Sherry Quinn is Lithium's Director of Education Services and leads the company's extensive customer training program, developing content with customers and the product teams.

You will be seeing a lot more of here in the Lithosphere where she is SherryQ.

Have you heard about Lithium’s training program? We haven't spoken about it too loudly in the past, but there is a great library of video courses available to help community managers stay on top of the platform features and the skills needed to run a successful and vibrant community.

So what do we have? Well for a start, there are a lot of courses available - and we’ll keep creating more so you can stay abreast of what’s new! [If you want to get a sample, we have some course details listed at the bottom of this post]

The offerings in the 100 series contain product overviews and demonstrations.  Courses in the 200 and 300 series cover implementation best practices and community management tasks; they include insights from people like Joe Cothrel and our most experienced customers. 

We know you’re busy, so each tutorial is short and to the point!  They average 4.5 minutes each, so you can sneak a few in, even if you have only 30 minutes to spare. You have full control to pause, go back, forward or rewind using the controls at the bottom of the page. 

Here’s an example from the Twitter Integration (106) course.

And pssstttt… they’re available to you any time!  Just send a note to 'education (at) lithium.com'.  You can also learn more here.
   
Why not start today? To view a sample tutorial, just click here .  This 4 minute tutorial shows you how community members can add value to Tribal Knowledge Base articles over time by collaboratively editing its contents.

Manjeera Patnaikuni is a Product Manager for Lithium Technologies. She is responsible for the End User Facing Features of the platform.

You can follow her on Twitter at @Manjeera 

Hello Lithosphereans! We have quite a few goodies lined up for you in terms of new features. I’d like to take the opportunity to let you in on two of the new feature enhancements coming up in the next couple of releases.

So let’s get started and take a sneak peak into what’s in store.

Unread Discussions

For users who visit the community often, we’ve made it really easy to keep track of all the discussions that are yet to be read by the logged in user.

Rather than sifting through all boards finding new content, you can now do it in one place.

It’ll save a lot of time, especially for moderators and super users who want to keep up with the latest activity in the community.

But wait... there's more!

There will also be a way to switch between discussion mode (thread view) and posts mode (message view) so you won’t be inundated with new messages for a discussion flooding the first page.

Thanks to Lithosphere user ‘Potential’ for the great idea and ‘jloyless’, ‘Laura’, ‘alissa’ and ‘Ryan’ for adding more meat to it. Keep the great ideas coming folks!

Autosave Drafts

Picture this.... You just spent hours crafting your new entry, only to see it disappear due to an accidental page load or just because your computer froze on you? I think we have all suffered from this at some point.

Well, not anymore!

All forum and blog posts will now have an Autosave function so your posts are automatically saved every few seconds. The time limit will be configurable by the community admin) so you don’t have to explicitly click the ‘save as draft’ button for blogs either.
 
As always, if you have product ideas let us know on the Lithosphere, by logging your suggestion in the Idea Exchange.

Dr Michael Wu Ph.D. is Lithium's Principal Scientist of Analytics, digging into the complex dynamics of social interaction and online communities.

He's a regular blogger on the Lithosphere and previously wrote in the Analytic Science blog.

You can follow him on Twitter at mich8elwu.

Hello everyone, and welcome to Building Community, Lithium's blog about the platform. Just as in the past, I will continue to cover analytic science and research here at Lithium. Today, I will talk about a refinement in the community health report that slightly altered the information displayed on the CHI compass that many community managers are receiving today.

The CHI compass is a radar chart that is a great for giving a compact presentation of multi-dimension data, namely the six health factors (members, content, traffic, liveliness, interaction and responsiveness) that goes into the computation of CHI.

However, some health factors have very different ranges of values, for example, weekly values for members may range in the hundreds, where as content (posts weighted by views) and traffic (views) may have values over 10K or 100K. On the other hand, liveliness, interaction and responsiveness, have much smaller values in the tens or, in some cases, less than one. With such large variation in the scale of the data, it means we have to normalize these values before we display the CHI compass. Otherwise the small weekly variations in the smaller values would not be visible against the large variations in the diagnostic health factors.

Here is an example that I've generated with Excel. In this example, liveliness had a very significant 100% weekly increase (from 1 to 2, 2 to 3, then 3 to 4) during the first 4 weeks. But it is relatively unnoticeable when plotted in the radar chart against the other diagnostic health factors, which have much higher un-normalized values. 

Community Intention

One of the insights that came out of Lithium's research was that even though you may launch a community with a specific purpose (such as key benefits like Innovate, Promote, or Support,) depending on the interaction at any particular period in time, you will find that a community can adopt the multiple traits, and these traits can change over time. For example, support communities often behave like enthusiast communities after product launches. Likewise marketing and sales oriented community can also behave like a support community and answer technical questions from the enthusiasts.

The shape of the CHI compass was designed to actually show what type community behavior is taking place. Thus, the shape can tell you whether your community is trending towards an innovation, promotion, or support community. However, this design requires a particular normalization scheme that normalizes the predictive health factors as a group. Because the liveliness factor generally has a smaller range of values than interaction, it appears much smaller when normalized and put on the same scale as interaction. People often interpret this low value as the community not being healthy whereas the community might be perfectly fine. The relative position on the compass is purely an artifact of trying to show the typology information on the CHI compass.

Under this normalization, the same community would look like the following. The outward bulge in interaction is the characteristic signature of an promotion community.

Rethinking the Visualization

So, on to the changes. After hearing many inquiries about how to interpret the CHI compass, we've decided to go with a simpler and more intuitive normalization scheme and remove the community typology information from the CHI compass.

We simply normalize each health factor to its best previous performance (over a 6 month window). If you outperformed the 6 month best score, your health factor will be 100% that week. This will redefine the new standard for which to normalize your future scores. So a growing community should hit 100% periodically, which indicates improvements over the last 6 months.

With the new normalization, the very same community would look as follow.

Some of you may ask..., if the shape of the compass doesn't indicate the community type anymore, where do we get that information? Don't worry, the community typology information is not lost, we will design another widget specifically for displaying that data!

I hope that gives you an insight into the changes in the CHI compass, and why we thought it is important. As always please let me know if you have any questions or would like me to cover particular topics.

Hello, and welcome back from the Thanksgiving holiday. As the year comes to an end, we are also planning a restructuring of our blog program for next year. So today, there are two items on my agenda.

First, I like to thank all of you readers out there for following my blog on Analytic Science. I know it is not a subject that appeals to everyone, and numbers and symbols can be, at times, technical and hard to digest. So thank you again for your interest.

Second, I also like to inform you of the upcoming changes to our blog program for the new year.

Announcement: Blog Program Changes

Starting in the New Year we'll be running a new, restructuring blog program on Lithosphere.  Full details can be found here where we outline all the changes.

Thanks for following up till now, and we're looking forward to talking with you in the new blogs.

The SF Bay Area is turning really cold now, and it's been three months, since I was invited to the Lawrence Livermore National Lab (LLNL) as a DOE CSGF alumnus. Today, I'd like to finish sharing the rest of that day with you. If you missed the first part, look here.

After the NIF tour, we took a short bus ride to the Terascale Simulation Facility (TSF). This facility is so highly-classified that one of our CSGF fellows was not allowed on this tour because there wasn't enough time to obtain clearance due to his foreign national status. The TSF houses the world's most powerful supercomputers. And they are constantly building and acquiring the newest, fastest supercomputer in the world. For example, the TSF still houses IBM's Blue Gene/L, which was the fastest supercomputer in the world back in Nov 2007. This supercomputer has a peak performance of 478.2 TeraFLOPS (478.2 trillion Floating-point Operations per Second). Today, Blue Gene/L ranks #5 on the top500 Supercomputer list. The Roadrunner at Los Alamos National Lab takes the lead with a peak performance of 1.1 PetaFLOPS (1.1 quadrillion FLOPS). However, earlier this year, TSF has already installed Dawn, a 500 TeraFLOPS initial delivery system of the Sequoia. Sequoia is the next generation Blue Gene supercomputer capable of computing at 20 PetaFLOPS, and it will be delivered early 2011. Scientists believe that at this rate, supercomputers will be powerful enough to simulate human brain function by 2013 and full-brain neuronal simulation would become possible by 2025.

Figure from LLNL.

The TSF houses its supercomputers in a 48,000 square-foot clear-span vault. The vault entrance has a large panel of electrochromic smart glass that turns transparent with a flip of a switch, but is otherwise completely opaque. This panel is fail-safe, and power must be applied to keep it transparent. So in the event of a power failure, the vault would remain secured. The vault's interior is filled with motion sensors, and we were told that the alarm had been tripped a few times because the computer's cooling fans had blown off small pieces of stickers and tapes from the new computers.

With so many supercomputers, heat becomes a serious problem. The supercomputer vault basically sits above a giant air conditioner. The vault floor is tiled with both perforated and solid aluminum tiles. The precise placement of each type of tile and the arrangement of the computers is optimized so that the airflow from beneath the floor provides maximum cooling efficiency. All computers are also mounted on seismic suspension racks, and they will simply wobble around during an earthquake.

The TSF is critical to the stockpile stewardship program, and it is used to simulate the physics of nuclear weaponries to an extremely high degree of accuracy that could supersede the need for actual underground testing. Basically the simulation must be as good as real. TSF also enables scientists to study many physical phenomena that occur at multiple scales through molecular dynamics. At the conclusion of the tour, Frederick Streitz (director of Institute for Scientific Computing Research at LLNL) showed us 3 amazing large-scale (up to billions of atoms) simulations and visualizations that lead to a better understanding of our physical world. The first was the Kelvin-Helmholtz Instability, for which Dr. Streitz and his team receive the Gordon Bell Prize. And the other two were on the fluctuation of electric fields in plasmas, and the domain formation during solidification.

After the tours, we were treated to wine tasting, dinner, and networking with DOE scientist and officials at the Wente Vineyards. I must say that every year when I gather with the CSGF fellows and alumni, I always feel deeply honored to be a part of this very distinguished group. Although many CSGF fellows do end up working at a national lab, such as LLNL, LANL, NASA, etc, a significant number of them go into academia and joint top rated universities, such as UC Berkeley, and Stanford. The rest work at some of the best companies across all major industries: Google, Microsoft, Intel, Boeing, IBM, HP, Fair Isaac, GE, Procter & Gamble, Goldman Sachs, Boston Consulting Group, AREVA, Exxon Mobil, Merck & Co, and Lockheed Martin, just to name a few. And I am very proud to bring Lithium Technologies to this list among the CSGF employers.

OK, that was the rest of my day at the CSGF Bay Area chapter gathering. After the Thanksgiving holiday, we will go back to community and social analytics. Follow my updates at mich8elwu.

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.