(Editor’s Note: This is the blog post I gifted as a secret santa blog. I have republished it here, with a few small embelishments.)
I had the great fortune to attend the Cognitive Colloquium in early October of this year at the IBM Watson Research Center in Yorktown Heights, NY. It was one of those life-changing moments when you feel like you’re sitting on top of a mountain and you can see much more distant horizons. In my case, the horizon I saw involved using some of my mental energy to solve the grand problems of digital content using the methods of cognitive computing.
What are these methods? Well, at IBM, we describe cognitive computing as a cluster of practices that use machine learning, natural language processing and high-performance computing to change the way computers work and how humans work with them. Heady stuff, I know.
Before you abandon this blog for more comfortable pursuits, please consider a ready example of this in Watson, the supercomputer that competed in Jeopardy! last year and beat the top champions the show had ever had. The IBM team taught Watson the rules of the game and he proceeded to improve his play through many months of live competition leading up to the televised show. He used natural language processing to understand the clues presented by the host, and devised likely questions for them. He used machine learning to get better and better at the game. He’s now being employed in medicine, marketing and several other domain-specific specialties, including our line of work.
Job 1 for my new mission was to read Nobel Laureate Daniel Kahneman’s thick book Thinking, Fast and Slow. Kahneman was a keynote speaker at the Cognitive Colloquium. His talk triggered several new insights in me about the relationship between human psychology and content strategy. As I read the book (primarily on my train ride between my home in Beacon, NY and Grand Central station), I continue to solidify these insights. I can now articulate several of them. In the interests of space, I will cover one of them for the content strategists who are likely to read this blog. If you’re still interested, please read on.
(If you’re interested in the complete set, look for my forthcoming book: Outside-In Marketing: Using Big Data to Drive Your Content Marketing. I also highly recommend reading Kahneman when you find yourself with a hundred hours or so of unstructured time.)
The central framework of Thinking, Fast and Slow
The central thesis for Kahneman’s life’s work, spanning over forty years of research of practitioners of fields too numerous to list, is a kind of mental dualism. Our minds have two distinct systems, which Kahneman calls System 1 and System 2.
System 1 is the set of processes that happen automatically, in a flash. They are so automatic, we often can’t recall afterwards intending to do them. We just do them. Examples include the habits of driving, like putting on your turn signal prior to a turn. You don’t have to think about it, you just do it. Most of our lives and much of our communication is governed by System 1. We are faced with so much uncertainty in life and it all comes at us so fast, we need a system to make sense of it in the rough. Kahneman calls System 1 “a machine for jumping to conclusions,” because that is what it does. It judges things automatically before all the data are available.
System 2 is the logical and systematic part of our minds, which has been modeled by cognitive scientists since the discipline was conceived. Though it is accurate and precise, it is slow and lazy. There are times when we doubt the knee-jerk responses our System 1 provides. And these are the times we engage System 2 to analyze all the facts at hand and make a reasoned decision. But System 2 is so lazy, we don’t use it as much as the philosophers and other idealists like to believe. In his book, he documents decisions made by experts in a variety of fields based almost entirely on System 1 thinking, and laced with the biases that it uses to jump to conclusions.
Kahneman was the keynote speaker at the cognitive Colloquium because his framework serves as a new way to model human thinking. As he said, “If you want to build systems that think like humans, start with understanding how humans think.”
Computers have always been devices that needed to be right all the time, without fail. So of course we patterned them after System 2 thinking. The trouble is, it takes huge supercomputers to do somewhat ordinary human tasks, like scanning encyclopedic knowledge for a likely question that matches a cryptic answer. Watson takes up a decent sized room and consumes massive amounts of electricity. The machines of tomorrow need to get ever smaller and more efficient, approaching the efficiency of the human brain. To do that, we need to build systems that do much of their work like System 1, fast and imprecise. Only when accuracy is needed will they engage System 2.
Practice: How do users interact with websites?
Beyond the implications of Kahneman’s work for cognitive computing, some of his work has more direct practical applications for content strategy. Indeed, his framework can be used to approximate how users consume websites. Consider this scenario:
Lizzy is a highly educated millennial who works as an editor in the publishing field. She searches for “structured mark-up” in Google and gets a ton of results. She scans the first search engine results page (SERP) and clicks the most likely link without really reading the results. When she lands on the page, she scans it to determine if it is worth the effort. She decides that it is, and begins reading the long-form content on the page.
What does Lizzy’s mental state look like? Well, she uses both System 1 and System 2 in the process of her information journey. System 1 is the primary mechanism of her scanning and clicking behavior. Scanning SERPs and clicking is so familiar to Lizzy, it’s like using your turn signals while driving. She doesn’t need to think about it. System 2 is what she uses to read and digest the content.
A whole UX discipline has grown out of Steve Krug’s imperative, Don’t Make Me Think. If you make Lizzy think when she lands on your page, you force her to engage System 2, which is slow and lazy. Not only is Lizzy in a hurry, she really doesn’t want to waste mental energy either. If you force her to think, she will jump to the conclusion that your page is not relevant before even engaging System 2, and she’ll bounce back to the search engine to try another result.
When Lizzy does find your page relevant, she is ready to engage System 2. This means providing enough data, case studies and other stuff to help her complete her information task. Once she engages System 2, she does not want to have to go back to the SERP again. Ideally, she can get everything she needs on your site. Once she engages System 2, long-form content is what she needs.
For the longest time, we have had a raging debate in our field of whether users read on the web. All kinds of studies showed that “users don’t read” on the web, they just scan. I have tried to replicate these studies in ibm.com with mixed results. After analyzing the results, I came to a conclusion that seems obvious after the fact: If you get the Lizzy use case right, users do read on the web. They’ll even download a longish whitepaper and read it on the web if it is relevant and compelling. But if you don’t get the Lizzy use case right, they bounce off your page before reading regardless of how close the content is to the query.
I have not done a complete analysis. Provisionally, the studies that suggest that users just scan on the web suffer from the fallacy of small samples. They happened to choose content that was not easy to scan as the basis for the studies. It forced users to do something they were not willing to do: To engage System 2 prior to deciding whether the content was worth their time and attention. Since these users never relented to engage System 2, they never “read” in those studies.
As pages improve and the body of evidence approaches critical mass, similar studies have come to different conclusions. Thanks to Kahneman, we now have a framework for understanding these studies. The inflection point between scanning and reading seems to be a System 1 process that determines whether a page is worth a users’ time and attention or not.
Theory: Digital content relevance works like typical human psychology
Those of you who are familiar with my work know I have based much of it on Relevance Theory, which is a kind of psychology of communication. It is the keystone of my book Audience, Relevance, and Search: Targeting Web Audiences with Relevant Content. The theory defines relevance as a sliding scale with two extent conditions, which I sketch below:
- The stronger the cognitive effect in the audience, the more relevant the linguistic artifact to that audience
- The more effort a linguistic artifact requires, the less relevant it is
A cognitive effect is just a change in the mind of the audience. When we learn or are influenced or make a decision, there is a corresponding cognitive effect. Most of these are small and incremental. Some are breakthroughs. All things considered, breakthroughs are more relevant than small changes to our attitudes. The actual theory is quite a bit more complex than this, but we can gloss over that complexity for the time being.
As I read Kahneman’s book for the first time, it struck me that Sperber and Wilson—the authors of Relevance Theory—were describing communication in terms of System 1 and System 2. They just hadn’t made that connection. When they talk about cognitive effects, they are talking about System 2. Relevance Theory is based on work by H.P. Grice that describes how we reason when we communicate. Because reasoning falls into System 2, cognitive effects are, by definition, System 2 processes.
The extent condition that is more interesting to me is the one about effort. It seems to me that determining whether a page is nominally relevant—that is, whether it is worth the effort or not—is a System 1 process. The content buried within an opaque UX could answer Lizzy’s questions exactly, but she will determine it is irrelevant in a flash if it lacks the visual cues System 1 requires—tight punchy headings, bolded keywords, etc., in short, all the things Google’s algorithm looks for.
The one correction I would make to Relevance Theory after reading Thinking Fast and Slow is to reverse the extent conditions. I would put the one about effort first, because on the web, a page is functionally irrelevant if it doesn’t convince System 1 to devote the effort. And if it requires too much effort for the time being, it loses relevance fast. Only after it is deemed worth the effort do users judge to what extent it is relevant. If the page helps Lizzy make a breakthrough about structured mark-up, it is highly relevant to her.
The blog medium prevents me from stating more. All I hoped to do is plant a few seeds in the minds of enterprising readers to take these thoughts further than I could in this medium. As I said, I will have a great deal more to say in my book when it comes out this year. In the meantime, if one reader had a mountain top experience with this blog, I feel it is doing its job.
James Mathewson is the program director for search and content marketing for IBM.
(Editor’s note: This is the blog gift I received as part of the Blog Secret Santa program. Enjoy!)
Dashing through the web
On a laptop or a phone
Googlin’ all the things
Rarely see the home (page!)
Content’s just the thing
Conversion is the key
Turn prospect into customer
And make us all happy!
Ohhh, visitors, site users,
Tell us what you need!
We’d like you to think of our brand
With positivity (YAY!)
Visitors, site users,
Please tell us what you need
Prove to my executives
This project will succeed
We will not interrupt,
We’ll help you make your choice.
Teach you about our product line
With friendly tone and voice
Organic search is great
Our landing page you’ll see
You’ll read it and then you will know:
Ohhh, visitors, site users,
Tell us what you need!
We’d like you to think of our brand
With positivity (YAY!)
Visitors, site users,
Please tell us what you need
Prove to my executives
This project will succeed!
I’ve had a rant stewing in me since I saw a TED Talk by Larry Smith on why you will fail to have a great career. The gist of his unpleasant talk is that you will fail to have a great career because you will compromise on what you are most passionate about. And if you don’t do what you’re most passionate about, you will never have a great career.
When I first saw the video, I said “Yes!” So much so that I posted it on my Facebook page. But after a few days, lingering doubts about it caused me to delete it from my Facebook page. These doubts have only grown in the interceding months. It took an interview with Martha Stewart in Parade magazine this past Sunday to inspire me to express these doubts in a blog post. She says:
My father was the smartest guy, he said: ‘you can do anything you set your mind to.’
I know you have all heard these words from your parents and teachers. And I don’t want to discourage you from pursuing your dreams. But I’m here to tell you if you insist on doing whatever it is that you are passionate about, you are more likely to fail to have a great career. Great careers are made by people who listen to what the world needs and who learn to provide those things. They are not necessarily made by people who create things they are passionate about and hope the world needs them. If you think that is the way things work, you are setting yourself up for disappointment.
It certainly helps to be passionate about what you do. It is important to any happy career. And you probably can’t have a great career if your work makes you miserable. But learning to do unpleasant things well, and learning to enjoy success in things for which you are not gifted are essential to cultivating a great career. If you only do what you like to do and what comes easily to you, you are likely to fail. This is the gist of my rant against Larry Smith.
Since before I started this blog with my co-author Frank Donatone, I’ve been engaging in a long and fruitful virtual debate with a group of people I lovingly refer to as the search haters. My latest blog about this can be found on Biznology: “Five Critical Roles that Need SEO Skills.” Not that the group of search haters is organized or has its own user group. But there is a long line of folks who are willing to trash the practice of SEO on the basis of two facts:
- SEO has sometimes been practiced by unscrupulous agencies to try to gain unfair advantage for their clients, thus this is what most SEO amounts to
- Search results are sometimes wildly irrelevant to search queries, thus search is not all that helpful in providing relevant content to audiences
I write this in the hope that I might influence a few search haters into a more sympathetic understanding of SEO. As the above Biznology post indicated, I spend the majority of my time training folks on SEO. Much of this is in countering myths 1. or 2. above. If I can preempt some of this training by influencing a few people now, I just might be able to get down to business with new hires in digital marketing sooner.
You know I am passionate about digital marketing. But you might not know that I am also a baseball blogger. I am cmathewson at the Minnesota Twins blog Twinkie Town, one of the SB Nation sites that recently went through an unfortunate redesign. I don’t write there very often anymore, in part because of the redesign. But at one time, I was one of its most prolific contributors, when the baseball world was going through a controversial culture clash between insular scouting paradigm to one based on math. In the center of that controversy was Nate Silver.
I have been a Nate Silver fan for almost 10 years, when he developed PECOTA, a system that uses statistical analysis to forecast the performance of baseball players based on their past performance. At the time, what passed for baseball analysis was performed by “baseball men,” scouts who had grown up around the game and learned its nuances. Most of their insights were based on what they saw with their own eyes and their gut feelings. Silver was one of the young Turks of a sabermetric revolution among baseball analysts, people who used math to analyze players and predict their future performance, often more accurately than the baseball men.
Eventually, sabermetriccs became an established practice in baseball. Every team uses methods developed by Silver and others at least as a check against the errors of their scouts. Most rely more heavily on math, using scouts to fill in the blanks. Billy Bean of Moneyball fame is one such baseball executive.
Apparently, Silver loves controversy. When sabermetrics became an established practice, he set out to do to political analysis what he helped do to baseball analysis, with his fivethirtyeight blog in The New York Times. Political analysis has long been ruled by the scouts of the politics world, aka pundits–highly educated men (mostly) who glance at polling data and form intuitive opinions about them. Until the 2012 elections, pundits ruled. But in this election, Silver ruled, correctly calling all 50 states a week before the election and getting very close on the popular vote. According to Silver, the electoral vote would be a landslide for the President. The pundits called it a “tossup” using such mixed metaphors as “razor tight.” More pundits predicted a Romney win than an Obama win.
Some say there was one true winner in the 2012 election and it was Silver. No pundit in the history of political commentary was as accurate as Silver was on that night. I don’t recall an election in which the old guard pundits were so far off, either. The contrast was stark. He was so good, he managed to make his brand of analysis an established practice in less than half the time it took him to do the same thing for baseball. In fact, Silver’s success in the 2008 election influenced enough people that his models were in wide use behind the scenes in the Obama campaign. Future campaigns will take notice, and ignore the kind of analysis Silver performs at their peril.
Since Silver likes controversy so much, perhaps his next challenge can be digital marketing. Digital marketing is in the throes of the same kind of conflict baseball and politics have gone through. Mad Men on one side, geeks on the other. The Mad Men flaunt their experience using their particular brand of creativity to develop and push content on unwilling masses, hoping for a small percentage of them to engage. Geeks work to learn the willing audience in detail and target them with content that helps them make smarter decisions. I fall firmly in the geek camp. But I struggle to convince the Mad Men. Enter Silver, who could cut through their BS and transform digital marketing.
If you’re interested in how, please read on.