I do apologise for the momentary lapse in blogs, but I’ve been extremely busy see, and so while I get my affairs in order, here is an oldie but a goodie. When I first saw Malcolm Gladwell I couldn’t believe my eyes. What an impressive fro!!
Marketing & Media Ecosystem 2010, identifies the priorities, capabilities and partnerships required across the marketer – agency – media value chain to optimize now and prepare for the future. The first cross-industry partnership of its kind, MME 2010 is a joint study between the ANA (Association of National Advertisers), IAB (Interactive Advertising Bureau), AAAA (American Association of Advertising Agencies), and management consulting firm Booz & Company.
Heard of Communispace? They have taken the concept of Social Networking, think Facebook, created hundreds of private communities for their clients including over seventy companies from skin care to breakfast cereal, banking to technology services and then sold the wisdom of these communities for about $180K for the first six months, and then $20K a month thereafter.
It works like this: I have a new Organic Skin Care product I want to sell to women from the age of 18 to 90. I hire Communispace who then recruits anywhere from three hundred to five hundred people, all women, aged 18 to 90, to form a community that includes profiles, discussion forums, online chat, and uploaded photos. Sounds familiar yeah, but the difference here is that this Social Network is a Research Network. No body else in Internet-land can see it except for the members, the Communispace moderators and the client.
Oh there one more thing, the members get rewards, typically gift cards, from places like Amazon.com.
These focus groups have been around for sometime, and the concept isn’t new. It’s just that the technology has matured such that, it becomes effortless and a little bit cooler.
James Surowiecki wrote a book on the subject:
The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations published in 2004, ISBN 978-0385503860.
He goes on say:
It’s about the aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate its argument, and touches on several fields, primarily economics and psychology.
Some of these anecdotes are quite interesting.
A contestent at a country fair: guess the weight of an ox after it had been slaughtered and dressed. It was predictive and needed to account for the butcher’s skill. 800 guessers, including expert butchers and farmers and also merchants and family members. After the contest, taking the average you got 1197 lbs – and the actual weight was 1198 lbs. It wasn’t a coincidence, nor limited to ox-weight-guessing.
At racetracks, the odds on horses predict almost perfectly how likely a horse is to win. Odds are determined collectively by correlating all bets and establishing statistical judgements.
Eli Lilly‘s think-tank has an internal stock market for predicting which drug candidates will make it to Phase 3 clinical trials: vital to narrowing down which pharma product to invest in. They open the market to 100 “semi-experts” who have some info but aren’t on the inside, and collectively they identify which candidates will win.
But then given all this what can be said about mob or herd mentality, like, for example, during demonstrations that turn violent or when the stock market crashes? How do we ensure the wisdom of the crowd? Is it something that can be moderated and controlled or is it something organic and fluid?
Social psychologists studying group behaviour tend to prefer terms like “herd behaviour” or “crowd hysteria”. And, one of the key catalysts to a misinformed and irresponsible crowd is an “information cascade” a concept that was introduced in an article by Sushil Bikhchandani, David Hirshleifer, and Ivo Welch back in 1992. Based on observational learning theory, information cascades occurs when people observe the actions of others and then make the same choice that they made. Because it appears safer and more sensible to do what others are doing.
This is definitely true for the Japanese who will cue in line for ages for a [insert store/cafe here] because why? Well everyone else is so it must be good!
But Surowiecki does acknowledge the dangers of pack mentality and so in a session entitled Independent Individuals and Wise Crowds, or Is it Possible to Be Too Connected? he asked “how do you ensure wisdom of the crowds without information cascades?”
1. Keep your ties loose.
2. Keep yourself exposed to as many diverse sources of information as possible.
3. Make groups that range across hierarchies.
So coming back to Communispace, the rules of engagement would have to be slightly different then social networks, because as we’ve just seen, having people that are tightly coupled, say friends and relatives may sway your views. The wrong information, especially from more vocal persons may also need to be tempered by other views, making the role of the Communispace moderator all that more important, and lastly, selecting people from diverse demographics, but yet ensuring they are your target audience is also important.
Last but not least, you need to have a really good senior analyst on hand to organise the data, retrieve the information and present the right level of insight for companies. Otherwise you could end up with another iSnack2.0 disaster.
The PageRank of a webpage as used by Google is defined by a Markov chain. It is the probability to be at page i in the stationary distribution on the following Markov chain on all (known) webpages. If N is the number of known webpages, and a page i has ki links then it has transition probability for all pages that are linked to and for all pages that are not linked to. The parameter α is taken to be about 0.85.
Markov models have also been used to analyze web navigation behavior of users. A user’s web link transition on a particular website can be modeled using first- or second-order Markov models and can be used to make predictions regarding future navigation and to personalize the web page for an individual user.
Hmm… more on this later.