Basis: This publication documents an ongoing (ten years to date) study of social network lifecycles and what is required for any given social network to thrive.
Background: The number of extant social networks increases along well-defined rules that are dependent on the number of social media channels and the technology required to access any given social network. This translates to a change in the past ten years from a few social media channels with a diversity of internal networks to a diversity of social networks each with their own social media channel.
Whenever there’s a proliferation of similar organisms the laws of evolution kick in with an unmatchable ferocity. A few social media channels with a diversity of internal networks demonstrated a user preference for the interface (usability) above the information (content value). A diversity of social networks each with their own social media channel demonstrates cladic growth that in turn is subject to evolutionary methods.
This is demonstrated in both online and offline worlds in how social networks form, grow, die and evolve into new social networks. Note that for the purposes of this study social network “stability” is defined as a creation-evolution cycle, meaning the social network thrives (a YouTube video that receives 1MM hits in two days then fades into oblivion does not constitute a thriving social network). “Healthy” networks are those that grow while maintaining focus and direction. “Vital” information is information required to keep a conversation going.
Objective: To determine if any specific requirements exist for the health of social networks regardless of social media channels (what is required for healthy fish regardless of the pond they’re in?).
Method: This research is an outgrowth of NextStage’s previous and ongoing social network studies, and is built on the mid 1980s-1990s cultural anthropology studies performed on such social networks as CompuServ, AOL, Genie and the like.
Five hundred differentiable areas of interest were identified across automotive, destination, entertainment, food, motorcycle, science and travel meta-networks. Similarities of subject matter (content, focus), contributor (voice, style, tone, knowledge-base, experiential-base, post/comment frequency), structure (interface, posting requirements/mechanics, alerting mechanism) and visitor (income level, education level, geographic location, life experience, age, gender) were isolated and routinely measured to determine social network mechanics.
Results:The greatest factors contributing to the longevity of a social network regardless of social medium are
Three “golden ratios”
The ratio of contributors to entire network population must be between 1:100 and 1:30. Social networks with contributor to population ratios in this realm demonstrate a reasonable dialogue is taking place. Fewer indicates unguided conversations, greater indicates a dearth of vital information.
The ratio of influencers to entire network population must be greater than 1:3,000. Influencers are required to inject source-recognized vital information to generate discussions among network participants.
The ratio of influencers to contributors should be within a few points of 1:100. Greater and there aren’t enough “Watsons” to support the “Holmses”, fewer and there are too many “Watsons” (see Another Ommaric Intersection – Holmses&Watsons).
The regular injection of vital information
Vital information must be “forward thinking” information. It must recognize a community challenge and offer direction for its solution. It does not need to solve the challenge, only demonstrate a possible solution path. Consensus solutions indicate there’s nothing left to talk about and are death to social networks until a new challenge is identified.
Injection to general conversation ratios should be within a few points of 1:55. Fewer and the conversation collapses, greater and the conversation becomes confusing.
Networks without regular injections of vital information first stagnate and eventually collapse.
The collapse speed is related to the size of the network. Larger networks collapse more quickly (relative to their size) than smaller networks due to higher social bonding factors usually present in smaller social networks.
Too many or ill-timed vital information injections cause confusion in the general population. This confusion translates to
a decrease in the general population.
an increase in the level of conversation among the “literati”. Note that this is a demonstration of a stable, evolving network.
The information gradient (dispersion vector) should be directly proportional to the size of the network.
Smaller, stable networks will demonstrate gradients of 1:10,000 (”instantaneous” for the population size). More gradual information gradients tend to indicate a social network preparing to collapse.
Key TakeAways: Brands (and others) wishing to maintain stable, healthy and growing social networks should focus their efforts on maintaining the necessary mix of
influencers, contributors and visitors to insure necessary conversation ratios
general comment to vital information posts/comments to insure necessary social growth incentive ratios
Basis: A one year study of twelve (12) international websites (none in Asia), M/F 63/37, 17-75yo, either in college or college educated, middle to upper income class in all countries studied
Objective: To determine if people were more decisive in their navigation when an image or text was used as a primary navigation motif (menu).
Method: Four separate functions were evaluated
Presentation Format Preference (a simple A/B test)
Sensory to Δt Mapping (time-to-target study)
Teleology (how long did they remain active after acting)
Time Normalization (determines what brain functions are active during navigation)
Results: Key take-aways for this research include
Visual (graphic or image)-based menus cause a 40.5% increase in immediate clickthrough, site activity is sustained an additional 32% with site-penetration being an additional 2.48 pages ending in a 36% increase in capture/closure/conversion.
Although not tested with Asian audiences, it is doubtful this technique will work with ideographic language cultures
The graphics/images used must be clear, distinct and be obvious iconographic metaphors for the items/concepts they open/link to. Example: Images of a WalMart storefront, a price tag with the words “Best Price” and people shopping resulted in greater activity than a simple shopping cart (too familiar as a “What have I already selected?” image) and the simple words “Store” and “Shop” to drive visitors into buying behaviors.
Existing sites with text-based menu systems need to use both systems (at the obvious loss of screen real-estate) to train existing visitors on the new iconography until image-based menu items are used more often than text-based menu items.
Basis: This publication concludes a two year study of visitor adaptation to and adoption of new technologies and site redesigns on similar product or purpose sites in the US, EU, GB and Australia. No Asian, South American or African sites were part of this study.
Objective: To determine if neuro-cognitive information biases exist in certain cultures and if so, is there benefit or detriment to those biases?
Method: Twenty sites (monthly visitor populations between 10-35k) were monitored in the USA, Italy, France, Germany, Great Britain and Australia. The sites included social platforms, ecommerce, news-aggregator, travel-destination and research postings. Activity levels were monitored before, during and after design changes were instituted, as well as before, during and after new technologies (podcasts, vcasts, YouTube feeds, social tools) were placed on the sites.
In addition to activity levels a study was made of viral propagation vectors to determine if changes to the site promoted new influencers or demoted existing influencers.
Results:
Announced changes to the sites increased adoption and adaptation rates among all visitors (in some cases by as much as 65%)
Announced changes most greatly benefitted US, GB and Australian audiences with adaptation and adoption rates increasing 12.5% on average.
Site previews increased adoption and adaptation rates among all visitors
77% of EU based visitors who chose to preview site changes became influencers regardless previous social standing on site.
35% of US based visitors who chose to preview site changes became influencers regardless of previous social standing on site.
32.5% of Australian based visitors who chose to preview site changes became influencers regardless of previous social standing on site.
27.5% of GB based visitors who chose to preview site changes became influencers regardless of previous social standing on site.
EU audiences demonstrated the highest rates of adaptation to and adoption of new technologies and site redesigns in all categories at 92.5% and 85% respectively.
Australian audiences demonstrated the lowest rates of adaptation to and adoption of new technologies and site redesigns in all categories at 30% and 7.5% respectively.
Key take-aways for this research include
Travel destination sites should provide a good deal of lead up time to site changes.
This lead up time should include previews and announcements.
This is especially true for US audiences.
Sites introducing social tools should select, train and promote influencers from within the existing visitor community before the social tools are made public.
The introduction of social tools to news-aggregator sites recognizably slowed the adaptation and adoption rates of EU audiences.
US based audiences were most likely to contact site admins, web admins, managers, etc., criticizing site redesigns and new technology implementations although they were the least likely to abandon sites due to those changes.
Australian audiences were the least likely to contact site admins, web admins, managers, etc., criticizing site redesigns and new technology implementations although they were the most likely to abandon a site due to those changes.
EU based audiences were the most likely to visit several sites all serving the same purpose.
EU based audiences were the most likely to give a site “time to settle” during redesign and new technology implementation before returning to it on a regular basis.
One thing we’re repeatedly told is that our problem solving methods are unique and very different from what everyone else does so we decided to offer our methodology’s high level form here.
The methodology is simple, adaptable and expandable. What is offered here is a core that can be used in any discipline with little modification.
Background Study
When presented with a challenge or a question to be answered or investigated, learn as much as possible about everything that’s been done before, regardless of how seemingly irrelevant to the task at hand. Be thorough, be detailed. Find out what’s failed and why. Find out what got close to solving the problem or answering the question and why it didn’t go all the way.
Learn this, study the background (even if it’s obvious. And if it’s that obvious to you, have someone not familiar with this particular paradigm do the study. You’re missing something if you think the background is obvious), study the personalities, the models, the methods, the politics, everything.
If you’re not willing or don’t have the time, don’t take on the research, project or task.
Necessary Data
This one, we’ll admit, causes people the most concern. Many people attempt to solve problems with either available data or easily obtainable data.
Stop. Go no further until you honestly answer this question:
What data — existing or not, obtainable or not — best solves the problem, answers the question or furthers the research?
Talking with researchers and analysts world wide, the above question is the greatest stumbling block. People defer to what data is currently on hand, previously obtained data made public by other researchers, data that current methods make easily obtainable or collectable, etc.
However, “ease of collection” or “prevalence of availability” should not be equated with “solves the problem”, “answers the question” or “furthers the research”. Agreed, it would be great if the exact data that would do all three was there for the taking and yes, solution vendors make wonderful cases for their data collection methods.
The first challenge to solving any problem, answering any question, etc., is to determine what kind and how much data is necessary to provide a solution or answer. Find out how to measure what you really need to measure to solve what you really need to solve and you’re 90% of the way to answering the question, solving the problem or furthering the research (there are two corollaries to this and they go into the third step in this research model).
I’ve seen research come to a halt until the investigators could determine what they really needed to measure to answer what they really needed to answer. Take your time at this stage. I’ve heard that “We have money to do it over but not enough to do it right the first time” and, while I know several businesses accept that concept, and while I agree with Jeff Bezos’ “Anything worth doing is worth doing poorly” I don’t believe or accept that these two statements are congruous at all.
Equals Must Be Equals
The number of times projects fail, results are erroneous or research flounders due to people forgetting the simple rule that “1=1″ is staggering. Using an online analytics term, “clicks” here must mean the same thing and be measured the same way as “clicks” there.
The first corollary is
Make sure you’re measuring what really needs to be measured.
The second is
Units must be the same — and have the same meaning — on both sides of the equation.
The “1=1″ requirement most often fails because people mix Categorical, Rank, Metrical Analysis techniques, measures and methods as if one is identical to the other and they are different.
And if you’re not sure of the differences, I’m sorry, you should not be doing research. NextStage is often called in to help businesses make sense of some research they performed or contracted with another group, and more often than not the solution comes from clearing up categorical, rank and metrical overlaps. Categorical, Rank and Metric are basic measurement concepts. I explain them briefly in The Social Conversion Differences Between Facebook, LinkedIn and Twitter – Providence eMarketing Con 13 Nov 2011. Learn them and learn them well.
To that end, the market is contributing to poor research models and measurement methodologies. The number of solution providers promoting self-serving “equations” as solving industry problems that
change critical term and KPI definitions midstream,
measure irrelevant (at worst) or very loosely (at best) correlated elements and profess a one-to-one correspondence between measurement and claim,
include data having no direct relevance to the problem yet are included because they’re easily obtained, and
make up their own KPIs and claim relevance
is mind-numbing.
Companies can find vendors whose definitions make the companies’ failures look good and aren’t we all a little tired of naked emperors?
Research Quotes
People who know me or NextStage know we love quotes. Here are some our researchers keep on their walls for easy reference:
We are continually faced by great opportunities brilliantly disguised as insoluble problems. – Lee Iacocca
Simple solutions to complex problems are often wrong. – Jeanne Ryer
The cause of a problem is the system that produced it. – Tom Bigda-Peyton
Judgement consists not only of applying evidence and rationality to decisions, but also the ability to recognize when they are insufficient for the problem at hand. – Tom Davenport
Should you encounter a problem along your way, change your direction, not your destination.
For every complex problem there is an answer that is clear, simple, and wrong. – H L Mencken
NextStage routinely makes its research available to Members. Research that’s been published in the Members area for more than a year will be moved here, to The Analytics Ecology, as time and tide allow.
Basis:
This publication reports on an ongoing (sixteen years to date) study of market fluctuations due to decreases in product/service versus feature diversity resulting in increased marketshare.
Background:
Markets arise under two conditions:
Products or services are developed that meet a specific need within a given population
Existing products or services are redefined or modified to meet the expectations within a given population
Emerging markets move from need-based to expectation-based in direct relation to the spread of product/service information within the given population. Note that this replaces the “adopter” model with a social contagion model — markets increase proportionally to the information level within a market. Early adopters are individuals who require minimal social information about a product/service, late adopters are those who require maximal social information in order to become market members.
Markets establish themselves when multiple vendors recognize possible revenue sources and expend resources to first enter then maintain marketshare. Traditionally market establishment followed an organic dispersement model due to minimal channels (information transmission vectors). The past sixteen years has seen an explosion of channels.
The traditional model dictates that the vendor able to saturate a market’s chosen channels will claim more marketshare. However, channels are proliferating with the end result that vendors must create their own channels to insure controlled information dispersion.
The social contagion model dictates that an uncontrollable information exchange be met with decreased marketshare and decreased product/service diversity while proliferating features and brands to meet consumers at different social contagion levels within the market population.
Objective:
To determine if branding concepts, product/service or feature diversity is more adept at establishing marketshare in socially engaged markets.
Method:
Eleven markets (agri, auto, construction, home electronics, personal apparel, personal communications, pharma, real estate, recreation, sports, travel) were observed from Jan 1995 to Jan 2011. Analysis was done on vendors in those markets, messaging, market reach, marketshare, channeling, brand imaging and management shifts.
Results:
Brand allure continues to play a role in marketshare
However brand allure is rapidly giving way to feature diversity (the brand that supports the largest feature set wins)
Feature diversity is becoming the new standard for opening markets and increasing marketshare, especially when features are tailored to a given market
Feature diversity benefits are increasingly communicated socially rather than through “traditional” channels
Product/service diversity benefits are decreasingly communicated socially although they maintain their place in “traditional” channels
Key TakeAways:
Brands able to demonstrate the greatest feature diversity within a market will maintain the greatest share of that market moving forward
Emerging markets will best be captured/maintained by products/services that are app enhanceable rather than those coming with a diversity of built-in features
There will be an increasing move to “app platform” devices as feature diversity moves from “what x can do out of the box” to “tailoring x to do what you want”
This app platform move will be the vector of future market segmentation
Mass marketing is a uniquely US invention. It was an invention of necessity because up until the 1970s the USA was a uniquely monolithic culture. People immigrated to the US to be American and the definition was both supplied and propagated worldwide via TV, radio, print and movies (the only media and channels available at that time). The definition of being “American” itself was a product of US based marketing and intended to foster a consumer culture (it was very successful).
The US’s late 19th century and early 20th century economic power and geographic isolation (equivalent to information isolation or “islanding” at that time) enforced that “American” stereotype (material rich while being psycho-emotionally independent of others). Indeed, pop cultural icons were not deemed to have “made it” unless they conquered the American market. The end result was that everyone strove to market this ideal and marketing didn’t need to be a science — the market was so big that any effort was destined to be successful with only minor modifications to the definition of “success”.
Meanwhile, immigrants continued to celebrate their ethno-cultural uniqueness and usually in private, behind closed doors, in ghettoes with festivals that eventually became tourist attractions, etc., but rarely was ethnicity celebrated on main street.
Enter Consumer Choice
The late 1970s introduced a perfect storm of socio-technical events that worked to destroy the monolithic US market; the oil crisis, the rise of cable television and emergence of cellphones (both forms of information technology) and the influx of populations who wanted to maintain their ethno-cultural heritage at all costs. Where immigrant status was once considered a curse it was now considered a blessing. Uniqueness was transferring from the population as a whole to the individuals in that population.
A self-cannibalizing cycle emerged. Marketers started to market towards the influx of self-actualized immigrants arriving in the US due to their numbers providing them with previously unknown economic power. Self-actualized immigrants re-established ethno-cultural identity to existing peer minorities who had been taught to be quiet about their unique heritages and a generation emerged who gave up their americanized names for either assumed or given ethno-cultural names.
By creating and marketing to self-actualized immigrants and existing minorities, marketers and businesses acknowledged and validated both their ethnicity and economic power. By acknowledging and validating ethnicity and economic power, self-actualized immigrants and existing minorities were increasingly able to maintain their (in some cases emerging) pride in their ethno-cultural identities. Increasing pride in ethno-cultural identity allowed ethno-cultural exemplars to be demonstrated on main street. It was no longer a symbol of being “right off the boat” to walk downtown in ethnic dress and regalia.
The end result of this acknowledgement of ethnicity was an acknowledgement of diversity and individuality — again, uniqueness. This shift demonstrated itself in the market by giving all consumers more choices. Grocery stores that once only provided shelfspace to “American” brands and palettes are increasingly giving over real estate to ethnic specific foods and products.
The other place consumer real estate is demonstrating ethno-cultural diversity is in information resources. This first appeared with cable television systems. Originally solely in American English (the US produced and distributed the majority of information for this technology), cable television systems have become increasingly culture specific.
The mid 1990s brought the advance of cheap information distribution via the web and widespread cellphone technology. Now consumer choice is “complete”. The web has moved from an English only medium to a truly culturally diverse medium and if you want “the complete story” you can get the news from separate US, British, French, Japanese, Indian, South African, Russian, … sources with the click of a mouse. Macbeth is rewritten for Japanese and Nigerian audiences and Tales of the Monkey King are available with an American Western twist. The increase in web-enabled mobile devices means a diverse information pool is available 24×7x365 and the consumer demonstrates their information source preference more clearly than ever before.
Markets as Scarce Resources
When presented with scarce but necessary resources, technologies will emerge to exploit those resources as economically as possible.
European, African and Arabic businesses have been well aware of this for decades, centuries and millennia, in that order. Tailoring marketing messages and campaigns to a total possible market of seven million consumers is the standard and getting a response of 700,000 is considered a success. Traditional american marketing would see a seven million response as a failure.
Geomarketing or “local” marketing — emerging in the US as yet another innovation and big thing — has been the standard elsewhere in the world for quite a long time.
Redefining Information Hegemonies
One of the greatest challenges to marketing become a true science (at least in the US) is the existing information hegemonies — megalithic broadcast companies that own multiple information outlets owning multiple media channels.
These hegemonies purchase successful culturally specific outlets and channels to increase the hegemonies’ reach and economic power. Unfortunately, they then apply marketing methods based on media and information consumption models developed in the first half of the 20th century (when first radio and then TV were in every home).
These models will not thrive long into the 21st century. The cost of information production and distribution — once extremely prohibitive and therefore making information itself a scarce resource — is now, like Macbeth and Tales of the Monkey King. Information sources from anywhere in the world are a click away and available for anyone with an internet, wifi, etc., connection. YouTube, FaceBook, Flickr and related sites turn everyone into their own marketing company. A few nods from PayPal and basement efforts are internationally financed sensations “overnight”.
Technology and information distribution infrastructures were already defined by cultural constraints elsewhere in the world so the american style hegemonies didn’t exist even though “american” cultural standards did. It was possible for other cultures to leapfrog the US in applying concepts of cultural anthropology, linguistics, ethnic studies and other sciences to exploit “small” markets because their infrastructure required it1.
And nowhere was the concept of “small marketing” becoming more obvious than in the online and mobile world due to the advent of social “small world” models (a concept borrowed from biopharmic clinical trials methodologies, mathematics, social anthropology and a few other sciences).
Changing Models
The increasing global awareness of cultural identity and diversity, the rise in ethnic pride and awareness, the acceptance of minorities and their requests for equal recognition, etc., are the psychological results of the information-accessibility explosion.
The increase in media outlets, methods and channels is destroying the old, american mass-marketing concepts and forcing marketers to use more and more scientific approaches and methods. Marketing is moving from a “…cast your bread upon the waters” mentality to a “choose your bread and waters carefully, and determine ahead of time how far to cast…” paradigm with the latter being something the rest of the world has been doing for a very, very long time.
Conclusion
Marketing will become a science. The rise and fall of disciplines (”neuromarketing” is the latest of these) attempting to explain marketing from a “scientific” paradigm is an example of evolutionary forces in the market looking for an answer to the “how do I best exploit this environment” question and not yet finding it.
Much as evolutionary forces caused biologies to answer “how do I best exploit this environment” with big muscles, big teeth and big brains so will several new disciplines take hold and only for as long as there are marketing companies willing to pay the prices they demand 2. I offer an E3 — economic-ecologic-environmental — model because co-evolution, ecologic diversity and resource economics will always apply.
As with our world so with marketing as a science. Humans fall into a wide variety of ecological niches that range from the obvious (age, gender, language, … essentially hangers on from the mass-marketing model) to the increasingly subtle (decision styles3, intender status4, psychological needs5 — extremely rapidly.
The language of commerce has changed throughout history and it can be thought of mathematically as a function of “which cultural-language groups had the best information-distribution technologies” X “the largest audience ready and willing to accept the commerce message”. Any change in the information environment creates new opportunities in the information-environment. Only those willing to create the technologies necessary to explain the changes/new opportunities will thrive there.
Markets will have to become a science — first borrowing from existing disciplines then giving to them — just to keep up.
1 – A favorite anecdote that demonstrates this is Atlantic Canada making digital phone and internet technology available to everyone who wanted it decades before the US. Atlantic Canada had never made the investment in copper wires and telephone poles as the standard communications technology. As copper based communication technology didn’t exist, installing a digital optical and wireless infrastructure was both possible and economical.
2 – I recently learned of a major brand who’s jumped on the Neuromarketing bandwagon in a big way. Realizing that a single person placed in an fMRI and flashing brand images at them doesn’t demonstrate “group” behavior, they’ve purchased ten fMRI machines — probably a US$10M investment in “cheap” machines alone, not counting training, staff, users, housing, maintenance, … — and placed them all in one room. This way they can get ten people into them at once at the same time and flash the exact same image to find out how the “group” responds to the brand!
Ah…yeah…
While the efficacy of fMRI, CT and related technologies for marketing purposes is still greatly in doubt (see Brain imaging skewed, “Nearly half of the neuroimaging studies published in prestige journals in 2008 contain unintentionally biased data that could distort their scientific conclusions, according to scientists at the National Institute of Mental Health in Bethesda, Maryland.” among others), the methodology itself is a legacy american paradigm — the only real answers are big, expensive answers…regardless if they’re answering the correct question.
I’ve been studying The Calculus of Intentions (it’s where semiotics and mathematics intersect) with some remarkably learned people over the past few months. A core question of the study is “How do we create a working definition that can serve as a baseline of knowledge while allowing us to create new knowledge?”
I believe this question is ignored in many disciplines today, especially in those disciplines where business mixes with science (see Why hasn’t Marketing caught on as a “Science”?). I’ve worked in pure research (work that had no obvious ROI) and applied research (”Solve this problem because we can productize the solution”). The former must create working definitions that are expandable, the latter works to create definitions that are brandable. Very different. The two can go into conflict.
A Valid Definition Must Be So General as to Encompass All Variants
In Reading Virtual Minds Volume II: Theory and Online Applications (still writing it, folks), I define “Usable” as
Something is usable when the individual using that thing achieves a goal both known and recognized prior to the usage event.
and “Usability” as
Usability is a measure of an individual’s conscious and non-conscious recognition of the pleasure derived from achieving their goal.
Creating as general as possible definitions is crucial to Reading Virtual Minds Volume II: Theory and Online Applications because I provide non-NextStage examples1 of how to do what I’m describing and I want readers to know ahead of time what they can expect as outcomes.
Readers will notice that “Usable” is objective and digital (you either did or did not achieve a known and recognized goal), “Usability” is subjective and analog (did you get a lot or a little pleasure? What do you mean by “a lot” and “a little” and is it the same as what I mean?) and I go into the reasons for this in the book.2
Readers will also (I hope) notice that the two definitions above exist in that borderland where pure becomes applied research. The goal is to create something general enough to be wholly true and restrictive enough to be uniquely identifiable as true.3
Pure “Definitions” versus Applied “Definitions”
What else is required? Pure research is usually interested in creating a definition for what hasn’t been in experience before, applied research not so much so, hence any definition used in business, etc., should encompass all previous similar experiences and definitely should not negate any previous similar experiences.
The classic business blunder example of this is “New Coke”. There was no question in consumer’s consciousness that the New Cok wasn’t “the real thing” and the debranding halo went from New Coke to Coca-Cola to Bill Cosby himself. The moral can be found easily in the Calculus of Intentions; instead of “Trust me, this is the real thing” (when “the real thing” was the existing definition of the old Coca-Cola formula) using “Trust me, this isn’t for everybody, so give it a taste. This could be the real thing for you” with Mr. Cosby’s finger first pointing at the Coca-Cola can then at the audience would have both captured the existing Coca-Cola audience and integrated it into the new formulation.
Integrate existing experience into the new definition and — from a marketing standpoint — you bring the existing audience with you (this is the heart of redesign and rebranding, also covered in Reading Virtual Minds V2). An example of not integrating existing experience into a new definition was something I heard in a radio spot earlier today (21 Jul 10). Some company in the Boston area is publishing a report, “The 25 Most Powerful Businesses in Massachusetts”. The ad then referenced their website with “Learn what makes a brand powerful at …”.
Essentially they use an existing term, “powerful”, then redefine it into something they lay claim to. It doesn’t matter if their definition of “powerful” is accurate or meaningful to anything else we may apply that term to because they’re also telling us what the term means when they use it.4
Organization and Structure
Next comes a definition’s ability to organize a body of knowledge into a clear, irrefutable structure. Such things are called “elegant solutions” in mathematics, meaning the definition demonstrates simple, easily repeatable solutions. This tends to be where pure and applied research — especially when the application is intended for branding — diverge greatly. Pure research works to create foundations, applied research builds on those foundations in the hope that nothing else will be built. Boston’s Hancock Tower, New York’s Empire State Building and Chicago’s Sears Tower (I think it has a different name now) are all buildings (foundational definition) and each has a separate name (applied definition).
The fact that what was Chicago’s Sears Tower for many years is now known as The Willis Tower is a demonstration of an applied definition’s mutability and temporality. People of a certain age will always reference that building as “The Sears Tower” and, if asked about “The Willis Tower”, will have to pause and perform the definition translation before answering with any confidence. Another example is The Boston Garden. I have no idea how many name changes it has gone through and to most people within a 100 mile radius of Boston who are over 35 years old, it will always be “The Boston Garden” (if for no other reason than “The TD BankNorth Garden” does not lend itself to alliteration and syllabation. It officially went from the full “The TD BankNorth Garden” to “The TD Garden” over a year’s time, I think, perhaps longer. Such is the strength of pure and applied definitions as brands). Very often, when placial applied definitions change, society imposes a foundational definition to replace all applied definitions.
Again using Boston area examples, Foxboro Stadium is Foxboro Stadium, not Gillette Stadium (readers specializing in search engines know such examples by heart). The Tweeter Center is the Comcast Center and was Great Woods. Most people have to guess where it’s located (Mansfield, MA). An example of pure and applied definitions going hand in glove is Gilford, NH’s, “The Meadowbook U.S. Cellular Pavilion” (once MeadowBook Farm. “MeadowBook” has always been part of the venue’s name so anybody and everybody knows about “The Meadowbrook”).
It is rare that a pure definition will change. Applied definitions are generational (as indicated above).
Recognize what is and what isn’t defined
Lastly, both pure and applied definitions need to clearly demonstrate what is not included in the definition. Binary definitions are great for this. “0″ is not “1″, (business) “male” is not (business) “female”. The definition of “Usable” provided at the start of this post is both binary and objective, good on both counts. Things like “Usability” and “New Coke”, being subjective, must always include the author’s intent as part of the definition. I enjoy math puzzles so their usability to me is quite high, lots of people I know find no enjoyment in them, so my intent must be included in my definition of math puzzle usability.
And it is the recognition of my intent, the pleasure I feel5, that brings us back to The Calculus of Intentions and creating definitions.
People as Programmable Entities
It is possible to determine usability for different personality types, meaning one can plot how much pleasure a group of people will derive from a given object/device/tool, meaning it’s possible to determine what features said object/device/tool must have to have penultimate usability, what features to change and how when introducing that object/device/tool into a new market, …
The same can be done for utility.
I was asked recently, “What sort of prison have you constructed, where the communications of people make such sense to you that their actions are programmably obvious…?”
I responded with “The foil here is probably an element of Cassandranism; if things are that obvious you’ll know who can be communicated with and who not.”
Such research is, I think, a ship and not a prison, although the two are only different based on definition and intent.
1 – A non-NextStage example is one where NextStage’s Evolution TechnologyTM (”ET”) isn’t required to achieve the result. The result may have been proven with ET and ET isn’t required to achieve the result.
2 – “Usability” as defined is not “utility”, the measure of relative satisfaction. I may be incredibly satisfied by something but derived absolutely no pleasure hence never want to use/do it again, such as being extremely satisfied that I survived a plane flight through a hurricane. However, I’ll never do it again, therefore the usability is zero.
Utility is a measure subjective and analog, and it provides no cycle for improvement. “Usable” provides a binary measure of improvement — it wasn’t usable before and now it is. “Usability” provides an improvement cycle — if usability is low (there is little to no pleasure in something’s use) we can go through iterations wherein changes to some object/device/tool increases usability (each change allows greater pleasure in its use).
As a further example of the difference between usability and utility, note that usability is sensory in nature (another reason it’s analog), utility is psychological in nature. We are prewired for usability (pleasure/pain), we have to learn utility.
3 – ET and humans move from “wholly true” to “uniquely identifiable as true” (the phenotype-genotype continuum) regularly and both do so via identity-relational models. For example, there exists a “business” definition of gender that is binary and has nothing to do with psychological, neurological, endocrinological, biological, …, science. By it’s definition, I am male and that is wholly true because it is a binary definition. Either I am or I am not.
When we say “You remind me of …” we’re dealing with “uniquely identifiable as true” and our conscious and non-conscious thoughts are using identity-relational models. We’re basically comparing our memory of person A with our immediate awareness of person B who’s standing in front of us. Are A and B a one-to-one match? Then they are uniquely identifiable and we say “Oh, you’re …”. When the match isn’t one-to-one we say things like “You remind me of …”, “You’re a lot like …” or “I knew someone (just) like you …”
The slide from “I recognize you’re a male” to “You remind me of …” to “You’re …” is the slide from wholly true to uniquely identifiable as true and uses identity-relational models (how many unique elements are required to uniquely identify this as “not that”? See Chapter 5 Section 3, “The Toddness Factor” in Reading Virtual Minds Volume I: Science and History for a description of this).
4 – Shades of “Pornography is what I’m pointing at when I say it.” I pretty much believe redefining something to suit your needs is obscene and pornographic. In this case, by going to the company’s website we learn “This national ranking is the first of its kind, … and provides a new benchmark for marketers”. Excellent! There’s no real validity to their “metric” other than self-promotion and the desire to become a standard. Wonderful! Truly! Therefore the basis of the metric is the audience’s acceptance of the company’s statements as valid.
But wait… I knew an emperor like that…
And truth in advertising here; I have at times advised clients to do something similar. The dissimilarity is that the clients so advised could back up their definitions and claims with long, well documented evidentiary trails.
There is an interesting model in evolution theory with lots of evidence to back it up. It deals with the fact that (in most cases) things evolve faster in competitive environments and things evolve fastest in antagonistic environments.
An antagonistic environment occurs when there’s active and intense competition for resources. For example, two top predators (commonly called “apex” predators, meaning nobody messes with them) vying for supremacy in the same food chain. At some point the two apex predators will stop preying on prey and start preying on each other. They’ll have to because two apex predators will quickly deplete all prey species and the only food resource left will be each other, hence somebody’s messing with somebody, hence apex-envy ensues and there can be only one “king of the mountain” in evolutionary terms.
So these predators will rapidly evolve (think “arms escalation”) until one gains the top spot. Sometimes (and rarely) will cooperation be the result and when it does occur it usually takes the form of one predator species become alpha and the other becoming beta. An example of this is scavenger species that help top predators cull herds of the weak, wait while the top predators dine then go in for the scraps. Humans and dogs are examples of this in the modern world (ie, the past 12-15k years or so). Wolves and humans vied for top predator status, wolves evolved into dogs (and right quickly, too. Wolves have been around as “wolves” for close to a million years) because humans left enough from their kills to warrant the evolutionary change.
That’s another thing to be aware of in these evolutionary, escalatory exchanges; the species that chooses the less dominant path tends to thrive because the more dominant species needs it in order to insure apex status. The fact that humans domesticated wolves into dogs then created so many varieties of dogs to do such a variety of jobs is a demonstration of this. The downside to such relationships is that the beta species will become prey to the alpha in hard times.
Antagonistic environments are usually unstable, meaning “something’s got to give”. Unstable environments are the bane of ecologies because ecologies survive best when things are in balance. Balance occurs in competition — sometimes A wins, sometimes B wins — but never in antagonism — somebody’s got to become “top dog”, to reach the apex, so to speak.
Business Environments
Mature business environments are competitive, rarely antagonistic. Antagonism is a hallmark of early stage evolutionary systems. There’s extreme competition for resources, predators haven’t evolved to match the opportunities of select prey because (in early evolutionary systems) everything is prey and everything is predator. Keystone species — the species that support ecologies at a fundamental and necessary level — haven’t evolved yet.
Even in mature markets something will occur, some tipping or tripping point triggers environmental/ecological change and the mature market will spawn a new market that is highly immature and antagonism ensues.
Think “land grabs”. Think “speculative markets”. Think “junk bonds”. Think “mortgage crisis”. Think about much of what has happened in the past ten years.
Think any maturing industry and you’ll first see antagonism once a new market opportunity is recognized, eventually followed by competition once the market is stabilized (again, balance). I’ve even heard of market competitors forming “co-opetitons”, co-operative competitive ventures. I’ve seen the contracts involved in such things and graciously shy away. As a friend once told me, “The purpose of those contracts is to decide where and when the mutual f?cking will begin.”
I consider such market realities to be necessary evils. They are fascinating to watch “over there” and tend to be not much fun “right here”. Environments and ecologies evolve and there’s nothing anybody can do to stop them. Any human intervention means new balances will occur. Bailouts and market reforms are (I believe) sorry examples of this. Concepts of market tending and stabilization grew out of long proven agrarian husbandry concepts that are (typically) misapplied.
The challenge in moving the concept from barnyard to main street is that the barnyard is a fixed and intentionally static environment with a highly monitored ecology. You can grow bigger beef, taller wheat, tastier corn or fleeter salmon because nothing is allowed to deviate from well established norms.
Main street is neither fixed nor static. It is a free market (or at least claims to be) hence environments and ecologies can be highly monitored and that’s about it. Some fox or coyote sneaks through the fence, some crows or chickadees recognize your owls can’t be everywhere and baboom your whole world changes. Whatever your monitoring must obey the barnyard rules but the predators are free to do whatever they want. Predators may not even be recognized by your monitoring systems (think “Bernie Madoff”).
I bring all this up because NextStage is about to get (what I consider) a good review from Gartner in their “Cool Vendor” report. The lines I especially like are
Analysis of content before publishing ensures that it will appeal to the desired audience.
Analysis of the users to a web site will help align content to the audience and their expectations.
These products provide empirical data about user profiles and reactions — which is difficult to obtain other ways — while protecting anonymity.
It’s going to be interesting (think “Chinese Curse”), these next few months. I already know what new products NextStage will be releasing this year. I have no idea how they or the markets we’re entering will evolve although I do know both will.
The very introduction of new species (regardless of that species survival fitness at the time of introduction) into an existing ecology changes it forever (think “invasive species”, think “zebra mussels”, think “kudzu”). All that’s required is that the invasive species be more opportunistic with the available resources than species already established in that environment. The odds are in favor of the invasive species. They’re coming in prepared to evolve. Existing species have evolved to a stasis condition with the existing environment (balanced ecology). The introduction of the new species necessarily disrupts the ecology, changes the environment, new ecological niches are demonstrated, …
A good research project. Or two, me thinks, releasing NextStage’s chimeras (a nod to things hybrid, born of four parents, like NextStage’s Evolution Technology) from the barnyard onto the main streets, don’t you?
Knowledge will forever govern ignorance, and a people who mean to be their own governors, must arm themselves with the power knowledge gives. A popular government without popular information or the means of acquiring it, is but a prologue to a farce or a tragedy or perhaps both. – James Madison
There was never suppose to be a part 3 to this arc (Ben Robison was correct in that). Part 1 established the challenge (and I note here that the extent of the response and the voices responding indicates that the defined challenge does exist and is recognized to exist) and Part 2 proposed some solution paths. That was suppose to be the end of it. I had fulfilled my promise to myself1 and nothing more (from my point of view) was required.
But many people contacted me asking for a Part 3. There were probably as many people asking for a Part 3 as I normally get total blog traffic. Obviously people felt or intuited that something was missing, something I was unaware of was left out.
But I never intended there to be a Part 3. What to cover? What would be its thematic center?
It was during one of these conversations that I remembered some of the First Principles (be prepared. “First Principles” will be echoed quite a bit in this post) in semiotics.2
According to semiotics, you must ask yourself three questions in a specific order to fully understand any situation3:
What happened?
What do I think happened?
What happened to me?
More verbosely:
Remove all emotionality, all belief, all you and detail what happened (think of quis, quid, quando, ubi, cur, quomodo – the six evidentiary questions applied to life).
What do your personal beliefs, education, training, cultural origins, etc., add to what actually and unbiasedly happened?
Finally, how did you respond — willingly or unwillingly, knowingly or unknowingly, with all of your history and experience — to what happened.
The power of this semioticism is that it forms an equation that is the basis of logical calculus, the calculus of consciousness4, modality engineering5 and a bunch of other fields. I use a simplified form of it in many of my presentations, A + B = C.6
Talking with one first reader, I realized that Part 1 was “What happened?” (the presentation of the research) and Part 2 was “What do I think happened?” (my interpretation of the research). What was left for part 37 was “What happened to me?”
And if you know anything about me, you know I intend to have fun finding out!
All Manner of People Tell Me All Manner of Things
The above is a line from Oliver’s Travels (highly recommended viewing), something said by the Mr. Baxter character. Mr. Baxter is himself a mystery and — although his true nature is hinted at several times — it is not revealed until the last episode. There we are told about The Legend of Hakon and Magnus. In short, Mr. Baxter could be a good guy, a bad guy or the individual directing the good or bad guy’s actions. His role entirely depends on what side you are on yourself, a true Rashomon scenario. I found myself in something similar to Mr. Baxter’s situation as how people responded to my research, its publication and myself also depended greatly on what side people were on when they contacted me.
I was both dumbfounded and honored by the conversations Parts 1 and 2 generated. The number of people who picked up on or continued the thread on their own blogs (here (and alphabetically) Christopher Berry (and a note that Chris continues the conversation in A Response (The Unfulfilled Promise of Analytics 3) ), Alec Cochrane, Stephane Hamel, Kevin Hillstrom, Daniel Markus, Jim Sterne, Shelby Thayer and if I’ve forgotten someone, my apologies), twittered it onward, skyped and called me was…I could say unprecedented and remind me to tell you about a psychology convention in the early 1990s (nothing to do with NextStage, just me being me, stating what is now recognized as common knowledge yet way before others decided it was common. Talk about unprecedented results. I had to be escorted out under guard. For those of you who know Dr. Geertz, his comment upon learning this was “I’m not surprised you’d have to be escorted out by guards. You have that subtle way about you…”8).
But to note the joy means to recognize the sorrow (as was done in Reading Virtual Minds Vol. 1: Science and History Chapter VI, “The Long Road Home”). While the majority of people honored me and a good number of people appreciated that I had done some useful research and donated something worth pondering, there were a few (just a few, honestly) who damned me.
The damning per se I don’t mind. It’s part of the territory. It was the manner and the persons involved that truly surprised me.
I was accused of possibly destroying a marriage (Susanism: If you think this is about you, it’s not. We know a lot more people than just you), maligning certain individuals (usually by people who maligned other individuals during the research. I guess I wasn’t maligning the correct individuals in their view), not demonstrating the proper respect to industry notables (same parenthetical comment as previous and you guessed it, another NextStage Principle), that I better post an apology to these same industry notables (two people wrote apologies in my name and strongly suggested that I publish them), …
Whoa!
Who gave me such power and authority to make or break people’s lives? Certainly I didn’t give it to myself, nor did I ask others to give it to me. And if anybody did give it to me without my knowing I gladly give it back. As I’ve said and written many times, I do research. When new data makes itself available and as required, I update my research. But until such new data comes in, the research stands.
What I really want to know is if, when the results of research are discomforting, the industry’s standard and usual procedure is
to change either the research or results so that people feel warm and fuzzy — hence have no impetus to act (according to one person at yesterday’s NH WAW, “Don’t measure what you can’t change”. An interesting statement that I disagree with. Doing so means to throw out meteorology, astronomy, … much of what has been historically measured without any change-ability allowed us to create the technologies that would produce change in previously unchangeable systems)
or let the discomfiting research stand — so that the challenge can be recognized and either action can be either taken or the challenge go ignored.
Seems to be the “change either the research or results” is the standard (or at least done when required) because while few asked that I rewrite research or results so that certain individuals appeared more favorably, the ones who did ask sure were some high-ranking industry folks.
Heaven forbid these folks wanting different results published or do complimentary research that either validated or invalidated my results.
Wait a second. What am I thinking? Obviously it would be impossible for them to do research that validates mine.9
Of course, publishing research would also mean publishing their methodologies, models, analytic methods, … and the reasons that ain’t gonna happen will be covered later in this post.
And if that is the standard and usual procedure — at least among those in the high ranks — then
congratulations to all the companies hiring high ranking consultants to make them feel good rather than solve real problems and
be prepared for those coming up through the ranks to learn this lesson when it is taught them.
For the record, not much upsets me (ask Susan for a more honest opinion of that). The sheer stupidity of arguments that resort to emotionalism or are nothing more than attempts to protect personalities and positions, though… Them they do offend me (can’t wait to learn how our Sentiment Analysis tool reports this). And more about stupidity later in this post (Let me know if you recognize Joseph’s “I’m mad as hell and I’m not going to take it anymore” persona).
When the Stories Meet the Numbers (Statistics, Probability and Logic)
I originally surveyed about sixty people for Part 1. That number grew to about one hundred in Part 2 due to responses to Part 1. Currently I’ve had conversations (I’m counting phone calls, Skype chats and calls, email exchanges and face-to-face discussions at meetings I’ve attended as “conversations”) with a few hundred people about those posts.
I noticed something interesting (to me) about the conversations I was having. Lots of people made statements about statistics, probability and logic but were using these terms and their kin in ways that were unfamiliar to me. Especially when I started asking people what their confidence levels were regarding their reporting results.
I’ll offer that search analysts (I’m including SEO and SEM in “search analysts”) seem to have things much easier than web analysts do. “We were getting ten visits a day, changed our search terms/buy/imaging/engines/… and now we’re getting twenty visits per day.” Granted, that’s a simplification and it’s the heart of search analytics — improving first the volume and second the quality of traffic to a site. Assuming {conversions::traffic-count} has standard variance, search analytics produces or it doesn’t and it’s obvious either way.
Web Analytics is the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage.
The analytics organization I see most often cited, SEMPO, doesn’t even attempt to define (”SEMPO is not a standards body…”) or police (”…or a policing organization.“) itself. It does offer search courses but the goals of the SEMPO courses and the WAA recognized courses are greatly different (an opinion, that, based on reading their syllabi as someone having taught a variety of courses in a variety of disciplines at various educational levels in various educational settings).
There are twenty-one words in the official WAA definition and a philologist will tell you that at least ten require further definition.
Definitions that require definitions worry me. Semiotics and communication theory dictate that the first communication must be instructions on how to build a receiver. Therefore any stated definition that requires further definition is not providing instructions on how to be understood (no receiver can be built because there is no common signal, sign or symbol upon which to construct a receiver. If you’ve ever read my attempts at French, you know exactly what I mean10).
One of the statements made during the research for this arc was “[online] Analysts need to share the error margins, not the final analysis, of their tools.” It expressed a sentiment shared if not directly stated by a majority of respondents and it truly surprised me. It states as a working model that any final analysis is going to be flawed regardless of tools used therefore standardize on the error margins of the tools rather than the outputs of the tools.
So…decisions should be made based on the least amount of error in a calculation, not what is being calculated (does the math we’re using make sense in this situation?), the inputs (basic fact checking; can we validate and verify the inputs?) or the outcome (does the result seem reasonable considering the inputs we gave it and the math we used?)?
A kind of “That calculation says we’re going to be screwed 100% but the error margin is only 3% while that other calculation says we’re only going to be screwed 22% but the error margin is 10%.
Let’s go with the first calculation. Lots less chances of getting it wrong there!”, ain’t it?
More seriously, this is a fairly sophisticated mathematical view. Similar tools have similar mathematical signatures when used in similar ways. When a tool has an output of y with fixed input x in one run and y+n with that same fixed input x in another run but a consistent error margin in both runs, standardizing on the error margin e is a fairly good idea. It indicates there’s more going on in the noise than you might think.11
Of course, this means you better start investigating that noise darn quick.
My understanding of “statistics, probability and logic” was often at odds with what people were saying when they used those words. The differences were so profound (in some cases) that I asked follow up questions to determine where my misunderstandings were placed.
Serendipity doing it’s usual job in my life, over this fall-winter cycle I took on the task of relearning statistics12, partly so I could understand how online analysts were using statistics-based terms. As noted above, the differences between what I understood and how terms were being used and applied was so different that I questioned my understanding of the field and its applications.
And to whither I wander, I offer a philologic-linguistic evidentiary trail for all who will follow. For those who just want to get where I’m going, click here.
Web Analytics is Hard
Of course it is. Anything that has no standards, no base lines, no consistent and accurate methods for comparisons is going to be hard because all milestones, targets and such will have to be arbitrarily set, will have no real meaning in an ongoing, “a = b” kind of way, and therefore Person A’s results are actually just as valid as Person B’s results because both are really only opinion and the HiPPOs rule the riverbank…
…until a common standard can be decided upon.
Web Analytics is easy
Of course it is. Anything that applies principled logic, consistent definitions, repeatable methodologies that provide consistent results, … is going to be.
Online Analytics Is Whatever Someone Needs It to Be
Ah…of course it is.
And this is the truest statement of the three for several reasons. Consider the statement “(something) is Hard“.
It doesn’t matter what that “(something)” is, it can be driving a car, riding a bike, watching TV, playing the oboe, composing poetry, doing online analytics, … . What that “(something)” is is immaterial because the human psyche, when colloquial AmerEnglish is used, assigns greater cognitive resources to understanding “Hard” than it assigns to “Web Analytics”, and this resource allocation has nothing to do with whether or not “Web Analytics” is easier to understand than “Hard”, it has to do with what are called Preparation Sets13. The non-conscious essentially goes into overdrive determining how hard “Hard” is. It immediately throws out things like “iron”, “stone” and “rock” because the sensory systems don’t match (iron, stone and rock involve touch-based sensory systems, transitive expressions such as “(something) is hard” don’t) and starts evaluating the most difficult {C,B/e,M}14 tasks in memory — most recent to most distant past — to determine if the individual using the term “Hard” is qualified to use the term as a surrogate for the person being told “(something) is Hard” (ie, our non-conscious starts asking “Do they mean what I think they mean when they say ‘Hard’?”, “Do they know what ‘Hard’ is?”, “What do they think ‘Hard’ means, anyway?”, “Do they mean what I mean when I say ‘Hard’?” and so on).15
What I will offer is what I’ve offered before; any discipline that defines success “on the fly” isn’t a discipline at all (at least it’s not a discipline as as I understand “discipline”). Lacking evidentiary trails, definitions and numeric discipline, comparisons of outputs and outcomes degenerates to “I like this one better” regardless of reporting frame.
Teach Your Children Well
Where statements like “(something) is Hard” and “(something) is Easy” really make themselves known is when teaching occurs.
Let me give you an example. You have a fear of (pick something. Let’s go with spiders because I love them and most people don’t (Only click on this link if you love spiders)). Phobias are learned behaviors. This means someone taught you to be afraid of spiders. It’s doubtful someone set out some kind of educational curriculum with the goal of teaching you to fear spiders (barring Manchurian Candidate scenarios). It’s much more likely that when you were a child, someone demonstrated their fear of spiders to you. Probably either repeatedly or very dynamically, so you learned either osmotically or via imprinting. Children demonstrate their parents’ behaviors in hysteresis patterns. This means that if you measured a parent’s level of arachniphobia and assigned it a value of 10, chances are the child would demonstrate their arachniphobia at a level of 100 or so in a few years’ time. Children who learn their parents’ fears and anxieties do so without understanding any logical basis for those fears, only the demonstration of them. When there is no logic to temper the emotional content, hysteria results.
However, if a parent demonstrates a fear response and the ability to control it, to explain to the child that fear response’s origin, etc., most often the child learns caution and not fear (not to mention that the parent usually learns to control their fear). The difference can be thought of as the difference between teaching a child to “Be careful” versus hysterically screaming “EEEEK!”
What’s so fascinating about this is that it’s also how we pass on our core, personality and identity beliefs whether we mean to or not (I cover this in detail in Reading Virtual Minds Volume I: Science and History). We can be teaching physics, soccer, piano, bread-baking, … It doesn’t matter because all these activities will be vectors for our core, identity and personal beliefs and behaviors. If we are joyful people then we will teach others to be joyful and the vector for that lesson will be physics, soccer, piano, bread-baking, … And if we are miserable people? Then we will teach others to be miserable and to be so especially when they do physics, play soccer, the piano, bake bread, …
Thus if any teaching/training occurs intentionally or otherwise, the individual doing the training/teaching is going to de facto teach their internal philosophies and beliefs — both business and personal — as well as their methods and practices to their students. This can’t be helped. It’s how humans function. If the philosophy and belief is that things are hard, then that philosophy and belief will be taught de facto to the students. Likewise for the philosophy and belief that something is easy. There will be no choice.16
The point is we protect others from what we fear. Humans are born with precious few fears hard-wired into us (heights and loud noises are the two most cited. Heights because we’re no longer well adapted to an arboreal existence and loud noises because predators tend to make them when they attack).
So the statement “(something) is hard” either means we fear “(something)” or we wish to protect others from having the difficulties we have when we do “(something)”, and if difficulties existed then the non-conscious mind is going to place a fear response around whatever “(something)” is to make sure we don’t put ourselves into unnecessary difficulties yet again.
The statement “(something) is easy” generates the polarity of the above and I, dear reader, I am the neuro- and philo-linguist’s nightmare because my training is simply that “(something) is”. My training is that both whatever exists and whatever state it exists in are mind of the observer17 dependent. Thus things simply are and our perceptions, experience and decisions make them hard, soft, easy, whatever, to us individually.
More colloquially, whatever your perceptions of the world are, it’s all you and precious little of anything else (a favorite quote along these lines is “What if life is fair and we get exactly what we deserve?” Ouch!).
The Trail Leads Here
There are lots of errors I can understand. A lack of knowledge, of mathematical rigor, of logic training, of problem solving skills, … These and a host of others I can appreciate. Especially in those junior to any given discipline.
But unprovable math, a lack of basic fact checking, outputs that have no meaning based on what’s come before and (let’s not forget) emotionalism? This really blew me away. Math can be taught, junior people who don’t fact check can be trained, making sure units match can be taught and comes with experience, … but emotionalism?
I’ll accept any of the above in junior players with the caveat that the first to go has got to be emotionalism.
But senior people failing any of these before offering something for publication? Then defending this lack of rigor with an emotional outburst? And when it happens more than once?
Talk about abandoning First Principles!
First Principles? We don’t need no stinking First Principles!
Challenge logic, challenge research, challenge findings, sure. Challenge a person if they challenge you, sometimes maybe. I’ll tolerate a lot, folks (ask Susan for confirmation), and I have a real challenge with such as these — Arguing emotionally and telling me it’s logic, arguments based on no facts at all… I’ll accept, entertain and work with ignorance, arrogance, discomfiture, anxiety, joy, love, appreciation, anger, … quite a wide thrall of human response.
But arguments such as these are, in my opinion, stupid.
There, I typed it.
Yet because such arguments were presented as such I must recognize that in some camps doing web analytics means to heck with fact-checking, logic, … That it’s acceptable to ignore truth and common practice to base outcomes on what one needs them to be. I mean, when someone with title and prestige does it, the overt statement is that others should, will or do do it, as well. Definitely people in the same company should or will do it. Whatever’s lacking in the master’s portfolio won’t be found in the student’s (in most cases).
Want to know why I stopped attending conferences? See the above.
I had been wondering if it was worth my writing a little bit on elementary logic, probability theory, problem solving or some such. A previous draft of this post contained an explanation of elementary statistics and problem solving as it might be applied to online analytics. Now I really had to question such an effort. If the notables don’t know how to apply these things…
Where the stories meet the numbers, there Understanding dwells
The power of logic, knowing problem solving methods, basic statistics, probability and so on is that they provide basic disciplines that prevent or at least inhibit mistakes such as listed above. You have the tools and training to basically “…draw an XY axes on the paper, chart those numbers and the picture that results points you in the direction you need to go.” You can be emotional about your research and your findings and you can’t defend your research emotionally. The research and findings are either valid or they ain’t.18
As for drawing an XY axes, charting numbers and getting some direction…what can you do with such evidentiary information? There are lots of things you can do. Determine the relationships between the numbers and you can exploit their meanings.
But if the basics are beyond the industry greats
then explaining the differences between cross-sectional studies and longitudinal studies (cross-sectional studies involve measuring a single (x,y) pair, meaning x is fixed for all y. Longitudinal studies involve countably infinite (x,y) pairs. Longitudinal studies are greatly more expensive than their cross-sectional cousins and is why cross-sectional regression models are often used when longitudinal regression models are needed) won’t do much good19,
nor will explaining the need for creating a “standard” site for calibration purposes,
models can only be standardized once methods themselves are analyzed and an accuracy “weighting” is determined (allowing all models to be compared to a “gold standard”, meaning comparing my results to your results actually has analytic meaning),
explaining the meaning of and how to “normalize” samples is out (doing so allows you to see where the normals fall on your standard curve. You put your normals in the middle to lower part of the curve because a) this is where population densities are greatest and b) no naturally occuring line is going to be straight so you shoot for placing your normals on the straightest part of the curve to get some kind of linearity (that y = mx + b thing). Every naturally occuring phenomenon follows mathematical rules that produce curves. Between the two blue lines is where standards occur. Below the bottom blue is “below standard”, above the top blue is “out of standard”. Between the bottom blue and green line is the normal range. You calibrate your methods against the gold-standard normals and anything above is where the money lies),
It takes more effort to reorder a partially ordered system than it does to create order in an unordered system (bonds, even when incorrect, have existing binding energy).
I completely understand why so many of NextStage’s clients couldn’t document the accuracy of the online analytics tools they were using at the time they contacted us for help. This lack of documentation was something I was very uncomfortable with. If there’s no proven methodology for demonstrating a number’s validity then you’ve essentially moved away from the gold standard and declared that the value of your dollar is based entirely on what others are going to value it at (pretty much determined by your political-military-industrial capabilities or in this case, those guarding the riverbank). Your numbers only have meaning so far as others are willing to accept them as valid and if lots of money is being paid for an opinion, that opinion is going to be gold regardless if it’s based on invalid assumptions or documentable facts.
The online analytics field is partially ordered — it’s been around long enough for a hierarchy to appear — so only those willing to expend the energy are going to attempt fixing it for the sake of getting it fixed rather than changing it to suit their own objectives.
And this is where
The detritus encounters the many winged whirling object
NSE was seeing so many erroneous tool results (my favorite example was the company that was getting 10k visitors/day and only 3 conversions/month. Their online analyst swore by the numbers) that it lead us to come up with a reliable y = x ±2db that we could prove, repeat and document. It relied solely on First Principles. This led to our in-house analytics tools, which is why we’re analytics tool agnostic. We really don’t care what tools clients use. If we don’t believe the numbers we’ll use our own tools to determine them because we know and can validate how our tools work. As a result we now often use our tools to validate the accuracy of other tools.
I have no dog in this fight (both the “Web Analytics is…” and whether or not a promise existed and has gone unfulfilled fights because I’m a recognized industry outsider) and won’t be dragged into it (I mean, would you really want me involved?). My agenda is making sure that those coming to NextStage for help either bring with them some mathematical rigor or allow NextStage to invoke it. There is little that can be done when a tool lacks internal consistency (given a consistent input it generates different outputs).
It really is that simple, folks. This is First Principles and they always work. Don’t believe me? Ask Ockham. First Principles have to work. As long as the sun rises in the east and sets in the west, as long as there are stars up in the sky, as long as the recognized laws of reality are valid, …
And because mathematics is a universal language, the stars are in the sky, etc., etc., these rules have to apply to online analytics and the tools used therein.
Unless you’re happy with high variability in results sets given a known and highly defined set of inputs.
Which is fine, if that’s what your values are based on.
And I doubt it is, so be prepared for companies to use HiPPOs only for political purposes (”Our methods are valid because they were installed/given to us/updated/validated/… by HiPPO du jour“), not for accuracy purposes.
I mean, people make a living out of these things, right? When someone talks about a regression curve and that a decision was made because the probabilities were such and so, does it matter if they know what they’re talking about?
Or is being able to use a tool the same as understanding what the tool is doing?
And I know there are online analysts out there who take high variability and weave it into gold. Good for them (truly!). They have a skill I lack. And they’re performing art, not science, and as someone who walks in both worlds I will share my opinion that science is lots easier than art. Science has rules. Art is governed by what the buying public is willing to spend and on whom.
Ahem.
That offered, HiPPOs du jour should be prepared for highly defined and validatable game-changing methods and technologies to un-du jour them because such methods and technologies will, given time and regardless of where they originate and how they emerge. In this, like stars shining in the sky, there is no option, no way out. The laws of evolutionary dynamics apply in everything from rainstorm puddles on the pavement to galactic clustering (I can demonstrate their validity in the online analytics world very quickly and easily; start with the first online analytics implementation at UoH in the early 1990s and follow the progression to today. Simple, clean and neat. I love it when things work. Don’t you? Gives me confidence in what I think, do and say).
My suggestion (note the italics) is that the online community create an unbiased, product agnostic experimental group. All empirical sciences that I know of have experimental disciplines within them (physics has “experimental physics”, immunology has “experimental immunology”, …). NextStage is not part of this community so again, we have no dog in this fight. Let me offer NextStage as an example, though — we do regularly publish our experimental methods and their results in our own papers and in business-science journals and in scientific conference papers. This allows others to determine for themselves if our methods are valid and worthy. Granted, NextStage comes from a scientific paradigm and perhaps taking on some of science’s disciplines would benefit the industry as a whole, or at least bring more confidence and comfort to those within it.
But what about the Third Semiotic Question?
Answering “What happened to me?” follows the trail of asking trusted others (my thanks to Susan, Charles, Barb, Mike, Warner, Lewis, Todd, Little-T and the Girls, M, Gladys and Dolph) many questions to bridge holes in my understandings.
All the ills referenced in parts 1 and 2 demonstrated themselves to their full — people who didn’t like what I wrote triangulated. They contacted others whom they thought were socially closer to me or “might have an in” but heaven forbid they contact me directly. Others focused their frustration at me because (probably in their minds) I was something concrete and tangible, something they could point at, instead of something they felt powerless against; the industry as a whole. Still others because they consider me an industry leader (I’m not. I’m an outsider, remember? I can’t lead an industry I’m not a part of. Or will Moses start telling Buddhists how to behave?). And (I’m told) I became the subject of klatch-talk on at least two continents (obviously, I need to start charging more for my time).
All of these things add up to determining the human cost of the unfulfilled promise of online analytics. As I quoted before, Coca-Cola® Interactive Marketing Group Manager Tom Goodie said “Metrics are ridiculously political.” He was correct and not by half. The cost is high. It is highest amongst
those unsure of the validity of their methods, their measurements and their meanings who want to be accepted and acknowledged as doing valuable work yet are unable to concisely and consistently document what they’re doing to the satisfaction of executives signing their checks
and those who are cashing those checks to buy new clothes.
Do I think online analytics industry will change because of my research and its publication?
Did you read what I wrote about accountability in The Unfulfilled Promise of Online Analytics, Part 1? People are being paid without being accountable for what they’re being paid to do. The sheer human inertia put forth to not change that model has got to be staggering, don’t you think?
And I doubt anything I could do would bring such a change about. My work may contribute, it may be a drop in the bucket helping that bucket to fill and that’s all.
The industry itself will change regardless (surprise!). As a WAWB colleague recently wrote, “For a field that’s changing rapidly, based on rapidly changing technologies, I personally feel that holding any expectations for the future is a set up for disappointment. The expectation of change is the only realistic expectation I can hold today.” and I agree. Things will change. They always do. To promise anything else is to lie first to one’s self then to others.
Final Thoughts
This is the end of the Unfulfilled Promise arc for me, folks. Please feel free to continue it on your own and give me a nod if you wish.
(my thanks to readers of Questions for my Readers who suggested this footnoting format over my usual <faux html> methods and to participants in the First NH WAW who, knowing nothing about this post, covered much the same topics during our lunch conversation)
1 – A constant promise to myself regarding my work — perform honest research, report results accurately and unbiasedly and (when possible) determine workable solutions to any challenges that presented themselves in either research or results.
2 – For those who don’t know, much of ET is based on anthrolingualsemiotics — how humans communicate via signs. “Signs” means things like “No Parking”, true, and also means language, movement, symbols, art, music, … . According to Thomas Carlyle, it is through such things “that man consciously or unconsciously lives, works and has his being.” You can find more about semiotics in the following bibliography:
3 – There is (in my opinion) no greater demonstration of this principle than in The Book of the Wounded Healers, a long forgotten book that I hope will become available again sometime soon.
6 – The simplest things often have the most power. The semioticist’s A + B = C demonstrates itself with three questions to form equations of meaning such as:
(what happened) + (what do I think happened) = (what happened to me)
(what happened to me) – (what do I think happened) = (what happened)
(what happened to me) – (what happened) = (what do I think happened)
9 – Note to Ben Robison: Still working on that sarcasm thing. We have what we think is a good go at it in the NS Sentiment Analysis tool we’ll be making public either this week or next (still waiting for the interface and may decide to go without it just to learn what happens).
12 – Periodic relearnings are part of my training and makeup. I put myself through periodic re-educations because I question my knowledge, not because I question someone else’s. My goal is to find the flaws in my understanding, not to pronounce someone else’s in error. Periodic re-educations keep subject matter knowledge fresh within me, brings new understandings to old educations, increases wisdom, all sorts of good things. Admittedly, this has enabled me to recognize flaws in other people’s reasonings. Two examples that the online community may be familiar with are Eric Peterson’s engagement equation (flawed definitions and mathematical logic) and Stephane Hamel’s WAMM (frame confusion).
In any case, the result of my own and others’ questioning was that I studied how that equation was derived (was the mathematical logic viable and consistent, were the variables defined and used consistently, …) and found it flawed. Eric asked if it would be possible for us to simply work together on the equation to remove some ambiguities and make it more generally applicable, thereby removing any questions of mathematical validity and provide business value.
The public response to my reworking of Eric’s original equation both confused and concerned me. My reworking was nothing more than turning it into a multiple regression model with the b0 and e terms set to 0 and all bn assumed to 1 (they could be changed as needs dictated). This allowed people using the reworking to determine by simple variance which models/methods weren’t valid in their business setting and ignore them. I kept thinking people would laugh at how simplistic my reworking was and the response was quite the opposite. It was at this point my concerns about basic mathematical knowledge among online analysts flared.
I read through Stephane Hamel’s WAMM paper (also because others entered it into a discussion) and recognized that by adding some consistent variable definitions that tool would have a great deal of power across disciplines. I asked Stephane if he’d mind my tinkering and so the story goes.
The challenge with Eric Peterson’s engagement equation and Stephane Hamel’s WAMM is (in my current understanding) that there is no “standard”, itself a theme I’ll return to in this post. As an example, my current work with WAWB involves applying some standard modeling techniques so a “normal” can be determined. This would allow Company A to measure itself against a normal rather than comparing itself to bunches of other companies (that might not be good exemplars based on differing business and market conditions) and determine upon which vector Company A should place its efforts to insure cost-efficient gains along all WAMM vectors. The first aspect (my opinion) would be organizational. Without people accepting recognized truth there is no truth (again, my opinion).
And each time I take on such a task I require myself to relearn the necessary disciplines so I can be confident that my understandings are as close to the original author’s as possible.
My method for learning and re-learning anything is to go back to First Principles (as mentioned earlier in this post). Some people may have heard or seen me talk about learning theory and how it can be applied everywhere. That’s a lot of what First Principles are about. Start with the most basic elements you can, understand them as completely as possible, build upon that. One thing this provides me is the ability and confidence to discuss my ideas openly, the freedom to ask questions honestly and truthfully, and to understand and accept conflicting views easily and graciously. Put another way, the more you know, the wider your field of acceptance and understanding, and the more fluid and dynamic you become in your ability to respond to others.
So I started relearning statistics by going back to First Principles, studying Gauss, Galton, Fisher and Wright, giving myself the time to understand how the discipline evolved, how the concepts of regression, regression to the mean, ANOVA, ANCOVA, trait analysis, path analysis, structural equations modeling, causal analysis, least squares analysis, …, came about, how they’re applied to different sciences (agriculture, eugenics, medicine, …), how bias, efficiency, optimality, sufficiency, ancillarity, robustness, … came about and how they are solved.
Harold Pashler; Mark McDaniel; Doug Rohrer; Robert Bjork 2008 Learning Styles: Concepts and Evidence, .Psychological Science in the Public Interest V 9 , I 3 1539-6053 %+ University of California, San Diego; Washington University in St. Louis; University of South Florida; University of California, Los Angeles
15 – And before I get another flurry of emails that I’m attacking one person or another, no, I’m not. An almost identical process occurs when someone says “(something) is Easy”. I describe the “(something) is Hard” version because it’s easier for people to understand. One of the wonders of AmerEnglish and American cultural training, that — it is easier to accept that something can be hard and harder to accept that something could be easy.
18 – I’ll use myself as an example. I’ve often become emotional when talking about research and results. But (But!) regardless of my emotionalism, the work stands or doesn’t. I can clarify, elucidate, explain, divulge, describe, … and in the end, the work stands or it doesn’t.
19 – If your model is a linear variation (all regression analyses are linear in nature) then you have something like y = mx + b, y = b0 + b1x + e, … and every change in one unit of x will cause a one unit change in y. Using the above equations as examples we get the textbook definition of the regression coefficient (either m or b1 in the above); the effect that a one unit change in x has on y.
20 – I have experience working with large data sets. Some of you might know I worked for NASA in my younger years. I was responsible for downloading and analyzing satellite data. The downloads came every fifteen minutes and reported atmospheric phenomena the world over. My job was to catch the incongruous data and discard it. I got to a point where I could look at this hexidecimal data stream and determine weather conditions any where in the world before it got sent on for analysis.
Basis: This publication concludes a two year study of visitor adaptation to and adoption of new technologies and site redesigns on similar product or purpose sites in the US, EU, GB and Australia. No Asian, South American or African sites were part of this study.
NextStage will be posting some of its research here (as noted in 
I am honored to be recognized by 
Annenberg’s Center for the Digital Future will have direct access to NextStage’s research and findings for use with their greater, international audience. 
I’ve been studying The Calculus of Intentions (it’s where semiotics and mathematics intersect) with some remarkably learned people over the past few months. A core question of the study is “How do we create a working definition that can serve as a baseline of knowledge while allowing us to create new knowledge?”
The classic business blunder example of this is “New Coke”. There was no question in consumer’s consciousness that the New Cok wasn’t “the real thing” and the debranding halo went from New Coke to Coca-Cola to Bill Cosby himself. The moral can be found easily in the Calculus of Intentions; instead of “Trust me, this is the real thing” (when “the real thing” was the existing definition of the old Coca-Cola formula) using “Trust me, this isn’t for everybody, so give it a taste. This could be the real thing for you” with Mr. Cosby’s finger first pointing at the Coca-Cola can then at the audience would have both captured the existing Coca-Cola audience and integrated it into the new formulation.
I responded with “The foil here is probably an element of Cassandranism; if things are that obvious you’ll know who can be communicated with and who not.”
There is an interesting model in evolution theory with lots of evidence to back it up. It deals with the fact that (in most cases) things evolve faster in competitive environments and things evolve fastest in antagonistic environments.
An antagonistic environment occurs when there’s active and intense competition for resources. For example, two top predators (commonly called “apex” predators, meaning nobody messes with them) vying for supremacy in the same food chain. At some point the two apex predators will stop preying on prey and start preying on each other. They’ll have to because two apex predators will quickly deplete all prey species and the only food resource left will be each other, hence somebody’s messing with somebody, hence apex-envy ensues and there can be only one “king of the mountain” in evolutionary terms.
Think any maturing industry and you’ll first see antagonism once a new market opportunity is recognized, eventually followed by competition once the market is stabilized (again, balance). I’ve even heard of market competitors forming “co-opetitons”, co-operative competitive ventures. I’ve seen the contracts involved in such things and graciously shy away. As a friend once told me, “The purpose of those contracts is to decide where and when the mutual f?cking will begin.”
There was never suppose to be a part 3 to this arc (Ben Robison was correct in that). 
The above is a line from 
For the record, not much upsets me (ask Susan for a more honest opinion of that). The sheer stupidity of arguments that resort to emotionalism or are nothing more than attempts to protect personalities and positions, though… Them they do offend me (can’t wait to learn how our 
More colloquially, whatever your perceptions of the world are, it’s all you and precious little of anything else (a favorite quote along these lines is “What if life is fair and we get exactly what we deserve?” Ouch!).
First Principles? We don’t need no stinking First Principles!
explaining the meaning of and how to “normalize” samples is out (doing so allows you to see where the normals fall on your standard curve. You put your normals in the middle to lower part of the curve because a) this is where population densities are greatest and b) no naturally occuring line is going to be straight so you shoot for placing your normals on the straightest part of the curve to get some kind of linearity (that y = mx + b thing). Every naturally occuring phenomenon follows mathematical rules that produce curves. Between the two blue lines is where standards occur. Below the bottom blue is “below standard”, above the top blue is “out of standard”. Between the bottom blue and green line is the normal range. You calibrate your methods against the gold-standard normals and anything above is where the money lies),
I mean, people make a living out of these things, right? When someone talks about a regression curve and that a decision was made because the probabilities were such and so, does it matter if they know what they’re talking about? 
Did you read what I wrote about accountability in 
You can find my
