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HOW KLOUT LEVERAGES MACHINE LEARNING TO CALCULATE SOCIAL INFLUENCE VIA THE 'KLOUT SCORE'

For the first time, Klout has published a paper on how they calculate and measure a person’s influence as an attempt to provide more transparency with its service.

Klout, owned by Lithium Technologies, is a website and mobile app that uses social media analytics to rank its users according to online social influence via the “Klout Score”, which is a numerical value between 1 and 100. For many, its a measure of how active and influential their social profile is across several social networks such as LinkedIn, Facebook, Twitter, Tumblr, and popular blog hosts such as WordPress.

The Klout scoring system assigns scores to 750 million users using machine learning that examines;

• 9 different social networks on a daily basis
• Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user
• Over 45 billion interactions from social networks every day

Adithya Rao, lead research engineer behind the Klout score said.

“Though the Klout Score has been around for years, this is the first time we have published a paper that provides this much detailed information on what’s behind it.  Getting this paper accepted is a step towards a more transparent view into the Klout score, and by publishing this information in a public forum others will be able to build upon the work we’ve done.”

Although there are multiple ways to consider what constitutes ‘influence’, how Klout defines it is as a way to to measure a users ability to drive action from their posts or social interactions. Many ‘influencer lists’ for example pull together people active on social media platforms based on the number of followers, ratio between followers and users they follow or simply by how many times they appear via a hashtag search. Klout digs a lot deeper by listening to the signals behind the noise using machine learning and data science to distinguish between people who claim to be an influencer and others who are more insightful the real influencers.

Who responds to your messages and interactions also adds to the algorithms behind Klout, for example, if Elon Musk responds to a message posted then they are influenced by your message and have acted on it.  This is weighted far differently than if someone who holds little influence across multiple domains, since Klout also validate that users with higher Klout Scores are able to spread information wider in a network.

When a user registers on Klout they associate their social identities on different social networks with the Klout profile;

• For Twitter, public data is collected via the Mention Stream;
• Anonymised data for opted-in Lithium Communities comes from in-house datastores
• Data for other social networks is collected via REST APIs on the user’s behalf, based on the granted permissions.

Data is collected continuously from interactions in a trailing window of 90 days using the Play Framework. The collected data is written out to a distributed file system, where batched parsing and processing is done using Hadoop MapReduce and Hive. From there the users Klout score is calculated and weighted according to further factors;

• The difference between the current time and the time at which the reaction occured.
• The social network on which the reaction was performed.
• The unit of original content or action on which the reaction was performed.
• The type of reaction.

Of course, there are still some elements missing from the scoring mechanism itself which Klout is working on integrating. When I spoke with Adithya he said that in the future Klout would be looking to attribute content from reputable sources like Forbes and weight this accordingly along with the other networks (Forbes has a Klout score of 99 for example, being associated with Forbes content would credit a contributing writer like myself with additional influence.)

Sentiment also isn’t tracked currently – negative or positive, which Adithya also acknowledged is a measure of influence but one which is very nebulous to calculate, since people will react differently to certain content. And it all hangs on the availability of the information to power the framework itself through the APIs provided by the social networks. Real-time and near real-time measurement is possible with some, but for example in the case of LinkedIn, Klout’s abilities are limited to certain interactions and data, such as the users job title and status updates. A users LinkedIn blog posts for example aren’t taken into account yet.

Where Klout’s strengths now lie is in the way it will tie in a users Klout score with transactional information and their overall digital footprint online, allowing businesses who use Klout to reach audiences and customers with a more personalised and contextual experience. 

Years ago Klout was purely used to measure and compare people online in a digital bragging rights context. There were other examples where a potential recruitment candidate’s Klout score was looked at for certain role profiles which demanded a command of an online audience or deep understanding of social networks.

Their next challenge now is to educate the wider online community and average user that they need Klout as part of their own generated data as retail and consumer companies seek to create and engage on deeper and personal levels. To an extent, many current users haven’t outgrown trying to play the system just for higher scores, and wear a Klout score like a birthday badge. I wrote about this on Forbes a while back in a previous article on social media habits.

"People have become obsessed with chasing meaningless numbers and Klout scores rather than just enjoy life. There was at one point a movement to use Klout as a means of measuring a candidate against a job application. Thankfully it failed.

Klout is a classic example of behavioural modification, where emphasis is placed on posting content that you know will hit the spot for a larger audience to get a push up the ladder, rather than post something you know will be engaging to the people that really matter (and consequently won’t even garner a blip on the Klout graph.)"

But Klout has now matured beyond this kind of audience now, and as a first step the publication of this paper of how their data scientist team employs sophisticated algorithms and machine learning techniques proves that Klout and Lithium Technologies is much more than just a number.

COMMENTARY:    This is not the first time that Klout has changed its social influence algorithm. After numerous complaints, Klout changed the algorithm in 2011, 2012 and 2014.  What I find very disturbing is that Klout places too much emphasis on the number of followers or fans that you have. I keep reading that users who bought followers on Twitter raised their scores significantly.  Here are a few examples:  

1. @BarackObama - President Barack Obama is the #POTUS, but according to Klout he commands a score of 98.  However, twitteraudit reports that the #POTUS has 37% audit score, claiming that over 41 million of his Twitter followers are "fake."
2. @HillaryClinton - Former Secretary of State Hillary Clinton has a Klout score of 94.  However, twitterdit reports that Madam Secretary has a 59% audit score, claiming that over 1.8 million of her Twitter followers are "fake."
3. @SenSanders - Senator Bernie Sanders has a Klout score of 81 with a 90% audit score, with only 90,000 "fake" followers. 

Granted, both Barack, Bernie and Hillary command tremendous influence by the nature of their current and past political resumes.  However, a lot of their tweets, if not most of them, are automated. This is not a level playing field, if you tweet 3 to 5 times per day like I do.  Does Klout take the volume of tweets into account?

When I first joined Klout, I got a score of 40, which is considered an average social influence score.  At that time, I spent most of my time tech blogging, and at one time achieved 40,000 unique visitors to my blog over a period of about one year.  I was averaging about two to three blog posts per day at that time.  Half of my fans were from the U.S. the remainder were international, primarily from the U.K. Canada, the E.U. and India.   I average 1.5 blog posts per day, and now average about 18,000+ unique visitors.  My Klout score has gradually increased from 40 when I started, to 44, 46, and 47 as of today. 

I spend most of my time on Twitter where I now have 2,800 followers (96% authentic).  I really never liked Facebook, so I only have 64 fans on Facebook, and this is deliberate on my part, and most of my posts from Twitter are fed to my Facebook page.  I also have a separate Facebook page, my Sharon Stone page where I am getting about 300-360 likes per week. The Basic Instincts star is quite a draw.  Klout can only key on one Facebook page to measure social influence.  As a result, I have switched from my Facebook home page to my Sharon Stone page.  I also have a lot of views on my LinkedIn page.  Hopefully, the combination of Twitter, LinkedIn an Sharon Stone (Facebook) will increase my Klout score. Get back to you on this one, but it should be interesting.  My goal is to hit a Klout score of 50 within 30 days.   

Courtesy of an article dated October 30, 2015 appearing in Forbes Tech

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GEORGE WASHINGTON UNIV SCIENTISTS DISCOVER THE ON-OFF SWITCH FOR HUMAN CONSCIOUSNESS DEEP WITHIN THE BRAIN

When the region of the brain, called the claustrum, is electrically stimulated, consciousness — self-awareness, sentience, whatever you want to call it — appears to turn off completely. (Click Image To Enlarge)

Researchers at George Washington University are reporting that they’ve discovered the human consciousness on-off switch, deep within the brain. When this region of the brain, called the claustrum, is electrically stimulated, consciousness — self-awareness, sentience, whatever you want to call it — appears to turn off completely. When the stimulation is removed, consciousness returns. The claustrum seems to bind together all of our senses, perceptions, and computations into single, cohesive experience. This could have massive repercussions for people currently in a minimally conscious state (i.e. a coma), and for deciding once and for all which organisms are actually conscious. Are monkeys conscious? Cats and dogs? A fetus?

When it comes to human consciousness, much like the rest of our brain’s operation, there isn’t a whole lot in the way of actual scientific knowledge. Despite a century of “modern” neuroscience, we still only have a rough sketch of how the human brain works. Most theories, though, generally agree that consciousness is probably created by a part of the brain that integrates activity from different regions of the brain into a single, holistic experience. To put it in (very loose) computing terms, this seat of human consciousness would be somewhat like a CPU; without it, you’d just have a bunch of different parts that are theoretically functional, but not really capable of getting anything useful done.

The claustrum, below the neocortex, in a human brain (Click Image To Enlarge)

The research, led by Mohamad Koubeissi at GWU in Washington DC, was originally tasked with analyzing a woman with epilepsy. The neuroscientists were stimulating regions of the brain with electrodes in an attempt to discover where her seizures originated from. Then, when they stimulated the claustrum — a thin region of the brain underneath the neocortex — the patient slowly lost consciousness. When the stimulation was removed, consciousness returned. When the claustrum was stimulated, the woman just stopped whatever she was doing (speaking, reading, moving) and stared blankly into space; when stimulation was removed, she continued as normal with no recollection of what had just happened. [DOI: 10.1016/j.yebeh.2014.05.027 – “Electrical stimulation of a small brain area reversibly disrupts consciousness”]

Speaking to New Scientist, Koubeissi said: 

“I would liken it to a car. A car on the road has many parts … but there’s only one spot where you turn the key and it all switches on and works together. So while consciousness is a complicated process created via many structures and networks – we may have found the key." Read: Human consciousness is simply a state of matter, like a solid or liquid – but quantum.

As you might expect when it comes to bleeding-edge neuroscience, there are some caveats to the research — most notably, the study only looked at the brain of one person, and due to her epilepsy (and previous removal of part of her hippocampus) she doesn’t necessarily represent a “normal” brain. In short, more research needs to be done — and following the publishing of this paper, you can be guaranteed that there will be more research into the claustrum.

COMMENTARY:  While most of us automatically identify the brain as the headquarters of our awareness, neuroscientists seek a more precise location (and understanding) for this unique if everyday phenomenon. A new study by neuroscientists of Vanderbilt University finds that consciousness does not make its home in just one brain region.  Instead, researchers say, awareness degrades the brain’s modular function and substitutes an integrated connectivity in which widespread communication arises across areas of the cortex. Consciousness, then, arises from cooperative and not solo brain activity.

Since the beginning of thought, philosophers have wondered where we derive our consciousness, and with the advent of sophisticated imaging technologies, neuroscientists have begun to explore this question in steadily increasing depth. Most recently, a 2014 study suggested that one region of the brain works as an on/off switch for awareness — when researchers electrically stimulated the claustrum of a patient (see video below), she instantly became unconscious. While this experiment does not prove consciousness resides in the claustrum, it raised many questions about the function of this unusual brain region: a thin, irregular structure of neurons hidden beneath the surface of the neocortex.

Francis Crick and Christof Koch, two pioneers in the field of human consciousness, theorize the claustrum functions as “a conductor coordinating a group of players in the orchestra, the various cortical regions.” Their hypothesis is based on the fact that the claustrum receives input from — and projects back to — almost all regions of the outside layer of the brain, the cortex.

Graphs and Images

For the current study, Vanderbilt University researchers investigated whether one or just a few areas of the brain might produce awareness. To accomplish their work, they used graph theory, a branch of mathematics focused on understanding highly complex, advanced networks, and a simple brain imaging experiment.

The experiment began with participants lying down on the hard bed of an MRI scanner. While researchers observed, participants performed a simple task of detecting a disk as it briefly flashed on the screen before them. After each participant completed a number of trials, the researchers compared all the results. They labeled those tests when participants detected the disk as “aware” and those when they missed the disk as “unaware.”

Upon analysis, the researchers discovered that no one area or network of areas in the brain stood out as particularly active during awareness. In fact, the whole brain appeared to become more connected following each report of awareness.  The authors wrote.

“These results provide compelling evidence that awareness is associated with truly global changes in the brain’s functional connectivity.”

Douglass Godwin, one of the authors of the study and a graduate student at Vanderbilt, told KurzweilAI.

“We take for granted how unified our experience of the world is. We don’t experience separate visual and auditory worlds; it’s all integrated into a single conscious experience.  This widespread cross-network communication makes sense.”

Courtesy of an article dated July 14, 2014 appearing in Extreme Tech and an article dated March 17, 2015 appearing in Medical Daily

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WHY FACEBOOK'S HUGE AUDIENCE AND POWERFUL NETWORK EFFECT CREATED A COMPETITIVE BARRIER THAT KILLED GOOGLE+

Is Google's de-emphasis of its social network proof of Facebook's competitive advantage?

While Facebook (NASDAQ:FB) is worth more than $270 billion today, its business is still a fairly new breed. Founded in 2004, the company quickly grew to become one of the world's first major online social networks. But many questioned whether the company's business was sustainable. After all, online social networks hadn't yet proved to be enduring business models. And even after the company's high-profile initial public offering in 2012, there were still a handful of critics who believed Facebook was lacking a sustainable competitive advantage. Today, however, it's clearer than ever that Facebook's competitive advantage is both powerful and sustainable.

What Is Facebook's Competitive Advantage?

Long-term investors are often searching for potential investments with durable competitive advantages. As famed investor Warren Buffett says, an investment should have a "moat."

Buffett has said. 

"In business, I look for economic castles protected by unbreachable moats." 

After all, without a competitive advantage, how can investors expect their current investment to be around years into the future, let alone appreciate in value?

But what is Facebook's moat? Investors have argued that its competitive advantage is a network effect.  A network effect becomes a competitive advantage when each additional member in the market makes a network stronger for all parties involved, and when, on the flipside, users are in a potentially disadvantageous situation when they aren't a part of the network.

Consider Facebook's network effect. With about one out of seven people in the world using the service on a monthly basis, there is a high probability that users who join the service will have friends and family already actively using the service.  So for every additional user, the network becomes increasingly useful, making it easier for existing members to connect.  On the other hand, if someone chooses to leave Facebook's network and use a similar yet less established social network, that person will have to leave behind many friends and family who use only Facebook. A network effect, therefore, makes it very difficult for new networks to woo users away from leading networks.

Perhaps the best illustration of the strength of Facebook's social network has been Google's failed attempt to compete with Zuckerberg & Co.

Google+: Far From A Threat

Hoping its sheer size and reach through Google search and Google products such as Gmail and YouTube would launch Google Plus into a position to eventually compete head-to-head with Facebook, Google initially required a Google+ account to engage with others on its products.

The idea was that Google+ would be a "platform layer that unified Google's sharing models." Even more, Google+ was aimed at becoming its own "product/stream/app in its own right," said Google executive Bradley Horowitz in a Monday post on Google+ detailing Google's decision to phase out the importance of Google+ as a platform across its products. Horowitz took over Google+ earlier this year.

He continued.

"This was a well-intentioned goal, but as realized it led to some product experiences that users sometimes found confusing. For instance, and perhaps most controversially, integration with YouTube implied that leaving a comment on YouTube (something users had obviously been doing successfully for years) suddenly and unexpectedly required "joining Google+."

Horowitz said Monday in a separate post on Google's official blog. 

"So in the coming months, a Google Account will be all you'll need to share content, communicate with contacts, create a YouTube channel and more, all across Google." 

The de-emphasis of Google+ as a platform across Google's products, as well as the clear lack of traction the platform has had in the past few years, highlights just how difficult it is to compete with Facebook's network effect. And if Google can't compete effectively with Facebook, who can?

Here’s an infographic that nicely illustrates the value of users and their impact on the network effect.

Click Image To Enlarge

COMMENTARY:   

Metcalfe's Law of Network Effects vs Zipf's Law

One of the surprising things about the valuation of Facebook in its IPO back on May 2012 is how the rationalization for the $104 billion valuation seems to have been based on some varient of Metcalfe’s law of network effects.  Anthony Wong Kosner, a contributing editor for Forbes said in an article dated May 31, 2012 said,  

"Surprising, I say, because this law (aka Metcalfe's Law), that states that the value of a network (network effect) is proportional to the square of the number of the nodes on the network, has been refuted quite convincingly as a method of evaluating social networks at least since Andrew Odlyzko and Benjamin Tilly’s groundbreaking paper in 2005 and the more widely cited article with Bob Briscoe in IEEE Spectrum in 2006. Their candidate for modeling value? The less sensationalist Zipf’s law."

Kosner goes on to say in the above referenced article:  

And yet, as recently as the day before the IPO, Tim Beyers of the Motley Fool defended the impending valuation on AOL’s Daily Finance channel on the basis of Metcalfe’s law. Consider that five years ago, Forbes Editor William Baldwin picked up the baton from Odlyzko and wrote in his column,

“Metcalfe’s Law is a very optimistic statement about connectedness. Double the number of nodes in a network—of computers, phones, rail stations or, perhaps, friends—and you quadruple its value. Too optimistic, says a revisionist mathematician, Andrew Odlyzko. His measure of networking value comes from a different law, described 75 years ago by the linguist George Zipf.”

What is Zipf’s law? It is one of a family of related discrete power law probability distributions that describe how in any system of  resources there are a small number of items of very high frequency and value and a “long tail” of many more of decreasing frequency and value. It is, in essence, an empirical description of hierarchical distribution of resources (i.e., rich get richer…). Zipf discovered the law through the observation of the frequency of words in the English language, but the law holds even for randomly generated “languages,” as well. The law states that,

“Given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, etc.”

Social networks are, in fact, more like languages than they are like the ethernet networks that R0bert Metcalfe based his law on. The difference is that the nodes of hardware networks are fairly homogenous. Each one has an equal relationship to every other one. But social networks are anything but homogenous. Dunbar’s number sets the size of a meaningful social group for humans at about 150. Beyond that size, additional members add diminishing value. But even within that 150 there is considerable range in affinity and sentiment.

Ben Kunz wrote about this recently in Businessweek in terms of the threat to Facebook of the rise of what he calls “unsocial” networks. He points to Path, which limits your network to Dunbar’s 150, and a new app for networks of only two, called Pair. These new micro social networks are bound to get closer to what their users really care about than sprawling sites like Facebook, but will probably be even more impenetrable to advertisers, which will make monetization difficult.

An example of the difference in valuation between Metcalfe’s law (n²) and Zipf’s law (n log (n)) is illustrated in that 2006 IEEE article:

"Imagine a network of 100,000 members that we know brings in $1 million. We have to know this starting point in advance—none of the laws can help here, as they tell us only about growth. So if the network doubles its membership to 200,000, Metcalfe’s Law says its value grows by (200,000²/100,000²) times $1 milion, quadrupling to $4 million, whereas the n log( n ) law says its value grows by 200,000 log(200,000)/100,000 log(100 ,000) times to only $2.1 million.”

To figure out what Facebook is worth based on this rationale would involve having a starting point valuation, and the first public milestone was Microsoft’s October, 2007 purchase of a 1.6% share of the company for $240 million, giving Facebook a total implied value of around $15 billion. This was clearly a wild overvaluation, considering Yahoo and others were offering $1-2 billion to buy Facebook outright at the same time. But just as an illustration of relative value, let’s call it $1 billion in 2007, and take Facebook’s 50 million monthly active users in October 2007 and its 900 million now. Based on the $1 billion valuation in 2007, the Metcalfe method would yield a valuation of a whopping $325 billion. The Zipf method would yield a much more restrained valuation of $21 billion. Further research has suggested that the “actual value” of a social network is somewhere between the Metcalfe and the Zipf numbers, with the growth line tending more linear than exponential.

But the difference in value between these two approaches is not just about market valuation, but, more importantly, about value of the networks to the users. Zipf’s law would suggest that most of what is coming through the Facebook pipe to users is of little value to them. But by another important metric, Facebook is clearly very valuable to its users. In an under-read but extremely valuable story this weekend on Forbes.com, contributor Nir Eyal talked about what he referred to as the most important “hacker metric.” Referring to a post by his friend Andrew Chen, Eyal says that the fact that “Facebook’s historical ratio of daily active users (DAU) to monthly active users (MAU)” has consistently been about 50% is highly significant.

Daily active users are, in Eyal’s terms, the agents of a successful startup. The timeframe is important because of what is called “Viral Cycle time,” or the amount of time it takes to complete a viral loop.

“Having a greater proportion of DAUs dramatically increases Viral Cycle Time for two reasons. First, daily users initiate loops more often—think tagging a photo on Facebook. Second, more daily active users means more people to respond and react to each invitation. The cycle not only perpetuates; with high DAU to MAU, it accelerates.”

Eyal contrasts the micro-engagements of users with the performance of the overall network,

“As opposed to distribution channels, the mechanics driving user engagement do not follow a power law.  In fact, it is the nuances of user behavior that make the competition irrelevant, just as it did in the case of Facebook’s early rivals.”

What Chen and Eyal are talking about are, in effect, quantum effects. But these viral effects do not scale up exponentially into the value of the entire network. The network encounters material resistance that act as a curb on exponential growth.

Perhaps Facebook and its evaluators have been so enthralled by the levels of user engagement that they have overlooked the way the value to individual users diminishes beyond their core network. This may explain why Facebook’s vast numbers do not translate into call-to-action advertising success. On Facebook we are actively engaged with what we most care about and we tune out the rest. The rest may be more than half of the content, and the vast majority of the marketing messages, but we are so in need of the wheat that it takes a lot of chaff to diminish our satisfaction. Beyond a certain point, though, the growth of the size of ones personal or Facebook’s global network does not increase a user’s happiness. It’s like the studies that have determined that over a certain threshold more money does not make people substantively happier.

Once you accept the fact of how differentiated the different members of a user’s network are in terms of their value and relationship to the user, the economies of scale of Facebook kind of break down. It is not a matter of general social clout (or Klout as the case may be) but of the specific user’s value overlay on their own network. If an advertiser really wants to target a specific type of user, given this complexity, wouldn’t it be easier to take out a quill pen and inscribe a missive on a piece of letterpress stationary and hand deliver it by horse-drawn carriage? At least that would get their attention!

Reverse Network Effects

NETWORK EFFECTS ARE the holy grail for Internet startups looking for venture-scale returns. On a platform with network effects, the value to a user increases as more users use it. Facebook, Twitter, LinkedIn, YouTube, Skype, WhatsApp and many others benefit from this dynamic.

In an age when more than a billion people connect over a network and new networks reach multi-billion dollar valuations with a handful of employees, one is tempted to believe that online networks are almost fail-proof. But as online networks grow to a size never seen before, many question their sustainability and believe that they are becoming too large to be useful.

To explore the future of online networks, it’s important to note how network effects correlate with value and the factors that make these network effects work in reverse.

Network Effects and Value

There is a strong correlation between scale and value in businesses with network effects.Greater scale leads to greater value for users, which in turn attracts other users and further increases scale. This rich-becomes-richer dynamic allows networks to scale rapidly once network effects set in.

There are three sources of value created on networks: Connection, Content and Clout.

Connection: Networks allow users to discover and/or connect with other users. As more users join the network, there is greater value for every individual user. Skype and WhatsApp become more useful as a user’s connections increase. Match.com and LinkedIn become more useful as more users come on board.

Content: Users discover and consume content created by other users on the network. As more users come on board, the corpus of content scales, leading to greater value for the user base. Content platforms like YouTube, Flickr and Quora, as well as marketplaces like AirBnB and Etsy becomes more useful as the number of creators and the volume of content increase.

Clout: Some networks have power users, who enjoy influence and clout on the network. Follower counts (Twitter), leaderboards (Foursquare) and reputation platforms (Yahoo Answers) are used to separate power users from the rest. On networks like Twitter, the larger the network, the larger is the following that a power user can develop.

Across these three drivers, a network with greater scale provides greater value in the form of:

1. More prospective connections for the user
2. A larger corpus of potentially relevant content
3. Access to a larger base of potential followers (greater clout), for power users

On most networks, value for users is created through more than one of these three sources. Facebook, for example, started with a value proposition centered around connection, but the introduction of the news feed has made content a central driver of value. In recent times, the addition of the subscribers feature has added clout for some Facebook users as well.

Why Network Effects Work in Reverse

One would expect that the bigger the network, the more value users derive from it.

However, as networks scale, the value for users may drop for several reasons:

1. Connection: New users joining the online community may lower the quality of interactions and increase noise/spam through unsolicited connection requests.
2. Content: The network may fail to manage the abundance of content created on it and may fail to scale the curation of content created and the personalization of the content served to users.
3. Clout: The network may get inadvertently biased towards early users and promote them over users who join later.

Just as network effects create a rich-becomes-richer cycle leading to rapid growth of the network, reverse network effects can work in the opposite direction, leading to users quitting the network in droves. Friendster, MySpace and Orkut bear testimony to the destructive power that reverse network effects wield.

Reverse Network Effects: Connection

Connection-first networks (dating websites like Match.com and networking communities like LinkedIn) build value by connecting people.

These networks may suffer from reverse network effects as they scale if new users joining the network lower the value for existing users. To prevent this, an appropriate level of friction needs to be created, either at the point of access or when users try to connect with other users.

On dating sites, women often complain of online stalking, as the community grows, and abandon the site. Sites like CupidCurated have tried to solve this problem by curating the men that enter the system, in a manner similar to restricted access at a singles bar.

LinkedIn creates friction by preventing users from communicating with distant connections. This ensures that users do not receive unsolicited messages. This also allows LinkedIn to offer frictionless access (OpenMail) as a premium value proposition.

ChatRoulette, in contrast, anonymously connects users over a video chat without needing to login. This lack of friction led to ChatRoulette’s stellar growth but also led to reverse network effects as anonymous naked hairy men took to the network, thus increasing noise and driving genuine users away from it.

Dating sites, as well as social networks like Orkut, have imploded in a similar manner after reaching scale, owing to noise created by fake profiles.

In general, networks of connection scale well when they create appropriate barriers to access on the network.

Reverse Network Effects: Content

On content networks like YouTube or Flickr, a larger network is likely to have more content creators, leading to more content for the user to consume. Networks like Facebook and Twitter, in addition to being networks of connections, are also networks of content.

Most networks of content have low friction in content creation to encourage activity from users and reach critical mass faster. To ensure that the content is relevant and valuable, the network needs strong content curation and personalization of the user experience.

Reverse network effects set in if the content curation systems don’t scale well. As more producers create more content, the relevance of the content served to consumers on the network shouldn’t decrease.

Curation

Content networks create a curation mechanism through a combination of moderation, algorithms and community-driven tools (voting, rating, reporting etc. ). Voting on YouTube, flagging a post on Facebook and rating on Yelp are examples of curation tools.

Curation mechanisms often break down as the volume of content increases. When curation algorithms and moderation processes do not scale, noise on the system increases. This leads to reverse network effects and users abandoning the system.

Quora has a very strong curation mechanism in place and benefits from a tech-savvy early user base. As Quora scales, many worry that less sophisticated users, entering the system, may increase noise leading to a rapid depletion of value for existing users. It remains to be seen whether its curation can scale as the network opens up to a broader user base.

Personalization

Content networks need a personalized consumption experience for users, that serves them relevant content.

An example is the news feed on Facebook or Quora or the recommendation system on YouTube.

Inability to maintain relevance of the consumption experience, with scale, may create reverse network effects.

The user experience on Facebook is centered around the News Feed.  However, Facebook’s frictionless sharing and cluttered news feed may lead to lower relevance for users as the network scales. Several factors contribute to this:

1. When a user adds friends indiscriminately, her news feed becomes cluttered with irrelevant posts.
2. Noise is further increased when marketers and app developers get access to the news feed.
3. When networks like Facebook and Twitter implement monetization models like Promoted Posts/Tweets, the signal to noise ratio suffers further as promoted content is less relevant than organic content.

Networks of content are constantly faced with the risk of reverse network effects as they scale. The poor signal-to-noise ratio in the news feed, not the size of the overall network, is Facebook’s weakest link as the network scales.

Reverse Network Effects: Clout

Networks of clout have a system of differentiating power users from the rest. Twitter, Quora and Quibb have baked in clout through the one-sided follower model. Active users vie for greater glory while using the network.

Networks of clout tend to be biased against users joining in late. Clout is a consequence of content that the user creates and early users get more time to create content and develop a following.

This is, ironically, aggravated by focusing on a high signal-to-noise ratio. Twitter recommends super users to prospective followers as these users are likely to create better content. Hence, the platform itself helps separate the power users from the rest.

Users who join later find it more difficult to develop a following and may stop using the network. These networks need a mechanism to ensure new users have equal access and exposure to the community to develop network clout. 500px, for example, differentiates Top creations from Upcoming creations to expose recent activity (often from undiscovered users) to the community.

Reverse network effects often cause a large and thriving network to implode. As a network scales, it’s ability to maintain a high signal-to-noise ratio is the leading indicator of its usefulness. Networks can, in fact, scale very well and prevent reverse network effects from setting in if they have

1. Appropriate level of friction in network access and usage, that prevents abuse
2. A strong curation system that scales well with the size of the network
3. A highly relevant and personalized user experience
4. A democratic model for users to build influence

Networks that have excelled in the above have scaled well. In a world where networks are reaching unprecedented scale, a keen focus on maintaining a high signal-to-noise ratio will enable them to remain valuable and effective as they grow.

Courtesy of an article dated July 28, 2015 appearing in MotleyFool.com, an article dated June 8, 2013 appearing in Pamorama.net, an article appearing in the March 2014 issue of WIRED, and an article dated May 31, 2012 appearing in Forbes Tech

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NEW STUDY: DITCH YOUR CHAIRS DURING MANAGEMENT MEETINGS, STANDING FOSTERS GREATER TEAMWORK AND EXCITEMENT

A NEW STUDY USING WEARABLE SENSORS SUGGESTS THAT TEAMWORK IS DONE BEST WHEN EMPLOYEES ARE STANDING UP.

As much as you might love slumping back in your chair and taking an open-eyed nap during long meetings, a new study suggests that teamwork is done best when employees are standing up.

It's the latest in a long line of research that examines the troubling effects of sitting. Evidence has piled up that “sitting is the new smoking”--that a sedentary lifestyle is linked to an increased risk for depression, heart disease, and diabetes. This is the first research we've seen that applies the anti-sitting argument directly to the boardroom.

The paper, “Get Up, Stand Up: The Effects of a Non-Sedentary Workspace on Information Elaboration and Group Performance,”  published recently in Social Psychological and Personality Science, analyzes teams of participants working for 30 minutes on developing and recording a university recruitment video. The teams either worked in rooms with chairs around a table, or with no chairs at all. While working, the participants wore sensors on their wrists that measured their physiological arousal based on the moisture produced by their sweat glands. Once the videos were finished, research assistants rated the teams’ collaboration and the quality of the videos, and the participants rated their team members’ attitudes--namely, how territorial they’d been. 

Lo and behold, the people who worked standing up had higher levels of physiological arousal, indicating excitement about the work, and were less defensive about their ideas than those who lounged in chairs. This lack of territoriality led to a better exchange of ideas and more engaging videos.  Lead researcher Andrew Knight of the Olin Business School at Washington University in St. Louis told Eurekalert. 

"Seeing that the physical space in which a group works can alter how people think about their work and how they relate with one another was very exciting," 

Knight himself had experienced the benefits of standing meetings while working for a software company, where groups would regularly crowd around whiteboards and work excitedly rather than lethargically. He found they always felt “more efficient and purposeful," which led him to want to back up this gut feeling with research.

Luckily, standing is free--so testing out these findings firsthand is as simple as getting employees off their butts.

COMMENTARY:

Strengths, Limitations, and Future Directions

Washington University of St. Louis integrated several types and sources of data – including physiological data, self-report data, and observational data – to draw inferences about how physical space influences interpersonal dynamics in groups. Our use of multiple data streams mitigates concerns of inflated relationships due to common method variance. Further, our use of a sensor to unobtrusively measure arousal highlights the potential for researchers to use new technologies to assess interpersonal dynamics in groups.

Despite these strengths, their conclusions must be interpreted with the limitations of their 
study in mind.   Their measure of territoriality was administered after the group task. In deciding to measure territoriality after the task, they weighed the costs of interrupting groups and sensitizing participants to the concepts under investigation with the benefits of measuring this pathway before groups completed their task.  Their ultimate choice to measure territoriality after the task may have influenced participants’ responses.  These concerns are mitigated by the fact that participants received no feedback prior to completing the post-task survey; nonetheless, they acknowledge that contamination may have occurred.  Future studies should replicate their findings using a measure of this mediator administered during the task rather than after it.

The University studied groups engaged in a 30-minute task, which may be a boundary condition of  their findings.  Estimates of typical meeting duration in organizations vary. Panko and Kinney
(1995) reported that nearly 75% of meetings are 30 minutes or less; Cohen and colleagues (2011)
found an average meeting length of 73 minutes (SD = 41). The 30-minute meeting length that they studied thus likely generalizes directly to many situations commonly found in organizations.
Still, future research is needed to explore the temporal boundary conditions around their finding
that a non-sedentary workspace enriches interpersonal processes in groups engaged in
knowledge work. It is possible that the benefits of a non-sedentary space would dissipate or even
reverse over longer periods of time if people become fatigued or irritable.  However, one 
promising approach for longer meetings might be for group members to oscillate between
standing and sitting over the course of the meeting.  Recent research (Oppezzo & Schwartz, In
Press) suggests that the beneficial effects of movement on creativity persist even after
individuals sit down.  Thus, it might be possible to use a standing format for the first 30 to 45
minutes of a meeting and then switch to a sitting format without sacrificing performance.
Research is needed to examine such questions.

Because the University examined one performance episode, their findings cannot speak to the
durability and permanence of the effects of a non-sedentary workspace across multiple group interactions. Although they do not have data on groups engaging in multiple tasks, they believe it is unlikely that members would become habituated to the effects that they proposed and found in this study.  The physiological and behavioral changes that they observed stem ultimately from physical effects (e.g., motion, physical location in space), rather than from perceptions of the novelty of the environment (Oppezzo & Schwartz, In Press). In post-hoc analyses they explored the possibility that the novelty of the environment might account for their results using a three-item measure that group members completed after the group task (e.g., “The room we worked in helped us feel creative.”). They found a non-significant effect of condition on members’ ratings of the novelty of the room (B = -0.05, p = 0.70), suggesting that people in the non-sedentary condition did not view their environment as more novel and conducive to creativity than those in the sedentary condition. Nonetheless, research would be useful to unpack how people respond to a non-sedentary environment across multiple group interactions.

Implications and Conclusions

The most important implication of this study is that the physical context in which a group
works can shape interpersonal dynamics and, ultimately, group performance.  Adopting a non-sedentary workspace may have benefits not just for individual physical health,  but also for group performance on knowledge work tasks.   By increasing arousal and reducing territoriality,   a non-sedentary workspace enhances the extent to which people engage in collaborative information elaboration – a key ingredient to high performance on knowledge work. These findings are important both theoretically and practically.  Theoretically, the physical space in which a group works is an important contextual input that scholars have, to date, largely ignored (Hackman & Katz, 2010). The manipulation that we investigated in this research – in which we simply removed chairs from the room – was relatively small, yet produced meaningful differences in group arousal and group idea territoriality.   Practically, office configurations and furniture are aspects of the workplace over which leaders often have direct control. Our results suggest that, if leaders aspire to enhance collaborative knowledge work, they might consider eschewing the traditional conference room setup of tables and chairs and, instead, clear an open space for people to collaborate with one another.

Courtesy of an article dated June 16, 2014 appearing in Fast Company Design

HOW BEYOND VERBAL MAPS THE PATTERNS AND FREQUENCY OF OUR VOICES EXPRESSING OUR EMOTIONS TO CREATE EMOTION-DRIVEN APPS

When a startup called Beyond Verbal began mapping the patterns and frequency of voices expressing various emotions, they noticed something odd: Love and hate looked almost identical. Yet despite the apparent similarities, our human minds can easily grasp the difference, and now machines are learning how to do it, too. In May, the startup revealed a technology that it says can identify hundreds of emotions in real time based on a voice alone.

The key to algorithmically deciphering the difference between love and hate? Beyond Verbal CEO Yuval Mor demonstrating the huffy tone of hatred says.  

“When you hate someone you really push the air and speak out.”

Then he switches to a kinder, cooing quality:

“When you love someone, this is the ‘pull’ kind of method.”

While to human ears the two tones are obviously different, on paper, their sound patterns look quite similar--except for these “push” and “pull” qualities, which the company built into its algorithm.

Yoram Levanon (Top) and Lan Lossos (Bottom)

Beyond Verbal’s technology is based on research that two PhD students, Yoram Levanon and Lan Lossos, began in 1995. At the time, they were investigating emotion’s role in decision-making. That developed into gigs as advertising consultants, and eventually, after realizing the connection between intonation and emotion, they started a business that improved call centers by identifying caller emotions. That company went into liquidation, but others saw opportunities for the researchers' data-driven emotion identifier beyond the narrow scope of call centers.

Dominance - This diagram represents the relative location of a voice filled with dominance in each aspect of basic acoustics (Click Image To Enlarge)

If, say, Apple’s Siri understood how you were feeling in addition to what you were saying, it could pull up not just a playlist, but rather a playlist that matched your mood. Politicians could use the technology to practice enhancing qualities such as leadership in their voices while giving speeches. People with Asperger's syndrome, who often have communication difficulties, could use it to understand verbal cues that extend beyond literal words. It could even help air traffic controllers identify when pilots were under stress.

Excitement - This diagram represents the relative location of a voice filled with excitement in each aspect of basic acoustics (Click Image To Enlarge)

Since a tiny startup couldn’t pursue all of these options at once, the researchers helped start a new company, Beyond Verbal, that focuses on making their technology available for developers to use in their own apps. The company has not yet announced its first customers.

Desire - This diagram represents the relative location of a voice filled with desire in each aspect of basic acoustics (Click Image To Enlarge)

During a demonstration in Fast Company’s offices, VP of Marketing & Strategic Accounts Dan Emodi played a video of President Obama talking about Mitt Romney during the 2012 presidential election. Beyond Verbal’s test app, Moodies, identified emotions in his voice like “provocation” and “cynicism.”

 

While listening to Princess Diana talk about her troubled marriage in a 1995 interview with the BBC, it noticed “love,“ “grief,” and “feeling of loss.“ 

 

You can try it on your own bit of monologue here.

The diagram below represents the relative location of a voice filled with Fear in each aspect of basic acoustics. The color represents the degree and location of the tested emotion, in this case fear, within that grid.  

Emodi says.  

"You think about fear, and fear is cold.  We didn't touch this...it so happened that fear is represented by blue colors [in our system for mapping the relationship between voice and emotion]."

Click Image To Enlarge

The app is not attempting to understand what you say, but how you say it. Researchers based the technology on an analysis of more than 1,000 speeches and questionnaires from several thousand study participants who listened to sound clips and chose corresponding emotions. Beyond Verbal has tested it by asking about 60,000 people online in 26 countries whether the machine had correctly paired sound clips and emotions. That research is continuing, says Emodi, and humans agree with the machine’s analysis 75% to 80% of the time, with success being slightly lower in countries such as Vietnam that speak tonal languages.

Is a machine that understands emotions creepy? A bit. Will politicians use it to practice their false apologies until they sound genuine? Probably. But, argues Mor, you could just as easily use the technology to practice sounding confident as a leader or loving as a parent, and through practice affecting these qualities, a la the “make it till you fake it” method, you may actually achieve them. In any case, you’ll be more in tune with the importance of how you say things.

Mor says.

“It’s not that you are necessarily [just] talking differently.  You’re listening. You’re listening to yourself.”

COMMENTARY:   In a blog post dated June 21, 2012, I commented on the neuroscience technology developed by Berkeley, California based NeuroFocus which measures the emotional responses of the human brain to external stimuli and how these differ between men and women. What is even more significant is how both sexes neurologically react to advertising and what aspects of an advertisement stimulates them, creating positive responses that ultimately lead to making a purchasing decision.  This new research into the human brain has led to the development of the new science of neuromarketing which explores the dynamics of consumer purchasing decisions, customer brand affinity, and customer brand engagement. 

Neuroscience research is making it increasingly clear that many of the decisions people make about what products they will buy or what services they will use are a result of intuition and unconscious mental processes rather than analysis or reasoning.  Consumers have emotional responses and logical responses to marketing and advertising.  And in both types of responses can occur on a conscious basis or an unconscious basis.

Experts at NeuroFocus suggest that approximately 11 million bits of sensory information is processed by the human brain every second. But only 40 bits of sensory information is processed consciously - this is all the conscious mind can handle - and all the rest of the information is processed unconsciously.

Advertising and marketing efforts are part of the universe of sensory information that the brain processes every day in a typical environment in the developed world. Images used in advertising, colors and designs used in packaging, music and fragrances associated with services, and the rich attribute sets of products themselves are all subject to the conscious and unconscious sorting that happens in the marketplace.

I believe that the creaton of emotion-driven apps for voice-activated apps in response to our voice patterns could eventually lead to customized marketing messages that increase the effectiveness of a brands advertising.  I could easily see a situation where I could make a phone call to order something and by decoding the emotional patterns of my voice, a salesperson or automated system could custmize its sales pitch to better serve my needs and increase the likelihood of a purchase.

I think that Beyond Verbal's voice pattern technology still have a way to go, and a lot of data still has to be gathered and analyzed, before generalizations can be drawn about the voice patterns of the population as a whole.  As NeuroFocus has discovered, there are distinct differences between how males and females respond to marketing messages.  The same thing maybe true in our voice patterns.   

Courtesy of an article dated July 17, 2013 appearing in Fast Company

BREAKTHROUGHS IN BRAIN SCIENCE SHED NEW LIGHT ABOUT CONSUMER BEHAVIOR, TIME TO RE-WRITE THE RULES OF MARKET RESEARCH?

Breakthroughs in brain science are challenging basic assumptions about consumer behavior. It's time to re-write the rules of market research.  

In February 1998, the late David B. Wolfe wrote an article in the now defunct American Demographics magazine headlined, “What Your Customers Can’t Say.”  The article challenged the assumption in most customer research that customers are the best sources about their motivations.   Wolfe wrote.

"Consumers don't choose rationally, [so] any research that forces rational answers has to be flawed."  

The article drew the largest response in the magazine’s history.

It has been 15 years since the article was printed but its argument is as relevant today as it was then.  Breakthroughs in brain science are challenging basic assumptions about consumer behavior. It's time to re-write the rules of market research. The following is an annotated summary of Wolfe’s original article.

If your job depends on market research, prepare for a shock. New discoveries in brain science are radically revising our understanding of how human beings think and make decisions, and these new models of cognition are rewriting the conventional wisdom about consumer behavior. 

Conventional marketing research depends on the assumption that people can accurately report their values, needs, and motivations. But many scientists no longer believe this. says neurologist Richard Restak.

"We have reason to doubt that full awareness of our motives, drives, and other mental activities may be possible."  

Bernard J. Baars, author of In the Theater of Consciousness, adds. 

"Our inability to accurately report intentions and expectations may simply reflect the fact that they are not qualitatively conscious." 

The idea that consumers have limited knowledge of their motives is shocking on both a professional and personal level. Everyone wants to believe they know why they do what they do. It wounds one's sense of personal autonomy to think otherwise. Also, evidence that average people cannot accurately describe their motivations is a direct challenge to established methods of conducting research. It calls for radical changes in the status quo of research. 

Marketing is ripe for a revolution because its failures are so apparent. One of the most important reasons for this breakdown is that research is not working because of flaws in its basic premises. Multivariate statistics that describe personality traits can account for no more than 7% of purchasing behavior, according to a paper published by William Massy, Ronald Frank, and Thomas Lodahl of Stanford, the University of Pennsylvania, and Cornell, respectively. 

Consumer research's problems originate in psychology, a field that has long struggled to define human behavior with the same precision physicists use to describe the movement of bodies from atoms to stars. But human behavior is too unpredictable to describe with such precision, because it depends on an almost infinite number of relationships.  Harvard psychologist Jerome Kagan writes. 

"An increasingly desperate search for cause-and-effect explanations leads many psychologists to "retreat to abstract ideas that ignore contexts completely." 

Consumer research reflects similar tendencies. 

Kagan is bothered by psychology's excessive dependence on behavioral models that conform better to statistical theory than to behavioral realities. Models of consumer behavior tend to extract their subjects from the complex, often unpredictable, but completely natural contexts in which people live and make purchasing decisions. The result is often an interesting manipulation of a hypothetical situation that leads to a marketing failure. 

One of the most famous marketing busts was the reformulation of Coca-Cola. Extensive consumer research predicted success for "New Coke" because people said it tasted better. But the research failed to disclose that people also saw "Old Coke" or "Classic Coke" as an important cultural icon that would lose value by changing the original recipe. This subtle value proved to be far more influential than taste in determining consumer response. 

Mainstream consumer research generally fails to take into account developmental changes in values and world views that happen across a person's life span. Research also tends to ignore the major changes in cognition, or how the mind processes information, that happen with age. The subliminal origins of these changes prevent consumers from adequately reporting them to researchers, but the changes are decisive in marketplace behavior. 

Another assumption that leads consumer research astray is borrowed from classic economics. Researchers assume that people make buying decisions to satisfy their self-interest, and that they use reason to determine which product best serves that end. Brain researchers see reason playing a much weaker role in personal decisions, however. In their book, Marketing Revolution, Kevin J. Clancy and Robert S. Shulman state the problem this way:

"Because consumers don't choose rationally, any research that forces rational answers has to be flawed." 

Our next post will discuss “hot buttons” and root motivators that should supplement current research methodologies.

COMMENTARY:    In a blog post dated March 8, 2012, I explained how developmental relationship marketing (DRM), a.k.a. Ageless Marketing was the key to tuning your marketing message so that it resonates with Baby Boomers.

The origins of Ageless Marketing stem from the five basic premises That define the origins of behavior, and its general path across the lifespan. They increase marketers’ effectiveness in linking product messages to the hidden (unconscious) drivers of consumers’ marketplace behavior by revealing behavior predispositions in various periods across the lifespan.

The five premises* contain benchmarks for testing the validity of what people report about their attitudes, needs and motivations. This is critical given that recent brain research indicates that all motivations are rooted outside the realms of consciousness. We can only speculate about the foundations of our behavior; thus overly relying on the literal meanings of consumers’ testimonies doubtlessly accounts for many marketing failures. 

• First Premise:  Origins of behavior - A person’s worldviews, needs, motivations and general approaches to needs satisfaction are predisposed – not predetermined -- by her/his current season of life, and originate in five systems of motivating underlying values (MUV Values).  MUV systems, from which all behavior emerges, are biologically innate and constitute the basic building blocks of behavior.  In effect, the five MUV systems are the DNA of behavior:  identity, relationships, purpose, adaptation, and energy.         
  
• Second Premise:  Origins of motivations - Urges to satisfy needs arise from root motivations that are activated by tensions between five sets of bipolar forces. The first force (objective force) in each set dominates behavior in the first half of life; the second force (subjective force) in each set dominates behavior in the second half of life.  
• Third Premise:  Domains of personal development - Personal development evolves in two domains of the self. These domains contain the roots of all developmental potential. The two developmental domains are:  physical domain and psychological domain.  
• Fourth Premise: Keeping information flow to levels the conscious mind can manage - The brain resolves this problem by conducting information triage. The criterion the brain uses to determine what information will be sent to the conscious mind is the relevance of information to a person’s survival scenario, a matrix of needs whose satisfaction is vital to a person’s comfort and pleasure and avoidance of discomfort and pain.   
• Fifth Premise: Seasons of life – stages of personal development - There are four seasons of personal development. The first two are dominated by social (psychosocial) development needs; the last two by inner (psychospiritual) development needs.

* Refer to my blog post dated March 8, 2012

In a blog post dated October 18, 2011, I profiled Neurofocus, a Berkeley-based firm that is making groundbreaking research in the field of neuromarketing.   Neuromarketing is a new field of marketing research that studies consumers'  sensorimotor, cognitive, and affective response to marketing stimuli like TV ads, billboard, online ads, etc.

Neuromarketing firms Neurofocus use technology like functional magnetic resonance imaging (fMRI) to measure changes in activity in parts of the brain, electroencephalo-graphy (EEG) and Steady state topography (SST) to measure activity in specific regional spectra of the brain response, and/or sensors to measure changes in one's physiological state, also known as biometrics, including (heart rate and respiratory rate, galvanic skin response) to learn why consumers make the decisions they do, and what part of the brain is telling them to do it.

In short, neuromarketing studies how a person's brain reponds to advertising messages.  It is the study of the brain’s responses to advertising, the brands encountered in our daily lives, and all the associated messages and images that are strewn throughout the cultural landscape of everyday life.   

Courtesy of an article dated April 1, 2013 appearing in MediaPost Publications Engage:Boomers and an a book published 2003 titled "The New Research: Understanding How Consumers Really View Your Products"

A LOOK AT THE RIDDELL 360 FOOTBALL HELMET, AND WHY NFL HELMENTS WILL NEVER BE CONCUSSION-PROOF

The Riddell 360 football helmet could help reduce brain concussions sustained by football players (Click Image To Enlarge)

EXPERTS AGREE, EVEN IF A CONCUSSION-PROOF HELMET WERE POSSIBLE, SUCH A SOLUTION WOULD BRING NEW INJURIES OF ITS OWN--TO THE SPORT AND ITS PLAYERS.

Imagine that you drive a car straight into a building at 40 mph. Despite airbags and seatbelts, you’d probably feel lucky to be alive. But when an NFL wide receiver meets a safety head-on, we expect them both to get back up to play second down.

What’s the difference?   One expert tells me.

“In a car crash, you stop in a matter of feet. In an NFL impact, you stop in inches.”

In the eyes of physics, a big hit on the field can be just as devastating as a car crash--or in many cases, worse. We’re expecting a mere 1.5 inches of foam and candy shell to decelerate a player’s head gently enough to prevent their brain from bouncing around inside their skull and causing poorly understood, but permanent and devastating injury. After talking to some of the brightest minds in helmet design, helmet testing and football physics, the elephant in the room became clear: A concussion-proof helmet is a pipe dream. If the NFL wants concussion-free football, they’ll need to redesign football.

That said, conditions have never been riper for disruptive technologies to increase player safety. And for the first time in football’s 200+ year history, we’re finally developing the methodology to separate our best helmets from the decorative chunks of plastic.

TESTING EVOLUTION

Just two years ago, a team led by Dr. Stefan Duma from Virginia Tech released the first ever five-star crash rating for football helmets (PDF). It’s one reason that Duma likens the NFL of today to the auto industry in the 1970s. Originally, all cars were rated by a basic pass/fail crash test, and it wasn’t until Congress passed legislature for a five-star crash rating system that car companies had the impetus to do better.

Before Duma’s testing, the same was true for helmets. The industry’s only concern was whether football players could take a hit without fracturing their skull or sustaining subdural hematoma. Concussions--or any other traumas--weren’t part of testing. So designing helmets became a limbo bar. If a manufacturer came in anywhere below crushed skull territory, their helmet was thrown in the approved pile.

Duma says.

“Our rating system is the first that actually shows people that some helmets are better.  One of the things we wanted to do was provide a mechanism for improved design. You’ll never have a concussion-proof helmet because injury is all about risk. But the better helmets lower your risk. That can be substantial. We found the best helmet lowered risk of concussion by 85%.”

THE WORLD’S BEST HELMET

Duma’s top-rated helmet today is the Riddell 360. Priced at about $400, it features a hard polycarbonate shell to bounce off an impact and an energy absorbing foam lining to absorb the aftershock. The pièce de résistance is its huge, spring-like facemask optimized for forward-facing collisions. The entire ensemble is so effective that it can cut the peak force of a head-on-head impact in half when compared to the league’s worst helmets. (And it just so happens, Riddell’s VSR-4, discontinued in 2010 but still supported for factory refurbishing, is one of football’s worst rated helmets.)

But why should players wear helmets at all, one might ask. In the mid 20th century, as many as 30 players were dying a year due to head injuries. Helmets were the byproduct of the NFL’s first head-trauma scandal. The first were thin, hard leather. Then they adopted plastics, padding, and face masks. As we learned more, the focus quickly became, not just hard protection, but the shock absorption of softer materials. This approach worked. Since the 1980s, we haven’t had a single skull fracture in the NFL. Today’s Riddell helmet is bigger than its predecessors, but it’s ostensibly the same design we’ve been building on for decades.

The Riddell Football Helmet (Click Image To Enlarge)

Each product Riddell releases follows about two years of R&D. They create a prototype. They fill it with a human head surrogate (complete with a brain-like liquid center) used in pass/fail certification testing. They smash it to simulate a head hitting the ground. Then they use another human head surrogate called the Hybrid III (found commonly in the auto industry’s crash test dummies), then they pummel the helmet again, focusing on specific angles of impact more like one might expect in an NFL game. Following that, Riddell seeds their new line to teams to test out in real-world conditions.

But with all this research and testing, can Riddell promise a concussion-free helmet?

Thad Ide, SVP of Research and Product Design at Riddell says.

“I wish we could.  With current technology and understanding, we’re just not there.”

The problem is ultimately one of physics. All helmets work under the same principle. The force striking one’s head--acceleration mixed with mass--can’t actually be prevented. Physics says that energy has to go somewhere, right? What good helmets do is lengthen the duration of the impact itself (in the hundredths of a second range), reverberating energy through various structures and materials, to smooth a hit from a sharp, high-g strike to a relatively smooth curve of deceleration. Consider landing on a concrete floor or a pile of pillows. Which impact takes longer and which impact hurts more?

Dr. Timothy Gay, University of Nebraska physics professor, writer, and industry helmet consultant said. 

“I think that it’s true that football helmets are 85% as good as they’re ever going to get.  The optimal football helmet won’t be much better than the helmet you can buy right now because there are just physics restraints on the kind of padding you can use. We have a pretty good micro, nanotechnological understanding of how materials work. And basically, there are limits on what padding materials can do for a given thickness.”

That’s an interesting point--what if we just increase the padding’s thickness?

MAYBE WE NEED A NEW PARADIGM

Gay tells me later, countering his own argument that had just sounded so convincing.  

"You can certainly make a helmet that’s concussion-proof.  All you have to do is put 15 inches of foam rubber on the outside of the helmet.”

This idea may seem as ludicrous as wrapping a fully padded football player in one of those novelty sumo suits. But it just so happens, there’s a company doing pretty much just that.

ProCap is a soft wrapper for football helmets that’s actually been around since the late 1980s. Most famously adopted by San Francisco 49ers offensive tackle Steve Wallace, this extra layer of padding is a means to further slow the impact of the blow. Its known downfall? The soft outer padding can look a bit silly. There are also claims--though no studies that I could find--that the friction generated by a soft outer shell could lead to a neck injury.

ProCap’s inventor wasn’t available for comment, but when I asked Duma about the potential of foam-wrapped helmets, he said that his lab hasn’t tested any, as the ProCap has generally existed as an add-on, not a self-contained helmet in its own right. But he did have the first reasoned red flag to this cult-worshipped design. In the 1990s, Duke researched the possibility of adding padding to the ceiling of cars to protect passengers in the case of a rollover. What they found was precisely on-point with football’s current fear, that the softness actually distributes pressure to the neck, which can cause horrendous spinal injuries. Duma says.  

“The notion was out there, ‘Let’s put 4 inches of foam in the ceiling!, but you may just end up with a bunch of quadriplegic people.”

It makes sense. Just look at your car. You have side doors full of foam or even airbags, but the ceiling is covered by a glorified sheet.

SO … ANOTHER NEW PARADIGM

Another, equally enticing possibility is to model football helmets, not after football helmets, but after motorcycle helmets. Most football helmets are designed to be reworn until they’re refurbished. They have to serve play after play. But motorcycle helmets are designed differently. They use a more drastic energy-absorbing foam and a hard shell that isn’t afraid to crack. The downfall is that these helmets completely disintegrate upon impact--they’re totally ruined--with the interest of absorbing every bit of energy possible.

But David Rogers, VP of Concept Development at Gentex, the leading manufacturer of helmets for the Air Force (that owns companies that have designed everything from football helmets to special ops helmets), argues that single-use helmets are not the panacea they appear to be. Beyond the fact that players would need to pause each play to swap out helmets, even our best motorcycle helmets are only designed for 18 mph impacts (not our theoretical 40 mph maximum hit of two players running 20 mph). And as Rogers puts it,

"Should you have a head-on collision in a motorcycle helmet at 18 mph, you probably still have a concussion, but you’re not dead.”

“Not dead” isn’t really any sort of improvement.

THE NFL CAN DO WHAT HELMETS CAN’T

After going back and forth with experts with every conceivable conspiracy theory I could imagine, the consensus was obvious: A concussion-proof helmet is either impossible, or its requirements would affect play so drastically that it would either impair the sport or lead to other injuries. Even if we can mitigate the impact of the average tackle (and current helmets actually do this exceedingly well), the worst hits in the NFL are exponentially harder than the baseline. I’ve heard estimates that 5 to 10 hits in any given professional game are of concussion caliber. It’s no wonder so many of our players have been out this year with brain injuries.

Dr. Duma tells me, when I ask what can change.

“The helmet is the third barrier of defense.  You have to work on the rules, and you have to work on the coaching.”

Football is a brutal sport, and it always has been. The hits seem big only because they are. Sooner or later, driving a car into a wall at 40 mph is going to hurt you, with or without a $400 piece of plastic on your head. Every single helmet expert agreed that we can redesign football much more quickly and effectively than we can redesign the football helmet--in fact, most suggested the idea to me. The only question that remains is, are we actually willing to change football? Or do we value the spirit of the game more than the lives of the people playing it?

COMMENTARY:    The fact of the matter is that the human brain was never meant to take the brutal hits of football players.  It is vicious sport where just one well placed hit can put you in the hispital, give you a concussion or make you a paraplegic for life.   There is not helmet that can be designed to protect the brain from that kind of damage. The brain is a jellatinous material that was never designed to take that kind of physical punishment.

Courtesy of an article dated February 2, 2013 appaearing in Fast Company Design

 

 

 

 

 

 

 

 

 

 

HOW NEUMITRA AND AFFECTIVA WRIST SENSORS CAN HELP PATIENTS SUFFERING FROM PTSD AND OTHER ANXIETY DISORDERS

Devices from two startups could be used to treat people with anxiety disorders—and one of the devices may eventually diagnose pain. 

Amid rising concerns over post-traumatic stress disorder (PTSD) and other mental illnesses, two MIT startups are developing wrist-worn sensors that can detect physiological changes—including perspiration and elevated temperature—that may signal the onset of events like anxiety attacks.

The data collected by these devices can be fed into an algorithm that aims to learn what triggers anxiety, or when people may be about to engage in a risky behavior. One goal is to head off destructive behavior, from drug abuse to suicide and violent outbursts, and to help with treatment.

The newest of these startups, Boston-based Neumitra, emerged from stealth mode earlier this year with a device that can measure proxies for excitement or stress, including increased motion, increased skin conductance from perspiration, and elevated skin temperature. The device, called bandu, sends readings to the wearer’s smartphone, which records them for later analysis. The device also includes a display that can be customized to suggest, say, that you take your medication, call a loved one, or listen to a song.

Neumitra’s wrist-worn sensor detects elevated perspiration and temperature associated with stress or excitement and can relay messages to the wearer (Click Image To Enlarge)

The challenge is teasing out exactly which physiological changes are linked to stressful events. Researchers also must filter out noise, such as perspiration from exercise or excitement from watching a favorite sports team. Some improvements will come from self-learning algorithms.

The Neumitra device is already being used in new research efforts at Massachusetts General Hospital, including ones to help patients suffering from PTSD and other anxiety disorders. The aim of the research is to create detailed records of what is triggering anxiety, says Darin Dougherty, director of MGH’s division of neurotherapeutics. While it is early days for the technology, he says, the device has the potential to fill a diagnostic gap.

Dougherty says.

“Previously, treaters had to rely on the patients’ subjective memory for the week, or weeks, between appointments as their sole measure of anxiety symptoms.”  

The device “provides moment-to-moment objective data regarding a patient’s anxiety symptoms,” and both the doctor and patient can see it.

Robert Goldberg, a neuroscientist and Neumitra founder, points to a grim national backdrop: one in three U.S. adults has a form of mental disorder—ranging from attention deficit hyperactivity disorder to PTSD—making it the third-largest category of health-care spending. More soldiers now die of suicide than in battle.  Goldberg says. 

“My goal is to bring mobile portable technologies to brain health.” 

Neumitra’s underlying technology is similar to that used by a commercial device made by Affectiva, a startup from MIT’s Media Lab that has made recent inroads in medical research.

Affectiva's Q Sensor 2.0 is a wearable, wireless biosensor that measures emotions via skin conductance (Click Image To Enlarge)

The company’s Q sensor has been used for more than a year in trials with an undisclosed company that is attempting to develop a physiological measure of pain. This would be an enormous advance, as pain diagnoses currently rely entirely on patients’ self-reporting.

The device is also in clinical trials to help measure drug efficacy in sufferers of Rett Syndrome, a genetic neurological disorder that can cause loss of speech, problems with motor coördination, and impaired cognitive function. The goal there is to see whether the drug is producing any measurable changes in patients’ stress levels, movement, and sleep patterns. 

Affectiva's Q Sensor 2.0 is an 'emotion' sensor that is worn on the wrist (Click Image To Enlarge)

Affectiva has made recent advances in other medical applications. In a recent study, the device showed it can serve as a proxy detector for a type of epileptic attack associated with sudden death. More than 50,000 people a year die of seizures, and most are unexplained. But in a few cases where the victims died wearing headgear that can measure the brain’s electrical activity (an electroencephalogram or EEG), clinicians found that brain waves were unusually suppressed. 

Rosalind Picard, a cofounder of Affectiva and director of affective computing research at the Media Lab, says that physiological responses measured by the Q sensor are associated with this same type of suppressed brain activity (which is not always fatal).  Picard says.

“We now have a correlate that is much easier to use than wearing an EEG all the time.”  

Clinical trials using the device in epilepsy patients are being planned, she adds.

The Q sensor can also help predict outbursts in autistic children, who often have difficulty verbalizing their emotions (see “Sensor Detects Emotions through the Skin”). Picard says her company is also working on ways to detect changes that may signal that a person is about to engage in risky or destructive behavior, such as drug abuse or even an outburst of violence.

Affectiva is currently focusing on supplying researchers—a single device costs $2,000, but the price is in the mid-hundreds when they are sold in quantity—including the software and support. The company is not currently marketing to consumers, but a consumer version might be available in a year or two, Picard says.

Goldberg says the Neumitra device will sell for $249 to $1,499, depending on features and analytics, and will be made available to consumers, as well as researchers and other organizations, sometime in 2013.

Goldberg says the company was inspired in part by a war veteran who was having anxiety attacks. Three years into therapy he realized that one source of his stress triggers were his visits to a Whole Foods supermarket. For him, the experience—strangers, suddenly appearing from a maze of lanes, holding objects—felt threatening.  He says.

“Whole Foods was a trigger point. We could have given him the same information in a week.”

Goldberg made a map of his own stress triggers, including several hot spots on and around the MIT campus.  He says he realized those were from rushing to events, and from being asked to speak publicly, as he did recently at a health startup event. 

He says.

“We see in healthy individuals—stress associated with real-world events. I can point to my own—public speaking is one of the major categories of phobia.”  

Ultimately, with enough people wearing stress monitors, he says, a company could find what was making employees stressed out, city officials could learn where women felt unsafe, and a nation could even take its collective stress pulse.

After the event, Goldberg invited me to put on the bracelet. He then asked me to recite the alphabet backwards in front of several strangers, who watched with a gleam of schadenfreude in their eyes. I lost my way around “W.” Goldberg, watching readings on his smartphone, said my wrist sweat and temperature indicated my stress was up 50 percent.

Although the technology is still experimental and the devices are used mainly in medical research, they herald the appearance of consumer versions and associated apps that let people monitor their mood and stress levels. Other emerging technologies aim to detect emotion using subtle cues from how people use smartphones (see “A Smartphone That Knows You’re Angry”).

COMMENTARY:   I know of a friend who is bipolar, and goes through frequent periods of emotional highs and lows.   It's when this person goes into depression that worries me the most because she drinks a lot, and has gone from one rehab program to another.  I hope that Neumitra and Affective can use their devices to detect when biopolar disorder patients are going into depression so that medications can be used to control their disorders.

Courtesy of an article dated December 20, 2012 appearing in MIT Technology Review

MIT VIDEO BREAKTHROUGH COULD GIVE BRANDS SIGHT, SOUND, EVEN MORE MOTION, AND HELP AD AGENCIES CREATE BETTER ADS

Software created by researchers at the Massachusetts Institute of Technology amplifies variations in video that are imperceptible to the naked eye, making it possible to exaggerate tiny motions. More telling, it could provide greater credibility for products.

MIT Computer Science and Artificial Intelligence Laboratory graduate student Michael Rubinstein designed the software, along with recent alumni Hao-Yu Wu, Eugene Shih and professors William Freeman, Fredo Durand and John Guttag. The researchers initially intended it to amplify color changes, but in their experiments found it amplified motion as well. The software makes visible the vibrations of individual guitar strings, or the ability to see someone's pulse as the skin reddens and pales with the flow of blood.  The new technology could assist online agency executives in creating more attention-grabbing advertising based on MIT's research on amplified emotions.

 

Eric Gulino, an ad executive at Skiver Advertising, said having the ability to see change without computer-generated graphics could increase the credibility of products because it would not require computer-generated art. He said it is not likely to revolutionize the way agencies create content, but it will open the door to demonstrate things not easily communicated and give consumers a whole new appreciation for products.

Take a car manufacturer, for example.  Gulino said. "Maybe the manufacturer had trouble communicating the effectiveness of technology in the car that takes over just before a crash,""The seatbelts might tighten. The brakes are applied and the airbag deployed. It's difficult to show it in real-time. It just looks like a crash."

Rather than a simulation, the manufacturer would show the event as it happens, giving the brand more credibility. Simulation would have been the only way to demonstrate a high-speed crash. Gulino said. 

"This technology opens up new avenues for creative folks.  It removes the fog from a consumer's perspective."

In a set of experiments, the software amplifies the movement of shadows in one frame of a street sequence photographed only twice, at an interval of about 15 seconds. Amplifying motion rather than color requires a different processing. The smaller the motion, the better it works.

Most of the research has been around imaging and monitoring medical conditions, such as in video baby monitors for the home, so that the respiration of sleeping infants would be clearly visible.

Agencies rarely work with universities like MIT. GroupM has begun to look into the idea, but doesn't work with researchers at the university yet. The ad industry already has a host of tools to create special effects, but even Needham Analyst Laura Martin agrees that

"Academic research demonstrates that diversity maximizes economic value."

While Martin refers to the TV ecosystem, her report on "The Future of TV: The Invisible Hand" highlights the need for more creativity in TV and video content.

COMMENTARY:    MIT's technical abstract describes the new video amplification technology as follows:  

"Our goal is to reveal temporal variations in videos that are difficult or impossible to see with the naked eye and display them in an indicative manner. Our method, which we call Eulerian Video Magnification, takes a standard video sequence as input, and applies spatial decomposition, followed by temporal filtering to the frames. The resulting signal is then amplified to reveal hidden information. Using our method, we are able to visualize the flow of blood as it fills the face and also to amplify and reveal small motions. Our technique can run in real time to show phenomena occurring at temporal frequencies selected by the user."   

Click Image To Enlarge

You can download a PDF file of the technical whitepaper describing MIT's Eulerian Video Magnification technology by clicking HERE. 

Courtesy of an article dated June 22, 2012 appearing in MediaPost Publications Online Media Daily

HOW NEUROFOCUS, A BERKELEY NEUROSCIENCE RESEARCH COMPANY, HAS IDENTIFIED HOW MEN'S AND WOMEN'S BRAINS DIFFER

 

It’s a known fact that there are vast differences between how men and women think and process information.  Yet some marketing experts believe many companies still can’t get it right when it comes to tailoring marketing to the sexes.  Dr. K.A. Pradeep, the CEO of Nielsen NeuroFocus, a Berkeley, California-based neuroscience research company, explains what companies should know about gender differences in the brain.

 

Dr. A.K. Pradeep of Berkeley's Neurofocus explains why he wrote the book about using neuroscience for marketing. Filmed by AVC with two hi-def cameras and a green screen, the video helps market Dr. Pradeep's book. To learn more about The Buying Brain, visit Neurofocus athttp://www.neurofocus.com/

In a blog post dated October 18, 2011, I profiled NeuroFocus and its newest product:  Mynd, the world's first portable, wireless electroencephalogram (EEG) scanner. The skullcap-size device sports dozens of sensors that rest on a subject's head like a crown of thorns. With Mynd, users can capture, amplify, and instantaneously dispatch a subject's brain waves in real time, via Bluetooth, to another device--a remote laptop, say, an iPhone, or that much-beloved iPad.

An attractive female subject is shown wearing NeuroFocus' Mynd portable EEG scanner:

Click Images To Enlarge

Here's a great video demonstrating NeuroFocus' Mynd, the world's first portable, wireless and wearable EEG scanner:

 

They found some major innate variances in the neuro make-up of women and men. For example, female brains have a larger prefrontal cortex which has an impact on emotions, and the regulation of emotions, so emotions are a primary way to get through to women.  And females tend to be more social, empathetic, and verbal than men.

 

In the above video titled, "The See-Through Consumer," A.K. Pradeep, the CEO of NeuroFocus, discusses the ways in which neuroscience, technology, and big data have enabled companies to know more about their customers than ever before. The session was moderated by Vijay V. Vaitheeswaran, China business and finance editor for The Economist, at The Economist's Ideas Economy: Innovation 2012 event in Berkeley, California.

Another big difference is that women have a larger corpus callosum, which is like an eight-lane superhighway connecting the right and left parts of the brain. Female brains distribute their thinking quickly, and can multitask - it comes naturally to them. It’s also significant that the hippocampus is bigger in women, so the level of nuance in emotional details is much greater. A man might remember the day he proposed; whereas a woman can likely tell you everything about that day

Advertisers have heard all the same Venus/Mars clichés and Louis C.K. monologues that consumers have. That said, some marketing experts, including A.K. Pradeep, believe that many companies still can’t tell their estrogen from their testosterone. The CEO of Nielsen NeuroFocus, a Berkeley, Calif.-based neuroscience research company acquired by Nielsen last year, tells Marketing Daily what companies should know about gender differences in the brain.

Q: What’s one of the major misunderstandings that marketers have about the way the brain works?

A: We’re all very keen to segment consumers into various groups, but that misses the fact that Mother Nature has already done a segmentation -- she calls it men and women. Marketing needs to honor that segmentation. 

Q: So what are some of the major differences?

A: Female brains have a larger prefrontal cortex. That has an impact on emotions, and the regulation of emotions, and so emotions are a primary way to talk to her. There’s also more intuitive reasoning. The portion of the brain that is responsible for worry is also somewhat larger. 

Another big difference is that women have a larger corpus callosum, which is like an eight-lane superhighway connecting the right and left parts of the brain. Female brains distribute their thinking quickly, and can multitask. It comes naturally to them. It’s also significant that the hippocampus is bigger in women, so the level of nuance in emotional details is much greater. A man might remember the day he proposed; a woman can likely tell you everything about that day. 

In men, there is a larger parietal lobe, which means they tend to be better at spatial perception. They like images better than reading. And the larger amygdala in men results in more aggression.

Q: How can that inform marketing?

A: Well, it helps to know that women tend to be more social, empathetic, and verbal. She is a multitasker and a big-picture thinker. So for example, we can show men and women an image of someone drinking a beverage, and their brains react about the same. But then if we show someone drinking that beverage, and then touching someone else on the shoulder, the women’s brains tend to really light up. The image of someone drinking something doesn’t create much of a response at all. But when it is shown with a social interaction, it does.

Q: What’s the impact of the emotive difference?

A: We don’t know why, but women have a better-developed suite of emotions. Women smile a lot more than men. Female infants make eye contact many times more than boys. Eye contact is critical in connecting to women. When girls play or do any cooperative activity, they are more than 20 times as likely to take turns as boys -- 20 times!

Q: But women are also very smart shoppers, and very rational. 

A: Yes. But they respond better to pitches with a combination of fact and feeling. A low price is a fact. A happy price is one that is low, and also emotive. 

Q: So that’s what she likes. What turns her off?

A: Stress. Conflict. We know that cortisol -- the stress hormone -- actually stays in women’s bodies longer than men, so she is conflict-avoidant. 

Q: Which companies get it right?

A: I won’t name names. But in general, I see many health and beauty companies getting it right, combining messages for women in a way that is most likely to appeal to them. The worst are CPG companies, which is interesting -- it’s a trillion-dollar space, and women do most of the purchasing. 

COMMENTARY:    There's no doubt in my mind that men and women think differently, and those differences are very important for brand marketer's to understand when marketing their products to either gender.  

The fact that so few brand marketers actually take the time to understand what goes on inside the human brain and how consumers respond when presented with similar product choices is a missed opportunity for them to design their products in such a way as to elicit the most positive reactions to their products.  

Neuroscience is still in its infancy, but you can bet that in another 10 years, the adoption of neuroscience in marketing decisions will not only become commonplace, but necessary in designing products, its packaging and even pricing it right.

The Science of Neuromarketing

The new science of neuromarketing explores the dynamics of consumer purchase decisions, customer brand affinity, and customer brand engagement.  Neuroscience research is increasingly clear that many of the decisions people make about what products they will buy or what services they will use are a result of intuition and unconscious mental processes rather than analysis or reasoning. Consumers have emotional responses and logical responses to marketing and advertising. And in both types of responses can occur on a conscious basis or an unconscious basis.

Experts at NeuroFocus suggest that approximately 11 million bits of sensory information is processed by the human brain every second. But only 40 bits of sensory information is processed consciously - this is all the conscious mind can handle - and all the rest of the information is processed unconsciously.

Advertising and marketing efforts are part of the universe of sensory information that the brain processes every day in a typical environment in the developed world. Images used in advertising, colors and designs used in packaging, music and fragrances associated with services, and the rich attribute sets of products themselves are all subject to the conscious and unconscious sorting that happens in the marketplace.

Brand Engagement Begins at Birth

American children are exposed to an enormous amount of advertisement. The marketplace is very good at schooling children in brand awareness. According to a research conducted by Nickelodeon, the time children turn about 10 years old, they know about 400 brands. Child psychologists at the Wright Institute in Berkeley, a private college offering masters and doctoral programs in clinical psychology and counseling psychology, say this figure is up from an average of 100 brand logos at the tender age of three. That is an increase of 300 percent in just three short years. As market researchers, we might ask how this level of brand awareness is developed.

Survey research conducted by SIS International Research indicates that people tend to use brands that they have vague memories about which extend back to their childhood. This is true for 53 percent of the adults and 56 percent of the teenagers who were surveyed. The survey data indicates that this most true for household good, beverages, and food - all brands that tend to be passed down from generation to generation. A review of when Proctor and Gamble introduced some of their brands gives a good picture of how this takes place. The age Proctor and Gamble's brands can come as a big surprise to consumers.

About 20 years ago, little children were as familiar with the Joe Camel character on a pack of cigarettes as they were with the cartoon drawing of Mickey Mouse, the Disney character.   Most people would agree that children are knowledge sponges and that they soak up much more information than can adequately be measured.  Some of the information that children process is subconscious, just as it is for adults. But a good share of brand-related knowledge acquired by children is consciously acquired, tied as it is to social status.  In fact, some market research firms that focus on learning about what children think about products and services relevant to their lives.

Courtesy of an article dated May 21, 2012 appearing in MediaPost Publications Marketing Daily, an article titled "Brand Affinity and Neuromarketing" appearing in About.com Market Research