Vuvuzelas are one of the top trending topics on Twitter (and has so early on established itself as the foremost Internet meme of the 2010 World Cup). We do have a new angle on this well-known irritant, but first we’ll block out the noise to focus on the players. We measured the relative online buzz for the top players and also noted their ESPN skill ranking. See the “Top 10 World Cup Buzzing Players,” below.

General Trends and Analysis

• Skill and popularity don’t always go hand in hand. Fernando Torres is the leader in Social Rank, but ranked 8th by ESPN in Skill. Chris Dempsey has a Social Rank of 6 but didn’t make ESPN’s Top 25 for Skill.
• There’s a new rooster in the yard: Fernando Torres beats out heartthrob Cristiano Ronaldo in percentage of women buzzing about him. Overall, it’s not surprising that 62% of the online conversation is generated by men. But Torres and Ronaldo both attract more women than men (See chart below, “World Cup Players — Buzz by Gender”).
• The World Cup is so low scoring so far, anyone who scores can become a country’s — and the internet’s — hero. Clint Dempsey ranks primarily because of the one goal he scored in the England vs. USA match.
• The strikers are the most buzzed about players — despite the lull in scoring so far. In fact the only two in our Top 10 list that do not typically play a forward position (Frank Lampard and Dani Alves) are both involved in negatively trending discussions.

Trending Themes Around Top 3 Players
1. Fernando Torres, Spain: Conversation trending POSITIVE
The buzz…

• Torres has been coming off the bench. Fans can’t wait to see him start and increase his playing time. (We’ll watch to see if his already substantial buzz increases as his minutes increase.)
• Including Torres, Spain possesses a bench filled with players that are better than most World Cup teams
• He’s talked about as part of the Spanish dream team, with Xavi, David Villa, David Silva, and Fabregas
• Spain will garner more supporters because Torres is a fan favorite from the Liverpool FC, his regular-season team
• Torres is the hottest/best looking footballer

Sample post…
“I just got an email from a coworker & all it says is “I love Fernando Torres. The End.” Hahahaha”

2. Lionel Messi, Argentina: Conversation trending POSITIVE
The buzz…

• Best player, best moves, best save so far this World Cup (he is ESPN’s #1 ranked player)
• Argentina relies upon him more than Barcelona does.
• He’s been playing great; it’s the team’s fault he’s not scoring more.

Sample post…
“Vamos Messi Vamos Argentina !!!”

3. David Villa, Spain: Conversation trending POSITIVE
The buzz…

• Great for Spain
• 3rd best striker in the world
• Good player

Sample post…
“The hairstyle gods really don’t like Villa today”

We’ve got similar data for the other players in the Top 10 of online buzz. Just contact me and I’m happy to share.

Fans can be fickle and fortunes change with the bounce of a ball. Watch for our weekly updates of the buzz around key players.

And to bring it full circle…
It all comes back to vuvuzelas… Using our discovery-based insights to uncover emerging conversations, we found that Coca-Cola might have “blown it” with their campaign graphics for the World Cup. Last month when Coke’s vuvuzela-inspired designs were released, they were praised for their sleek appropriation of a cultural artifact dear to South African football. However this association became a liability as soon as the first vuvuzela was blown, because frustrated World Cup fans are doing whatever they can to lower the noise level on the pitch. Many fans are publicly protesting on Coke’s Facebook Page to stop purchasing Coke products until Coke helps ban vuvuzelas!

See the discussion topic “Pepsi until vuvuzela is banned” for more.

The disparity between shows high in viewership and shows high in engagement is reflected in the fact that more than half the shows in the SocialSenseTV Top 20 don’t appear in Nielsen’s top 20. The Hollywood Reporter covered the report and noted how ratings and fan-base don’t always match up. (digiday:DAILY also wrote about the report and our new SocialSenseTV offering.)

So how does this help a media buyer or a network? What can social engagement tell us that matters to the bottom line of those engaged in this spring rite of traditional media?

This is the time of year when networks, agencies, and brands all try to assess what a TV show is worth — and what it’s likely to be worth months later — so they can buy inventory now, at upfront prices. “Scatters,” or ads bought closer to air date, can cost 20 to 30% more than upfront buys. Shrewd, informed buying now saves agencies and brands a lot of money. Bad, misinformed choices will stick them with ads on shows that are dogs.

Social media provides a rich vein of conversation to mine that can measure engagement and passion around a show, an actor, or any other topic you care to explore. We can listen to the social conversation to discover why a show is popular, which shows are a good match for a brand, and ultimately, where to buy or sell TV inventory.

We can even analyze and “rate” shows that haven’t aired yet, by listening in on the early buzz about new shows and their stars.

Advertisers are seeking highly engaged audiences with the idea that they are more likely to watch live, see commercials, and appreciate the connection of brands to their favorite shows. Social media listening provides the perfect platform to find those engaged audiences and then discover what they are talking about.

For networks, the same dynamics are at play. If a network is cultivating shows with highly engaged fans, they’ll want to measure that and capitalize on it. They can use the data to prove value and legitimately increase the cost of TV ads. They can also find good matches between brands and shows by finding those shows with fans that care about a given product or category. Note (in the chart below) how network ranking changes when measured by social media ranking or Nielsen ranking of shows.

In a time when advertisers are worried about viewers skipping over commercials, branded content is a hot topic. We looked at two instances. It’s well known that Subway rescued “Chuck” from cancellation. And the fans seem grateful. They’re still talking about Subway when they talk about the show.

We also looked at the “Modern Family” iPad brand integration episode. Though iPad brought a lot more equity to the partnership (word is Apple didn’t pay cash for the deal, but their buzz is much higher online than that of the show), they did get a substantial lift: $516K in earned media.

These are just examples of the kind of campaign evaluation that social media analytics can provide. It’s a powerful new way to measure the impact of a campaign or tv event, because it aggregates effects from multiple channels: if you saw it live or delayed, on TV or on Hulu, in a bar or on a plane, the buzz registers the same online.

The full report includes more analysis of the uses of social media in informing upfront buying and selling, and more insights around specific shows. Please download it and let us know what you think. And if you are ready to enlist social media in your media buying or selling, check out SocialSenseTV, our new listening platform and custom reports services tailored to television.

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.

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.

The 2010 Winter Olympics were a laboratory for testing how events and advertising play on the second screen. Most of the “experiments,” the way we interpret social media data, were focused on how consumers behave in social media. But what we can learn from 300 million people interacting online should inform all aspects of marketing.

Networked Insights conducted a study to help agencies and brands find the best athletes to sign celebrity endorsement deals. We measured online engagement around particular athletes and combined those results with demographic data from the sites where the posts appeared to find out not only who is most popular — but with whom.

The study was picked up by Adweek, Wired, and Digiday.

The full results are included. Here are a few items of note:

• Sidney Crosby is the hands-down winner in engagement. At his level, he’s still pulling in more women than any other athlete, even though his audience is predominantly male. He’s clearly a good candidate for sponsorship for many types of campaigns.

• For a company wanting to get women’s attention, Apolo Anton Ohno is a great pick. The former Dancing with the Stars champ is particularly appealing to women ages 25 to 44.

• To capture the coveted 18 to 24 market, consider Bode Miller.

• For high-end products, Evan Lysacek brings home the gold with the $100K+ income crowd. Not surprisingly, these wealthier fans skew slightly older with a big group at 35 to 54. There are slightly more males in the audience.

This data can fuel insights to solve a real marketing problem: Whom should we choose to be a celebrity endorser for our product? Like any important decision around marketing strategy or tactics, it should be compared with other data and expertise. Agencies and brands will certainly consider the nature of the product and the celebrity personality, for instance.

This Networked Insights study combines engagement metrics with demographic data, which was averaged from sites with highest engagement for specific athletes.

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.

The New York Times picked up on part of our report. Here’s the rest.

This was Networked Insights’ second year reporting on the buzz around Super Bowl ads. The analysis from our SocialSense listening platform reports on engagement, a measure beyond frequency of posts. It factors in other post consumption (eg, reads) and influence of the poster. Like last year, we’re also calculating Social ROI — what a company spent on ads in relation to the lift they did or didn’t get.

Teleflora gets an “Insighty” for winning two years in a row. Volkswagen and Focus on the Family also sparked high engagement for dollars spent, placing second and third.

The data shows Motorola bringing up the rear. And there were other players, like the e-Trade baby ads, whose online buzz didn’t measure up to the pre-game hype.

Both Coke and Pepsi (the latter who famously didn’t buy an ad this year) were in the middle of the pack. But, comparing the spend on Coke’s two ads to Pepsi’s social media spend, we rate Coke at a Social Media ROI of +26 (160% increase in interactions) and Pepsi at virtually flat ROI (+10% interactions).  So, though Pepsi’s campaign certainly had merit and created buzz in the weeks before the game, they might have done better to top it all off with a traditional TV ad.

See the chart below for the big winners and losers.

Google introduced Alerts in 2004 and became the first true technology solution for the PR industry. The digital clipping service has now become an indispensable tool for PR practitioners everywhere. In May of this year, Google rolled out near real-time search with their search-options functionality. Whether or not this was in response to the growth of Twitter, it made the search giant even more of a player in the crisis-management game. Now, hot on the heels of real-time search comes Google’s patent on sentiment analysis — a clear sign of their desire to own the monitoring space.

This combination of features now competes head-to-head with most of the monitoring companies at a price none of them can match. Ever wonder why all of them give away a free month? They have to; they’re competing with free. For PR, this is the land of milk and honey: one of the most innovative and technologically advanced companies on the planet providing the exact tools needed for crisis management… for free!

Advertisers and marketers need more sophisticated tools geared toward marketing and business strategy though. Google has yet to explore things like semantic search (understanding the meaning behind words, not just the words themselves) because the Google infrastructure is focused on searching for specific things and retrieving that data as fast as possible instead of using large-scale analytics to uncover themes in the data. In addition, Google still has not moved beyond its rather simplistic influence analysis and towards ideas like the evaluation of idea propagation. All this means that analytics companies need to get focused.

Share elsewhere...

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…

Share elsewhere...

Marketing and advertising, on the other hand, have more strategic needs that can only be addressed by deep analysis and trend discovery. Marketing organizations are not structured to respond to tens of thousands of mentions, they are structured to work on the 25-30 major themes and customer needs that cut across the entire conversation. Marketers need to gain understanding about their customers and their market place in order to create messages and campaigns that play out over several months (at least). While PR practitioners are often forced to live from crisis to crisis and spike to spike, marketers have the freedom to sit back and watch how things develop, casting a critical eye on the relevance of groundswells.

Think of it in terms of stock investing — if monitoring is the best day trader, then analytics is Warren Buffet. Day trading keeps a close eye on the sharp peaks and valleys of daily stock prices and relies heavily on tips, rumors, and hearsay. As day-traders are acutely aware, one bad guess or reaction can spell disaster. Strategic investors are not concerned with the daily spikes but with the much smoother trend line of activity spread out over weeks and months. Lots of day traders make and lose their wealth many times during their lifetimes; their day-to-day lives are full of fear and turmoil. When it comes to your brand, what type of investor are you?

Share elsewhere...

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?

Share elsewhere...

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.

Share elsewhere...

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.

Share elsewhere...

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!

Share elsewhere...