Xavier Jimenez is Director of Social Solutions Consulting at Lithium. He is nominated for a board position with the Digital Analytics Association so we thought it was a good opportunity to get to know him a little better. Preview: he loves rugby and chili peppers. Oh yes, and digital analytics!
On November 11th, Lithium is hosting the largest online conference for social media and CRM professionals at the Social CRM Virtual Summit. The change brought about by the social media revolution has impacted every area of industry, including how customers choose to interact with companies and each other online, particularly where they turn for trusted information.
This virtual summit comes at a time when companies are starting to see tangible financial results from deeper online engagement with customers, and will explore the current capabilities of and future for Social CRM.
With webcasts by Social CRM thought leaders and live chats with industry experts and practitioners, the Virtual Summit will be a ground breaking event. Remember you can sign up for this free event here, and jump into the conversation on Twitter using #vscrm.
To introduce you to the experts presenting at the event, we’ll be running a series of mini profiles here in the Lithosphere.
Expert:Dr Michael Wu - Principle Scientist, Analytics, Lithium Technologies
Dr Michael Wu is the Principal Scientist of Analytics at Lithium Technologies. Michael received his Ph.D. from UC Berkeley's Biophysics graduate program. His graduate research focuses on modeling the human brain, specifically the visual cortex, with techniques from math, statistics, and machine learning. He is one of the principle minds behind Lithium’s Community Health Index standard of measuring the success of a community.
He will be hosting an expert chat session on Analytics, giving practitioners the opportunity to talk about the science of measuring community, and what the metrics really mean for their business.
Q: What got you involved in community and/or social media to begin with?
The better question might be ‘what got me into social analytics’? I’d have to say, it’s all about the data. Because of the SaaS platform run by Lithium – it has recorded a huge data set over the 10 over the past decade. 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, everyday people. This is what got me excited about social analytics.
Q:What are you currently working on?
Primarily my research is focused on two groups of users, superusers, and lurkers and the interactions they have. Superusers are obviously interesting because they contribute so much and bring so much value to the community. But why are they involved in the way they are? What drives them? It’s safe to say that nobody joins a community as a superuser. Yet, in every community, we observe the emergence of this particular group. Can we accurately predict who will become a superuser soon after they join the community?
Lurkers on the other hand are interesting in their own right - because up to 90% of a community displays this behavior. What keeps them engaged even though they don’t participate? Can we incent lurkers to participate and move up the rank ladder? These questions are something that have all community practitioners searching for answers. We can already measure superuser interaction, but if we can predict which lurkers will become superusers you have an incredibly powerful targeting metric.
Q: What is big community topic on your mind at the moment?
As well as investigating superuser/lurker behavior, I am very interested being able to derive predictive models for business value. The goal being to discover the mechanisms where our platform can bring values and quantify the value they bring to a company. Some of these mechanisms, such as call deflection, are well understood and their return on investment are readily quantifiable.
But the value of ‘word of mouth advocacy’, and people who are influenced through lurking are less tangible. Currently I am working a model that quantifies the value of word of mouth in a community. I am hoping that this will lead us down the road to ultimately quantifying the value of a superuser to a business.
Michael writes regularly in the Lithosphere blog, ‘Analytic Science’ – he is currently writing a two part article on his participation at the Virtual Summit.
Michael Wu joins us again for the second installment describing how the new Community Health Index was developed:
I wrote previously about how I came to start the development of the Community Health Index (CHI), through my background in the science of the brain and through Lithium's extensive data set of online communities. Picking up the task, I will start by defining what it means when we talk about community health.
The performance of any enterprise communities has two dimensions:
meeting the needs of members (customers), and
meeting needs of the business (enterprise).
Community health addresses the first dimension, and it measures how well the community meets the needs of its member. It is very important, because without customer satisfaction, there is no business success.
With this understanding of community health, I set two basic criteria to narrow down the data we must plow through. Otherwise, the most complete picture of community health would be a consummate of all the data about the community. First, because it is our objective to make the community health index universal, we must use basic data that every community has. This eliminated many of the metric data that only Lithium keeps bringing the number down to about 20 (I actually analyze more than 20, but only about 20 are universally available). Among these are the usual metrics plus some less common ones such as percent of unanswered threads, average thread depth, average number of unique participants in a thread, average post length, etc. Although these metrics might not be recorded explicitly by every community platform, they can be easily computed from aggregating and summarizing the record of all the messages and user data that every community must have.
After establishing the initial data set, the second criterion we applied is known as the Occam's razor. The goal is to come up with a minimum set of data that gives the greatest predictive power. This is a challenging problem in statistics, known as the bias-variance tradeoff. In plain English, it means that there is a tradeoff between the complexity of the model and the predictive power of the model. Although complex models that use many variables will always have greater explanatory power for the available data, their predictive power for unseen future data degrades. On the other hand, simpler model with few variables may not explain the current data as well, but they are more predictive of future trend. Why is that? That is just the nature of uncertainty and how it works, much like why gravity always attracts.
Next time we'll start the journey through the Lithium community data set. And I'll turn the number crunching crank to identify areas with the greatest predictive power!
For updates and discussion between Michael's posts, leave your comments here or you can follow Michael on Twitter at mich8elwu.
Another treat for you today: Michael Wu, resident scientist and chief number wranger behind the Community Health Index has agreed to drop by and tell the story about how this new open standard was developed. Enjoy part one of this special peek behind the scenes!
For the past six months, I have been engaged in a massive data analysis project at Lithium to develop an index that measures the health of online communities. I've subsequently refer to this index as the community health index (CHI), which I like to denote with the Greek letter Χ. This project began shortly after I joined Lithium when I received my Ph.D. at UC Berkeley in Biophysics. Although it was a dramatic transition from academic to industry, I thought that analyzing community data shouldn't be that difficult. After all, data are just numbers and the math and statistics required to gain insight from them are just equations and symbols, which are universal across all disciplines. I was in for quite a surprise.
I have been a brain scientist during my academic years, and I focused in an esoteric area called computational visual neuroscience. Basically, that just means that I use a lot of math, statistics, and techniques in physics to model, study and ultimately understand how the brain process visual information. Coming from this background, I see an obvious connection between a community and the brain: they are both complex networked dynamical systems.
The brain is made up of approximately 100 billion neurons talking to each other through a language of their own (action potentials, which are impulses much like the Morse code).
Each neuron also network with other neurons and form connections that create local cliques of friends and buddies.
The interactivity between the neurons is what makes the brain (viewed as a community of neurons) work. Without these interactivities the brain will wither and die of atrophy.
Although there are many more interesting analogies between the brain and a community, now that you see the connection, it is time for the surprise. To my astonishment, Lithium actually has a huge data set spanning the 10 years of its SaaS business operation. This is compounded by the fact that Lithium keeps about 240 different metrics that monitor every moving part of the community, and the metric list is growing as new features are being added. Moreover, there are copious non-metric data. These include moderator log files, notes from customer engagement, and annotations of PR or any event related to the customer. To my surprise, it turned out that these non-metric data accumulated over the years through active community management, moderation and customer engagement are most valuable and informative for the development of the community health index.
In later posts I'll describe my journey through this large and complex data set. But today I'd like to hear from you - what do you most want to know about the Community Health Index? What next steps would you like to see?
Something special today: a quest post from Lithium's own Neil Beam, Director of Enterprise Programs in our Client Services group. Neil's recently been spending all his time immersed in metrics and numbers to help us describe what makes your community tick (and more importantly, how to keep it going strong). Today he's here to share some of the though that went into developing Lithium's newly announced analytics offering:
We are releasing today our new Lithium Insights suite, and Scott invited me to talk about it in more detail here on his blog.
The Index is the first step towards an industry standard – focused on what community practitioners should measure, report and be held accountable to in their daily practice. It does two things: 1) serve as a absolute measure of communities where you can stand them up side-by-side and say – “okay, now I know how to compare these communities” and 2) provide actionable measures that tell practitioners what to focus on first and what to do next that are specific and relative to the individual community – 6 health factors do this. Three are predictive and three are diagnostic. Note, we picked these 6 factors (Liveliness, Interaction, Responsiveness, Members, Content, Traffic) because they are an universal denominator common to most community platforms, even Twitter could fit this paradigm.
Case in point, when I was the project owner of a community in my past position I quickly discovered that simple metrics (page views, posts, registrations) only gave my management team a partial snapshot and single dimension of a very complex and dynamic system. It never felt good. How did I compare for my executives the other 5 communities on the Lithium platform at this company which were built for different products and completely different audiences and launched at different times? The Community Health Index addresses this.
So what does the Community Health Index look like?
There is a lot going on here (it is the front page to a longer analysis) but this report shows a Community Health Index of 672 on a 0 to 1000 scale is a range of ‘healthiness’. We intentionally didn't scale the Index into the negative because this would immediately imply an unhealthy/healthy dichotomy, which isn't the case. Instead, the healthier a community is, the more likely it will accomplish the goals of the members and the company. Obviously a Community Health Index around 100 or 200 is not accomplishing as much for the members, guests and the company a community with a score of 700, 800 or even 900. You can always improve your health – and what is really important is that the 6 health factors tell you exactly what to focus on first. Here we point out Responsiveness and Interaction as target areas in the Compass. This customer got specific recommendations based on those health factors.
Finally, you'll notice that the methodology and formulation of the 6 health factors and the Community Health Index are fully disclosed in the white paper. We did this so practitioners could land on a common dialogue.
So, how did we do? We welcome the feedback because the point is to continue to improve methods for helping our customers derive value from their communities, and help the industry grow as a whole.