But from a marketing standpoint — and we’re in the business of providing consumer intelligence — his point is academic or even wrong. With media channels fragmenting at a staggering pace, the Holy Grail of marketing is the unifying insight. We seek a truth or message that can transcend media, that we can plug into an integrated campaign, from a Facebook fan page to a billboard on the highway.

I chose this topic for our first ARF webinar because I see too many marketers looking to social media  to inform only their social media efforts. Insights from social can drive every aspect of marketing: campaign strategy to product innovation.

To get the whole story, view the webinar here:

Part 1:
http://www.youtube.com/watch?v=s9oKoBE1veY

Part 2:
http://www.youtube.com/watch?v=lJ_8G9w8pkY

Part 3:
http://www.youtube.com/watch?v=gTgav1bmDhc

Part 4:
http://www.youtube.com/watch?v=Y80XV3ZxJHI

ARF members can view it on their site, but we’re pleased to make this freely available to anyone who’s interested. The full description is at bottom of this post. But first, a word on why we recently joined ARF, who co-presented the webinar…

The Advertising Research Foundation is a pillar of the research community. They’ve helped move the industry forward and set standards for the last 74 years. The field of social media analytics is coming of age, and we’re pleased to have a seat at the table to move the industry forward and guide its development alongside the other members of ARF.

The Medium Is NOT the Message: Social Media Analytics Deliver Insights Beyond Social Monitoring
Presented by: Dan Neely, Founder and CEO of Networked Insights

This informative webinar will describe the current state of social media analytics and provide case studies to illustrate real-world applications of analytics for market research. Learn:

• How analytics can deliver insights to provide core marketing research and drive strategy.
• The difference between listening (strategic and proactive) and monitoring (tactical and reactive).
• How analytics contrast with and complement traditional market research.
• How analytics can inform essential marketing use cases, such as campaign strategy, campaign evaluation, advising digital ad spend, and driving innovation.
• How the world has changed since Marshall McLuhan’s proclamation that “the medium is the message.”

If you’re just listening to consumers in social media online to drive social media tactics, you’re missing out on the real value of social media data: the largest focus group that’s ever existed. (And if you’re not yet listening online, learn why you should be.)

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We were an ad:tech sponsor. And I presented as part of the Marketing Masters series. We expected about 200—and got 500—at a session  expertly facilitated by Susan Bratton.

It seemed there were a lot of ad network folks at the conference and fewer vendors of, say, online video. But when it comes to figuring out what over 11K people are talking about, it’s best to bring in the listening platform. Our analytics identified these key themes, included here with commentary and sample quotes, ranked in order of prominence:

Parties
The ad:tech crowd is social in every way…

“The entire EWA crew is going there and sponsoring a big party on 4/20, hopefully we can meet some of you guys there. This is my personal favorite conference – you can actually breath at this one and meet people on a more personal level.” (From Warrior Forum.)

Mobile and social media
The increasing proliferation of mobile devices creates a boundless opportunity for marketers. Social media and mobile will develop together. For instance, social dating sites combined with mobile technology make it easier to connect when out and about.

“Driving this growth in usage is mobile access. I know you’re all thinking ‘here’s another mobile blog post’. But with the launch rate and uptake of new mobile devices, the web is an anytime, anywhere proposition that’s no longer about sitting in the corner on your PC.” (From Speaking Sensis.)

iPad: You have to go pretty far off the beaten path to escape this topic…
The iPad is creating plenty of buzz and technolust drooling even in the middle of the country, where we live. But Apple couldn’t find a more on-target demographic than the ad:tech crowd in San Francisco.

“…that glorious iPad…. I am going to pack loads of business cards and drop them in as many fishbowls as possible. That iPad is mine, baby.” (From Two Men and the Internets.)

(Note: We know this hopeful attendee didn’t win the iPad that Acxiom gave away on day 2, because our director of marketing did.)

Affiliate marketing
Affiliate marketers need to change with the technology. Mobile creates a new opportunity for affiliates. Their role hasn’t gelled yet, but those who can help serve up the right offer to the right person in the right place will get a piece of the pie.

“I’ve long wondered when affiliate marketing would enter the mobile space, and now seems to be the time.” (From Luffemann.)

Travel issues
Sometimes the real world interferes with the virtual one. The volcano gets the last word.

“There is no excuse for missing an Ad:Tech meeting quite like a volcano blew up so i have to stay home.” (From Wicked Fire.)

Activities that are more strategic and proactive are best informed by an analytical listening platform like Networked Insights’ SocialSense. We cover the orange section of the chart. Those activities in the gray section are typically associated with monitoring solutions. Sure, there’s some value in the gray area. But with all the interest in quantifying and evaluating ROI from social media, the orange activities are worth noting for their high ROI value.

Being smart and informed early in the process (regarding Market Research, for example) pays greater dividends than being smart later (Crisis Management, for example).

Still, a marketer’s first thought about using social-media data is often found at the bottom left of the chart around PR activities. I hope this chart encourages marketers to think more broadly. As you begin to track patterns over mentions, moving rightward and upward across the landscape of the Analytics chart, you’ll reap greater rewards and realize significant, measurable ROI from your social-media listening efforts.

Where do you fit on the continuum? Are you operating in the orange area? I’d love to hear your feedback. And please share this chart, either by linking here or passing it along.

Five themes top the list. And there’s  a thread running through these topics: Foursquare and Gowalla. They create a lot of buzz across multiple categories. Were you there? Did the buzz in the clubs and the auditoriums differ from the forums and blogs? Let us know. Here’s the Networked Insights Buzz Report from SXSW 2010. The patterns and themes are outlined below and we’ve added a few mentions for some color commentary.

Foursquare vs. Gowalla
You could say they’re territorial: it’s a location-based battle for supremacy.  Foursquare appears to be the “must have app,” despite issues early on. Many media outlets have reported that the conversations around Foursquare this year rival those of 2008 when Twitter began its journey on the relatively short road to social-media dominance.

“I’d love to see the source for the claim that Gowalla and Foursquare are neck-and-neck at SXSW. I’m a Foursquare loyalist but am trying Gowalla for the first time to check it out. At all the SXSW events that I’ve checked in using both, Gowalla shows 10% of the number of checkins as Foursquare. For the record, i think Gowalla has done a good job creating awareness at SXSW through all the sponsorship, but it doesn’t look to me like all the awareness is translating into conversion.”

Android applications
Mobile applications from Foursquare, Gowalla, MOG, and Last.fm ranked high.

“Music service MOG lifted the lid on its new mobile applications this morning at SXSW, promising premium subscribers the ability to stream any song, anytime, anywhere, on both iPhones and Android devices.”

Twitter announcement that wasn’t
They were rumored to be releasing information about an ad service, but instead they released @anywhere, a modest enhancement to help integrate Twitter on third party sites. At some point, people might want to make some money from all their tweeting, right? Maybe even a better question (or statement) would be that, at some point, Twitter would like to make some big money, right?

“The Twitter keynote had SxSW attendees lining up an hour beforehand, with many being turned away from an auditorium fitting thousands, and volunteers flagging every last empty seat for a willing occupant, it wasn’t the festival highlight many had hoped.”

Social gaming
We’re seeing huge growth in this industry. And the online discussion mirrors that. Both pioneers and new-media experts agree, the lines between traditional and social gaming will continue to fade as the popularity of the gaming industry grows exponentially.

“Foursquare is positioning itself primarily as a social utility and city guide, while Gowalla is leaning toward its gaming roots and attempting to bridge the gap between virtual and tangible goods.”

Danah Boyd and privacy issues
Boyd called out Facebook and Google for invading user privacy and lots of people took notice and have been talking about it in great detail.

“Technology has made a mess of what is private and what is public; technology has in essence removed our various selves from the safety of context.”

Well, that concludes our findings on the buzz about from the SXSW Festival last week. I hope to see you there next year. But right now I can’t go too far. I’m covering the phones…

From our point of view, the situation is an excellent example of how listening to social media is a critical part of a holistic approach to campaign design, helping to fill in the gaps left by traditional methods. The auto industry has long known that, while men purchase more than half of all cars, women exert influence over more than 80% of purchases. Beyond that, the February sales figures show Toyota declining 8% while Ford sales increased 43%!

So while traditional research and metrics let marketers know that they should be focusing on women, they fall short in understanding how to talk to those women. Our data showed that while men seem to be willing to give Toyota a bit more leeway — talking about fixing and loving, women use harsher words like failure and damage. In addition, Ford comes up a lot in these conversations, furthering bolstering the argument about the importance of speaking to women.

So what does this mean for the marketers designing Toyota’s post-recall campaign? Just as Roehm pointed out, they need to talk to women; they should focus on quality and reliability; and they should probably go head-to-head with Ford.

But outside of the Kodak Theatre in Hollywood, winners are decided by thousands of movie buffs posting their opinions online. That online buzz doesn’t necessarily predict the Academy’s winners. But we’ll be eager to see how the Oscars land compared to the Networked Insights list.

We searched for and focused on the sites with the most discussion of Oscar nominated movies: sites like IMBD, Awards Daily Forums, and AwardsDaily. In our work with agencies and brands, we strive to find relevant content discussed by relevant audiences. In this case, we chose people engaged and passionate about movies.  The data was gathered from February 2, when the nominations were announced, through March 3.

The drama between behemoth Avatar and upstart The Hurt Locker plays out with high tension. In categories where the films square off, each finishes in first or second place in share of conversation. And each gets a social win three times: Avatar wins for Best Director, Best Picture, and Sound Editing, while The Hurt Locker has the edge in Cinematography, Film Editing, and Sound Mixing.

Christoph Waltz (Inglorious Basterds) is the leading social favorite in an individual category. Engagement around Waltz is 21% higher than any other leading actor. He accounts for 39% of the share in conversation.

Categories that were short on engagement included: Live Action Short, Documentary Short, and Animated Short. Engagement in these categories is 10-15% of the average. Who says attention spans are growing short?

And now, the rest of the Social Media Winners…

We would like to thank all the people who tirelessly posted their opinions on social media sites for the great gift of their collected wisdom (and we thank our mothers, drama coaches, and husbands and wives). We couldn’t have done this “Measuring the Social” report without you.

For a comprehensive report on share of conversation for all categories and nominees, email Jonathan Zarov.

Now, you’re probably wondering why I italicized the little quote about highly regarded sources, and if you are the foreshadowing type, you may already be able to guess where I’m going with this. First, let me say that most of the effective algorithms for extracting entities, and almost all of NLP, rely on fairly well formatted, “predictable” text. By “predictable” I mean that it follows formal grammar rules, etc. So, in selecting highly regarded sources (say, CNN?) you are constraining your statistical frame to sites that you can processes. There isn’t anything terribly wrong with doing what you’re good at, but I would like to argue that, at least at Networked Insights, we fight to keep away from this restriction. In part, formal writing carries a bias. It carries a message motivated by some latent motivations. Keeping a high reputation, for instance. It is an immensely more difficult task to harvest information from the drips-and-drabs of informal writing such as is found in twitter and forums (or even blogs).

It’s because of this that, while the thought of Evri is exciting, I don’t think it will tell you anything that you didn’t already know. Now, my goal isn’t to pick on Evri, but I think it is fascinating to realize that the reason that analyzing more formal and easily analyzed text on the web is a bit of a losing battle is because the formalism comes in part from the author knowing that we are watching. Fox or CNN write with a specific audience in mind, and that audience is the same audience that TV seeks to entertain, and on, and on, and on. What is so powerful about the social web is that it’s text produced with only an audience of two or three people being expected.

I say this is powerful in two ways. First, biases equal out by the sheer volume and diversity of publishers. If someone is trying to catch the attention of a particular audience and tailors their text to fit, then that intentional word-smithing is likely reduced by countless other authors with similar overall ideas to express, but different spins to put on the text. Second, since most of this text is being generated quickly, and admittedly not always that well thought out, the raw feelings of people tend to leak into the text. It’s the living room conversations had without thinking. It’s the reflexive “boo” at the stadium. This rawness, if you will, if far more valuable because it’s never what you were expecting.

Obviously cheap energy is highly desirable and there are lots of ways to get it.  The problems come in when you need enough energy that is cheap enough and clean enough.   That’s the interesting part (and the crux of the problem):  recognizing that there are lots of legitimate needs and many of them are in direct conflict (cheap vs. clean, clean vs. high quantity).

To solve problems like enough energy or accurate analytics, you have to optimize the competing variables or invent a way where those variables are no longer competing.  As we work on our analytics, the competing variables are accuracy, analytic power, speed, and cost.  The important thing to realize is that these are not either-or situations (either you have accuracy or you don’t), they are continuous values and you can give up a little of one to get more of another. Accuracy is not about a number on a page or a reported percentage, its about a give and take between accuracy and all the other competing variables.

So, an interesting discussion of accuracy would define it, talk about ranges of acceptable values, and discuss the cost of having high accuracy and how to optimize that problem.  Without doing that, we’re just saying to companies “try to power your offices with free energy… yeah, that will make things better”.

So what are we doing to make our analytics more accurate?  There are four areas of social-media analysis affected by the accuracy problem: semantic search, organizing information, influence, and sentiment.  These are exactly the four areas we are focusing on to constantly improve the accuracy of our analytics for the future.

To give you an idea about where we (and the market) are heading, Senior Research and Development Engineer T.R. Fitz-Gibbon will be sharing more over the next few months about how we are evolving these four main concepts.  Stay Tuned…

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Such a great point. Counting brand mentions is only the tip of the iceberg yet it’s what so many of the current crop of social media monitoring tools are trading on. They count brand mentions, aggregrate content, and report on it. While they get you looking at social media, they are decidely slim on contextual information unless you have time to do a LOT of reading. Malcolm talks about the importance of framing social dialogue and I think that is key to the next step in the evolution of these tools.

Framing (Malcolm’s take may be slightly different so I won’t speak for him) is essentially a more holistic view of data. It’s putting data in context – relative to a market, a segment, a shared affinity or a goal. This is a shortcoming of many current monitoring tools as they focus on mentions, popularity, sentiment etc. but don’t do much to help you understand the implications of all this data.

Searching for brand mentions and verbatims is relatively cheap and easy to do (as evidenced by the price pressure in monitoring right now). Marketers need more context than brand mentions to make sound business decisions and large companies just coming to the social media table are reluctant to bet significant sums of money on such thin evidence when it comes to informing a large marketing campaign.

So what’s next? Malcolm mentions the new feature from Scout Labs called “Quotes”. It’s a good first step. They are attempting to push the envelope a bit in terms of giving their data more value to the end user. But what can you do to really push the envelope?

How about trend discovery and text analytics? Monitoring solutions rely on keywords – that means that you will find what you look for. What current monitoring tools lack is the ability identify trends or patterns in data that you didn’t know to look for. Text analytics opens this door.

Text analytics is basically a trend discovery engine of the best kind. It identifies trends or patterns in data that you didn’t know to look for. It is a very compelling and powerful technology. Combine deep text analytics with a segmented approach (something we do with SocialSense) and you have what we think is the next step in the evolution of social listening tools. I’m curious, what do you think is next?

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There is power in having concrete numbers, even if they do not mean much in isolation, for at least the sake of comparison. One of the best examples of a measure without absolute meaning is baseball’s OPS. A player’s OPS is the sum of their On-base percentage (which is a pretty self-explanatory number) with the Slugging Percentage. The Slugging Percentage (SLG) is the sum of the total bases reached by a runner on a single at bat divided by the total number of at-bats that the batter has. The concept that SLG captures is the hitter’s overall effectiveness and power ^[1].

If you close your eyes and get near to that crevasse where conscientiousness and lucid dreaming meet, you may start to understand what on earth SLG or OBP meant in the minds of their creators. They are, as numbers without a context, meaningless. That said, if you give them either the context of Other Players or the context of Chance We’ll Win This Game their value is unmatched in today’s baseball. An OPS near 1 is fabulous! What does 1 mean? Well… uh… it means we win a lot, and that’s good enough (unless you are my Mets who have great stats yet constantly defy the odds). Sigh, I digress…

Software, like baseball players, is magnificent, often times mysterious, and occasionally questionably worth the sum you paid for it. And to evaluate it, like  baseball players (who’s behavior also often seems peculiar and irrational), you must boil your application down to some simple numbers you can digest.

For instance, if you were to compute a health metric to digest your system’s state, what might you use? Some insights gained from the recent software sprint I was involved in point to more than your typical CPU usage, Memory usage, and network usage. While these numbers are valuable, they do not give you a good idea as to how your system is behaving compared to how it should be behaving. That is to say, if your application uses 90% of the CPU most of the time, then that is normal and acceptable. It is not until you establish the mode water mark that you can start reasoning about what high and low look like.

To begin our application’s instrumentation we first built a restful webpage to pump our statistics out via XML. We also wired up simple serialization of all the available system JMX server named object values. This gives us, essentially for free, the max and current values for memory, garbage cleanup statistics, thread statistics, and a host of other diagnostic bits of information.

In retrospect, we should have wired our application metrics into JMX as well, and perhaps that will be a future task, but for now they reside in series of iBatis calls to the database storing simple double-precision values, their weighted moving averages, their min, max, and current values as well as some time stamping information about how long the interval is between calls to that statistic and how often the statistic has been updated.

Of particular interest is that the weighted moving average consists of only two data points: The current average and the new value. Also stored with the statistic is the weight given to the new value (the value of which should be >= 0 and <= 1). The larger this weight is, the faster the average will move toward the new value. For daily job runs this value should be high. For frequent calls to the database, this value should be low. This causes the average to move quickly for the infrequent calls and slowly for the very frequent calls.

One added bonus, errors and exceptions, regardless of language, should be treated as data to be handled and preferably captured and stored. The statistics engine does provide an exception handling routine to store an exception, though this system needs refinement as we learn more about how we will be using this information in the future.

The goal of all this a dashboard with fields not unlike the following:

• exception count for the last day
• current memory divided by peak memory
• current threads divided by peak threads
• batch job timeout count + batch job completion count = total job count
• user calls per minute

These are all instrumentable values and, at a glance, can tell you some profound things about the general activity of your system. It will tell you something about throughput and something about how close to your hardware limitations you are.

What is even more profound is that once the application holds this information, the application can “learn” and take action when the situation changes. It can throttle user queries to let the database catch up. It can send warning emails. It can stop running jobs. It can signal other clients of the DB or other resources to “back off use” for 10 minutes. These are all very simple reactions to resource strains, but impossible to do intelligently without some very basic measurements.

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This is a great debate and is definitely worth checking out, along with Nathan Gilliatt’s response. I would like to add a few points that may have been under emphasized. In particular, I would like to address misconceptions about the volume of data we’re talking about, the use of statistics, validity vs. power, and human bias.

Data Volume

The main issue around data volume was covered in my last blog post, “Does monitoring provide the confidence and omniscience you need? One of the main points of that article is that there is simply too much data out there for humans to analyze effectively, in many cases.

As a concrete example (which I used in the last article), a search of only Google Groups for “Macintosh OR Mac -cheese -Fleetwood” returns over 17,000,000 posts over the last three months (more than twice as many as I found while writing my last article). That averages out to over 180,000 posts a day for one brand, from only the sources searched by Google! (While the number of sites covered by Google is impressive, it is not exhaustive.)

The point is, to perform analytics around one topic or brand (such as Macintosh), let alone multiple, means analyzing staggeringly large volumes of data! And, it is only increasing with time. It is important to keep the magnitude of data in mind as we move forward.

Statistics

The response to the above point is often something similar to Mike Daniels response: “But there’s an unstated assumption behind the technology promise: that it is necessary to analyse all or a very large percentage of these conversations in case we miss something”[sic].

Do we need to analyze a large percentage of posts? Yes and no. There are really two use cases that we are talking about.

1. Finding the PR or marketing emergencies that require immediate action
2. Understanding trends

For the first use case, finding emergencies, we really do need to analyze a large percentage of posts, and this is why the volume of data is relevant (This point was also covered by my last blog post). For example, let’s say you can only analyze 10,000 posts per day because we are using humans, and if we are receiving 180,000 posts per day, that gives us less than a 6% chance of finding an emergency post when it happens. How much are you willing to pay for a 6% chance?

For the second use case, understanding trends, it is true that we do not need to analyze a large percentage of posts. We have well founded statistical methods of sampling a subset of posts, analyzing them, and then relating our results back to the whole in a valid way.

However, saying that we don’t have to measure all posts is an abuse of statistics. Statistics is not meant as a shortcut to avoid processing more data points. Instead, it is a way to still derive some value when you CANNOT process all the data points (for whatever reason). Statistics is a fall back, it is a safety net. As a general rule we should process as many data points (posts) as we can and only use statistics when we are unable to process all of the points (due to constraints of time, money, feasibility, etc.)

Validity vs. Power

This discussion of statistics feeds directly into my next point: analytic validity and power. This is really the heart of the matter. While in some cases we may not need to process a large percentage of posts (as I discussed above), we do want to process as many posts as possible.

In analytics, we talk about “validity” and “power”. Statistics provides methods and rules for finding valid results when you can not process all the data points. Analytic power comes from processing more data points. “Power” in this sense is the ability to detect a difference, trend, etc. when it is exists. So, yes, we don’t need to process a large percentage of the posts to be valid. But, with more posts comes more analytic power and, hence, more value. Thus, there is much to be gained from taking advantage of machines to perform analytics.

What about humans, then?

Human Bias

Even if we could use humans to analyze all of the post we have, we still may not want to. Computers have the ability to put new data points in context with all of the other data under consideration. Humans can’t do that (especially when there are 180,000 other data points); we put things in context with all of the other things we know, which is very different. This is where bias can creep in.

There is a natural flow in analytics from data, to information, to knowledge, to decisions. We begin with data, organize it, analyze it, and put it into context with the other data to produce information. Then, a human consumes that information and combines it with what she knows to produce new knowledge. This knowledge can then drive decisions.

Regardless of whether your analytics are performed by human, machine, or other, this is the general flow; there is human knowledge used at some point. But, it is important to insert this knowledge into the flow at the right time. Human knowledge, in the form of expectations, inserted too early into in the the process I’ve described can drastically bias the results.

To sum up, I think Mike Daniels and Mark Westaby both bring up some great points, as do many of the people commenting on their post. At the end of the day, both humans and machines are needed, one cannot proceed along the data, information, knowledge, decision chain without them. The trick is to use the right tool for the right job. Machines are great at processing large amounts of data, putting it into context, and producing information while people are unequaled in our ability to create knowledge and use it to drive decisions in situations where the answer cannot be reduced to a one or a zero.

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People come to the internet with an established view on a particular topic. We do not care precisely what the topic is, but suffice it to say, that it is one that the Internet user has some knowledge about, perhaps would like to know more about, and has some preconceived notions about. When they come to the Internet with their question, they punch it into a search engine and get back a few hundred results.

They read the headlines and queue in on the ones that match their preconceptions. They may click on a few that are not in agreement with what they believe is true, but they feel compelled to check them out also. The vast array of disagreeing sites are as a hydra, too numerous, and in this case too varied, for our Internet-searcher to make much sense out of. In the end, they are most likely not persuaded by any of them (mostly because they are all lumped into one category of “opposing views” and the single category of “my view” has some definite, clear, coherent representation).

The result? People find what they already know on the Internet. This is how forums become so mono-thought-esque and this is why people shout-down or ignore opposing views that appear in their specialized forums. This is, of course, a generalization, but a prevalent pattern.
I point this out because it is a re-presentation of a fundamental pair of challenges that the Internet faced when it was just the World Wide Web. Social Media offers some ways to alleviate the problem, but not many.

1. How do you find a trusted voice for your point of view? That is, how can you be sure that you have a valid point of view and have found on the Internet someone who represents it with reason and balanced judgment?
2. Second, very similar to the first, and indeed necessary to satisfy the first point, how can you find balanced, well-reasoned opposition claims to the view point you are searching for? There are simply too many views that do not agree to single out the few that are perhaps actually challenging to your perspective, when considered.

To put it another way, to solve #1 you need #2. To solve #2, currently, you need to read through a lot of information and most people simply do not have time for that. As a result, they tend to form their opinion off-line and validate it online.

The result is online communities that are essentially echo chambers in which opposition perspectives have a very tough time gaining any sort of traction. Is this a bad thing? Well… not really, so long as you don’t think that your online community is some sort of truth-broker.

Social media offers some methods by which you can more easily find valid voices of agreement and opposition by creating continuity of relationships. If you can find one person with well-reasoned arguments for or against your position, chances are that they only dialog with like-quality people. If they disagree online, they most likely will do so at the same level at which they argue for their position. Thus, if you try to find valid opposition, find those who disagree with your initial social network contact on the subject matter.

Spidering along the social web, you should be able to find a much smaller set of points of view with higher quality arguments. The problem them becomes finding only that entry person, something you could perhaps accomplish in the “real” world and take online.

One last thought: Malcom Gladwell rightly points out in Tipping Point that there are different people who serve different roles in societies. He breaks them up into three groups one of which are Mavens and the other of which are Connectors. The Mavens are the subject matter experts whose opinions you want. The Connectors are social butterflies looking to bring together people. If you can find not only a subject matter expert who argues at the level you are looking at, but if you can identify who among his connections is a Connector-type of person, you will have a fabulous initial sub-network of people to read for opinions.

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Until recently, the best solution for many of us has been something like Google Alerts. You enter your search terms (“Macintosh OR Mac -cheese -Fleetwood” and so on) and you get tens, hundreds, or even thousands of items a day; you know everything.

Or do you?  How capable are you, as a human being, at finding the most important information in a sea of data? Let’s take a shot at figuring that out.

On the plus side, people are very sophisticated text processors. We are highly skilled at reading a piece of text, understanding its meaning, and placing it in context with other information about the brand we are monitoring. We are very good at knowing what’s important.

But how do those skills scale at the volume we’re dealing with on the web? The problem isn’t “how skilled are you?” but “how much can you read?”.

Let’s say that you need to monitor the Macintosh brand. A quick search of Google Groups for “Macintosh OR Mac -cheese -Fleetwood” returns about 5,750,000 posts over the last 3 months; that’s about 64,000 posts per day. So, if you read 10 posts an hour, for 16 hours a day, for three months straight, you’d cover less than 0.3% of the posts about Macintosh computers!

Even with your tremendous ability to identify important content, you would be missing up to 99.7% of the posts concerning your brand. And, don’t forget, your time has been completely monopolized by one brand, so you are completely ignoring 100% of the rest of your brands (not to mention your family, social life, and general hygiene).

So, if you truly want confidence and omniscience, you do not want a service that gives you a bunch of posts to read, you do not want a monitoring solution. What you need is a system capable of processing all of your posts and finding the important information for you. This would free up your time to perform the important in-depth analysis for which there is only one tool: your human brain!

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