interaction you have with that user, but talking to different users also increases the level of interactions within your community. In fact, the latter has a greater effect on the overall health of the community. The interaction health factor measures the average number of unique participants per topic weighted by the amount of conversation between them. Thus, this health factor provides an estimate of the average number of members a potential user is likely to interact with and the estimated amount of messages that will be exchanged between them. Accordingly, this health factor may be interpreted as the expected scope of the engagement for potential visitors.

Different Modes of Interactions on the Lithium Platform

Because different communities have vastly different kinds of interactions, the interaction health factors also vary greatly across communities. For example, the dominant mode of interaction of a support community consists of a troubled user asking a question and a knowledgeable user answering the question. This kind of interaction usually involves few users with relatively short diagnostic dialogs before arriving at the solution, so support communities tend to have a steady and modest value for the interaction health factor. In contrast, the dominant mode of interaction for an enthusiast community often involves extended discussions among many users. Also, because the topic and amount of discussion is usually event-driven, enthusiast communities tend to have a higher, but more volatile value for their interaction health factor. B2B and internal communities also tend to have lower level of interactions than that of enthusiast communities.

The Current and Future of Interaction

Besides the difference in community purpose, the different web applications, such as forums, blogs, ideas, and tribal knowledge base (TKB), also have drastically different modalities of interaction. However, the current health factors and CHI were developed based on analyses of primarily forum interactions. Blogs, Ideas, and TKB are relatively new products in our platform that do not have 10 years of historical data. Consequently, the expected level of healthy interaction in the current algorithm may be too stringent for these new interaction modalities. As a result, the interaction health factor for communities that use these new applications may appear slightly lower than expected. We have made note of this issue and we will address it in the next revision of our formulae.

I hope this blog gave you a better understanding of interaction and addressed the concerns you may have concerning this health factor. Next time, we will move on to the last health factor, responsiveness. Watch for updates at mich8elwu.

After a month of digression, let's come back to the topic of health factors. Previously, we have covered the three diagnostic health factors:

1. Traffic
2. Content
3. Members

In the next three post, we will explore the predictive health factors. Base on all of your valuable feedbacks, I got the impression that Liveliness is the factor that has raised the most questions. So I will begin with this health factor.

The Liveliness Health Factor

A number of people have asked:  what is liveliness? The liveliness health factor is a perception of the amount of activity within a given space, so it is a measure for the concentration of activities. This health factor is extremely important, because it strongly influences a user's propensity to participate. In fact, liveliness gives an estimate of the likelihood of active participation for community users. The more lively the community appears to be, the more likely an individual is to participate. It's logical to think that a lively community is more likely to respond to the user's question or comment.

Concentration of Activity = Perception of Liveliness

Although modern communities furnish users with many activities, posting messages is still the most visible action that is going to influence a user's perception of the community's liveliness. Since other activities (such as rating contents, tagging messages, chatting with other members, etc.) are less noticeable, they have much less effect on the perceived liveliness of the community.

Look at the above picture and ask yourself: what make it lively? This picture looks lively to a spectator because the children are concentrated, and there are a lot of visible actions. This playground would be much less lively if the same group of children were dispersed throughout the park.

Since liveliness is a concentration measure, the absolute amount of post is not very important. For example, if your community has 200 posts per week and there are 4 boards, then each board gets 50 posts per week on average. That is more than 7 posts a day! Users would perceive this as a pretty lively community. In contrary, if there are 50 boards (same 200 posts per week), then each board will only get 4 posts per week. That is less than 1 post a day! Even though the absolute amount of posting activity is the same (200 per week), the former example has a much higher concentration of post, and therefore gives the perception of greater liveliness than the latter example.

The Current and Future of Liveliness

The question that is often raised is "Do you excluded private boards, archived boards, read-only boards, etc, when computing the liveliness health factor?" In the current version of CHI, the liveliness calculation only excludes boards that are hidden or under a hidden category. These hidden boards may contain some private or archived boards, but they probably do not encompass all of them, and it certainly does not include read-only boards or announcements boards. Consequently, communities with many of these "non-public" boards may appear to have a lower liveliness, because our current calculation may not excluded all boards that are not intended for public participation.

Like the content health factor, liveliness is currently computed at the community level, which prohibits drilling down to identify of categories or boards that are not lively. These two items are already on our roadmaps of to-do items. If you can think of others, please let me know. Also, if there are any questions about liveliness that I did not cover, please bring it up . Let's try to make this a lively discussion.

Earlier this month, I was invited to give a workshop at Stanford University. This workshop, titled New Metrics for New Media: Analytics for Social Media and Virtual Worlds, is one of many that are sponsored by Media X. It was held at Wallenberg Hall on Aug 5 and 6.

The workshop was organized by Martha Russell (Associate Director of Media X) and Marc Smith (Chief Social Scientist of Telligent), and consists of panel discussions from industry leaders, various talks, and hands-on tutorial sessions. I was very privileged to be invited by Martha Russell to give a talk on Measuring Social Media & Digital WOM. Besides giving my talk on CHI, and the mechanism and benefit of community word-of-mouth, I also learned a lot from the workshop. Since my days are still overwhelmingly busy, I've decided to write a short blog this week to share with you one of the most interesting metrics that I've learned.

Inferring User Intentions:

One of the most intriguing metric I learned from this workshop is intention: what does a user want to accomplish. Intention is not really a metric in the traditional sense, but it is something that we can potentially infer based on other metrics. Although this might sound a little scary, I believe it is beneficial. Information retrieval system could benefit greatly from understanding exactly what the user is looking for.

Although some business may leverage this information and spam their audience, but a spam to one may turn out to be useful information to another. A key for Google's success in ad targeting is that the ads are sufficiently relevant to the user that they are no longer treated as spam. The reason businesses spam today is precisely because they are unable to accurately infer user intent. If we can truly infer intention, there is no need to spam at all. In fact, spamming would become impractical and no one would do it because its efficacy is so low.

However, intention inference is not easy. The only algorithm I know that is capable of performing such inferential task is belief propagation. However, this algorithm is computationally very expensive. Even with modern cloud style supercomputing, running a large-scale belief propagation may still be infeasible. Yet, I am fascinated by this inference problem from a theoretical point of view. Much can be analyzed before one finds a practical implementation. That is why we need researchers!

OK, that is it! Told you this will be a short one. Let me know what you think about this metric. If someday we discovered an efficient algorithm for intention inference, would you be excited or afraid? How would you use this information?

Photo by Marc Smith

You might know from my short bio that I am an alumnus of the Computational Science Graduate Fellowship (CSGF) program sponsored by the US Department of Energy (DOE). Last week, the CSGF fellows and alumni in the Bay Area were invited for a tour of the Lawrence Livermore National Lab (LLNL). We visited the National Ignition Facility (NIF) and the Terascale Simulation Facility (TSF), and it was eye opening to learn about the big science and the challenging research that were conducted at LLNL. After the day tours, we were invited to a wine tasting and dinner at the Wente Vineyards. Although this has nothing to do with community or analytics, it was a full day of inspiration for me. So let me share some of my excitement with you.

LLNL is a classified national lab, so I had to send in my personal information for a background check weeks ahead to obtain the proper security clearance. The lab is so secured that no cell phones, cameras, video cameras, or any electronics were allowed beyond the guarded fenced surrounding the facility. Basically, after I picked up my visitor badge outside the fence, I become completely isolated from the outside world. I'd like to draw your attention to the blue casing hanging above the badge. Any guesses on what that might be? It is a radiation sensor. Although I had one of these badges when I was at the Los Alamos National Lab in 2003, I never had one with a radiation sensor, because I was working in the Machine Learning Group under CCS-3 Division, which is safely shielded far away from all radioactive materials.

Alright, let's get on with the tour. Stop #1: NIF. As we arrived at the NIF, we saw a big sign saying "Bringing star power to earth." Let me explain what that means. The NIF is a nuclear fusion research facility that houses 192 lasers, including four of the most powerful lasers in the world. Using the Inertial Confinement Fusion technology developed at LLNL, these lasers are used to ignite a fusion reaction (at a temperature of 100,000,000 degrees) that creates a miniature sun (a medium sized star) inside a 10 meter spherical target chamber (see photo from NIF). The fusion chain reaction in this miniature sun will then generate clean energy much like the sun has power life on earth for millions of years. This animation by NIF explains how these lasers work.

Current nuclear power plants use nuclear fission technology, which produces a radioactive byproduct that remains hazardous for millennia and can be used in nuclear weapons. However, the byproduct of fusion is just Helium-4, a perfectly clean, safe, non-reactive noble gas. Moreover, fusion technology could eliminate our dependence on fossil fuels, because fusion power is much more efficient. The fusion energy released from 1 gram of hydrogen fuel is equivalent to roughly 2 tons of coal!

The NIF is a colossal project! Commissioned in 1993, the construction of NIF began in 1997, took 12 years and $3.5 billion. The last of the 192 laser beams were completed on March 31, 2009. I feel so privileged to be able to see the NIF in its entirety so soon after its completion. Currently, hundreds of engineers and scientists at NIF are using simulation shots to test all the lasers, the diagnostics, and the control system. There is absolutely zero tolerance for errors. Future experiments are aimed at replicating the nuclear ignition process reliably and rapidly enough so that it can be a viable source of commercial energy.

Are you excited? As a scientist, I'm totally thrilled! You may admire the futuristic look of this magnificent structure, but what I see is the culmination of a Herculean scientific endeavor. To me, the NIF is a product of thousands of passionate scientists and engineers from all sorts of backgrounds who have dedicated their lives to working together for the well being of humanity. I just couldn't help but be in awe when I think about the difficulties they must have overcome to reach this stage.

Let's save some of my enthusiasm for later. This is the first time I have posted something unrelated to social analytics, but I thought it might be interesting for you to get a peek at the life of a scientist. Let me know how you like my little adventure. Stay tuned at mich8elwu.

Hello, TGIF. I think this is the first time I've try posting a blog on a Friday. I had a hectic week!

So, this is the fifth and the last article in the miniseries Ranks Designed to "Flow". Previous blog articles from this miniseries can be access through these links:

1. Spacing the rungs of your ranking ladder

2. Know your superusers!

3. Flow with your most prolific superusers

4. Give your superusers a little surprise

We've covered a lot of ground, and I've introduced many design principles for building an optimal ranking ladder for engaging your superusers. I must emphasize that it is very important to implement these rank design principle in the order that they are presented. It is meaningless to flow with your superusers, if you don't know your superusers' capability. And it is useless trying to surprise your superusers with special privileges if the gaps between your ranks are so large that it takes them years to get a promotion. They will never get there and never be surprised! However, the first two steps do involve some analytical work, and they are the most difficult and most important step (especially step 2). Once you know your superusers, everything that follows is easy.

Building a complete ranking structure

Until now, I have been talking about the principles for designing a single ranking ladder that rewards the posting behavior of community members. Although message posting is a common participation within online communities, modern community platforms now furnish their members with a host of activities. Consequently, superusers may come in many flavors depending on the kinds of activity they participate in. The superusers that we've considered so far are content creators who excel in posting messages. But superusers may be critics who rate contents by giving kudos and report inappropriate contents, and others may be organizers who label and tag contents. Therefore, an ideal ranking structure should have multiple branches for rewarding different kinds of participation.

To create a multi-branch ranking structure, you simply juxtapose everal ranking ladders together. Each ladder has a set of ranking criteria that is based on different participation metrics. For example, rather than post requirement, some ladders may use kudos requirements, and others may have a tag count requirement. Based on the superusers' participation, they will climb different ladders. Some well-rounded superusers may even excel on several ladders. Moreover, ranking ladders can be merged by creating participation criteria using the logical AND.

So how many branches should you have? According to Forrester Research, online participation pattern can be segmented into 6 categories via the social technographic profile: inactives, spectators, joiners, collectors, critics, and creators. Since inactives do not participate, and spectators only consume contents passively, there are at least 4 categories of active participation that you can reward. But in theory, there is no limit to how many branches you could have in your ranking structure. The more ladders you have, the more unique your superusers will feel about their contribution (and reward). But more ladders required more management. A multi-branch ranking structure should be the last step in the design of your ranking structure. Having many poorly designed ranking ladders is much worse than having one that is well designed. My advice is to start with one ladder for your creators. When you are able to manage steps 1 to 4 with all the yearly adjustment, add one for your critics, then collectors, and finally joiners.

Congratulation! This concludes my miniseries on the optimal design of your ranking structures. Next time we'll explore something different. Have a great weekend.

Photo by Jon Wiley

After 3 weeks of Health Factors, I guess (from the fact that my last post received no kudos) you are probably a little fed up with it. So let's take a break!

It's been a while since we talk about rank design, so this is a good time to revisit the topic.  This is the 4th article in the miniseries on designing the optimal ranking structure for your community. Previous blog articles from this miniseries can be found here: 

1. Spacing the rungs of your ranking ladder

2. Know your superusers!

3. Flow with your most prolific superusers

Give your superusers a little surprise

Part of the fun and the challenge in gaming is the unpredictable elements in games. The player can never truly know the outcome of his play. When the gamer has just figured out the game, he moves into the control state. When he is able to predict the outcome, the game is probably too easy for him. And soon he will find the game boring and move onto something more challenging. Likewise, a fun and challenging ranking ladder should be cryptic, and it is best when there are some elements of surprise built in. In a previous post, I talked about switching after year 2 to a very regular and predictable arithmetic progression (a.k.a. linear progression) for our ranking ladder. The problem is that your smart superusers will most likely figure out this ranking scheme.

So, how do you keep the superusers engaged under such a predictable (boring) ranking criteria? I will describe 4 things that you can do to spice up your ranking ladder even when it is a predictable and boring arithmetic progression.

1. Name your ranks creatively.

It is inevitable that some superusers will eventually figure out the post requirement for promotion, and will expect a promotion at the right time. But if you name your ranks cleverly, they still won't know what they are going to get next. So, don't name your ranks in any obvious progression. For example, a terrible choice of rank names would be bronze, silver, etc. Without even writing it down, it is blatantly obvious that the one after silver is going to be gold, then probably platinum, titanium, and then diamond. Give your superusers some serendipitous joy when they get promoted and use your imagination when naming your ranks.

2. Make some noise.

For statisticians, there is a simple trick to make things less predictable. Just add some noise (or randomness)! Rather than following any formula strictly, you just need to randomly jitter the number a little bit. For example, it is very easy for anyone to figure out the pattern in the following linear progression: 10, 20, 30, 40, 50, 60, 70, etc. By add some random noise, the sequence becomes much harder to predict: 11, 18, 32, 41, 49, 63, 74, etc. Notice that the challenges between the ranks will remain roughly the same (compare the figure of the jagged ranking criteria here with the figure of the smooth ranking criteria from my previous blog post). The shape is virtually the same, but this one is much harder to figure out. You can start jittering the post criteria from the first rank if you wish, but please be sure to "make some noise" when you switch over to the highly regular linear progression.

3. Privileges do matter.

Even with added noise and the most humorous and interesting rank names, superusers may still get tired of the routine rank changes without other incentives. This is especially true after you switch the ranking criteria over to the highly predictable linear progressions to avoid over challenging your superusers. If you follow the recommendation in this miniseries of blogs, this should happen roughly 2 years after the superuser first participated in the community. After 2 years, don't you think it is time to show your superusers that you recognize how special they are? So tell them (it only takes an email) and grant them access and customization privileges that are not available to others. Start with something simple, such as allowing your superusers to use a personalized icon. Then as they move up the rather boring rank ladder, they will be intermittently rewarded with different privileges that are totally unpredictable.

Attaching special privileges to a rank through our permission system is an extremely effective way to engaging your superusers. However, you might want to let time test their loyalty and goodwill before giving out too many privileges. Therefore the perfect time to give out special privileges is after the superusers have moved through the first 24 ranks. However, if you really trust your superusers, you may do this earlier on your ranking ladder. This also applies when switching your superusers into a MVP program.

4. Show your trust.

An important reason to give special permissions to your superusers is to establish trust. A deep relationship built on trust is likely to be more lasting than a superficial relationship that is built on other incentives. So show your superusers that you trust them. Invite them to beta test programs in private boards and treat them as your most valuable assets. Give them power to take action in the community, such as moving posts and perhaps deleting inappropriate content. Let them help you moderate your community. Can you really trust the superusers with that much power? I believe that you should. Because it is highly improbable that a superuser would just throw away all the reputations and privileges they've earned through hard work over long period of time. The more they have invested, the more they will treasure their unique social status.

Now that your rank ladder is all spiced up, you should be able to keep your superusers engaged "indefinitely" (contrast the above figure with the figure from my previous blog post). Next time we'll wrap up with the grand finale: synthesizing all the rank design principles we've explored to build a complete ranking structure. Stay tuned at mich8elwu.

Last time we talk about the content health factor and how we plan to reformulate it to enable drill down capability for the health factors and CHI. This time, I will explore the third health factor: Members.

The Members Health Factor

After achieving a steady stream of traffic and copious amount of high-value content, a healthy community should accumulate registered members, grow, reach critical mass, and then become self-sufficient and self-sustained. The members health factor is intended to measure the growth rate of the community.

Recall that traffic is a measure of passive engagement, and content is a measure of active participation. In Lithium-powered communities, login is not required for passive engagement, but it is required for active participation. This means that users must register to become members of the community and must sign in before they can post. However, because people tend not to register  or log in unless they have to, the members health factor is good estimate for the conversion rate from passive engagement to active participation.

What about Member Churns and Lurkers

Although growth is a sign of a healthy community, member attrition is inevitable. Some users register merely to post a question, and they will leave as soon as they have the solution to their problem. Therefore, even healthy communities will have some natural member churn rate. But a high attrition rate is indicative of poor community health because the community is not able to retain members and grow its active core of user base. This issue has been brought to my attention at the Persuasive2009 conference.

Another question I often hear is that "what should we do about lurkers?" If you are not familiar with social media you might think that lurking is an undesirable behavior. But in a series of studies, Prof. Blair Nonnecke and Prof. Jenny Preece have shown that lurkers do provide value, and they are important to the success of community. So let your lurkers lurk! When the time comes--when they have a specific question to ask or some information to contribute--some of them will de-lurk and become members.

The Current and Future of Members

Currently, the members health factor is the same as the weekly registration metric, so it only measures the passive to active conversion rate. To get a complete picture of community health, it is important to track member attrition in addition to member accumulation. So in our next iteration of CHI, the members health factor will account for member churn.

This covers the three diagnostic health factors: Traffic, Content, and Members. They are called diagnostic because they describe the result of user behaviors and tell you the current health of the community. As such, they are lagging indicators of community health. Next time, we will start with the predictive health factors: Liveliness, Interaction, and Responsiveness. They are referred to as predictive because they describe the intrinsic social dynamics within the community, so they tend to be leading indicators of community health.

Let me close by reaffirming that all comments are welcome, so let me know what's on your mind. We do take your comments seriously, and you can help us make CHI more accurate and better suited to everyone's needs.

My previous post focused on the "traffic" health factor - where it is today and where it's headed in the near future in terms of reformulation. Today I'll be talking about another health factor: Content.

The Content Health Factor

Once critical mass is reached in terms of human traffic, the next thing to focus on is building content within the community. Visitors won't return time and time again without an abundance of interesting, useful and highly desirable content. The content health factor is a measure of both the quantity and the quality of the messages posted within your community. Contrary to traffic, which measures the passive engagement of visitors, content measures the active participation of your community members. Because posting (whether it is a message, a reply, or a comment) adds consumable information that is persistent within the community. This is a form of active participation.

Measuring the Quantity and Quality of the Posts

The post count metric provides a straightforward measure of the quantity of posts, but how do you measure their quality? We leave this decision to the readers. Using a marketplace metaphor, when the number of consumers (readers) in a community is large, the "economics" of the community can give an accurate estimate of the relative demand (whether they are useful or interesting) for the posts. Since the demand for a post strongly correlate with its viewership, the demand for a post must be reflected in the page view metric. However, highly viewed pages tend to draw more random views. This snowball effect will inflate the estimate of consumer demand. Therefore, the post quality can be approximated by a dampened version of the page view metric, which we call viewership.

The Current and Future of Content

In the current implementation of CHI, post counts and page views are aggregated over the weekly window, and then computed at the community level. However, this computation prohibits any drill down capability for this health factor. Yet, drilling down to a category or a board and seeing the content health factor at different hierarchy of the community can provide actionable intelligence for the community manager. Because this is a common use-case, and personally I've been asked this specific question at our customer conference earlier this year, I will make sure that we address this issue in the next reformulation of CHI. This drill-down view can be achieved by computing the product before the aggregation.

Can you guess what is coming? Yes, we will talk about the member health factor next. In the mean time, please do tell me if there is a feature you want with regards to measuring community health. Stay tuned at mich8elwu.

Welcome back. I hope everyone had a relaxing Fourth of July holiday. Last week, Lithium delivered a new report, the community health report, to our clients. This report contains two important pieces of new information: 

1. The community health index (CHI): a score that reflects the "health" of your community.
2. The community health compass: a radar chart that shows the relative value of the six health factors (traffic, content, members, liveliness, interaction, and responsiveness) that went into the computation of CHI.

Earlier this year we published a whitepaper on CHI. Since then, however, we've refined the CHI algorithm based on data from real-world use cases along with customer feedback. So for this post, I'd like to talk about the current state and future direction of CHI.

The Traffic Health Factor

Let me begin with the health factor you are most familiar with: Traffic. This health factor is most important when you are launching a community. If no one comes to your new community, the community will fail. As the community matures and acquires a large core of active users, the effect of traffic may become less important. This is because traffic reflects only the passive engagement of the visitors with the community. In a public community, visitors can accomplish quite a bit without any active contribution. For example, they can read messages, search for answers, navigate the community, checkout other members' public profiles, etc. These activities are considered "passive" because the visitors merely consume existing community content, but do not add content of their own. 

Measuring Human Traffic is Not That Simple

In short, the traffic health factor is intended to measure the amount of human visits to the community. However, traditional page-view metrics also counts non-human visits by web robots and crawlers. Moreover, counting page views on a modern AJAX-filled website is not trivial. Because Web 2.0 technology furnishes users with a wide variety of interactions on a very dynamic webpage, users can potentially visit many places and perform many activities without refreshing the page. As a result, behind-the-scene REST API calls, RSS feeds, or any server-rendered pages go unnoticed to conventional page-view trackers such as Google Analytics.

The Current and Future of Traffic

In the current formulation of CHI, traffic is measured by Lithium's own PageView metric, which includes REST calls and any server-rendered pages. Although our preliminary study suggested that robots and crawlers do not significantly affect the final CHI score of the community, we intend to remove their contribution to our PageView metric in our next formulation of CHI. However, since robots do not always declare their identity (in fact some crawlers may even intentionally disguise themselves as human visitors), it is not possible to completely remove the effect of robots and crawlers. Despite this, through iterative reformulation, we hope to derive a traffic health factor that can track the passive engagement of human visitors more accurately.

I hope this post gave you a better understanding of the traffic health factor. Next time, let's talk about the content health factor. In the meantime, feel free to continue this conversation in the comments section below.

This week, I'm going to digress from the topic of flow and rank ladders in order to share an interesting talk I heard at a conference. Last week, I was at the C&T2009 meeting at Penn State University with our Chief Community Officer, Joe Cothrel. Although this meeting was rather relaxing for me, because I didn't have to present, I still can't believe it took me 12 hours to get there (9 hours of travel from SFO to State College connecting at DC, plus 3 hours lost from PST to EST). I stayed on campus at the Nittany Lion Inn that is a 5 minute walk to the IST building, the meeting venue location. I will not bother to recap the meeting, since a concise summary can be found in the conference program. However, one talk sparked some thoughts in my head that I'd like to share with you.

Day 2 of the conference opened with a keynote, titled "Knowledge Reuse and Novelty in Community Settings," delivered by Prof. Karim R. Lakhani from Harvard Business School. Prof. Lakhani presented an interesting experiment on collaborative innovation in the form of a MATLAB programming contest for solving an NP hard problem. Each code entry's performance is evaluated, scored, ranked, and displayed immediately with the contestant's name. Since this is a collaborative effort, any contestant may reuse and modify code submitted by others and then resubmit it as their own entry. The contest is closed after about a week and the top score at that time wins regardless of how many times one submits, how many lines of code one adds, or how much performance gain one contributes. The competition is all about reputation, collaboration, and learning; the winner only gets a T-shirt or a cap.

The results of this experiment are quite interesting!

• Novelty per entry is quite low: 3.5% on average.
• Borrowed code per entry is rather high: 71% on average.
• Small chunks of novelty code are often reused, so they tend to have high social values. But code entries that are too novel (have too many novel blocks of code) are often not reused because they are too hard to understand. Therefore their social value decreases.
• In contrary, small chunks of borrowed code are not often reused, so they tend to have lower social values. However, as the sizes (number of lines) of the borrowed blocks of code increase, they become reused more often, so their social value increases.
• Winning entries tend to have few lines of novel code and many chunks of borrowed code. In fact, the amount of borrowed code is twice as predictive of top performance as novelty.
• Finally, collaborative innovation almost always leads to a more optimal solution in shorter amount of time.

Since all communications in a community are persistent and are made available through the internet to the rest of the world, a community is a fertile ground for collaborative innovation. Although the amount of novelty per post is usually negligible, through many iterative refinements by many users from different backgrounds, the solution is often highly optimized and very innovative. This method of innovation and optimization is actually very similar to how evolution optimizes certain biological motifs through natural selection. Computer scientists have found this optimization method so effective that they invented the field of evolutionary computing through biomimicry.

Now, how would you like to run a similar type of collaborative innovation "contest" on your community? Lithium is geared up for a new product that will enable you to reuse the great content in your community, collaborate, innovate and produce highly valuable knowledge base articles. Watch out for our Tribal Knowledge Base (TKB) products announcement soon!

Previously, I have blogged on using the principle of flow to build ranking ladders and scale them to match the skill level of the superusers in your community. Because these blog articles are the foundation for this blog, I strongly recommend reading them first if you haven't. They can be found here:

1. Spacing the rungs of your ranking ladder

2. Know your superusers!

Flow with your most prolific superusers

In a benchmark study I conducted last year, we found that healthy and vibrant communities that have many superusers generally have a large number of ranks. In fact, the benchmark list of top communities has an average of 31 non-role-based ranks (ranks that are achievable through participation). If we include role-based ranks (that were assigned), the average is 59. Among these top communities, the number of ranks goes as high as 134 ranks. But does this apply to your community? The important questions are: How many ranks does YOUR community need, and how many is enough?

The answer is that you need as many ranks as necessary to keep your most active superuser engaged. The exact number will depend on how prolific your most active superuser is. I will illustrate this with the calculation on Lithosphere again. Last time we've designed an optimal ranking ladder for Lithosphere. The post requirement for each rank are: 3 posts, 9, 18, 30, 45, 63, 84, 108, 135, 165, 198, 234, 273, 315, 360, 408, 459, 513, 570, 630, 693, 759, 828, and finally 900 posts at the 24th rank. We've also calculated the post rate for all users who have been in the community for more than 2 weeks and sorted them. If you look at the previous blog, you will see that ScottD is the most active superuser on Lithosphere (he also happens to be our community admin, but let's ignore that for now). He has posted 451 messages in total and his post rate is 1.16 posts/day. If ScottD wasn't our admin, he would be on the 16th rank now. So the proposed rank ladder with 24 levels is definitely enough for now.

A natural question is when will this rank ladder become insufficient? If ScottD posts 450 more messages, his post count will exceed 900. After that, his contribution will no longer be rewarded by this ranking ladder. Soon after, he may become bored with the community. How long would that take? Since we have ScottD's post rate, 450 more posts would take him about (450 post)÷(1.16 posts/day)=388 days. So in a little more than a year, it will be time to adjust this ranking ladder. What do we need to do a year from now to keep ScottD engaged in his personal flow state? Add more ranks! But how should we set the rank criteria? Remember, if it's too easy, ScottD will be bored, and if too hard, he might become frustrated and leave.

Note that the gap between the 23rd and 24th rung of the ranking ladder is (900-828)=72 posts. For typical Lithosphere superusers with post rate of 0.851 posts/day, this will take them about (72 posts)÷(0.851 posts/day)=85 days, which is almost 3 months. Even for ScottD, the most prolific superusers on Lithosphere, it will take him about (72 posts)÷(1.16 posts/day)=62 days, which is about 2 month. This means the gaps between the top rungs of our existing ranking ladder is already challenging for our superusers. So after the 24 linearly incremental ranks that are designed to engaged the superusers of Lithosphere, it is a good time to switch to the arithmetic progression (a.k.a linear progression), which has a constant growth rate. The logical choice is to continue the rung spacing between the top ranks of the existing ranking ladder. How many ranks we add depends on how soon we want to adjust the ranking ladder again. If we want to challenge ScottD with the appropriate difficulty for 1 more year, we will need to add at least 6 ranks, starting with the 25th rank requiring 972 posts, and then spaced evenly every 72 posts thereafter.

Why wouldn't we just add 60 more ranks and be good for the next 10 years? You can, but I certainly would not recommend that because ScottD's capability may change. Perhaps another more prolific superuser will come along, or maybe some other superusers will surpass ScottD. So we may need to change the spacing of the linear progression again the following year. In general, it is a good idea to re-compute the post rate of your top superusers yearly (or semi annually) and adapt the post criteria to keep the "flow" with your superusers. Alternatively, if you have a superuser MVP program, you can also switch your top contributors to an assigned-rank system to build a more personal relationship with your superusers.

Next time we will add some mysteries and surprises to spice up your ranking ladder and explore what happens after we switched the ranking criteria over to the arithmetic progressions. Come and follow my update at mich8elwu. May the flow be with your superusers!

An important mechanism for getting into the state of flow is to have a balance between ability and challenge. In a community, this means having a set of ranking criteria that matches your superusers' ability. If the criteria are too easy, superusers will quickly reach the top rank and become bored. But if the criteria are too difficult, superusers will become frustrated over their lack of advancement. In either case, the risk is that superusers will give up trying and abandon the community eventually.

Know your superusers!

Knowing your superusers is the key to designing a ranking structure that matches their capability. This is done by computing the post rate for all users that have registered for more than 2 weeks on the community. I will illustrate this calculation with Lithosphere as an example (see screen shot). As of June 6, 2009, Lithosphere had 524 members who have registration age of more than 14 days (2 weeks). Column B shows the total post count by these users (including blogs, ideas, comments and replies, but excluding deleted messages). Then I divided each user's post count by their respective registration age in days (column C) and sorted the resulting post rate (column D).

Now, if you believe the 90-9-1 rule, the top 1% (the 99 percentile) should be your superusers. In our experience, the fraction of superusers consists of only about 0.1% of the community population. Sean O'Driscoll also claimed that only 0.5% of the members in his data are superusers (see Interview with superuser guru Sean O'Driscoll from our 2009 Customer Conference). If we use our conservative estimate of 0.1%, the capability of Lithosphere's superusers is simply the 99.9 percentile of the post rates. Capability, in this example, means how fast your superusers can post. Using Excel's percentile function, you can calculate the 99, 99.5, or 99.9 percentile values from the post rates (column D). From the screen shot, we can see that the 99.9 percentile post rate is about 0.851 posts/day. This is the approximate capability of the superusers on Lithosphere. The superusers on your community will have a different number, and communities with very active superusers will have a larger number. I've certainly seen communities with superusers posting up to 35 posts/days. Armed with this knowledge of your superusers, you can now scale your community's ranking ladder so that its ranking criteria match your superusers' capability.

Our empirical data suggests that, on average, community members expect a promotion to higher rank once every month. This would require an average of 12 ranks in 1 year, or 24 ranks in 2 years. Using the ranking criteria formula I presented last time, we can calculate the post requirement for the 24th rank. If we use the incremental difference of 10 as in my previous blog, then c(24)=(10/2)×(24+24²)=5×(24+576)=3000 posts. For Lithosphere superusers (posting 0.851 posts/day), this would take them roughly 3525 days or 9.7 years to reach a post count of 3000, because (3000 posts)÷(0.851 posts/day)=3525 days. Clearly this ranking criterion is too challenging for the Lithosphere superusers. At 0.851 posts/day, we can only expect the Lithosphere superusers to post about 621 posts in 2 years, since (2 years)×(365 days/year)×(0.851 posts/day)=621 posts. Even if we make a wild speculation that the superusers capability will double in 2 year, this would only brings the expected post count to 621×200%=1242 posts, nowhere near 3000 posts.

Clearly, we need to rescale this ranking structure. With simple algebra, I solved for the incremental difference as a function of the post criterion from the formula I presented in my previous blog article:

  .

But what do we plug in for the post criterion c(n)? Since Lithosphere is a young community, the superusers definitely have room for improvement. I will assume a 20% yearly increase in the superusers' capability, so that their expected post count over 2 years could potentially reach (621 post)×(120%)×(120%)=894 posts. Therefore roughly 894 posts should be the proper post requirement for the 24th rank, hence d=2×(894 post)/(24+24²)=1789/600=2.98. Rounding this to 3, we may now generate the optimal ranking criteria that are match to the capability of the superusers on Lithosphere. The post requirement for the first rank is 3 posts, then 9, 18, 30, 45, 63, 84, 108, 135, 165, 198, 234, 273, 315, 360, 408, 459, 513, 570, 630, 693, 759, 828, and finally 900 posts at the 24th rank. Even if Lithosphere's superusers did not improve their capability at all, they would have gotten approximately 621 posts in 2 years and ascend through 19 ranks requiring 570 posts.

So, is your ranking ladder too easy or too difficult for your superusers? A sanity check by computing the post rate of just the top users on your community and comparing that to the post requirement of the top rank can quickly tell you if your ranking structure makes sense. A ranking structure that is design to "flow" could engage and captivate your superusers with superior efficacy. Next time we will answer a question that every community manager once had, "How many ranks do I need, and how many is enough?" Stay tuned at mich8elwu.

Hello and welcome back. It's been a while, so we'll review a bit throughout this article. Last time I described the concept of flow and discussed how it is governed by an individual's abilities and the challenges he encounters. I also talked about the connection between flow, gamers and superusers. In this miniseries of blogs, we'll apply this to optimize the ranking structure for your community. We all know that reputation matters. Here, we will show you the details that make it work.

Spacing the rungs of your ranking ladder

Previously, we discussed how games transport players into flow. A well designed game usually has many levels. with the difficulty between levels increasing slowly so that the gamers can easily find challenges that match their skills. By extrapolation, an engaging ranking ladder for the superusers should mimic the gradually increasing difficulty levels of a game. Although the ranking criteria may depend on any combination of metrics we collect, I will use the most common criterion, post count, as an illustrative example.

 A common mistake that many communities make is to use the convenient geometric progression as the post criterion for promotion to successive ranks. A geometric progression is a numerical sequence where successive terms are obtained by multiplying the current term by a fixed common ratio. For example, the post requirement for the first rank might be 10 posts, and then successive ranks require 20, 40, 80, 160, 320, 640, 1280, etc (blue ladder). Geometric progressions are terrible as ranking criteria because they grow very rapidly. In fact, the growth rate of geometric progressions is exponential! The example sequence above, with a common ratio of 2, grew over 1000 in just 8 terms.

So how should you space the rungs of your ranking ladder? There are two possible solutions. First is an arithmetic progression (a.k.a. linear progression), where the successive terms are obtained by adding a fixed value to the current term. For example, the first rank might require 10 posts, and then the higher ranks require 30, 50, 70, 90, 110, 130, 150, 170, 190, etc (red ladder). Because arithmetic progressions grow more slowly than geometric progressions, they are better suited for ranking criteria. However, because such ranking criteria are very regular, they may be too predictable to challenge highly competitive superusers.

If you want to challenge your superusers, I recommend using a sequence with a linear increment, where the difference between successive terms grows linearly. For example, the first rank might require 10 post, then subsequently, 30, 60, 100, 150, 210, 280, 360, 450, 550 etc (green ladder). Unlike the arithmetic progression, where the difference between successive terms is always 20, the difference between successive terms of this sequence increases linearly: (30-10)=20, (60-30)=30, (100-60)=40, etc. This sequence can be generated by the following ranking criteria formula,

,

where d is the incremental difference between successive terms and n is the rank number. The example I presented above has an incremental difference of 10, so it can be generated by . You can easily check that the third term is 60 = (10/2)(3+3²) = 5×(3+9) = 5×12, and the forth term is 100 = (10/2)(4+4²) = 5×(4+16) = 5×20, etc.

Keep in mind, the key is to have small gaps between the rungs of your ranking ladder. An ideal ladder might start with a geometric progression, since the early terms of a geometric progression are fairly closely spaced. As the gaps between the ranks increase, you can control them by switching the ranking criteria to a linear incremental scheme. And finally, you can move to an arithmetic progression to prevent the gap size between ranks from growing so large that it is nearly impossible to move up the ladder.

Now that you know how to build your ranking ladder, next time we'll scale it so that the ranking criteria are tuned specifically for the superusers in your community. Stay tuned at mich8elwu.

Have you ever experienced a time when you were so immersed in what you were doing that you forgot about your physical feelings and the passage of time? This highly-rewarding mental state is known as flow, and it is studied and characterized by a renowned psychologist Mihaly Csikszentmihalyi. I had the great pleasure of hearing Prof. Csikszentmihalyi himself speak on this topic at the Persuasive2009 conference. The talk was enlightening and made me understand why I sometimes forgot to eat or sleep when deeply absorbed in solving a problem.

According to Csikszentmihalyi, flow is an optimal state that can be attained when the challenges we encounter are matched to our ability. When the task is slightly too easy (or too hard) we fall out of flow and go into a state where we feel in control (or aroused if the task is slightly too hard). When the task difficulty greatly exceeds our skills, we are likely to experience anxiety. And if the task challenges do not come close to our ability, we will often experience boredom (see figure).

As illustrated by the figure, this also implies that when we are in a state of control or relaxation, we simply have to challenge ourselves and pick a more difficult task to get back into flow. However, if we picked a task that is too hard, we must learn and increase our skills gradually in order to move back into flow. Therefore, we learn the most when we are in the arousal state.

Picking a task that is just challenging enough for us to move into the flow state is not easy because the tasks we encounter do not have a continuous range of difficulty. Moreover, the exact level of challenge for a task is difficult to gauge. In an attempt to challenge ourselves, we often pick a task that is too hard and go into a state of anxiety. This is why many people like to stay in the comfort zone of control and relaxation and do not like to challenge themselves. Consequently, flow is not a common mental state.

Although flow is not common, Prof. Csikszentmihalyi has mentioned that they are more prevalent in creative professionals, such as artists, composers, poets, scientists, mathematicians, etc... This is because these professions require much self-challenge to create something novel and original. Due to the distinctive gaming heritage of Lithium, we know another group of people who often experience flow. Can you guess? Yes, they are the gamers. If you know friends who are into gaming, or if you have teenage children who are addicted to computer games, you will know what I am talking about. They will play tirelessly for hours, if not days, straight.

So what is it about video games that enable people to move into flow so easily? Actually, games in general (not limited to video games) can create an artificial environment where the task difficulty is well-controlled and increase gradually. This makes it much easier for gamers to pick a just-challenging-enough game to move them into flow (B2 in figure). Even if a gamer accidentally chose something too difficult, it would most likely not be something totally beyond his skill. So, they would experience arousal (B3) rather than anxiety or worry (B4), which is undesirable. In the arousal state, gamers only have to learn a little bit to increase their skills sufficiently to move back into flow (C). This will in turn encourage gamers to take on more challenges. This feedback dynamic is what makes so many gamers addicted to playing their favorite games.

As a practitioner of this theory, Lithium knew all along that the reason a superuser would spend 8 hours online answering questions is precisely the same reason that a gamer would play for days without sleeping. In fact, the Lithium platform is built upon our deep understanding of various gaming and social dynamics. The control--arousal--flow dynamic is just one of many that are deeply ingrained in our rich and flexible reputation engine. This is the reason we are able to attract and keep those superusers who will spend many hours on our communities. Moreover, because flow is inherently a rewarding and desirable mental state, superusers are often happy to volunteer their time and effort. To them, it's just like playing a game.

Despite my personal rediscovery of the connection between flow, gamers, and superuers, I must clarify that I am not claiming that a superuser answering questions online is necessarily experiencing flow. Whether superusers truly experience flow is a research question that can only be addressed via the scientific method. I was just inspired by Csikszentmihalyi and wanted to share the spark in my mind.

Having discussed the relationship between flow, gamers, and superusers, next time we will apply the theory of flow to help us design the optimal ranking structure that engages the superusers. Stay tuned at mich8elwu.

Couple weeks ago, I was invited to participate in a panel at the Persuasive2009 conference. The panel was on new metrics for engagement and I was to speak about the community health index (CHI). However, the audience was primarily social psychologists from both academia and industry. And all of them have a common interest in Persuasive technologies, which is defined by its inventor, Prof. B.J. Fogg, in his book to be any technology "that is designed to change attitudes or behaviors of the users through persuasion and social influence, but not through coercion." So, I was challenged with the task of relating CHI to engagement and persuasion. As a scientist, I did my homework. I read up on the most authoritative research papers in this field and came up with the following strategy.

First, I evaluated the Lithium platform by a persuasive system evaluation framework. This framework was published by Prof. Harri Oinas-Kukkonen in last year's conference proceedings and has already been adopted by researchers in this field. So I thought this would be a good place to start. In addition to some basic requirements, the paper outlined 28 persuasive design features that are grouped into 4 categories. To my surprise, the Lithium platform actually met all the basic requirements. Moreover our platform currently has 25 out of the 28 persuasive features. This allowed me to confidently conclude that our community platform is in fact a very persuasive system!

The next step was to relate all this to CHI. Since the panel was on metrics for engagement, I have reinterpreted the 6 health factors of CHI as measures of engagement: 

1. Traffic: is a measure of passive engagement.

2. Content: is a measure of passive engagement.

3. Members: is the conversion rate from passive to active engagement.

4. Liveliness: quantifies the likelihood of any user to engage actively.

5. Interaction: is an estimate of the scope of the engagement.

6. Responsiveness: measures the quality of the engagement.

By reinterpreting the health factors as measures of engagement, CHI can take on a whole new meaning. Since every engagement provides an opportunity for persuasion, CHI is actually a measure of "persuasibility". Although the Lithium community platform was not designed nor thought of as a persuasive system, it can certainly be used as one. In addition, we can now measure the persuasibility of this system.

My research really paid off at the end, because it has made the Lithium platform and CHI very digestible to the audiences. I was pretty thrilled to find my framework for CHI was tweeted and blogged, by Maury Giles from Pursuit, another panelist at the conference.

Next time, I will tell you a bit more about some of the fascinating things I've learned at the conference. For updates, come and follow me at mich8elwu.

Photo by David Lin

Hello everyone. I'm Michael, the principal scientist working on analytics at Lithium. With most scientific endeavors, the principal investigator is only one member of the team, and analytics at Lithium is no exception. By the way, Lithium has the witty culture of naming our development teams after superheroes. So can you guess what the analytics team is called? Hint... there is a recent movie about this group. You guessed it: the X-Men!

We chose this name because the variable X is commonly used to represent an unknown quantity. Also, X has a visual resemblance to the Greek letter chi (χ), which is what I used as the symbol for the Community Health Index (CHI).

After being a guest blogger on ScottD's blog for the past few months, we decided that I should have a blog of my own. For the sake of bookkeeping, I've gathered here previous blog articles that I've written on the development of CHI.

1.       From the Brain to Community Analytics

2.       Criteria for Creating the Community Health Index

3.       Crunching Numbers for the Community Health Index

4.       Interpreting the Statistics for CHI

5.       Formulate, Predict, and Reformulate

I will use this blog to share my passion and love for science and analytics. I will explain, and try hard to explicate in laymen terms whenever possible, some of the fascinating research that is conducted here at Lithium. This blog will also be home to tidbits of interesting scientific findings that never made it out the door as products or white papers. Finally, I will share some of my personal experiences as a scientist in the industry. This will cover everything from the people I meet while attending conferences, to interesting conversations I have with fellow practitioners, to topics I’m thinking about.

I will end this first blog post with a quote by Sir. Humphry Davy, recorded when he was defending the so-called "useless experiments" conducted by his student, Michael Faraday. This quote was also recited by Brian Cox at the end of his short TED Talk this week.

"Nothing is so dangerous to the progress of the human mind than to assume that our views of science are ultimate. That there are no mysteries in nature, that our triumphs are complete, and that there are no new worlds to conquer."

Likewise, nothing in this blog is absolute. If you disagree with me, let's discuss and talk about it. That is how science makes progress.