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Social 2011 Recap, the Radian6 User Conference

By Ken Burbary ⋅ April 11, 2011 ⋅ 12 comments

Last week I had the pleasure of attending and participating in Social 2011, the first ever Radian6 User Conference. I’m happy to report it was a smashing success, on many levels. Don’t jump to conclusions, this wasn’t an analyst / data geek event. While social listening platforms like Radian6 are certainly used by these roles, this conference targeted marketing executives, strategists, brand managers and community managers as well. It was a terrific blend of practical know-how, detail combined with strategy and insights for operationalizing social business. Radian6 spent time announcing some big news, and launching new products that enable putting social business to work more easily. The event also included stellar keynotes from Mitch Joel and Paul Greenberg in particular, along with informative and entertaining panels (the panel I was on about ROI turned into some heated debate) through the day. To give you an idea how active and enthusiastic the crowd was, in the 2 days that the event spanned, there were over 15,000 mentions on Twitter using the #social2011 hashtag.

The main highlights of course centered around product news about the Radian6 platform. Let’s take a quick look at them and the impact they will have.

Insights Platform – Radian6 launched a full blown insights engine. It extracts more relevant meaning from the mountains of social data that are harvested via social listening. Instead of being limited to knowing share of voice or total volume(s) of relevant conversation, one can now easily (with a click or two) drill-down into the data on a relevant topic and get much more granular, to answer very specific questions about sub-topics, themes, etc… on a given bit of conversation. I’d never do it justice in a paragraph or two, so take a look at the product overview video found here, as it will surely impress.

Why is this significant? For 2 primary reasons. The first being, it gets decision makers the answers they need quickly. Historically, to get to the answers the insight engine will spit out, one was required to do a lengthy bit of many searches/profiles, just to find the relevant data, then manually analyze it as well. The second reason? Efficiency. The insights engine will save significant amounts of time and energy to get key marketing, customer insights and customer support questions answers. In the era of the real-time organization, this capability will fast become the norm, not an advantage. Think table-stakes.

Summary Dashboard - The summary dashboard is an another new product, and attempts to provide an easy to digest, high-level view of a brand’s entire social footprint. It pulls key information from several parts of the Radian6 platform, and brings them to the user in beautiful, data cubes (built in HTML5, say goodbye to the Flash!). The types of information that you can get from the summary dashboard are conversation volume, overall sentiment, key audience demographics, influencer and content/topic analysis. This is significant again for the efficiency gains. It’s simply a better way to view important information over the previous alternative, which was to construct much of this manually via the widget gallery, which can be time-consuming and tedious.

Enterprise Engagement – This product announcement may not be relevant to all, but for those that are not only doing social listening and analysis, but also response and engagement, the expanded engagement console is a worthy consideration. It now offers full access to the social web. Respond to customers in Twitter, Facebook, blogs, forums, etc… It also comes with expanded workflow and notifications, team management capabilities, and an extensible widget gallery. That’s right, 3rd party developers can now build specialized plug-ins for the engagement console. An example shown at the event was the Klout plug-in added to the engagement console, enabling one to view an integrated Klout score for each author contained in the conversation results.

Mobile – One thing that’s always been missing from social listening platforms is a good mobile app, to easily monitor events and pull of key information. Not anymore. Radian6 launched their first mobile app for the iPhone (video overview at this link), available soon in iTunes. Before dismissing the relevance of this, consider the end user. This isn’t meant for analysts that need to get neck deep in the data, but for those company representatives on the front lines, as a means to always be in touch with what’s happening, and have the capbilities to respond. This is an extension of the real-time organization concept I described above.

Key Observation: The critical and growing 3rd Party Ecosystem tied to social listening success. While impressive and capable, Radian6 didn’t achieve these innovations alone. They spent time discussing how imperative it is for them, going forward, to identify the right specialized partners, and integrate partner technologies and capabilities into the platform, rather than develop it internally. There were 3 partners brought on-stage at the event to describe their integration and benefits. Radian6 has made substantial increases in their text analytics, semantic analysis, and influencer analysis by adding OpenAmplify, ThomsonReuters OpenCalais and Klout technologies to their platform. They hinted these 3 were only the beginning, and they were more to come in the future. This places Radian6 in a position to do what they do best, focus on great data coverage and customer support. Then integrate the best of breed specialists into the mix for analysis.

A few more items. The conference was incredibly well organized and run. There wasn’t a single snafu or hiccup that I can recall. This means the folks at Radian6 responsible for organizing the event worked their tails off, and I’m happy to give them the kudos their deserve. This means people like Lauren Vargas, Cory Hartlen, and Craig Comeau to name a few. I’m sure there are about a hundred others I’ve missed but please know I appreciated everything!

3 Tips to Improve Your Online Influencer Analysis

By Ken Burbary ⋅ March 3, 2011 ⋅ 15 comments

Identifying influencers is an important part of social marketing, and becoming big business for some companies like Klout . Yet, influencer identification and analysis isn’t well understood, or easy to optimize. That’s because no two influence analysis efforts are the same. Like fingerprints or snowflakes, an influence analysis program is unique, specific to the goals and priorities of the organization executing it.

To date, automating influence analysis has relied heavily on quantitative measures (example: like followers/friends, RTs, mentions/replies). While these metrics can be valuable inputs into determining whether or not someone is influential, they aren’t worth much at all without their counterpart, qualitative metrics. If influence is a coin, then the 2 sides are quantitative and qualitative. Focusing purely on quantitative metrics tells you if someone is popular, not influential.

A recent and fitting example of this is the news of celebrity Charlie Sheen joining Twitter. Sheen amassed 1 million followers in approximately 24 hours. An impressive feat, no? Is he popular? Definitely. Is he influential? Well, according to Klout he is. Sheen currently has a Klout score of 88 (higher than most people, including industry thought leaders like Chris Brogan (81), Jason Falls (72), and Brian Clark a.k.a Copy Blogger (73). Does this mean Sheen is more influential than any of those 3? It depends on the topic.

Enter relative influence. Relevance brings the qualitative perspective necessary to complete the influence picture. Without it, you cannot be sure that you have an actionable list of influencers. The 3 individuals mentioned above are respected, trusted voices on topics such as marketing, social media and blogging. If you were to take a simple quantitative approach, and rely on Klout’s score, you could make the argument that Sheen is the more valuable influencer to focus on. Would you take his advice on any of those topics over Brogan, Falls, Clark? Absolutely not! Relative influence should be the focus on influencer analysis. You’re looking for individuals with deep, strong ties to the community on specific topics, not individuals with weak ties to many communities. Don’t confuse popularity with influence, they aren’t the same.

In addition to using automated tools to improve the effectiveness of your quantitative influencer analysis, here are some tips to gain a more robust qualitative view and gain a balanced perspective.

Define relevancy first - Before you begin, think hard about the topics, types of conversation or actions that are relevant to your business goals. Create a relevancy list to act as a compass, guiding your analysis away from anything that doesn’t meet those criteria. You want relative influence, not popularity (Example: Nike recently targeted absolute influence, not relevancy, including David Armano in a basketball outreach program, details here). Also, think critically about what qualitative metrics are relevant to you. Not all may matter, especially if you’re focusing on a single channel. As an example, if targetting Twitter, then exclude Facebook and/or LinkedIn data from your quantitative analysis.

Use Influencer Tools are a starting point – Don’t throw out the baby with the bathwater. Tools like Klout may not be where we want them to be, yet. However, they can be used as a starting point to help you find the general direction to move forward. A more comprehensive list of influencer analysis tools, can be found here at Jason Falls blog.

Integrate with other data to gain clarity – Social data is a great start but represents only a small slice of the digital data spectrum. Combine social data with other digital inputs to validate your influencer analysis. Two good types of data are search and web analytics. Pull web analytics data for the website/blog of a suspected influencer. Are there many inbound links? Traffic? Search volume? etc… The point is, you should find a corresponding level of activity in these other data types to support the social data findings and make the strongest possible case for them as an influencer.

Finally, if you’re interested in a rich, in-depth perspective on influencer analysis, take the time to read this eBook, authored by @chuckhemann and @radian6. It will enlighten you on the finer points of influencer analysis, including specific methods and metrics to consider in yours.

Radian6_JAN2011_Book

The Dirty Little Secret of Social Media Monitoring

By Ken Burbary ⋅ July 13, 2009 ⋅ 213 comments

Social Media Monitoring (sometimes called social media listening or conversation monitoring) is widely recognized as one of the first social media best practices. It is the first step in getting to know and understand the behavior and needs of an online audience. Most companies that are engaged in this activity have an internal team or outsource the effort to their agency.

There is no shortage of tools/platforms available to monitor your key terms. Companies like Radian6, Techrigy, Nielsen and Cymphony are quite busy these days rolling out new partnerships and product features, all aimed at providing more meaningful data and better ways to use it. It’s the no-brainer call right? Fire up a tool and plug in some terms, and voila, instant answers.

However, I’ve begun to notice inconsistencies in the data that different social media monitoring tools produce. The dirty little secret or so it seems, is they aren’t all working with the same data sources. For this post, I want to discuss some differences between Radian6 and Techrigy SM2. This is not meant as a criticism of either tool, because I think they are both fantastic at what they do, but rather an attempt to highlight an issue, generate a public conversation around it so that everyone using Radian6 or SM2 can benefit.

Some background: While doing a recent set of searches for a brand using the EXACT same keywords and phrases, without using source filters, the results look different. Take a moment to view the charts below for R6 and SM2 results.

Radian6 Results

SM2 Results

Now, ignoring the fact that both tools have some differences in categories, focus on the main sources of online conversation. Blogs, forums and micromedia (Twitter).

You’ll see a dramatically higher number of blog results for Radian6 compared to SM2 (266 to 91). And for forums, vice versa (SM2 has 396 to Radian6 200).

While I expect to find subtle variations in the results between tools, I DO NOT expect to be put into a position to question which tool is “right” and which is “wrong”. Perhaps that isn’t the way to look at it though. Is the real answer an aggregate of both data sets? If so, how does one easily filter out the unique data versus duplicates?

This may raise additional questions that you need to ask yourself before selecting a monitoring solution.

Which subset, or channel, of social media does a particular tool specialize in?

Does this data mean if you know your audience is spending time on forums, SM2 may be better solution? Perhaps. Perhaps not. I suspect there is more to this than it appears on the surface. Hopefully someone else that is also monitoring can shed some light. With any luck, we can get some folks from both companies to chime in (David Alston, Amber Naslund, Jimmy Rey, Connie Bensen – any takers?)

UPDATED: I’ve been contacted by both Radian6 and Techrigy representatives, and am working with them to identify the source of the problems. After we have resolved the issue and feel confident about the source, I will update again.