The Business Technology blog over at WSJ reports on a recent study of more than 100 corporate social networks. Ed Moran, a Deloitte consultant, found that:

Thirty-five percent of the online communities studied have less than 100 members; less than 25% have more than 1,000 members – despite the fact that close to 60% of these businesses have spent over $1 million on their community projects.

Moran’s conclusion is that companies get seduced by the technologies involved without understanding the terrain. These sites fail, he believes, because companies don’t invest enough money or manpower in supporting them, and because the things the companies measure don’t really align with their professed business goals.

The title of the article - “Why Most Online Communities Fail” - is misleading, since Moran is talking specifically about corporate social networks, and the very premise of these sites is flawed if you ask me. I haven’t seen the list of companies he looked at, but I would guess that most of them actually have thriving online “communities” whose activities just happen to be distributed across the Internet. People are twittering. They’re posting about those 100 companies on their blogs and MySpace pages.

I understand the urge that companies have to contain this activity, but it’s a pipe dream. You can build the snazziest playground in the world, and most of your community still won’t show up. If you want to connect with them, you have to do it on their turf. If you want to quantify their effect on your brand perception or your sales numbers, you have to find tools that can do that.

That’s what we’re aiming to provide of course, and that’s why I believe in this product. Companies are willing to spend millions on the fantasy that they can bring their communities to them because they don’t have very good ways of tuning in to the communities that are already out there.

But that’s changing.

In this week’s installment of his ‘Circuits’ column, David Pogue asks, “Are you taking advantage of Web 2.0?” By ‘you’ he means your company, and he describes the response this question got from the attendees at a recent PR conference:

“…within seconds, there were 132 responses on the screen in a huge, scrolling list. ‘Not enough money.’ ‘Don’t understand it.’ ‘No technical resources.’ ‘Not enough manpower.’ ‘No visible return on investment.’ ‘Fear of ridicule.’ ‘Fear of slander.’ ‘Fear of permanence.’ ‘Fear of the public running amok.’”

There are lots of common fears in there, and they’re all reasonable at first glance. Companies are understandably afraid of opening themselves up to ridicule and slander from a public running amok, knowing that all the messy results will live forever, just a Google search away. And they’ve seen some embarrassing failures from companies who’ve tried to embrace the new paradigm - like the Chevy Tahoe debacle, and Wal-Mart’s fake blog (or flog) scandal, to name just two incidents. So the safest bet is to simply stay away from all things Web 2.0.

The problem with this approach, obviously, is that the public is already running amok. That’s what the public does. If they want to slander you, they have YouTube and MySpace and a million other places to do it. Sticking your head in the sand doesn’t make all this stuff go away. It just makes your company look silly - or worse, aloof, uncaring and behind the times - and ultimately more vulnerable to whatever mud they might be slinging.

So if it’s unwise - or unrealistic - to stay out of the fray, then what’s the best strategy for jumping in? The other questions from the PR conference attendees fall into this category. More and more companies have recognized the need to participate, but they don’t know where to focus or how much to invest.

There are lots of success stories. Big companies like Dell and Mariott have generated good will and good press through their forays into Web 2.0, and this has surely translated into dollars. But it still comes down to the question of ROI. If one of the ultimate goals of embracing Web 2.0 is to engender good will, then how do you quantify it? How do you measure success?

Does anyone out there have a story that starts to quantify the actual value of good will?

The February issue of FastCompany magazine includes an article provocatively-titled, Is the Tipping Point Toast? about the work Duncan Watts has done researching influence. The article doesn’t exactly torpedo Gladwell’s hypotheses, as the title suggests, but it does argue that influence is a much more random phenomenon than Gladwell and a string of high-profile marketing gurus - not to mention our own intuition - would have us believe:

[Watts] has written computer models of rumor spreading and found that your average slob is just as likely as a well-connected person to start a huge new trend. And last year, Watts demonstrated that even the breakout success of a hot new pop band might be nearly random. Any attempt to engineer success through Influentials, he argues, is almost certainly doomed to failure.

Strong words, and not ones that marketing folks want to hear. But let’s back up and look at the two schools of thought at odds in this debate.

The Gladwell school (previously put forward by Ed Keller and Jon Berry in their book, The Influentials) holds that a relatively small number of elite and well-connected tastemakers is responsible for igniting the first small flames of buying or behaving that eventually spread like wildfire to become mainstream trends. Marketers like this model partly because it makes sense intuitively. We can all think of people in our lives who are consistently ahead of the curve with things, or whom we depend on as consistently reliable sources of information. It’s nice to think that if you can, as a marketer, put your message or product in the hands of these elite few, then they will do the rest of the work for you.

Watts, however, isn’t buying it. His research - a variety of computer models as well as social experiments using real people - doesn’t support the existence of this special class of powerful people. As far as he can tell, a trend can start anywhere and with anyone, as long as the marketplace is primed for it. This is borne out in a well-known experiment he conducted by building two identical online music communities where users could rate unknown songs from unknown artists. In one community, the users couldn’t see anyone else’s rankings. In the other, people could see how everyone else rated each song. He wanted to see whether word of mouth would affect the rankings in this second community, and whether any of the participants would emerge as the tastemakers.

In the first community, people rated the songs fairly evenly. But in the second community, as one would expect, favorite songs did emerge, as word of mouth took hold. Even more interestingly, in eight repeats of the experiment, different songs emerged as the favorites each time. For the most part, it wasn’t even close. The #1 song in one community, for example, was ranked #40 out of 48 in another. And there was no evidence to suggest that any participant in any community was significantly more influential than anyone else.

Watts’ experiment confirmed that word of mouth is powerful but, to the chagrin of marketers, it also seemed to show that it’s completely unpredictable.

So is the Tipping Point toast, like the article says? The most likely answer of course is no, and that both arguments are correct. There certainly are people who are influential by virtue of a large audience or expertise with regard to a particular subject. On the other hand, there are certainly many trends that started with seemingly random people.

Watts’ solution is to forget about trying to identify or engage with any supposed influencers and to focus instead on the masses. To this end he has developed a form of advertising with built-in sharing (and tracking) mechanisms designed to facilitate their spread.

Perhaps he’s onto something, but I think that developing a good mechanism for sharing is much less important than developing a good message that people will want to share. The “why” is more important than the “how.”

The currency, so to speak, of influence is the message. There is a science to crafting a good message, or meme. I like the formula offered by Chip and Dan Heath in their recent book, Made to Stick, which states that a good message is:

• Simple
• Unexpected
• Concrete
• Credible
• Emotional, and
• a Story

If marketers follow this formula, the chances that their messages will go “viral” are much greater, whether influencers are specific and identifiable elites or just random folks on the street.

The last piece of the puzzle is the marketplace, and this is something we’re trying hard to make more predictable too. Or, if not predictable, then transparent. Understanding what makes an effective meme is key to spotting them as they develop, but it’s still very difficult without reliable visibility into the marketplace. We’re aiming to provide this with some of the tools we’re developing, because this is at least as essential to the influence problem as attempting to identify some elusive special people at the top of the chain.

We always enjoy a good data visualization, especially when it’s elucidating what we are doing here at Scout Labs! On Wired Magazine this weekend was a infographic of what happens after you hit “Publish” on your blog page. It’s called “The Life Cycle of a Blog Post, From Servers to Spiders to Suits - to You”. If you can figure out how to click and hold your mouse down to zoom it and scan around, you’ll see a category called “Data Miners” and I guess that’s partly us — the ones who analyze the blogosphere (and social networks and image-sharing sites and video sharing sites and user reviews) to make sense of it for clients overwhelmed by the sheer volume of it all. But we are also the “Corporations” (yes, “the Suits”), because real people at real companies are using our service to Scout what people love, hate, want, think and feel about their products, brands and services. What we are NOT: an ad network or aggregator trying to sell ads. We figure there are plenty of those out there desperately trying to get ads in front of eyeballs. Inspiring people to build better products and to build stronger relationships with customers sounds much more fun to us.

First came the Nature vs. Nurture debate: what makes us who we are? Is it nature — what our parents, at the moment of conception, brought to the genealogical table (or, bed, for those of us with more traditional parents)? Or is it where we lived and how those parents treated us in our early years and beyond? Then came the Taste Great, Less Filling conundrum. Today, at least in the web universe, rages the human versus machine controversy. At its core, the questions is: what returns the best, most relevant results (search results, relevant news, etc.)? Machine-generated algorithms or human “editors”? Google, with its mysterious search algorithms and millions of servers, is the poster-child for the “Machine” camp (although the reality is that their algorithms rely on human editors). In the “Wisdom of the Crowd” camp are the Web 2.0 likes of Digg, Wikipedia and del.icio.us. Like in the early days of the Nature vs. Nurture debate, (or Taste Great, Less Filling, for that matter) people are polarized over the issue — as if it’s one or the other. John Battelle sparked another round of debate on the matter just last week. Of course, the right answer is “both”.

Machines can do things that humans just can’t do very efficiently. They can process huge amounts of data very quickly (like the massive amounts of consumer-generated media that exist) . And machines don’t need to sleep or take latte breaks, so they can monitor things around the clock, in real-time. Of course, the categorizations and the kinds of processing that machines can do are very “gross” — they can count things, extract links, and look for patterns and run analyses that humans devise (like, say, an Influence algorithm or a Significance algorithm or patterns in language to determine sentiment). Machines can also count, measure and incorporate HUMAN (user) actions and behaviors — both explicit and implicit — along with the technical data, and that’s where the lines begin to blur and where things get interesting. So, we desperately need smart machines, directed by far-smarter humans, for complex things things like search or monitoring the voice of the customer out across the Internet.

But people, especially teams of like-minded people with a common purpose, can do what no machine could ever do — draw conclusions, add insight and strategize. Humans can add the “so what”. A founding belief of Scout Labs is “Let technology do what it does best, and people do what we do best. Together, we’re a pretty good team.” We have architected the service to offer the best of both worlds, working together seamlessly. Of course all this is a means to an end. Our users just want to know what stuff she needs to pay attention to right now and to collaborate with her team to do something about it. We are excited that very soon, everyone will get a chance to use Scout Labs and see Man and Machine working together in peace and harmony. Taste Great vs. Less Filling will still be there to fight about.

The interesting thing about data is not the “what.” It’s the “so what?”

As a case in point, this graph shows US government spending on document shredding contracts, which amounted to $452,807 in 2000 and ballooned to $2.9 million in 2006.

Now, one could suppose that the cost of shredding has skyrocketed. Maybe the shredding workers unionized for higher salaries and company cars. From the data alone, you couldn’t rule this out.

Of course we know enough about the current political climate to understand what’s really behind the data.

For more, visit usapending.gov, the government’s brand new and utterly fascinating database of federal spending.

Nielsen, the television ratings monopolist, finally made its way to my mom. She called me, THRILLED that she had been selected to be a Nielsen family! When I think Nielsen, i think set-top boxes that measure your every channel surf, so I asked if they were going to help her with the installation of the box. She said that she was supposed to just write down what she’s watching every minute of the day. WHAT? I assumed she was embroiled in some mail-in phishing operation, but when I got there and looked at her paperwork — it was legit. Nielsen sent her $15 and a little paper diary so that she could record what she watched, exactly when she changed the channel, when she channel-surfed, where she ended up and for how long, etc. I simply could not believe that that is what was behind the billion-dollar advertising decisions that marketers make.

I’ve since looked into it, and it’s true. Nielsen uses set-top boxes for national prime-time TV ratings, but for the thousands of local markets across the country, TV and radio, these hand-scored diaries have been used for nearly 20 years. (Some images on people’s Diary Packs on Flickr, in case, like me, you find this hard to believe). Recently, Nielsen has tried to move to a more automated collection methodology for local markets as well, but the ratings were SO drastically different from the hand-scored methodology of the past (no surprise, I’d say), that Nielsen has taken major flack from both advertising networks and audience groups (such as minorities) whose favorite shows’ ratings dropped drastically in the conversion. A good overview of the saga is in Wired magazine.

I asked my mom a week later how she did on her little assignment. Of course, she said she kept forgetting to write things down, so she had to back-fill — trying to remember at the end of the week the things that she had watched and when.

So, Nielsen has a few issues.

1. The accuracy of the data issue — my mom is trying to watch TV while she mosaics her whole house and puts (the golf kind) in her living room — she doesn’t have time to journal. Not even for $15.
2. The representative sample issue: is my mom — who is the only hard-core Republican in Santa Cruz county — truly a representative sample of the pot-smoking college town by the sea? Well, now that she’s a Nielsen family, maybe she does finally get her voice heard.

We at Scout Labs certainly believe in more listening to customer feedback that is already naturally occurring, and less contrived research scenarios and abstract and laborious user tasks. So good job, Nielsen, pushing to automate — better later than never!

Initial findings from a new study presented at the Society for New Communications Research (SNCR) symposium over the weekend validate what we already know about the importance of social media to businesses: It’s important…

Fifty-seven percent of respondents said that social media tools are becoming more valuable to their activities as more customers and influencers use them. Twenty-seven percent reported that social media is a core element of their communications strategy.

But the study, funded by the Institute for Public Relations and Wieck media, also sheds some light on some of the ways and reasons companies are adopting strategies to address social media. The respondents talked about proactive and reactive strategies, and the findings suggest some clear priorities for both.

Respondents reported that the most effective tools for their social media initiatives are currently:

• Blogs
• Online video
• Social networks

Surprising to the researchers was the fact that criteria that measured online engagement for blogs and podcasts were among the least important to the respondents.

However, for online communities and social networks, the top three criteria for evaluating influence do reflect the importance of online engagement:

• Participation level
• Frequency of posting by the community member
• Name recognition of the individual

Furthermore, 51% of those surveyed are formally measuring the effects of their social media initiatives, with a particular interest in how successfully they are engaging with key audiences. They want to understand and, of course, enhance their brand’s reputation (and product awareness, etc.) with those audiences. They want to know how well their own forays into blogging and social video are faring, and they want to know who is writing or commenting about them, how much they are writing and what they’re saying. It’s also interesting to note that near the bottom of the list was traditional media coverage.

This is why it was important for us from the very beginning that our application focus not only on finding and measuring consumer generated content, but also enable companies to engage with the consumers who are generating it.

Yesterday’s word of the day, apparently, was “influence.” At least that’s what was on the mind of a few big bloggers. Steve Rubel started the conversation by declaring dead the notion that link count equals influence. He argues that counting inbound links is irrelevant outside the blogosphere - in Facebook or Twitter for example - where many conversations go down. His bottom line is that using inbound links as the barometer of influence misses too much.

I disagree with Rubel’s suggestion that link count is or has ever been synonymous with influence. I’ve always seen it as just part of the formula. Maybe a disproportionately large part, but only because there’s so little else that can be quantified. Traffic data can tell you the reach of a source - which is another mark of influence - but publicly-available traffic data is pretty unreliable. Right now, Rubel is right to point out that the formula for measuring influence is oversimplified, but I don’t think it’s due to a lack of sophistication on the part of those who seek to do so. The problem is a lack of measurable data, and as that improves over time, so will influence metrics.

A companion piece by Rubel’s colleague, David Brain, lays out the beginnings of a more nuanced formula for measuring influence that starts with the top 30 bloggers (using rankings published by various sources) and attempts to factor in a variety of social networking activities carried out by said bloggers, as follows:

• Blog - analysed Google Rank, inbound links, subscribers, alexa rank, content focus, frequency of updates, number of comments
• Multi-format - analysed Facebook - number of friends
• Mini-updates - analysed Twitter - number of friends, followers and updates
• Business cards - analysed LinkedIn - number of contacts
• Visual - analysed Flickr - number of photos uploaded from the person/s or about the person/s
• Favourites - analysed Digg, del.icio.us

This implicitly accounts for some notion of reach, along with some rather ad-hoc and unscientific social media metrics, but since it’s limited to just 30 blogs, it fails to address anything but the most general questions around influence and influencers. Many of the top blogs are technology or marketing focused, so while they might be highly influential to a few key audiences, they are totally irrelevant to, say, urban teens or middle-class parents of toddlers.

Rubel and David Brain also touch on the “Facebook phenomenon,” and there’s no question that Facebook, MySpace, Twitter and the other buzzing social services deserve a place in any discussion about influence, but what place? Because these services enjoy such a high profile right now, it’s tempting to overestimate or misunderstand how they factor in. Facebook user profiles are not accessible to the general public or even to other Facebook members who are not connected as friends - unlike the top bloggers, who enjoy large reader audiences and a lot of visitor traffic. So the only way influence can exist on Facebook is virally - with memes spreading gradually across overlapping circles of friends, through direct contact between individuals. Basically like the real world, and probably just as difficult to predict, detect and measure.

Jeff Jarvis chimes in and pokes fun at Rubel, calling him a sort of “grim reaper of measurements” who “likes declaring things dead.” On the subject of influence, Jarvis discusses some of the complexity involved in quantifying it. He points out that there are different and dynamic spheres of influence that have to do with things like an individual person’s reputation, subject matter expertise and credentials, nature and reach of their forum (e.g. traditional media vs. blog), and nature of the audience.

This brings us to a more fundamental problem that underlies the challenge of measuring influence. Namely, that there’s no consensus on what “influence” actually means. What do we want to measure? What exactly are these mysterious influencers influencing? Brand perception? Purchase decisions? And whom do they influence? Furthermore, a lot of the work on this problem is focused on identifying influential sources - people or publications that tend to influence other people. But a single well-written product or movie review, or one disastrous customer service call can be hugely influential, regardless of the author’s credentials.

It’s clear that no one has influence figured out, which makes it an endlessly fascinating problem area. We’ll keep tinkering with our own formula, and as we’re able to factor in more data, our influence metrics will get better and better.

The notion that consumer-generated content lacks authority (journalistic, academic, scientific) is not a new one of course. Wikipedia has been in the middle of this storm for a while now, and then there was this article in the New York Times about the proportion of web content ranked highly on social search sites like Digg and Reddit that is generated by teens.

This morning I found this collection of bad user-generated Amazon.com reviews of great books (that is, books commonly understood by scholars to belong in the canon of great literature).

Do the traditional definitions of authority apply to what Scout Labs is doing? And what is authority in the context of buzz and influence?