Demographics Fail

We forget, now that our reach is wide, that all purchasing is done by individuals.  Since we don’t know the individuals, and locating and selling to each and every one of them (us) is too expensive, we developed marketing to help us select the people, the individuals, most likely to purchase whatever we are selling.  We do that by carving up the population into demographic segments.  We do that by creating images and messages our testing tells us will appeal to those demographics.  As you noted, I am using the word “demographics” loosely – as it can just as easily mean single white 18-24 year-old men when selling video games, as it can mean general practitioners in the rural parts of beef exporting states when selling Lipitor.

759460300_63ca1caac9_mBut, why is this important?  Demographics provide us with statistically probable individuals.  Using these expected values are a great way for describing groups, but the value breaks down when talking about individuals.  We all know the story about the man who drowns crossing the river that is, on average, six inches deep.

The second failing in demographics is the pure focus on the individuals.  If the goal of sales and marketing is to convince individuals to take action (purchase, vote, visit, etc.), demographics alone does not provide the context under which we, as social animals, make decisions.

The number one factor that we as consumers use in making purchase decisions in consumer packaged goods, automotive, everything is our peers.  The younger we are, the better demographics reflect our peers, but that starts to break down rapidly once we leave school and enter the work force.

One place where we, as marketers, do a great job taking peer context into account is children’s toys.  Think about how they are advertised.  Is the latest and greatest StarBot 7000 action figure advertised with a static image of the figure with a voiceover talking about the high durability injection molded plastic construction and the die cast elbows capable of withstanding 30,000 hours of continuous play in -40°C conditions?  No, they show bunch of kids running around having a great time with the StarBot.  Children do not have long-standing deep networks of peers, so advertisers create a potential peer group in the advertisements.  Even as children get older, more media savvy, and create deeper relationships with their peers, all parents will recognize the plaintive cry of, “But, Billy has one!” Continue reading “Demographics Fail”

Why is an influence metric hard to decide on?

Why is coming to common metric for measuring influence so hard? Short answer: because measuring influence is not only nuanced, but it’s also really hard.  Maybe we’re asking the wrong question, maybe we should be asking how susceptible to influence are we?

First, a matter of semantics: authority is power bestowed by an outside source. Police, judges, your boss, etc. all have power over you in their own contexts. In most cases, authority is external to Social Media, so what we really want to know is how influential (power regardless of authority) a person is. So, I’ll stop talking about authority and start talking about influence.

Influence in SM is created through exertion of control over content that reaches you by modifying the content, or adding additional context such as your opinion. Modifications can be explicit, or implicit; merely passing along a piece of information indicates you have some interest in it. The social part of this information flow dictates who sees your content. So your influence is relative to your network. That’s bad for good metrics. What worse, from the following diagram, you can start to see that influence is also relative to the individuals within your network. Fortunately, combining influence and reach seems to be promising.

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Imagine you are A. You’re going to have a lot easier harder time exerting control over information flowing through your network than B. That’s an example, why reach (or count) alone is a poor measure of influence. But, clearly if you have a larger network, you are likely to influence more people. So how do quantity and influence work together? One strong way is path independence.

Turning influence on its head, it is much easier to measure how likely we are to be influenced depending on where the content comes from. Hearing from two independent sources will have greater impact on our decision forming than two related sources. In marketing, this is called the media multiplier when advertising is spread across multiple channels, e.g. Radio + TV.

So maybe the real question is, how influencable are we? One easy measure is network efficiency.

Andreas Kluth and the Campfire

Great podcast interview with the Economist‘s Andreas Kluth contrasting social media and the communication around the campfire.

“We were all awkward as teenagers … if I had had already Facebook and such media available to me I would have probably been completely impossible to talk to now. Because of course, there is a certain brain exercise involved in communicating face to face: cues and voice and body language and so forth. Knowing not when to interrupt someone.”

From way back at the end of oh-7, and still a great listen.

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/Message: Authority Is A Highly Charged Particle

I’ve discussed my thoughts on authority before and I think follower count is a poor measure; but Stowe Boyd as has a great post (where the name for this post came from) summing up much of the controversy.

Two things I particularly like about the post: his spelling out why follower count is not without merit as a measure, and his unshy conviction that influence is a good thing.

To these I’ll add one short thought and one quote.  Follower count, for all of its failings is the single measure we can all agree on.  That alone is powerful. As for influence:

It is the pressure of our peers, after all, that gives us the support to try things we otherwise wouldn’t have.  — BILL TREASURER, Right Risk

A very happy, healthy, and prosperous New Year to you and your social network. Keep connecting.

Random Graph Generation in Perl

If you ever find yourself needing to generate random graphs in Perl (quite the ice breaker, I can tell you), I recommend checking out Matt Spear’s Graph::Maker, which has generators for everything from Erdos-Renyi and Watts-Strogatz to Lollipop graphs. The only downside is the use of Graph which is s-l-o-w for graphs of even moderate size, so makes using it directly for simulations of Social Networks out.

Watts Strogatz Network from Wikipedia

[Image from Wikipedia]

Happiness is Contagious in Social Networks

Social Happines (From the NY Times)
Social Happiness (From the NY Times)

Knowing someone who is happy makes you 15.3% more likely to be happy yourself, the study found. A happy friend of a friend increases your odds of happiness by 9.8%, and even your neighbor’s sister’s friend can give you a 5.6% boost.

“Your emotional state depends not just on actions and choices that you make, but also on actions and choices of other people, many of which you don’t even know,” said Dr. Nicholas A. Christakis, a physician and medical sociologist at Harvard who co-wrote the study.

…quoth the LA Times; and there’s more coverage over at the NY Times (including a full size of that great image).

Game Theory + Network Analysis = ?; An Example

During one Saturday in the beginning of November, I took part in a multi-party negotiation, which had some surprising results. Out of curiosity, I mapped who wanted what from whom, and ran a basic network analysis.  The second surprise of the day was the analysis was really close to the observed results. I hope this description of my network analysis of a common game theory problem spurs discussion about how use network analysis and game theory in combination.

Code Blue: Swift Trust and Team Dynamics of a Crash Cart Response

Swift Trust, much like it sounds, is the concept of rapidly coming to intra-team trust.  A doctor friend of mine who introduced me to the term, explained it with the context of the ad hoc team of MDs and nurses responding to a cardiac arrest, a code blue.

I have been thinking about this throughout a book I am reading now, Honest Signals, by Alex (Sandy) Pentland from the Human Dynamics group of the MIT Media Lab.  In it, Prof. Pentland discusses physiological social signaling, and one point particular to swift trust stood out:  with great accuracy, one can predict behavioral outcomes using a “thin slice” of observation.  One study was able to predict six-year marital success based on just the first three minutes of a marital conflict.  There are many more studies showing similar success including job interviews, therapist competency ratings, and courtroom judges’ expectations of trial outcomes. My guess is there are things about the crash cart scenario which take advantage of this.

Some thoughts about this applied to code blue teams:

  1. the roles are well defined, so the amount of politicking is reduced
  2. time pressure pushes you to trust your colleagues, as there is little other choice
  3. the desired outcome is constrained, so you are only asked to trust in this specific situation
  4. trust develops rapidly with success
  5. trust develops when you don’t have a choice about the team over the long term. (time frame is short, so don’t know if this comes into play).

If these are right, here are a few predictions about the crash response process:

  1. there are a number of quick steps taken as a group before administering to the patient.  That would help establish some trust right at the beginning.
  2. the team members know each other at least by reputation, that goes a long way to giving the benefit of the doubt.
  3. the outcome is critical, so everyone is pushed to excel. This works in the the trust/success feedback loop.
  4. team members talk about crashes with their non-team colleagues.  this helps the reputation feedback.

Are there any MD’s or RN’s out there who care comment?  I have only the most cursory knowledge about the way the team is conducted, not to mention the actions team members take.  Does this fly?

[Photo credit: Simon]