NextStage Member Tools Explained

For anybody keeping track, here are our current tool offerings to NextStage Members. We keep adding tools as they become available and will update this list periodically. Some tools require training, some tools require our tracking code be on a digital property, some tools are so new they don’t have their own icons yet.

For those who don’t know, NextStage Membership costs $250US/year. There are lots of other benefits. Come play with our toys. They’re lots of fun and so are we.

Posted in Ad Placement, Age Persuader, Analytics, Audience Finder, BlueSky Meter, Client Prospector, Compatibility Gauge, Entrepreneur Gauge, Experience Optimizer, Gender Persuader, Immediate Sentiment, Job Prospector, Looking Glass, Love Finder, Love Jones, Marketing, NeuroPrint, NextStageology, OnSite, PersonaScope, Political Analyzer, Political Reader, Predictive, Predictive Echo, Resume Rater, SampleMatch, Sentiment Analysis, Social Interferometer, TargetTrack, Tools, Veritas Gauge, {C,B/e,M}sTagged , , , , , , , , , , , , , , , , , , , , , , , , , , ,

Nostra Culpa re NextStage Sentiment Analysis

NextStage’s Evolution Technology calls for human help whenever it encounters something new, unique, or out of its normal experience. Reading Virtual Minds Vol. 1: Science and History readers know our technology does this because I’ve documented it in that book.

This time our system alerted me about a specific Confidence value (from the Intermediate Sentiment Analysis Report) that was a little askew compared to other values it had determined, so I sent an email to the user who’d run the report and offered to go over it with them so we both could learn what that Confidence value applied to.

On Friday (2 Jul 10) afternoon, after our coders had left for the July 4th weekend, the user wrote back very graciously (thanks!) that they’d need to learn how not to fabricate in their writing.


Their response threw me. What did “fabrication” have to do with this Confidence value?

Development History

Readers who’ve followed NextStage Sentiment Analysis development and beta users may remember that NSSA’s Confidence report grew out of a request from FindMeFaster CEO Matt Van Wagner for a tool that could determine if a blog author was full of BlueSky (Matt had another term) or not.

It took a long time to come up with something that I was comfortable with as determining blue sky because there are so many different factors to determining intentional BS from unintentional BS from joking BS from … This discomfort showed up with almost daily rewrites of the Confidence descriptive text. The rewriting process was similar to Mark Twain’s “The difference between the right word and the almost right word is like the difference between lightning and a lightning bug.”

What we came up with was a Confidence equation that included various BS factors because I couldn’t figure out how to completely separate the two (we can discuss the Confidence-BS link at a convention or training sometime, if you’d like. It’s pretty interesting). I wasn’t completely satisfied with the formulation we came up with, could accept it for what it was and told everyone who was using the tool about my concerns.

Then in early May 2010, during conversations with some brilliant researchers specifically about how BS is formed in cognition, we came up with a way to separate BS from Confidence and proceeded to completely spin off Matt’s BS Meter into a separate tool that dealt with whether or not some writing was fabrication or not.

Mea Culpa

But I focus on the charts whenever I look at our reports, not at the descriptive text included in the reports. I’ve been seeing these charts and such for better than ten years at this point so I simply look at the charts, know what’s being reported and respond to that.

I don’t look at the text anymore.

And I obviously should. When this user emailed me that they needed to work on fabrication I went “Huh?” and looked at the report again. “What does this have to do with fabrication?” Then I looked again. Then again. Then I read the report.
The descriptive text for the Confidence report was:

Confidence Gauge – The above gauge indicates (on a scale of -100 to 100) the author’s confidence in their own material. Values from -100 to -80 can most likely be considered pure fabrication although this may not be the author’s intent. Also note that someone writing fiction is intentionally fabricating information. Skilled authors and dramatists can write pure fiction and this meter will indicate confidence is high merely because they have high confidence in their work. This chart is most applicable to people with moderate to no creative writing training.

That descriptive content was the best we could come up with prior to spinning off the BS Meter. The funny thing (to us) was that the suggestions (not shown here) were based on Confidence metrics, had nothing to do with BS and had been part of the Confidence report from the start. Those never changed.

But we’d spun off the BS Meter.

And we’d written new, more accurate descriptive text for the Confidence report:

Confidence Gauge – The above gauge indicates (on a scale of -100 to 100) the author’s confidence in their own material. Some examples:

  • Values from -100 to -75 can occur when the author believes strongly in their material (is confident) and also believes it will not be well accepted, understood or acted upon by their audience (isn’t confident about its reception).
  • Most research and technical writing will score between -20 and 0 because researchers and technical writers tend to have an “I should check this one more time” mindset.
  • It is common for natives of the USA to score between -15 and +10 when analyzing casual, “every day” writing.
  • Truly confident writers will score between 15 and 35.
  • Scores higher than 80 often indicate the author will come off as either sarcastic or vain, based on the author’s acceptance by their audience.

This chart is most applicable to people with moderate to no creative writing training.

And we (I) completely forgot to put it in.

Let this be proof that I’m not as clever as (it seems) many people think.

Making Amends

It’s amusing that this mistake was discovered after we reported our best sales month ever.

But Principles are Principles and when squeezed, one discovers the flavor of the juice.

So by the time this post sees the light of day, everyone who purchased NextStage Sentiment Analysis use since 12 June 2010 (when the BlueSky Meter was released) will have received email notification that their subscription has been renewed. Please contact NextStage if your subscription isn’t renewed (and have your purchase data handy).

Hey, it’s not exactly an oil spill in the Gulf and we do what we can to make things right.

Posted in Analytics, NextStageology, Sentiment Analysis, ToolsTagged , , , , , , , , , ,

Once Upon a Beta: My NSSA Experience

I had the privilege of being one of the beta testers for NextStage Sentiment Analysis (NSSA) over recent weeks, and this is a total rewrite of my original report of my experience. Doing sentiment analysis on your own writing can be quite a revelation – it can definitely put you in your place. First of all, let me tell you a couple short things about me. I have worked in web analytics for about ten years. I view NextStage’s work as a kind of brave new world (that has such people in it!) that – even with careful and patient guidance – feels just beyond the edge of what I can comprehend. I will tell you honestly that, unlike (probably) other beta testers, I have not experienced a need for sentiment analysis in my job. What I am saying is that my testing approach and my desire to be involved came simply out of analytical curiosity. I didn’t come with a pile of case studies, or anything like that. I am going to do my best here to tell you about my experience. I invite you to ask me any questions that you think I may be able to help with, and I understand if you’d feel more comfortable ignoring this kind of layman-style review.
I am going to start by telling you why this is a total rewrite, even though you’ve probably guessed. I ran sentiment analysis on my first draft and it flagged high for some things I felt were fairly negative, and indicated I would be doing any readers a disservice. I will start with what was not surprising, and explain it to you. I flagged very low for confidence (the scale originally conceived as the BS meter). This is not surprising – as I described above, I am no expert on sentiment analysis (by a LONG shot) and I don’t think I spent enough time being clear about that in my original draft. And despite Joseph Carrabis’s efforts, I always feel a bit of an interloper here in this world of neuroscientific analysis. However, I grasp what my scientific value to the beta effort is: analyst. So I hereby am attempting to provide you with analysis, which should go better. I am sure my confidence will still be low, because that’s my nature, but it shouldn’t be quite so abysmal.
What surprised (and cowed) me was that I also scored very highly for “Retribution” and “Troll”. I see why I scored high on Troll. My tone in the first write was flippant, in a failed effort to project a bit of confidence. I made a few jokes inside, mostly of the variety that would only be funny to me, referencing Dr Seuss and throwing in statements informing you that you could ignore that, etc. Retribution really knocked me over though, and I’ve spent a few days pondering it (without reaching out for help, just as a test). I’ve decided that it was two factors: because of the research I did for my review and my references back to web analytics (which were largely flippant and probably didn’t help my Troll score either). But let me talk about that separately. I hope you’ll stick with me here!
If you’ve read the previous postings on sentiment analysis (I put a list of the ones I found most useful at the end of this posting), you know that in NextStage’s view you need multiple dimensions of data to determine sentiment truly. In preparation for writing this review article, I read a few articles on sentiment analysis. I didn’t do this in a particularly scientific way, I just sort of “turned up” my sensitivity to the term in my regular reading. I took this approach because I wanted to get a feel for what my peers (web analysts) thought sentiment analysis was. Or where it was failing, what was needed, etc. Every article I read talked about scoring a statement (usually a Tweet) as positive, negative, or neutral. Sometimes neutral was omitted. Because I did this “research” after doing my beta testing, you can imagine I had some preconceived notions about why this was inadequate. I likened these people to those that use web analytics only for Hits! (pretty cruel, really) Now, you (or heck, even Joseph) may argue that that can’t cause the retribution flag. That was the best I could come up with after pondering.
Which brings me to a very key point: I had the benefit of Joseph’s help through the beta process. The NSSA results (in current form – I understand plans are to work on this) are really not information that the average analyst can walk up and interpret. I don’t tell you this to trouble you; I am just telling you that the data is nuanced. My first beta interpretation was wrong in so many ways that it could easily fill another post. And, even though I’ve described reports and values individually, they are actually interdependent. For instance, not only was my confidence low, but I scored low on trust and affinity. That’s because I have a very limited idea of who follows Joseph – I know you’re not all web analysts. There’s a good chance some of you think web analytics is total bunk pseudo-analysis! I do not expect that number to improve on this review. I am struggling to give you a clear picture of how the various values interweave, but they truly do, which is a key point. Just like you cannot rely on a single isolated metric or KPI in web analytics, you cannot rely on a single metric in sentiment analysis.
So, to summarize before this gets so long as to be unreadable: the analysis is eerily accurate and eye-opening to say the least. If you write or read, you will probably find yourself in need or at least in want of this tool at some point in your life. I find no evidence out there that there is a comparable tool at your disposal, so when you find yourself needing to know author sentiment (including your own sentiment) you will come back to NSSA.

Thank you for reading,
Twitter: @jdaysy
Skype: cmjenday

P.S. As promised, the four postings that I leaned heavily on during the beta:


NextStage Sentiment Analysis, Beta Test, Phase 2

First my thanks to everyone who took part in the Phase 1 Beta test of NextStage’s Sentiment Analysis (NSSA) Tool. This post covers modifications we made thanks to their comments and follows on Understanding and Using NextStage’s Level 1 Sentiment Analysis Tool.


  • Our developers installed the high-speed data system. Analyses that use to take 10 minutes now take about 60 seconds.
  • We added the Level 2 reports (beta testers will be seeing them in their outputs).
  • Level 2 users will also be able to download a XLS of the results (per Chris Berry’s request).
  • We modified two of the Level 1 reports.

First the newly added Level 2 reports.

Level 2 Reports

Rene suggested using The 10 Must Marketing Messages, Trust, Affinity, Author Rich Persona, Target Rich Persona and Worst Rich Persona.

Author Rich Persona

The Author Rich Persona report lists both the author’s Rich Persona and key elements of their {C,B/e,M} matrix. “{C,B/e,M}” is a shorthand notation for the Cognitive, Behavioral effective, Motivational matrix, a tool that calculates how an individual thinks about, responds to and is driven by any information in their environment. Knowing any individual’s or group’s {C,B/e,M} grants unprecedented knowledge of how to craft a message in order to generate a desired response or propagate a message to that individual or group (I can provide a long bibliography for those interested).

For example, a typical Author Rich Persona report looks like the following:

Author Rich Persona – This report will present the type of RP that has written the text (eg. V3) and a bulleted description of his characteristics.

This material was most likely written by an individual with a V14 Rich Persona. Key features of their {C,B/e,M} include:

  • These people are strongly motivated by what they see
  • They are success oriented
  • Presentations with emotions must be positive in nature
  • They make decisions based on what feels “right”, “correct” or “best”

Lastly, this individual probably falls into the following Myers-Briggs categories:

You can think of The Author RP Report as a kind of Me casa e su casa, meaning that people communicate best with those whose RPs and {C,B/e,M}s are identical to their own. The more identical, the easier the communication and the more easily shared complex cognitive and emotional concepts. Part of my training was learning how to shift my {C,B/e,M} at will to match those of people I was communicating with. Doing so enable me to better understand and respond to them, what is called establishing rapport.

So the above is telling you the author’s {C,B/e,M} casa. They will most effectively communicate with people whose casa is their casa. This is great if their {C,B/e,M} is the same or relatively close to the {C,B/e,M} of the largest possible population segment.

But if it’s not, then the most they can hope to immediately and directly engage is the population segment corresponding to their own {C,B/e,M} casa. They will capture the attention of population segments with {C,B/e,M}s close to their own and how much attention is captured (and then turned into engagement) depends on how psychographically distant the author’s {C,B/e,M} is from reader {C,B/e,M}s.

And before going any further, remember we’re just analyzing the Author’s RP. Including Target and Worst Rich Personae would have expanded that listing some 40 times! And without training?

Desired Intent and PsychoGraphic Desired Intent

Instead we’re offering a variant of some things Chris Berry requested in his original “Boy, if only I could find a Sentiment Analysis tool that did this” list , Desired Intent and Psychographic Desired Intent. Chris’ specific requests were:

click for larger imageWhat I came up with is the chart on the right (and it helps if you know some social mechanics. I can provide a bibliography if you’d like). The leftmost column indicates how much of the best audience will respond as the author desires. The center column indicates how much of the next best audience will get the message and respond. The rightmost column indicates how much of the worst audience will get the message and respond.

The concepts being used in these determinations involve psychological distance. The leftmost column indicates people in the target audience who think the way the author thinks, believes what they believe, learns the way they learn, decides the way they decide, …. all that exact-matching {C,B/e,M} stuff. The middle column can be likened to you listening to someone and responding that you think you agree with them and there’s a few things you need clarification on. The rightmost column can be likened to you listening to someone and disagreeing with them but not knowing why you disagree.

The 10 Must Marketing Messages

click for larger imageThis chart shows the relative intensities of ten messages that must be communicated in all media if the audience is going to positively respond.

I emphasize relative intensities because (my opinion) showing a scale of 0-100% doesn’t indicate how strongly a message was communicated, only that it had a certain intensity when compared to other messages. Normalization (such as scoring 0-100%) is useful in some metrics and not in this on (my opinion again). Someone may be communicating “I Can Help You” at 50points and let’s say that all other messages sum out such that the “I Can Help You” message is 50% of all messages being communicated. The next person is communicating the same message but for a different brand and their message is at 500points. Same other rules as above and it also sums out at 50%, but depending on lots of other factors that second message for the different brand wins because of its intensity, not because of how it normalizes when compared to all other messages. Currently NSSA produces normalized because I was out-voted. I’d love to hear your thoughts on this.

Also, I provide more examples of these ten messages in Reading Virtual Minds Vol. 1: Science and History.


click for larger imageTrust (for the purposes of this tool and thanks to Chris Berry) is defined as “the degree of trust between a person (brand) and a social network contained in the message”. What is being calculated is the author’s non-conscious belief that the audience will accept the message. A low score can indicate that the author doesn’t believe the audience will accept the message, that the author believes a small percentage of the audience will accept the message and so on. It doesn’t make much difference with high scores, you’re good any way you look at it.


click for larger imageLevel 2 also includes an Affinity Graph (shown on the right). An author’s affinity to their audience is a measure of how much the author believes they are a member of their audience’s greater community. What’s particularly interesting about this chart is that it should not score high for people who also non-consciously think of themselves as either Influencers or GateKeepers because both functions indicate a non-conscious recognition of being separate (in some way, shape or from) from “the herd”. Author’s who score high as Hubs should score high in Affinity because the function of a Hub is to channel knowledge within a community, hence will have a greater self-concept of being a member of their own audience.

Changes to Level 1

Feedback and observing Level 1 users caused me to rethink some of the information in Level 1 and how it was displayed. The changes are to the Confidence (BS) meter and Message Retention Probability.

Message Retention Probability

click for larger imageThe Message Retention Probability chart originally showed two data points, how much of an audience will remember the message for 3 or so days and how much of an audience will be branded by the exposure.

Rene suggested I expand this to include some other options. What made sense (I’m open to suggestion on this) was a measure of how much of an audience will

  • Understand but Not Remember the message
  • Remember but Not Understand the message
  • Remember for 3 or so days
  • be Basically Branded

Each of the above are rough translations of how much of a message goes into what parts of the brain, long-term (“deep”) memory and cognition. The goal is to have the message lodge in both deep memory and the cognitive centers simultaneously, which is “basically branded”. Note that how large this value is depends a lot on who the intended audience is and how well written something is for that audience.

Suppose what is analyzed shows strongly in “Remember for 3 days or so”. Whatever the message is, it needs to be repeated inside that audience at least once a day for three days in order to shift things to “Basically Branded” (and remember, we’re not monitoring the audience, only the author. The audience would need to see the author’s message three times in three days to internalize the message). An analysis that shows strongly in “Remember but Not Understand” usually indicates that whatever the message is, it needs to be repeated through different channels. Lastly, “Understand but Not Remember” will normally take the lion’s share in any analysis. Note that that audience is not the audience for the message for any of several reasons, it’s simply the largest audience segment out there.

Confidence (BS) Meter

click for larger imageAs you can see, the Confidence (BS) Meter is now horizontal and clearly shows the 0 mark. Visually more informative with much less cognitive effort, I think.

Eating Our Own Dogfood Dept

Just for kicks, I ran the original version of this post through NSSA (sans blog interface, just the content). Can you say Ouch!.
So I went in and made edited. Four versions later, this post is what you get.
The differences are in the numbers:

V0 Version V4 Version

Love Factor

Positive 33.98 0.87
Neutral 1.01 98.76
Negative 65.01 0.36


-72.32 -18.64

Message Retention Probability

Understand But Not Remember 21.46 19.63
Remember But Not Understand 0 0.25
3 Days or so 0 0
Basically Branded 0 0

Message Intent

Referral 22.55 25.81
Retribution 28.96 23.53
Love -1.54 18.3
Constructive 24.09 12.43
Troll 25.93 19.93

Author Influencer Type

Influencer 28.57 62.41
GateKeeper 63.23 37.59
Hub 8.2 0

10 Must Marketing Messages

We Trust You 8.19 10.23
You Can Trust Us 18.21 17.02
This Is Important 2.17 1.26
This Is Important to You 7.16 6.63
We Can Help 9.24 12.12
We Can Help You 24.72 20.86
You Are Good People 8.24 8.41
We Are Good People 7.53 8.86
They Are Not Good People 6.97 5.68
We Are A Leader 7.57 8.92


0 10.00935


0 7.732702

Author Rich Persona

A15 V1

Desired Intent and PsychoGraphic Desired Intent

Desired Intent (First Circle)- A15 71.72 (V1) 29.97
PsychoGraphic Desired Intent (Outer Circle) – A9,A10,A11,A12,A13,A14,A15,A16 4.95 (V2 ,V1 ,V3 ,V4 ,V5 ,V6 ,V7 ,V8) 2.03
PsychoGraphic Desired Intent (Outmost Circle) – K23,A7 ,K7,V23,A15,A23,K15,V7 ,V15 0 (A1 ,A9 ,K1 ,A17,K9 ,K17,V1 ,V9,V17) 0

Major changes through the revisions were removal of massive bibliographies, caveating, general de-sciencing of the content (I can email the V0 post to any insomniacs with a need). Of particular note is the big change in Desired Intent. The First Circle value scored about half the V0 version of this file. Why? Because I was shifting my {C,B/e,M} from an A15 to a V1 {C,B/e,M} (ResearcherJoseph to BusinessBloggerJoseph). This is a tip of the hat to long time editor Brother Brad Berens who’s been telling me to do the same for years now.


Beta testers will once again be turned loose by the time this post goes live.

Enjoy and please let me know your thoughts. Tools evolve through use and interaction, and as I explained in Eight Rules for Good Trainings (Rules 1-3) and Eight Rules for Good Trainings (Rules 4-8), I learn from others more (I’m sure) than they may ever learn from me. Example: One of our beta testers is a fellow in his early 20s. My reasoning for including him? Whatever else he does during the day, his interests are going to be very different from mine. He’ll put material through analysis that I don’t even know exists.

Again, thanks and enjoy.

Understanding and Using NextStage’s Level 1 Sentiment Analysis Tool

For those of you who weren’t in the loop, NextStage has been taking it’s desktop tools and turning them into web tools. The first to come out of that particular shoot is NextStage’s Sentiment Analysis Tool. I’ve written about that tool before in Sentiment Analysis, Anyone? (Part 1) and Canoeing with Stephane (Sentiment Analysis, Anyone? (Part 2)). Here I’ll be sharing how to use and understand the Level 1 version of that tool.

NextStage’s Level 1 Sentiment Analysis tool provides the following information (per Rene):

  • Love Factor – This report will provide an horizontal histogram composed of 3 items: Positive, Neutral and Negative. It will thus show on a scale from 0 to 100 the positive, neutral, or negative degree of the message. As in real life things arent black or white we expect that any message will score in the three dimensions but at different levels.
  • BS Meter – This report will present a gauge with a scale from -100 to 100 with shade of colors ranging from red to yellow and to green. It will present if the author of the analyzed text believes (actually, whether or not the author is confident in what they’re writing) or not what he has written and to what extent. We believe that a score over 25 means that the author believes what hes written and under -25 the opposite. In between, the author is not really sure
  • Message retention Probability – This report will present in a gauge in a scale from 0 to 100 the probability that the conveyed message will be retained by the readers of this message. It is stated as a probability as this will depend upon the type of visitor reading the text (its Rich Persona) and it will be reported against the whole population of our Synthetic Users.
  • Message intent – This report will provide an histogram composed of 5 items: Referral, Retribution, Love, Constructive and Troll. It will thus show on a scale from 0 to 100 the intention of the message for each of these items. The same consideration as for the first report applies (non Black or White).
  • Author Influence type – This report will provide an histogram composed of 3 items: Influencer, Gatekeeper and Hub. The scale will go from 0 to 100 and will present the type of author that has written the text.

As always, I’ll use my own writings for demonstration purposes in the beginning. The reasons for this are simple:

  1. the NextStage Sentiment Analysis tools are reporting on the non-conscious of the author when that author was composing the information.
  2. I’ve had several dozens of years of training to recognize, understand and report on my own non-conscious activities and behaviors hence will be able to describe whether or not I believe what NextStage’s Sentiment Analysis tools are reporting.

I’ll analyze some other online material (again, for demonstration purposes) once I’ve analyzed a few of my own (let me know if you’d like something you’ve written analyzed).

You Found It!

Our first lookLet’s start with the first Triquatrotritecale post, You found it!. What is there now isn’t what we original had (the original is shown on the right and we’re still working on it). But what did that original post have to say about me when I wrote that post? What say we find out.

  • Neutral meLove Factor – First you need to know that I’m a stickler for accuracy. I’m very uncomfortable with (what I call) marketing truth, the tendency of people to put their spin on things so that what is horrible doesn’t sound so. Example: President Obama’s stating that the Copenhagen accord was “Meaningful and unprecedented.” Both are true and I’ll concede that both are accurate. I’ll also offer that neither are accurate truths. It’s kind of like a sin of omission mixed with caveat emptor and some it’s what you don’t know that’ll hurt you thrown in. One of our NextStageologists chides me that I’ve become very good at marketing-speak and it wounds me, truly. Or accurately.Accuracy versus truth comes into play with the Love Factor chart. You see that the Neutral reading is high compared to Positive and Negative, yes? That’s true and not accurate. Human beings are not naturally “neutral” to much of anything, not non-consciously anyway. What NextStage’s Evolution Technology (ET) really recognized was that my Positive reading was about 52 points and my Negative was about 48 points. The truth of this is that I was working at being neutral. To most people it would come off as neutral and only because the conscious brain takes the non-conscious information “He’s working at being neutral” and mentates “He’s neutral”.

    My position (ahem) is that there’s a lot of difference between someone working at being neutral and truly being neutral. Susan and Charles disagreed with me. More accurately, they agreed with me and also offered that the subtlety would be lost on most people (I think better of you, dear readers, than do they).

    The accurate truth is that my Positive value was 210, my Negative was 192.5, my Neutral somewhere around 20. These values indicate that while I was working at being neutral, I wasn’t really busting my gut over it, more like I was just another human being being human.

    But in any case, NextStage’s Sentiment Analysis tools will report something like you see above unless the Positive and Negative values are “arithmetically” different, meaning the author recognizably writes one way or the other.

  • BS MeterBS Meter – First, I’ve never been comfortable with the term “BS” or any of its variants. Also, what NextStage’s Sentiment Analysis tools measure is whether or not the author non-consciously believes (“accepts” is a more accurate term) what they’re offering as being valid information. Most accurately, ET takes a reading of whether or not the author is confident in the information they’re providing then matches that to some other things and the result is a measure of their confidence in what they’re writing.The distinction may seem subtle and I assure you it’s not. That distinction is shown on the above chart. What I wrote in You Found It! was quite true and accurate.

    But (!!!) I really didn’t have any idea where this blog would go or what I would post about when I wrote You Found It!, hence my confidence in what I was writing wasn’t as good as it could have been. For that matter, I wrote in I’m the Intersection of Four Statements “I consider myself one of the least confident people I know.” so my confidence levels should never be incredibly high.

    What can be gleaned from this metric is that when the author (yours truly) wrote that post they definitely were uncomfortable with the information they were presenting. The value (-52.81) indicates a probable lack of confidence in what they were presenting. Was it BS, though? I suppose that depends on what the person reading the charts thinks of the author. Neutralizing that “person reading the charts” bias is a lot of what NextStage trainings are all about (just an FYI, folks).

  • It's like water, except it's a message, and as you grow older you retain more.Message Retention Probability – This is another metric which can be true, accurate or both. This image is about as true and accurate as I can mathematize them. What’s showing is this: Taking the greatest population swath possible, basically 0% will remember what I’ve written.This is both true and accurate. Especially if you know what’s being calculated.

    Knowing only what is written and nothing about who is reading, the writing style I use and topics I write on suit such as small audience that when measured against the entire blog-reading population, I basically write for 0% of the population (the other truth involved in this is that most people don’t know how to write or design information for the largest possible audience. This more than anything else is why most websites are thrilled to get 3% conversions, why companies have to practically hit you over the head before you can remember their brand and act on it in any meaningful way, …).

    However, my regular readership retains about 90% of what I write. This isn’t to suggest that everything I write is understandable, only that it’s easily memorable.

    So, if the question is “what percentage of the general population will be remember what I’ve written?” the answer is 0%. If the question is “what percentage of my readership will retain what I’ve written?” the answer is 89.25%.

    The only way I know of to answer that last question, though, is if you have NextStage’s OnSite tool tracking your site (that’s a description of the limits of my knowledge, not a plug for NextStage technology).

    So we can resort to something that is either true or accurate and both depend on how fine you want to cut things. For example, if you’re not interested in the tightest possible segmentation, my writing (or at least that one post) would be memorable to just under 17% of the general population. That’s true but not accurate because of how the brain retains information. Information (such as a webpage) may be presented visually (for example) and it will not necessarily be remembered (think of how much you see in a day — heck, in a minute — that you can remember seeing three days — heck, three hours — later). What has to occur is that the information be presented in a way that is both stimulating (to lock attention on it) and memorable in the way that the greatest percentage of the population remembers information.

    Or presented as you know your audience will remember it (read “configured so that both brain and mind assign high enough “survival” value on the information that said information is quickly placed in deep or ‘long term’ memory”).

    So the question becomes, do people using NextStage’s Sentiment Analysis tools want things true, accurate or both. We can do them all…oh heck, why not just do them all and have done with it?

    By the time we release these tools to the general public this particular report will provide true, accurate and both true and accurate results. Let me go do that now, in fact…

    (about half an hour goes by)

    Okay. This report will now (“now” meaning as soon as our programmers convert my math into working code. They’re very good at it. It’ll take them less time to get it installed and working than it took me to mathematize it) show “Understand But Not Remember”, “Remember But Not Understand”, “3 Days or so” and “Basically Branded”.

    (and I hope you all appreciate what I do for you)

  • Who loves ya, baby?Message Intent – This, thank goodness, is a fairly straightforward report to apply. What is shown here is that a) your author is pretty mild-mannered over all (the numbers are kind of equal) and b) wanted to get back at someone or some thing (the Retribution value). In this case, it was the KMM blog platform and for reasons I made obvious in both You found it! and Today I was asked if I was comfortable doing NeuroEngineering. The Referral, Love and Constructive values being pretty close to each other hearkens back to the “working at being neutral” versus “being neutral” thing mentioned above.
  • And what do I think about me?Author Influence Type – This is a metric that one needs to understand clearly. NextStage’s Sentiment Analysis Tools are metricizing whether or not and how much the author believes they are an Influencer, a GateKeeper and a Hub. This is not an indication of how their readership thinks of them, only how the author thinks of themself (learning what an author’s readership thinks of the author would require NextStage’s OnSite tool or something similar).The results for this post did surprise me (and remember, this is a non-conscious metricizing of myself). Consciously I don’t think of myself as either influencer or hub. I would accept having a GateKeeper mentality and recognize that would be my boundaries and limits kicking in (“boundaries and limits” as in personal boundaries and personal limits. Most everyone who knows me tells me mine are incredibly strong).

    But then I thought about it. According to Twitter I’m either influential or highly influential. This information amazed and baffled me. People take me seriously? People think I know what I’m doing? Wow.

    Even so, not knowing how Twitter comes up with such definitions I had trouble accepting it (although it was flattering). But then several conversations over the past months revealed what some people are calling “The Joseph Effect”. People want to emulate my methods and principles in their lives (very flattering). One person told me that they were actively incorporating The Joseph Effect in their life and the change has been recognized by others as both growth and positive. Okay, more than one person made such a comment to me. Several, in fact.

    This is truly incredible to me. Get to know me better and your attitudes will change, I’m sure.

    However, all that stuff had obviously been roiling in my non-conscious for a while. Whether I consciously accept it or not, I non-consciously recognize that I influence people.

    And then I remembered debating with myself for a good hour or so whether or not to make “public” that Susan and I had donated to what I consider a good cause. This was a real debate for me, the intersection of “Let your light so shine (don’t hide your light under a bushel)” and “Don’t let one hand know what the other is doing”. I finally decided that publishing our involvement might cause others to become involved hence I had concluded I was an influencer even though there was no conscious recognition of “I’m an influencer”.

    Le coeur a ses raisons que la raison ne connait pas. (Pascal)

    And no scarier a thought could one have on a sunny Tuesday afternoon.

    But what about what I wrote, forgetting the webpage part?

    I demonstrated in Sentiment Analysis, Anyone? (Part 1) that there’s a difference in what someone writes/designs and how all the blah-blah of the web interface shows people. Think of it as an equation:

    Informationwritten + Informationweb interface = Total Presentation

    The above was all for the Total Presentation shown in the webpage snapshot I shared close to the beginning of this post. What’s the Sentiment Analysis for just the written text?

    More truthFor one thing, once the mitigating influence of the page interface is removed, my truer feelings reveal themselves. Why such a substantial difference with the interface and without? Because I used colors and phrases (for the right column in the blog) that tend towards neutrality rather than offense or defense.

    That brings us to the Confidence (BS) meter. I won’t bore you with another picture. The original was -52 and change. The pure text value was -51. No matter how you cut it, I’m a very cautious person.

    Pretty much the previous result holds true for the revised Message Retention Probability metric. The general public will not understand nor remember what I’ve written for any period of time. Remember, that’s the general public. Perhaps we need to include the option of the tool user entering an audience from a pick list? Rene? Anyone? Of course, that might merely prove that not only can the author not write, the person running the test has no clue of what audience the author is targeting. On the other hand, if you’re in charge of marketing for a company blog, you’d have a great idea of who the audience is.

    This would be incredibly useful in determining who’d be best suited to write content. Take the naked webpage and plug in some content (3-4 pieces should be enough) from as many authors as you like. Run a test on each set. The author that scores the best with the desired audience is the one who should be writing your content.

    Yeah, I like that.

    And what I really meant was...Next on the list is Message Intent. Here I show both “Total Presentation” and “Just the Text” values side by side. It’s intuitively obvious to the casual observer and a well known fact among all my regular readers that I love you I love you I love you and it doesn’t matter whether an interface is used or not. In the case of this post, I care about each and every one of you equally (ahem).

    Equally interesting is the rise in Retribution. Strip away the interface and I was one unhappy camper.

    This is more accurately how I think of myselfAnd when you strip away all the artifice of the interface? As I wrote earlier, I’m a GateKeeper. Anybody who’s asked me to share someone else’s personal, private or similar information knows NextStage Principle #51 takes affect.

    Okay, enough for now. I know there are beta testers waiting to play. If you haven’t heard from Rene or me yet you will in a few days. Or email me or Rene and let us know you’d like to play.

    Next time out, an analysis of some other folks’ blog posts (let me know if you’d rather I not analyze your blog).

    And before I forget

    I’m writing this post because of my firm belief that people need training when encountering new tools (at least I require training…”require”? I actively seek it out). Susan suggests a mindset of “We’re not in Kansas anymore” crossed with Friendship Bread when using NextStage tools because our tools measure things that go “bump in the night” as far as most people are concerned and definitely are different from clicks, pageviews, cookies, …

    Have no fear NextStage will offer plenty of training opportunities and lots material when the next level is ready.

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