The Net-Savvy Executive

Everybody is Learning

2013-05-14T16:33:19Z

Another sketch from the whiteboard

A couple of years ago, a suggestion that I develop a maturity model for social media analysis led to a different kind of model. My approach to this space has always been to explore its edges, looking for what might be next. One effect I've noticed is that change circulates through the ecosystem of companies, their customers, and their suppliers. Where change keeps coming, everyone's learning together.

A linear maturity model defines development stages toward a known destination, but in a system where everyone is learning, the destination is still unknown. We react to others, and others react to us. Change reverberates through the system, and we don't yet know what maturity looks like.

What this means in social media analysis
If social media analysis were good for one thing, we could have a simple maturity model. The products would progress toward a theoretical ideal, and clients would mature toward efficient, effective business practices. But the technology stack is built on areas of active research, new platforms are driving new consumer behaviors, more business functions are showing interest in how to use social media to do their jobs, and vendors are trying new ways to distinguish themselves.

Virtually every piece of the puzzle is moving.

Let's go to the whiteboard to see if we can visualize it.

There's a lot going on here, and this is the oversimplified version. Here's the basic dynamic: on the right, new capabilities become practices; on the left, new expectations become requirements. In the overall system, we expect more from our suppliers as we adapt to new capabilities and adopt new practices.

Think about what is being learned in each loop.

Tool vendors combine their own R&D with new capabilities from research labs and partner companies to expand their products' capabilities, enabling new tactics for their clients, who provide feedback and new requirements based on real-world use of the products.
Consumer-facing companies experiment with new tools and capabilities, and they learn from both operational results and customer reactions.
Customers react to companies' online tactics, adjusting their behavior to maximize their own benefit. When they find a practice they like, they may expect other companies to mimic it.

The catch is that this is all happening at the same time, and the companies, at least, are trying to predict how their customers will want next.

Who's learning fastest?
We know of some unintended lessons, such as teaching customers to complain publicly for a quicker response, and redefining like. But where do we look if we want to get ahead of the market? Try these key areas:

Outside innovation - New research and inventions may provide answers to questions you've wanted to answer.
Product capabilities - What's possible keeps changing, but don't look only at existing suppliers. Look at adjacent markets for capabilities worth adapting to new applications.
Client requirements - It's always worthwhile to pay attention to what companies say they'll pay for.
Client capabilities - Watch what companies are actually using, too.
Competitor actions - Watch early adopters for practices that may become standard. Is there a better way to do it?
Customer expectations - How are people reacting to new business practices? What issues are being raised? What new expectations?

Like any model, this one raises more questions than it answers. That's the point. What will it help you discover?

More ideas from the whiteboard:

I'm sharing some of the frameworks that have been hiding on my whiteboard. Want to apply them in your business? Email me.

Discovering New Applications for Data and Analysis

2013-03-13T13:40:43Z

Do you have any of those mix-and-match books that let you remix parts of their pages? (It's ok; you can claim they're for your kids.) We once bought a story starter for my son (see, like that) that combines an opening quote, a character, and a situation. Put together a random grouping, and you have the beginning of a story.

That's sort of how I look at data and analytical methods.

Here's how it works: First, remember the basic building blocks of social media analysis: data, analytics, and application. Now, let's generalize from the social media example, because this isn't just about social media data.

We get three basic pieces:

Data
Internal and external sources, open (freely accessible) and proprietary (paid). There's a lot more here than most discussions get into.
Analytic methods
Sentiment analysis, topic clustering, source profiling, statistical analysis, geospatial analysis—the list goes on and on. This is a good area for And not Or thinking.
Applications
In a software business, this usually refers to the product, its features and their benefits. Here, though, think about the work that can be enabled through the application of data and analytics. Think about functional roles and what they need to do, and then you may get ideas about what a software application should do.

Put the three together, and you get data that can be combined with analytic methods to generate value in a particular application, or functional role.

We tend to get stuck in familiar modes of operation, thinking that a certain type of data implies a certain type of analysis, which is useful for a certain application. We fall back on social media + sentiment analysis + marketing. You might even think of it as a chemical reaction: social media + sentiment analysis -> value for marketing.

It's comfortable. It's familiar. It's not wrong. But there's more.

Time to mix it up
To find more value in the data and analytics, we need to start flipping the pages in the book. Which analytic methods could make this source of data useful for that function? I know what I know. What have I not yet found?

You can start with any piece first, and switching the order aids discovery. You might start with a functional role and ask what information would help them. You might start with a data source and think about how it might be useful. Or you could ask how an analytic method might turn data into something meaningful.

The secret is that each category has more options than you're probably using. More sources of data, inside and outside your organization. More analytic methods—some still being invented. More functional roles than the ones you're used to supporting.

Combine them, and you put familar data through unfamiliar analytics. New data through existing analytics. And you find ways to create value beyond the marketing, public relations, and customer service roles we associate with social media.

Do I have specifics? Sure, but not all in one blog post.

The mix-and-match book is similar to the Omniscience framework I proposed, which is all about understanding how intelligence and analytics can be useful at all levels in the organization.

Thinking Aloud in 2012

2013-01-23T00:12:10Z

Around the end of the year—or the beginning—I look at the numbers to see which blog posts people have looked at the most, and it's always the old posts that dominate the list. It's the same for 2012: only one of the top 10 posts in 2012 was something I wrote in 2012. Since the stats favor the old posts, here's a recap of some of the stuff I'd hope you didn't miss.

Three Buckets of Social Media Data
I've tried categorizing social media before, but this one is turning out to be more helpful than my previous attempts. When working around monitoring and analysis, think of social media as three types of data sources: about content, activity, and people. If you haven't considered all three, you have more work to do.
Why Government Monitoring Is Creepy
The meaningful distinction between private and public spaces is changing faster than our sense of privacy, both online and in the real world. The rise in drone activity around the world will make this an increasingly important topic.
What Happens After Your System Notices Something Important?
No matter how much intelligence we try to engineer into our analytics systems, most are still working toward putting data in front of a person. What if the system helped with the next steps?
Can You Trust Social Media Sources?
Finding meaningful insights in social media data is challenging enough, but there's more. Some of the sources you're finding may have been put there by people who intend to deceive you.
The Four Locations of Social Media
Putting social media data on a map is helpful, but remember that location might not mean what you want it to mean.

Previous years' lists
2011: Top 10 Posts, Revisiting 2011
2010: Top 10 posts, Thinking through 2010
2009: Top 10 posts

The Most-Read Posts of 2012

2012-12-31T16:34:25Z

As the year winds down, it's time to see what people found on the blog this year, and once again, the most-read posts are generally older ones. Clearly, search-engine traffic favors older posts, and the visits add up through the year. But look at it this way: these are the posts on topics people searched for this year. Does that say something useful?

What Does Salesforce-Radian6 Deal Mean for Everyone Else? - March 2011 (#3 in 2011)
Applying Social Network Analysis to Social Media - September 2010
Human vs. machine analysis - April 2007 (#5 in 2011)
Visual text analysis - April 2007 (#6 in 2011)
Visualizing networks based on communication - February 2008
Global Social Media Usage Patterns - January 2011 (#4 in 2011)
Professional-Strength Social Media Aggregators - June 2010 (#8 in 2011)
Monitoring Social Media Before You Have a Budget - May 2008 (#2 in 2011)
Why You Can't Measure Influence - January 2012
Five Modes of Listening - September 2009

In keeping with tradition, I'll highlight some of this year's new posts that I think should get more attention in a separate recap.

Previous years' lists
2011: Top 10 Posts, Revisiting 2011
2010: Top 10 posts, Thinking through 2010
2009: Top 10 posts

2012 Acquisitions in Social Media Analysis

2012-12-17T19:22:24Z

I've been tracking acquisitions in social media analysis for years. It feels like we've had a lot of deals this year, and based on what I've seen, it's true. The volume has gone up every year. This year, I thought I'd do something new: I wrote a recap of the activity, which you can find at The Year in M&A, Social Media Analysis 2012.

As with the big directory of over 400 companies, the list of transactions requires some judgment about which deals to include. The companies that offer turnkey platforms for monitoring social media are easy. Others offer some of the building blocks for developers who want to focus on other pieces or enterprises building their own tools. Most run on a software-as-a-service (SaaS) model, though some licenses software for on-site installation. The variation gives the market a fuzzy edge, so it's not obvious what to include.

For SMA, I've chosen to go with a coarse filter, which means that I tend to err on the side of inclusion. If I sometimes reach too far, its because I think there's value in knowing what's happening on the other side of the fence.

Looking back
2012 was about more than acquisitions, of course. The investment deal flow continues, which I plan to recap separately, and I'm still discovering new—and new-to-me—companies fairly regularly. At the other end of the lifecycle, I've noticed an increase in companies shutting down quietly and a few sales of "assets" (as opposed to operating companies).

In 2006, I thought I'd find every company in the world developing tools to work with social media data. By now, I think we've established that it's not possible, but it remains an interesting space to watch.

Update: the 2012 investment recap is now up.

Finding a Social Media Analysis Tool With Specific Capabilities

2012-12-07T16:52:56Z

Quick, name a social media monitoring tool that can monitor Instagram. Got one yet? Not sure? I found four in seconds. Here's how.

I launched Social Media Analysis in 2009 to move industry news coverage from my personal blog to its own site. A little over a year ago, I added a free directory of social media analysis companies, which continues to grow as I discover more companies in the market. In yesterday's webinar on choosing social media monitoring tools, I realized that the news archive is the better tool for finding specific product capabilities.

SMA's directory has its own search feature, which knows a few tricks, such as finding companies based on a search for their old names. But if you're searching for a feature, the directory is only as good as the descriptions that the vendors have written about themselves (in this challenge, a directory search found one result). For something as specific as covering a particular network, it's not likely to be a big help.

If you write long enough, you build a history
The good news is that the main site also has a search feature and nearly four years' archive of industry news. The weekly roundups of product updates are particularly rich in keywords for the search engine to use. A quick search for "Instagram" revealed four monitoring tools that have announced Instagram coverage.

Even industry observers who make a point of keeping up with the tools market can't remember every detail of what 400+ companies are doing. Is SMA on your go-to list of resources for keeping up with the social media tools market?

Vendors, have you checked your company's entry recently? Is it complete and up to date? Does it contain the right keywords for searchers?

What Happens After Your System Notices Something Important?

2012-10-25T20:50:19Z

I sometimes summarize the opportunity of social media analysis as using computers to "read the Internet." It's not an original idea, but it is one we still haven't mastered. I've seen many tools that find relevant content and apply some level of automated analysis, but we're not about to replace the analyst. One simple question I've started to think about is, "then what happens?"

The SocialSpook 9000 reads millions of blog posts, Facebook updates, and tweets every second. It finds every relevant mention in your space, extracting the facts, opinions, and needs that you're looking for. Its sentiment analysis engine provides 120% accuracy in 38 languages, and its graphics are so well designed that whole new awards contests have been created for it to win.

In 2007, I pointed out the need to link social media monitoring to customer service, because most of the problems that people were seeing as PR problems started with unhappy customers. Since 2010, I've been thinking about another application: blending social media data with other publicly available sources to create an automated view of what's happening in the world. It turns out to be a big challenge.

My own private news channel… or command center?
We can take this in several directions. At the low end, applications such as Flipboard generate personalized media based on activity in the user's social media accounts and selected topics or sources. In the middle, we might have a more dynamic version of the social media dashboard running in the conference room or reception area. It's the web-powered news channel that always shows something you might care about.

At the high end, we're looking at a valuable—but noisy and sometimes misleading—source of crowdsourced information about events in near-real time. The obvious applications are in government: national security, law enforcement, emergency management, and disaster response agencies are looking for fast and accurate information from social media sources. I see value in corporate applications, too, for functions like security, risk management, logistics, and business continuity that need information when things happen. Preferably without hiring an army of analysts to look at dashboards on the quiet days.

Now what happens?
The challenge in using social media for real-time awareness is that the volume level becomes overwhelming just when the information becomes most valuable. Forget looking for the needle in a haystack; this is the needle in the needlestack. Faster than you can read them, more messages arrive, and they're all relevant.

Existing tools generally emphasize either handling messages individually (think customer service or community engagement) or analyzing them in aggregate (think sentiment and leading topics). For this application, we want the system to help analysts deal with the volume without losing the detail, and that's where I started asking about what happens next.

For all the systems that can notice something happening and put it on a screen, I wanted a system that can notice and pay attention. So what would that look like?

Here's an idea (click to enlarge):

The inputs to this system can go beyond social media content; depending on your application, it might pick up data about natural disasters, weather, or market data. It might incorporate traditional news media, commercial intelligence services, or internal data. Its models will reflect the needs of its users, so a system that looks for, say, transportation-related incidents could be quite different from one looking for damage reports in weather emergencies.

This has a lot of moving parts, and it builds on what others have already built. The central idea is to go beyond the dashboard and think about how the system can relieve analysts of some of the burden of reading the alert queue. Step one is to consider what an analyst does with that information and how a computer could mimic that.

I'm sharing some of the frameworks that have been hiding on my whiteboard. Want the long version? Email me.

Be Careful with that Email

2012-10-07T19:09:24Z

I'm noticing a big increase in fraudulent emails, and they look more convincing than ever. October is National Cyber Security Awareness Month (via Coretta Jackson), so let's talk about email safety. If you already know this stuff, maybe this is the time to talk to friends and family about it. Parents, this is you, too.

When was the last time you got one of these?

The lottery you won without entering
The relative of an African dictator or member of the US military who wants your help liberating millions in ill-gotten wealth
The stock promoter with a hot tip
The fake speeding ticket from New York
The warning about your PayPal account
The family member who is trapped in a foreign country and needs cash now

Within the last hour? I don't even count how many of these I get every day. But you already know that none of those is what it claims to be.

Fish in a barrel
The most common online scams target the most gullible people. Hey, fraud is a business, and when you're sending out millions of offers, you need to screen your leads well. According to a new study from Microsoft Research (PDF), that explains why so many emails are so obviously fraudulent: they're targeting people who are too gullible to notice the scam.

An email with tales of fabulous amounts of money and West African corruption will strike all but the most gullible as bizarre. It will be recognized and ignored by anyone who has been using the Internet long enough to have seen it several times. It will be figured out by anyone savvy enough to use a search engine and follow up on the auto-complete suggestions… It won’t be pursued by anyone who consults sensible family or fiends, or who reads any of the advice banks and money transfer agencies make available. Those who remain are the scammers ideal targets.

—Cormac Herley, Microsoft Research (emphasis added)

So when you quietly delete that obviously scammy email, you validate the scammer's optimization method. But delete it, anyway.

Going after smarter targets
While the mass-market scammers are going for the easy marks, a different style of criminal is getting more aggressive about smarter targets. They're getting trickier, personalizing attacks on strategically selected targets and masquerading as services you probably use. You won't fall for the secret treasure of Idi Amin, but how about this private message reminder from LinkedIn? The email looks right—or almost right—so you click the link to go to your LinkedIn inbox… and end up installing botnet software on your computer. Ooops.

Your company won't believe it's won the European lottery, so these attackers mimic legitimate business services:

The UPS invoice
The payroll processing notice
The fake complaint notice from the Better Business Bureau
The Corporate eFax message

The image at the top of this post is one of two fakes I got on Friday, sent to separate addresses. It presents as approval for some payment system at Intuit, but by now, you know that Intuit had nothing to do with that message.

What does it do, exactly? I don't know, but nothing good. It probably has something to do with stealing a password or installing malware on my computer. We'll never know. <Delete>

Think before you act
Email-borne attacks are serious business. It's not some bored kid messing with your computer; it's hacktivists, criminal organizations, and even governments. As you're going through the daily slog in the inbox, take a few, simple precautions:

If an offer is too good to be true, it's not true.
If a need is unusually urgent, confirm that it's real before you commit resources.
You don't win contests you haven't entered.
Be careful about links in email, even from companies you trust. Look at the URL the link wants to send you to, before you click on it.
Even better, type in the main URL of the trusted site, and use their navigation to find your inbox, or account, or password reset, or whatever you think needs attention.
Be extra alert about attachments, especially ones you haven't requested.
Don't open compressed (.zip) or executable files from unknown sources.

Finally, if there's any doubt about something you get in email, stop and think before you do anything. Type the main keywords and "scam" into Google, and see if the results tell you something important. Look it up on Snopes, which has been investigating rumors and scams for years. Email can wait for a little due diligence, but it's hard to unfall for the trap once you start clicking on things. You have so much investigative power as close as the nearest web browser, why not use it?

You're good with all this? Haven't been tricked in a long time? Excellent. Go share your wisdom with someone this month. Keep your family and friends from becoming victims.

Whole Lotta Influence Going On

2012-10-05T18:28:26Z

What do you do when you disagree with a conference speaker? Do you tune out, start checking email, check the schedule for when the next session starts? Do you post snarky comments to Twitter and Facebook? Do you challenge the speaker in the Q&A time? What if the topic is well worn, and you're getting tired of hearing the same points you disagree with? Are you tired of the influence arguments yet?

While sitting through a presentation for nonprofits on communicating with online influencers, Heidi Massey got that familiar feeling, so she challenged the speaker, Justin Ware, in the Q&A. They continued the discussion after the session, and it's led to a great pair of point/counterpoint posts on using Klout scores:

Heidi: Klout: Not Even A Good Starting Point
Justin: Klout: It Does Matter and Here’s Why

I like this. Not only do they go to the effort of thinking through their arguments and writing something coherent, they do a service to everyone else by linking to each other's posts. It's a nice idea: post your position, and link with someone with a different opinion. Do you think it will catch on?

Speaking of influence, was there a memo about putting influence startups in Portland? First Tellagence, now Little Bird? If you need to identify the right people for spreading your message, the available tools are multiplying fast.

There should be a rule that if you have an argument on a Friday, you have to cite Monty Python.

The Summer Reading Post

2012-10-04T18:00:34Z

It seems that I'm late posting this year's "what I read this summer" post. That's to be expected, since I'm behind on the reading pile, too. But summer is giving us an encore this week, so here's my chance to share some of the interesting things I've read with you.

Attack of the killer drones
I read mostly nonfiction these days, but I did take a summer diversion with two books by Daniel Suarez, who's making a run for the techno-thriller trophy. The first was Freedom™, which is more part two than sequel to Daemon (recommended in the 2010 list). If you liked Daemon and haven't read Freedom™ yet, you need to find out how the story ends.

Suarez's new book, Kill Decision, takes the same approach of combining current technology with a dose of near-future science fiction, but this time the threat is from autonomous swarms of killer drones. This one's weaker as a novel, but it raises serious issues: Black-hat PR in social media. The use, abuse, and proliferation of armed UAVs, persistent surveillance, and open-source intelligence. The attribution challenge of cyber warfare. By pushing these themes (and others) to an extreme, Suarez creates an opening to think about where the limits are, and where they should be.

If the dawn of the drone age interests you, you can balance the fictional portrayal with Wired for War, P.W. Singer's 2009 book on UAVs and ground-bound robot warriors. You'll wonder what war even is, when one side is far enough from the action to avoid harm. From there, pick up Rachel Maddow's Drift (2012), which—despite the author's well-known leanings—is a generally conservative take on the vanishing checks (in the U.S.) on executive power to make war.

What I like about Suarez is that his novels tie into real-world issues in a way that gets you thinking. These other books prove that the topics Suarez raises are real, even if the specifics include some science fiction.

Random difficulty
How improbable is an enjoyable read in statistics and probability? Leonard Mlodinow's The Drunkard's Walk: How Randomness Rules Our Lives (2008) demonstrates how much of what we interpret as cause and effect may be the result of the expected variation in random processes. Baseball stars and hedge-fund winners look a lot like coin tosses, if you look forward into the future instead of backwards into the hindsight.

The Drunkard's Walk gives the best explanation of Bayesian reasoning I've yet encountered, even while using the cancer-screening example that must be required. You also get a chapter on the Monty Hall problem, which is a bit of a mind-bender even after the light comes on. We're really not used to problems that break the rules of the discipline.

Nassim Nicholas Taleb's first book, Fooled by Randomness (2001), covers much of the same territory—people misinterpret randomness habitually—but it returns too often to the financial markets for its lessons and examples. Taleb's forthcoming Antifragile, on the other hand, looks like a must-read this fall. Systems that actually benefit from chaos and black swans? Time for some solid-surface counterintuitive.

How's that prediction working out?
Flipping the empirical method around, Duncan Watts tears into our habit of backfitting our analysis to fit past events and calling it common sense in Everything is Obvious (Once You Know the Answer) (2011). In so many different ways, the world is too complex to predict—oh, and randomness is a problem that we don't handle well.

The answer is to respect the unknown, build flexibility into our plans, and get better at reacting quickly instead of trying to predict the future. These are themes I keep running into, and they make a lot of sense. Or is that just my confirmation bias speaking?

The queue is winning
I started Rohit Bhargava's Likeonomics: The Unexpected Truth Behind Earning Trust, Influencing Behavior, and Inspiring Action (2012) this summer, and while I got surprisingly useful ideas from the first couple of chapters, events got in the way. I'll get back to you when I finish that one.

This post has become a bit of a tradition. If you like this, you might enjoy these posts from previous years, too: 2011, 2010

Social Media Monitoring: Bespoke, Tailored, or Off the Rack?

2012-09-26T19:23:51Z

So much of the public discussion of social media monitoring and analysis focus on commercially available platforms that are more or less ready to go to work. Somebody has to set up the queries, and there may be some dashboard configuration, but the tools are generally pay-and-go propositions. A recent article points out that some companies are going for something much more customized to their needs.

In its big list issue on companies doing IT well, Information Week mentioned Toyota's new social media and CRM tool:

The tool took 60 hours to develop, largely using software Toyota already had. Oracle Endeca Discovery handles data discovery and search analytics, WiseWindow and DataSift aggregate social data, and Lexalytics analyzes sentiment.

Toyota is using the tool to improve customer service, product forecasting and quality, and lead generation. It plans to feed information to dealers in the future.

If the usual model is off the rack—software-as-a-service from a single vendor, up and running with minimal configuration in minutes, hours, or days—this is custom tailored software, using the available APIs to combine the strengths of multiple products and vendors. With so many companies offering the building blocks of social media analysis, a mind-boggling near-infinity of combinations could be assembled to do—well, what would you want to do?

The opposite of off the rack is bespoke, which in this context means completely custom software development. Even using some of the open-source components that everyone uses as important building blocks, it's a lot of work. Unless you're in the software business (or might enter it), I'm not sure why you would bother. It's hard to think of even narrowly specialized applications for social media analysis that don't have someone trying to address the market.

Custom is expensive, which is why the volume market goes for off-the-shelf products. But given a hint of what one company is doing to tailor a system to fit their needs, I want to know more. Do you know other examples that people can talk about?

Photo by Douglas LeMoine.

Using Live Data as Eye Candy

2012-08-24T20:11:38Z

At some point, every user of data fantasizes about an over-the-top command center (it's not just me, right?). The emergence of the social media command center concept is creating an excuse to indulge that desire for a NORAD/NASA/DOT mission control, replete with a constellation of flat screens and constantly updating charts. If you're thinking of jumping in, you'll want to read Jeremiah's lengthy post on the topic. But what if your needs—and budget—are more modest? What if you're looking for one very nice overview for a public place?

If you go out into the world as a customer, it's hard to avoid televisions in public places. The trendy business equivalent is the live dashboard that shows how things are going, from web traffic to sales to the stock price to online chatter. We're past the days of a single-column TweetDeck in a conference session; these offer tweets, pictures, metrics and more. If you want a live picture for the reception desk, team area, conference room, or trade show booth, it's now easy to put together something worth looking at.

Here are a few I find interesting:

Liveboard (Twingly)

Liveboard is all about the tweets, combining live-updating metics with sample tweets. The top-level metrics (total tweets, unique users, retweets, etc.) are animated with an analog odometer effect that serve as a sort of pulse for the display. Its charts list the top tweeters and hashtags associated with the topic, and visualizations depict volume by day and hour.

See the live demo, and be sure to click on the screen and move it around; there's another visual off the right side of the screen (or make it fit your screen by reducing the height of the window).
Multitude (JamiQ)

Multitude is a moving timeline of a Twitter search, illustrated with the images people attach to their tweets. JamiQ describes it as a wall, which would be a good use for it. The design is simple, clean, and not interactive, so it makes a reasonable backdrop or lobby display. The updates can move quickly, so it benefits from being shown on a wide screen.

See the live demo.
Tickr

Tickr combines the summary on the wall with the combined-source analytics dashboard, creating a live-updating view that can be tailored to different purposes. Load it up with sources of performance data—business, operations, or technical—and it's a constant reminder of how things are going. Point it at social media sources, and it's another candidate for the trade show display.

The company's "try it" page includes links to multiple live examples using social media data. The site also has case studies that show the use of other data sources.

Buy, adapt, or build your own?
The wall-mounted dashboard plays a different role than the analyst's interactive view. Once configured, it's meant to run without user interaction, and a clean, no-controls interface design makes it look more like TV than computer software. As always, it pays to start with some thought to what you want to accomplish with the display (beyond scratching that desire to show off your data). Even eye candy should have a purpose.

Realistically, many dashboards might be configured for this kind of use. If you can configure the widgets on the screen, and if they update without user action, you have the raw ingredients for this kind of application. If you're using a social media analysis platform, you might be able to set up a live view of people talking about your company or event. The newer dashboards that combine social media data with other sources could be set up for this. Leftronic, for example, seems to specialize in big-screen, non-interactive display applications.

Is this something you're doing? Have you seen an unusual use of this type of display? Where do you want to see live data?

And, as always, who have I missed?

Update: If all you need is a tweetstream, HootSuite's new HootFeed looks nice.

Working with Social Media Data: People and Groups

2012-07-30T14:42:32Z

From the first time I described the three buckets of social media data, I knew that one category was different. Content and activity analysis are built on the lessons from established schools of measurement, and while we argue about the specifics, the objectives aren't so alien. The last category—people data—seems more exotic, and it's the least discussed area of measurement. What do we do with data about people, then?

What are people data?
Social media data provide information about both individuals and groups of people: who they are, who they know, what they care about, what they have to say, where they go… Have you noticed just how much information people are sharing about themselves, both intentionally and unintentionally? Collect it from various sources, and you're looking at people data.

As I mentioned in the introduction post, the boundaries between categories aren't absolute, so you could look at much of the data that does into an analysis of people as either content or activity data. The difference comes about when we start thinking about the people as individuals or as identified groups—the focus is on the people, which is why it's useful to look at the data differently.

Analyzing data about individuals
When using the data to consider an individual, you have several basic options on how to approach the analysis. Remember to think and, not or; there's no value in deciding which approach is the right one until you have a specific objective.

Profiling
Compile a detailed personal profile from multiple sources, merging multiple social account profiles with customer data and content analysis of the person's online activity. The resulting information could provide context to customer service agents or sales reps as they interact with the person.
Scoring
Apply a model to rate someone's influence, authority, or relevance, which might help you prioritize efforts in blogger outreach. You might also view someone as a customer, scoring credit, lead strength, customer value, or loyalty.
Predicting
Activity data linked to an individual might be useful for predicting future behavior. How good is your crystal ball?

Working with data about individuals always runs the risk of turning creepy. I'll get into the balance between privacy and the value of data another time, but be sensitive to the risks as you decide how to use information about individuals.

Analyzing data about groups
Zoom out from the individual view to think about the what the data can tell us about groups of people. First, we might identify different types of groups, and then we can develop profiles that communicate why we're interested in particular groups.

Identifying
Groups come in various forms, both formal and informal. The easiest to profile are organizations with formal membership (which includes employers). More casual groups might form through social network sites, discussion forums, or meetup groups. Finally, we have the extended networks of indirect connections, some of which are conveniently entered into online social networks.
We might also find value in virtual communities implied by some characteristic, from interest in a common topic to locations, both real and virtual. How information travels in such a community could be useful to understand.

I've had some interesting conversations on the subject of social network analysis, and how its use in social media isn't necessarily in sync with the science on social networks (in the original, not online, sense). If you understanding that you're mapping something other than social relationships, though, I think there's underdeveloped value in applying network analysis to more data points.
Profiling
Profiling a group is less likely to turn creepy than individual profiling, but there's still a right way to do it. First, describe how the group was identified; for some uses, that may be all the information you need—if you're developing a targeted marketing promotion, for example. Going deeper, think about what the group is interested in and where they go (online and in the real world). Who are their leaders—and what is leadership within the group? What's important to them, and what's their history?

Before you interact with a group, make an effort to understand their norms. The unwritten rules vary by community, and what works in one setting can be precisely wrong in another. As you work to understand and interact with groups, you're dabbling in anthropology, so you might consider its methods.

Our society is producing an astounding amount of data about people, both as individuals and in groups. It's easy to cross the line into overly intrusive use of the data, but it's hard to find a common definition of where that line is. That's a topic I plan to explore in depth in the coming months.

Photo by James Cridland.

Working with Social Media Data: Activity

2012-07-27T05:10:34Z

Monitoring social media. Measuring social media. Social media analytics. All of these treat social media as data, but social media generate at least three types of data: content, activity, and people. In the last post, I wrote about content data, which is the starting point for listening. This time, let's talk about activity. What are people doing that we can analyze?

What is activity data?
Activity data is just what it sounds like: data about the behaviors of people as they use social media. When we're tweeting, pinning, tagging, posting, commenting, sharing, and liking, the systems we watch are watching us back. It's like web analytics, except that social media support many more activities than most web pages, and the activity takes place on social media sites instead of companies' own web sites.

Analyzing activity data
If you're used to measurement conversations with an unstated assumption that you're talking about content data, you probably talk a lot about sentiment and topics. If you listen to web analytics folks talk about social media for a few minutes, you hear about entirely different metrics: friends, followers, fans, likes, shares, retweets, and more.

Compared to content data, activity data presents a set of harder metrics, meaning there's not much doubt about the actual numbers. They're based on observing the use of features built into the software, rather than an interpretation of someone's writing. There's little ambiguity in clicking on a Like button, for example. It's either been clicked or not. The real question is what that means.

An embarrassment of metrics
The challenge in using activity data is less about the underlying technologies and more about tying them to business objectives. We have a lot of available metrics to choose from, and to complicate things, similar-sounding metrics from different social media sites can't always be compared. Always start with the most important question ("what are you trying to accomplish?"), and be sure you understand what the metrics really represent.

With activity data, the web analytics folks have an advantage, because their existing metrics tend to be closely tied to business performance. They already measure how well their web properties generate interest, leads, and sales. It's not too much of a stretch to extend the marketing funnel to include social media properties, too.

Besides its effectiveness in leading customers directly to the e-commerce store, you might measure social media activity as evidence of customer or community connections (engagement), or think of users as an audience for your messages (reach). Some metrics may have value with minimal interpretation, such as product ratings scores. Any tactic you employ that is designed to lead to an action has the potential to be evaluated with activity data, so—again—what are you trying to do?

Lines that go up and to the right make for successful presentations, if you understand what the line represents and how it relates to the business. Activity data can give you those charts; all you have to do is pick the right metrics. And as you're considering metrics, remember the three types of social media data.

Next: Working with Social Media Data: People and Groups

Screen capture by Darren Krape.

Working with Social Media Data: Content

2012-06-08T15:28:09Z

Before you can analyze, you need data. In thinking of what you can do with social media data, I find it helpful to think about three buckets of social media data: content, activity, and people data. Let's talk about content. If you look at social media from one angle, that's what it is: lots of content. What do you do with that?

What is Content Data?
When we talk about listening and how people express their opinions, we're talking about working with content data. From the text of tweets, blog posts, and product reviews to pictures, videos, and audio recordings, content is everything that people are posting and sharing online. When people ask about sentiment, opinion, and complaints, they're asking about content.

Analyzing Content Data
Remember consumer-generated media? That was the mindset in 2006 when I started looking for companies that worked with social media data. People were empowered by these new, "Web 2.0" technologies to share their thoughts and opinions with a global audience. The companies they talked about suddenly needed to pay attention, and the existing paradigm with the closest fit was media analysis. So, much was borrowed.

The media analysis world was about understanding media coverage, when media meant professional writers and paid publications. You could count things: how many articles mentioned you, how many times were you mentioned within articles, and how did that compare with the competition. You could rate mentions as favorable or not, and you could see if your messages were picked up by journalists. There's more to it, but you get the idea.

It turns out that a lot of established media analysis techniques work for consumer-generated media, too. The challenge is that the new media sources generate a lot more content, so you need to sample the data or automate the process to keep up.

The other paradigms that usually enter discussions of content data are opinion research and the customer service queue. You can hardly turn around without running into these, "the world's largest focus group" and the new channel where customers expect a response.

Turning Content Into Usable Data
The promise of all this content is that people are sharing their thoughts with anyone who pays attention. The challenge is in turning the data into something that can be analyzed. That's where we get into coding the data—scoring it for sentiment, identifying the topics and entities (such as people or companies) discussed, rating the opinions and emotions expressed. It's hard work, especially when you consider the need to work with foreign languages.

In the case of text—posts, tweets, and the like—turning raw text into usable data is the job of text analytics. Whether they use statistical approaches that compare new texts to previously scored texts, or they parse the grammar to "read" the content, text analytics systems take text in and give coded, structured data out. From there, the processing gets easier.

All content is not text, but more of it could be. Back in the professional media world, you might be able to get transcripts or closed-caption data to augment video content. Beyond that (and even deeper into the research lab than text analytics), you can find systems that extract speech from audio and video, converting it to text for further analysis. Finally, most content sources include hidden metadata, such as topic tags and author information, that adds context and clues for analysis.

There's a lot to content analysis, which is why it's a growing specialty. I've spent a lot of time blogging about it here over the years, too. But if we step back and look at the big picture, it's only one of three types of social media data.

Next: Working with Social Media Data: Activity

Photo by Michael Sauers.