Reading Virtual Minds Volume I: Science and History, 4th edition

NextStage: Predictive Intelligence, Persuasion Engineering, Interactive Analytics and Behavioral Metrics It’s with great pleasure and a little pride that we announce Reading Virtual Minds Volume I: Science and History, 4th EDITION.

Reading Virtual Minds V1: Science and History, 4th edThat “4th EDITION” part is important. We know lots of people are waiting for Reading Virtual Minds Volume II: Experience and Expectation and it’s next in the queue.

But until then…

Reading Virtual Minds Volume I: Science and History, 4th EDITION is about 100 pages longer than the previous editions and about 10$US cheaper. Why? Because Reading Virtual Minds Volume II: Experience and Expectation is next in the queue.

Some Notes About This Book

I’m actually writing Reading Virtual Minds Volume II: Experience and Expectation right now. In the process of doing that, we realized we needed to add an index to this book. We also wanted to make a full color ebook version available to NextStage Members (it’s a download on the Member welcome page. And if you’re not already a member, what are you waiting for?)

In the process of making a full color version, we realized we’d misplaced some of the original slides and, of course, the charting software had changed since we originally published this volume (same information, different charting system). Also Susan and Jennifer “The Editress” Day wanted the images standardized as much as possible.

We included an Appendix B – Proofs (starting on page 187) for the curious and updated Appendix C – Further Readings (starting on page 236). We migrated a blog used for reference purposes so there may be more or less reference sources and modified some sections with more recent information.

So this edition has a few more pages and a few different pages. It may have an extra quote or two floating around.

You also need to know that Reading Virtual Minds Volume I: Science and History is a “Let’s explore the possibilities” book, not a “How to do it” book. As such, it deals with how NextStage did it (not to mention things that happened along the way). It does not explain how you can do it. This book’s purpose is to open a new territory to you and give you some basic tools for exploration.

There are no magic bullets, quick fixes, simple demonstrations, et cetera, that will turn you into jedis, gurus, kings, queens, samurai, rock stars, mavens, heroes, thought leaders, so on and so forth.

How to Do It starts with Volume II: Experience and Expectation and continues through future volumes in this series. We’ve included a Volume II: Experience and Expectation preview with a How to Do It example on page 302 so you can take a peek if that’s your interest.

That noted, I’m quite sure that you won’t get the full benefit of future volumes without reading this one because unless you’ve read this one you won’t understand the territory you’re exploring in those future volumes.

Reading Virtual Minds V1: Science and History, 4th edThat’s Reading Virtual Minds Volume I: Science and History, 4th EDITION. It’s so good and so good for you! Buy a copy or two today!

Posted in Analytics, Consumer Psychology, Marketing, NextStageology, Predictive, Research, Social, Tools, {C,B/e,M}sTagged , , , , , , , , , , , ,

A Twittering (and Related Social Platforms) Update Part 4 – Twitter v LinkedIn v Facebook v FourSquare v Pinterest v … (If you invest here, do you need to invest there?)

NextStage: Predictive Intelligence, Persuasion Engineering, Interactive Analytics and Behavioral MetricsThis is the fourth post in a six part blog-arc about some recent research NextStage has done regarding Twitter and several other social platforms. Some of these posts appear on my BizMediaScience blog due to tone. This post is a little more researchy and we figured it should go here. We’re also wanting to spread the love a bit.

These posts will cover

  2. Watches
  3. “You don’t follow anybody”
  4. Twitter v LinkedIn v Facebook v FourSquare v Pinterest v …
  5. Private v Public Personae
  6. “You rarely point to someone else’s writing”

This post deals with the reason for this blog-arc, the marketing functionality of different social networks and NextStage’s research. We first discussed these concepts during our SNCR NewComm Forum 2008 presentation, Whispering to Be Heard: The Art and Science of Buzz Marketing so you can appreciate that we’ve been looking at this for a while.

Twitter v LinkedIn v Facebook v FourSquare v Pinterest v …

NextStage is completing a study on the sociality transfer between social platforms. Specifically, we’re investigating community detection by groups and individuals, how they determine which platforms serve them best, hence marketers can determine which social platforms will serve a given audience and message best. The end goal is an equation that determines cross-pollination between social platforms, as in “If you invest here, do you need to invest there?”

I presented The Social Conversion Differences Between Facebook, LinkedIn and Twitter at the Providence eMarketing Con on 13 Nov 2011 and explained that the greatest marketing cross pollination efforts at that time would be a Twitter-LinkedIn effort.

More so than any other effort. Twitter to LinkedIn and back.

The reason is due to Twitter and LinkedIn users having {C,B/e,M}s1 that are much closer to each other than the {C,B/e,M}s of any other combination, therefore a Twitter-LinkedIn campaign allowed for lower marketing costs (same material would engage both audiences) and allowed for multiple touchpoints in this single cross-audience (multiple touchpoints generate more activity than single touchpoints in a given audience).

NextStage Compatibility GaugeMore recent research was done quite differently from the above and dealt with sociality (the ability to recognize node-specific communities and a blend of community detection and recognition). If you recently received an invitation from a NextStageologist to join them on a social platform, you were part of the research. Chances are you were invited to join one of us on some social platform because you’ve generated an extensive “paper” trail — you blog, you publish whitepapers, you have more than one profile somewhere, you comment on other people’s blogs, you have an online resume, … and that paper trail could be analyzed by our Evolution Technology (most often NextStage’s Compatibility Gauge) to determine if you would or would not link, befriend, pin, tumble, so on and so forth, and what platforms you’d accept/allow contact on.

See how painless research can be?

Truth be told, some NextStageologists received a warning message from Facebook: we were attempting to befriend people we didn’t know and, evidently, enough Facebookers complained that several of our accounts got flagged. We could either quit Facebook, take back all our Friend Requests, take back all Friend Requests made to people with whom we had few friends in common, … Evidently research isn’t painless for everyone. The fact that there are people on Facebook who follow some of us on Twitter but won’t befriend us is worth a post in itself, don’t you think?

But in any case, the results are fascinating.

And if the results are all you’re interested in, click here.

Theory to Practice

One of the things we theorized, tested and put into some of our tools (NextStage ClientProspector and NextStage SocialInterferometer which are currently only available to Members (we’re hoping to make the SocialInterferometer public soon), NextStage LoveFinder which is publicly available and NextStage JobProspector (still in development) is that {C,B/e,M}s can fit together like puzzle pieces, sometimes like hands in gloves and sometimes they’ll grind against each other like gears shearing teeth as they clash.

This puzzling-glove-clash determines how messages will be communicated by smaller networks through larger networks — how groups can thrive within groups. Kind of like being part of a organization while having a group of closer friends within that organization. Everyone takes part in the organization’s activities and the closer group will engage in its own activities beyond those provided by the organization.

We’re interested (and you should be interested, too) in how messages within the smaller, closer groups get propagated through the larger groups. Let me give you an example.

Figure 1 - A network of 1Let’s say I have something I want to share. Immediately, I can only share it with myself. This is demonstrated by that one, little, solipsistic dot in the middle of the image on the right. I may have a great idea, an incredible product, a wonderful service, a great story, a good vibe, whatever. Immediately it’s just me who knows about it.

Oh, what to do?

Figure 2 - A small network of friendsWell, the first thing to do is tell a few friends.

But remember your own experiences sharing great news with others? Do you tell the first stranger you meet? Do you go looking for a stranger, someone you don’t know from Adam?2

Chances are you don’t go the Adam route. Chances are the first people you share your news with are close associates, people in your tribal network. People in your tribal network may be physically right next to you, a few feet away, a few doors down, in the next city, state or country.

What makes them participants in your tribal network is that you and they have lots of similar if not shared experiences and that you tend to respond similarly if not near-identically to anything that comes along. In other words, they’re in your tribal network because their {C,B/e,M} is either identical to or real close to yours. Because of this, you trust them to rejoice with you when you share your good news or give you solace when your news isn’t so good. This circle of friends is known as a psycho-social distance3 of 1 in social mechanics and the tribalness is called homophily.

Figure 3 - The network is growing!So you get feedback from them and it’s positive. So positive, in fact, that they want to tell some their friends and you risk sharing your joy with people just a little outside your normal social network. Now we’re dealing with people at a psycho-social distance of 2 from you and 1 from your friends.

Figure 3a - The network is growing circles!And some of those people at psycho-social distance 1 from you? The folks with similar and not identical experiences? They’re the people who’ll transmit the message to people who don’t quite know you at all (think of reTwittering a tweet). These people are more interested in the message than you, they are captured by the meme more than your personality. These people show up in the image as different colored dots.

Figure 4 - My gosh! Look at all those people you more or less know!Okay, now those people who don’t quite know you are spreading the message through their networks. These folks are psycho-social distance 3 from you. Notice that there’s more than three colors in the image? That’s because each time the message leaps a psycho-social boundary it does so by transforming a little (they don’t call it viral for nothing). The message (at this point some people will call it a meme and that’s incorrect. The message and the meme will travel together at this point and the two are different) has probably morphed slightly by going from your {C,B/e,M} through the next person’s {C,B/e,M} then through the next person’s {C,B/e,M} so on and so forth.

Figure 5 - And look what's happening to your great little thought!This is the point where marketing goodness happens. People start interpreting the original message. The source message and the meme it contains separate. The meme continues with none to very minor changes — it is the viral core of the message, that part that gives the message meaning in so many diverse markets and with so many different audiences. It does this by adapting a little bit but in ways that make big differences. Think of a virus that affects some people but not others that changes its viral sheath a bit. Now those previously unaffected are affected. Your good idea is doing the exact same thing only doing it to get inside people’s heads instead of their lungs or gut.

Those little bits and big differences appear because by now that meme has been slightly modified by everybody who hears the message. They’re all adding their little flare to it and it travels much like whispers in the childhood game “operator” (some call it “telephone”). The best known modern demonstration of this is the “spin” politicians’ surrogates put on their don’s gaffs and guffaws: they can’t control the message so at least get the best meme on it so that distortions and deteriorations are minimal and deniable. This is why the best spins are five words or fewer. Memes, the messages’ viral cores, are much like biologic viruses — the smaller they are the less stoppable they are (remember this if you’re in marketing or its close cousin, propaganda).

Figure 6 - And now everybody knows you!And if you’re lucky and you’ve done your work well and you know what you’re doing, your source message hasn’t changed all that much from its original form, has gone viral and you’re message is now making its way through groups and minds that you couldn’t imagine.

And it’s “making the rounds” because people who are now interacting with your message think completely differently than you think, their {C,B/e,M} so foreign to your {C,B/e,M} that you wonder how they learned about your message in the first place.

Now, take a good look at that last image. See that there are different subnetworks within the greater network? And that each subnetwork has its own subnetworks of different colors? That’s how small networks can propagate a given message through larger networks. The pictures from top to bottom are examples of community awareness (figures 1 and 2), detection/sensing (3, 3a and 4) and recognition (5 and 6). Without these steps your message ain’t going nowhere.

NextStage's RichPersonae Wheel of FortuneWhat we hypothesized, tested, put into some of our tools and what is demonstrated by those who linked, befriended, etc., and who didn’t is the puzzling-glove-clash of the {C,B/e,M}s that in NextStageology look like the sundial-like images in Looking for Love? Now You Can Find All the Right Places! (On the Evolution of Tools) (an example is on the right). Go to that post, look at those images and you’ll see what I described above writ in NextStage’s RichPersonae notation (NextStage’s RichPersonae are a systematic way of working with {C,B/e,M}s).

In short, the ability to predict who would connect with whom, on what platforms and in what time period.

Truly fascinating stuff (we thinks)! The ability to know who/where your viral message will get the most “push” and in what directions (kind of like viral vectors), how it will travel and where it will go!

Now you can literally pick your viral marketing targets.


All in all we targeted platforms based on 2010 CMO’s Guide to The Social Landscape, the 2011 CMO’s Guide to The Social Landscape and the 2012 CMO’s Guide to The Social Landscape. if nothing else, CMO’s presentation methodology has improved. We also included some unmentioned platforms because various NextStageologists use them.

  • Facebook is best for small, local businesses because The Human Touch4 — consumers’ directly interacting with brands — is doable
  • Facebook can be used by large businesses best if they create a destination page that provides local connectivity to local brand agents for local audiences. This is simply a reframe of the above
  • LinkedIn is excellent for B-B sales and promotion (be prepared for LinkedIn to increase its Spam factor exponentially)
  • 4Square, Pinterest and Twitter are best for special offers and give aways
  • Pinterest and Twitter are best for announcements and offers
  • Other platforms investigated haven’t demonstrated any specific uniquenesses yet. They may be amazingly affective for a given business and a given audience and in the whole they didn’t rise to the levels of those platforms mentioned above.

Note that none of the above deals with {C,B/e,M}s? The above is based on the type of personalities that will respond best to those platforms, when those platforms are used to communicate the specific types of information listed.

It’s also possible to specifically target the audiences’ psychologic, behavioral-effective and motivational types that frequent those platforms but an explanation of that is well beyond this post (contact us if you’d like more info).

By the Way

If you’re reading this and would like to link to me or befriend me or whatever, please do so. I always enjoy the company. Of course, Twitter, Facebook, LinkedIn, etc., etc., have all largely become marketing platforms, so there you go. The audiences on all platforms are splintering as demonstrated by the plethora of platforms and into the various rapidly spawning groups that are now crowding each platform (remember those images up above? Notice that they’re similar to the growth of online social platforms? Ever hear of culture-death? All communities fail to thrive when they exhaust their resources).

But those differently purposed groups are basically my xWatches organized by the subnetworks themselves. They are self-organizing which means without a strong, central message/meme they will fail. I provide the xWatch categorizations for my messages so my audience knows how to handle the information I’m given them, which message to which interest and so on, as explained in the SNCR NewComm Forum presentation mentioned above.

Marketers beware.

1: You can learn more about {C,B/e,M}s in the following links:

2: The truth is you will tell people who don’t know you at all if the information you’re sharing either violates or is in conflict with your Core and/or Identity. You’ll share with unknown others to seek validation of the conflicting and/or violating information. You can’t go to people you know with that kind of data, they’d never believe it. The only option left is to have it validated out of your network. This allows you to start creating new networks (if you like the information) or ignore opinions (if you don’t like the information). In either case, you’re giving yourself time to integrate the new information before sharing it with those you trust.

3: You can learn more about psycho-social distance in the following links:

4: You can learn more about The Human Touch and how it applies to social networks in any of the following:

Q&A for the Technology Driven Research Event in Chicago, 2-3 May 2011

Hello again. Sorry not to have posted here in a bit. We’ve been a little busy.

In any case and as often happens, I was interviewed for the upcoming Technology Driven Research Event in Chicago, 2-3 May 2011. Here’s a transcript for your reading pleasure. The questions are in italics, my responses in plain text.

Q: NextStage Evolution offers technology that understands human thought through any machine interface; that seems to be almost a Rosetta Stone for market research! Can you tell me a bit more about your approach and how it works?

A: The answer depends on what you mean by “works”. One version of it “works” by putting a little javascript tag on a client’s site (in the case of our visitor analytics tools). A completely different and equally truthful answer is that it “works” by having a very sophisticated understanding how people behave when they’re being themselves, quite similar to how human beings non-consciously understand each other through years of interacting with each other.

For example, you walk through a mall, glance at someone and “intuitively” know their gender, age, and can make some amazingly accurate guesses about their background, lifestyle, education, income, likes and dislikes, so on and so forth. You do this and your “guesses” have an accuracy that would make IBM’s Watson look like a low grade moron because Watson knows facts and can connect them but it doesn’t have experience, specifically human to human experience.

My research into such things started back in 1987. I was listening to some educational psychologists talking about a problem in that field. It triggered something in me, basically that there was a way to model how humans learned about each other, a way for a computer to go through the different stages of social learning that humans go through from birth throughout the rest of their lives. This model eventually became a set of rules similar to the sets of rules humans use when they interact with each other. When two people meet an incredible number of factors go into deciding the level of intimacy they’ll share. The decision to work together, play together, live together, etc., can be thought of as a “sum of the parts”. Different levels of intimacy are determined by the number of parts in the sum, whether the result is positive or negative, how positive, how negative and so on. Humans recognize one individual from another by summing all the available parts and matching that sum to a sum of the person they have in memory. Are the sums relatively equal? Then you know this person. Not so equal? Then either you don’t know this person or this person has changed and if so, do you still want to know them? This storage of sums became our first breakthrough, the identity-relational model. It mimics how people know each other and was scalable.

So you could say I was teaching the computer facts but instead of facts like “Barack Obama is the 44th President of the USA” — essentially an equation, A = B — I was teaching the computer social facts, what makes up human social intuition, things like “Sometimes when a person looks down and sighs heavily it means they’re sad, sometimes it means they’re tired, sometimes it means …”, and all these “sometimes it means” can be thought of as sums of the parts.

I remember telling those edpsych people that they’d never solve the problem from within their own discipline (I love Einstein’s “We can’t solve problems by using the same kind of thinking we used when we created them.”) and true to my word, to make our technology “work” I borrowed from disciplines so far removed from the traditional paths that, when I created the first working model of our technology, a friend counted elements from 120 “unrelated” fields involved.

We created a new data architecture, the identity-relational model, and some new mathematics to work it, and so far have two patents on how our approach “works”. If any of your readers are familiar with Feynman Diagrams, we made Feynman Diagrams of human interaction, human emotion and behavior, of social systems and social dynamics.

The end result is that our technology can read a document, watch a video, listen to a podcast and determine traditional demographics (age, gender, etc) of the best audience for that material. What’s amusing is that there’s usually a lot of difference between the audience marketers are targeting and the audience their creative is actually targeting.

Further, our technology can determine author intent, as in “what did the author really hope to achieve with this material?” Most companies are amazed at how many non-conscious messages marketers and creative plop into their content, or how strongly those non-conscious messages affect audience response.

On a more technical level, our technology can report on both author and audience RichPersona, a fairly complete description of their cognitive, behavioral and motivational psychologies. This is useful for marketers because it reports how the audience will respond to some creative, when they’ll respond (intender status), why, what exactly will cause the response, how to shape the response to the client’s needs, and demographically who. We’re currently betaing a “SampleMatch” tool that uses these aspects to help companies create test communities for their products and services.

Another part of our technology can observe website visitors and determine demographic and psychologic factors without cookies, without forms, without interrogating the visitor or other internet databases in any way, with no other equipment other than a browser session active (no cameras, no harnesses, no scanners, no …) with the visitor interacting in the most normal settings (sitting in their home, on the bus, in the mall, …) doing what they want, and our technology does this in real-time. An independent test determined that our technology was 98% accurate determining visitor age and 99% accurate determining gender simply by observing how visitors navigate a web site.

And that brings us back to someone sitting in a mall and making highly accurate guesses about people they see just by watching them. What NextStage does is recognize that visitors are “walking” through a website and our technology is the person sitting in the mall, watching others walk past and making highly accurate guesses.

This technology has been in use since 2001.

Q: What do you think are the major drivers of change in the market research space right now and how is NextStage Evolution planning to take advantage of those trends?

A: Major drivers of change…One is definitely the market itself. When audiences demand change suppliers must change in order to keep and increase their audience. An interesting example of this “audience-demand/market change” cycle is what’s happening in the Middle East (as I write this). The suppliers are the various governments, the audience is each country’s population and the market is each country’s economy. The audiences are demanding change and the suppliers — the governments — must change in order to serve those changing audiences. In a more traditional marketspace, if suppliers don’t change then the audience finds another supplier. Extreme cases are when a market fails and a new market takes its place. Some countries have been fairly successful at changing their marketspace and many of the former soviet economies are examples of this.

Another driver is the increasing accountability requirement of analytics. I wrote a three part blog (it starts with The Unfulfilled Promise of Online Analytics, Part 1) based on a long study of people’s attitudes towards online analytics and one of the outstanding elements in there dealt with “accountability”, specifically that no one really wants to be held accountable (surprise!). I wrote Why Isnt Marketing a Science, Part II about how marketing is being forced into an accountable model and that it’s kicking and screaming all the way.

But I do think marketing is going to have to become accountable because executives are demanding more and more of their marketing dollars as audiences — thanks to the ‘net itself — have become increasingly vocal and demonstrative. As I noted above, the audience is changing therefore the market will change.

In a way “marketing” suppliers are always changing. Every time somebody comes out with a “new” way of calculating something they’re offering a change in the market. I love 140Sweets co-founder Anna OBrien’s “Random metric names and symbols is not an equation” statement because it demonstrates a need for accountability in the analytics marketspace.

The latest change attempt is “neuromarketing” and as always the unspoken claim is “now we’re accountable”. Accountable? Great! But now the consumer has to ask the next set of questions; Accountable regarding what? Accountable to whom? With what kind of repeatable accuracy over time?

Shoving someone into a physically restrictive environment such as an EEG or fMRI, or sitting them in a chair with their head locked into an eye-tracking mechanism, etc., definitely provides data and does anybody honestly what to state that such methodology is demonstrative of the consumer’s real-world experience? It’s the difference between “Someday I’d like to learn how to dance” and taking dancing lessons. The latter teaches you what actually has to be done, the former demonstrates how well your brain can mimic (“imagine” or “remember” might be better terms) a concept it has called “dancing”.

The difference is that such methods provides data about (what I consider) extremely synthetic situations. Nobody engaging in commerce — e, intellectual, social, etc — does it strapped in some kind of synthetic environment unless the investigators are willing to accept synthetic results.

This brings us to how NextStage is poised to take advantage of those trends. I suppose the first is some 20 years of research into these things. By “20 years of research” I mean 20 years of studying how people interact with information presented via machine interfaces, about the last 15 or so of those years we’ve been studying how people interact with the web and about the last 6-7 we’ve been studying how people interact with mobiles. So the first thing is that we have direct experience with how people change their habits as their tools (desktop to laptop to netbook to mobile, web to 2.0 to 3.0 to x.0, Genie to AOL to email to Facebook to Twitter to …) change, we’re not talking about taking data from completely different models (Network TV or Print, for example) and saying “This is what happened here so it’s what’s going to happen there”.

So when it comes to accountability, between the patents, the scientific conference presentations, the peer reviewed publications, the kudos we’ve garnered since we started, the ongoing research, …, NextStage is pretty well covered.

NextStage also has a fairly decent lock on adapting to market and audience change because our technology is a basic (I’ve also heard the term “platform”) technology. One of our first investors said, “You’ve created plastic. It doesn’t matter if someone wants a baby bottle or a car dashboard because your technology can be shaped to whatever people require.” This belief is demonstrated by the fact that the majority of our tools came from client requests. We’d be in meetings and someone would say “It would be great if we could figure out…” and one of us would think about it and a few days later a prototype tool would be ready for testing. An example of this “if only we could figure out” attitude is demonstrated in Sentiment Analysis, Anyone? (Part 1). We said, “Forget about what ‘sentiment analysis’ tools do, tell us what you want done”, we created the tool along those lines and its been one of our best sellers ever since.

Another way we’re taking advantage of market changes is the price point of our tools. Right now, most senior level execs don’t use our tools because most aren’t willing to risk their jobs on a (relatively) low price point tool. It’s like going to the bank for a loan and not being able to make payments. You borrow 20k$US, can’t make the payments and it’s your problem, you borrow 20m$US can’t make the payments and it’s the bank’s problem. The same rules apply. A 100k$US solution goes wrong and it’s the vendor’s problem, a 499$US solution goes wrong and it’s the exec’s problem. This “who owns the problem” challenge is compounded by our established accuracy. What do you do if you go with a low cost solution that’s documented with a 90%+ accuracy and it doesn’t work? You look for a new job.

Where all of this works for us is that we’re the darling of mid-level management. They have discretionary spending that’s right in line with what our tools cost and they don’t have the responsibility of their management seniors. They can expense 10-499$US, get a result, report it and be done. There’s no budgetary delays, procurement meetings, tactical planning, resource allocation, etc., and it’s up to senior management to act. This is a win-win for us, especially since people who use us take us up the ladder when they move on to a new position.

So there you go and I hope it’s useful. Please let me know if you need more or other.