Category Archives: Search Behaviors

The Evolution of Google’s Golden Triangle

In search marketing circles, most everyone has heard of Google’s Golden Triangle. It even has it’s own Wikipedia entry (which is more than I can say). The “Triangle” is rapidly coming up to its 10th birthday (it was March of 2005 when Did It and Enquiro – now Mediative – first released the study). This year, Mediative conducted a new study to see if what we found a decade ago still continues to be true. Another study from the Institute of Communication and Media Research in Cologne, Germany also looked at the evolution of search user behaviors. I’ll run through the findings of both studies to see if the Golden Triangle still exists. But before we dive in, let’s look back at the original study.

Why We Had a Golden Triangle in the First Place

To understand why the Golden Triangle appeared in the first place, you have to understand about how humans look for relevant information. For this, I’m borrowing heavily from Peter Pirolli and Stuart Card at PARC and their Information Foraging Theory (by the way, absolutely every online marketer, web designer and usability consultant should be intimately familiar with this theory).

Foraging for Information

Humans “forage” for information. In doing so, they are very judicious about the amount of effort they go to find the available information. This is largely a subconscious activity, with our eyes rapidly scanning for cues of relevancy. Pirolli and Card refer to this as “information scent.” Picture a field mouse scrambling across a table looking for morsels to eat and you’ll have an appropriate mental context in which to understand the concept of information foraging. In most online contexts, our initial evaluation of the amount of scent on a page takes no more than a second or two. In that time, we also find the areas that promise the greatest scent and go directly to them. To use our mouse analogy, the first thing she does is to scurry quickly across the table and see where the scent of possible food is the greatest.

The Area of Greatest Promise

Now, Imagine that same mouse comes back day after day to the same table and every time she returns, she finds the greatest amount of food is always in the same corner. After a week or so, she learns that she doesn’t have to scurry across the entire table. All she has to do is go directly to that corner and start there. If, by some fluke, there is no food there, then the mouse can again check out the rest of the table to see if there are better offerings elsewhere. The mouse has been conditioned to go directly to the “Area of Greatest Promise” first.

Golden Triangle original

F Shaped Scanning

This was exactly the case when we did the first eye tracking study in 2005. Google had set a table of available information, but they always put the best information in the upper right corner. We became conditioned to go directly to the area of greatest promise. The triangle shape came about because of the conventions of how we read in the western world. We read top to bottom, left to right. So, to pick up information scent, we would first scan down the beginning of each of the top 4 or 5 listings. If we saw something that seemed to be a good match, we would scan across the title of the listing. If it was still a good match, we would quickly scan the description and the URL. If Google was doing it’s job right, there would be more of this lateral scanning on the top listing than there would be on the subsequent listings. This F shaped scanning strategy would naturally produce the Golden Triangle scanning pattern we saw.

Working Memory and Chunking

There was another behavior we saw that helped explain the heat maps that emerged. Our ability to actively compare options requires us to hold in our mind information about each of the options. This means that the number of options we can compare at any one time is restricted by the limits of our working memory. George Miller, in a famous paper in 1956, determined this to be 7 pieces of information, plus or minus two. The actual number depends on the type of information to be retained and the dimension of variability. In search foraging, the dimension is relevancy and the inputs to the calculation will be quick judgments of information scent based on a split second scan of the listing. This is a fairly complex assessment, so we found that the number of options to be compared at once by the user tends to max out about 3 or 4 listings. This means that the user “chunks” the page into groupings of 3 or 4 listings and determines if one of the listings is worthy of a click. If not, the user moves on to the next chunk. We also see this in the heat map shown. Scanning activity drops dramatically after the first 4 listings. In our original study, we found that over 80% of first clicks on all the results pages tested came from the top 4 listings. This is also likely why Google restricted the paid ads shown above organic to 3 at the most.

So, that’s a quick summary of our findings from the 2005 study. Next week, we’ll look how search scanning has changed in the past 9 years.

Note: Mediative and SEMPO will be hosting a Google+ Hang Out talking about their research on October 14th. Full details can be found here.

Learning about Big Data from Big Brother

icreach-search-illo-feature-hero-bYou may not have heard of ICREACH, but it has probably heard of you. ICREACH is the NSA’s own Google-like search engine.  And if Google’s mission is to organize the world’s information, ICREACH’s mission is to snoop on the world.  After super whistle blower Edward Snowden tipped the press off to the existence of ICREACH, the NSA fessed up last month. The amount of data we’re talking about is massive. According to The Intercept website, the tool can handle two to five billion new records every day, including data on the US’s emails, phone calls, faxes, Internet chats and text messages. It’s Big Brother meets Big Data.

I’ll leave aside for the moment the ethical aspect of this story.  What I’ll focus on is how the NSA deals with this mass of Big Data and what it might mean for companies who are struggling to deal with their own Big Data dilemmas.

Perhaps no one deals with more big data than the Intelligence Community. And Big Data is not new for them. They’ve been digging into data trying to find meaningful signals amongst the noise for decades. Finally, the stakes of successful data analysis are astronomically high here. Not only is it a matter of life and death – a failure to successfully connect the dots can lead to the kinds of nightmares that will haunt us for the rest of our lives. When the pressure is on to this extent, you can be sure that they’ve learned a thing or two. How the Intelligence community handles data is something I’ve been looking at recently. There are a few lessons to be learned here.

Owned Data vs Environmental Data

The first lesson is that you need different approaches for different types of data. The Intelligence Community has their own files, which include analyst’s reports, suspect files and other internally generated documentation. Then you have what I would call “Environmental” data. This includes raw data gathered from emails, phone calls, social media postings and cellphone locations. Raw data needs to be successfully crunched, screened for signals vs. noise and then interpreted in a way that’s relevant to the objectives of the organization. That’s where…

You Need to Make Sense of the Data – at Scale

Probably the biggest change in the Intelligence community has been to adopt an approach called “Sense making.”  Sense making really mimics how we, as humans, make sense of our environment. But while we may crunch a few hundred or thousand sensory inputs at any one time, the NSA needs to crunch several billion signals.

Human intuition expert Gary Klein has done much work in the area of sense making. His view of sense making relies on the existence of a “frame” that represents what we believe to be true about the world around us at any given time.  We constantly update that frame based on new environmental inputs.  Sometimes they confirm the frame. Sometimes they contradict the frame. If the contradiction is big enough, it may cause us to discard the frame and build a new one. But it’s this frame that allows us to not only connect the dots, but also to determine what counts as a dot. And to do this…

You Have to Be Constantly Experimenting

Crunching of the data may give you the dots, but there will be multiple ways to connect them. A number of hypothetical “frames” will emerge from the raw data. You need to test the validity of these hypotheses. In some cases, they can be tested against your own internally controlled data. Sometimes they will lie beyond the limits of that data. This means adopting a rigorous and objective testing methodology.  Objective is the key word here, because…

You Need to Remove Human Limitations from the Equation

When you look at the historic failures of Intelligence gathering, the fault usually doesn’t lie in the “gathering.” The signals are often there. Frequently, they’re even put together into a workable hypothesis by an analyst. The catastrophic failures in intelligence generally arise because some one, somewhere, made an intuitive call to ignore the information because they didn’t agree with the hypothesis. Internal politics in the Intelligence Community has probably been the single biggest point of failure. Finally…

Data Needs to Be Shared

The ICREACH project came about as a way to allow broader access to the information required to identify warning signals and test out hunches. ICREACH opens up this data pool to nearly two-dozen U.S. Government agencies.

Big Data shouldn’t replace intuition. It should embrace it. Humans are incredibly proficient at recognizing patterns. In fact, we’re too good at it. False positives are a common occurrence. But, if we build an objective way to validate our hypotheses and remove our irrational adherence to our own pet theories, more is almost always better when it comes to generating testable scenarios.

Are Our Brains Trading Breadth for Depth?

ebrain1In last week’s column, I looked at how efficient our brains are. Essentially, if there’s a short cut to an end goal identified by the brain, it will find it. I explained how Google is eliminating the need for us to remember easily retrievable information. I also speculated about how our brains may be defaulting to an easier form of communication, such as texting rather than face-to-face communication.

Personally, I am not entirely pessimistic about the “Google Effect,” where we put less effort into memorizing information that can be easily retrieved on demand. This is an extension of Daniel Wegner’s “transactive memory”, and I would put it in the category of coping mechanisms. It makes no sense to expend brainpower on something that technology can do easier, faster and more reliably. As John Mallin commented, this is like using a calculator rather than memorizing times tables.

Reams of research has shown that our memories can be notoriously inaccurate. In this case, I partially disagree with Nicholas Carr. I don’t think Google is necessarily making us stupid. It may be freeing up the incredibly flexible power of our minds, giving us the opportunity to redefine what it means to be knowledgeable. Rather than a storehouse of random information, our minds may have the opportunity to become more creative integrators of available information. We may be able to expand our “meta-memory”, Wegner’s term for the layer of memory that keeps track of where to turn for certain kinds of knowledge. Our memory could become index of interesting concepts and useful resources, rather than ad-hoc scraps of knowledge.

Of course, this positive evolution of our brains is far from a given. And here Carr may have a point. There is a difference between “lazy” and “efficient.” Technology’s freeing up of the processing power of our brain is only a good thing if that power is then put to a higher purpose. Carr’s title, “The Shallows” is a warning that rather than freeing up our brains to dive deeper into new territory, technology may just give us the ability to skip across the surface of the titillating. Will we waste our extra time and cognitive power going from one piece of brain candy to the other, or will we invest it by sinking our teeth into something important and meaningful?

A historical perspective gives us little reason to be optimistic. We evolved to balance the efforts required to find food with the nutritional value we got from that food. It used to be damned hard to feed ourselves, so we developed preferences for high calorie, high fat foods that would go a long way once we found them. Thanks to technology, the only effort required today to get these foods is to pick them off the shelf and pay for them. We could have used technology to produce healthier and more nutritious foods, but market demands determined that we’d become an obese nation of junk food eaters. Will the same thing happen to our brains?

I am even more concerned with the short cuts that seem to be developing in our social networking activities. Typically, our social networks are built both from strong ties and weak ties. Mark Granovetter identified these two types of social ties in the 70’s. Strong ties bind us to family and close friends. Weak ties connect us with acquaintances. When we hit rough patches, as we inevitably do, we treat those ties very differently. Strong ties are typically much more resilient to adversity. When we hit the lowest points in our lives, it’s the strong ties we depend on to pull us through. Our lifelines are made up of strong ties. If we have a disagreement with someone with whom we have a strong tie, we work harder to resolve it. We have made large investments in these relationships, so we are reluctant to let them go. When there are disruptions in our strong tie network, there is a strong motivation to eliminate the disruption, rather than sacrifice the network.

Weak ties are a whole different matter. We have minimal emotional investments in these relationships. Typically, we connect with these either through serendipity or when we need something that only they can offer. For example, we typically reinstate our weak tie network when we’re on the hunt for a job. LinkedIn is the virtual embodiment of a weak tie network. And if we have a difference of opinion with someone to whom we’re weakly tied, we just shut down the connection. We have plenty of them so one more or less won’t make that much of a difference. When there are disruptions in our weak tie network, we just change the network, deactivating parts of it and reactivating others.

Weak ties are easily built. All we need is just one thing in common at one point in our lives. It could be working in the same company, serving on the same committee, living in the same neighborhood or attending the same convention. Then, we just need some way to remember them in the future. Strong ties are different. Strong ties develop over time, which means they evolve through shared experiences, both positive and negative. They also demand consistent communication, including painful communication that sometimes requires us to say we were wrong and we’re sorry. It’s the type of conversation that leaves you either emotionally drained or supercharged that is the stuff of strong ties. And a healthy percentage of these conversations should happen face-to-face. Could you build a strong tie relationship without ever meeting face-to-face? We’ve all heard examples, but I’d always place my bets on face-to-face – every time.

It’s the hard work of building strong ties that I fear we may miss as we build our relationships through online channels. I worry that the brain, given an easy choice and a hard choice, will naturally opt for the easy one. Online, our network of weak ties can grow beyond the inherent limits of our social inventory, known as Dunbar’s Number (which is 150, by the way). We could always find someone with which to spend a few minutes texting or chatting online. Then we can run off to the next one. We will skip across the surface of our social network, rather than invest the effort and time required to build strong ties. Just like our brains, our social connections may trade breadth for depth.

The Bug in Google’s Flu Trend Data

First published March 20, 2014 in Mediapost’s Search Insider

Last year, Google Flu Trends blew it. Even Google admitted it. It over predicted the occurrence of flu by a factor of almost 2:1.  Which is a good thing for the health care system, because if Google’s predictions had have been right, we would have had the worst flu season in 10 years.

Here’s how Google Flu Trends works. It monitors a set of approximately 50 million flu related terms for query volume. It then compares this against data collected from health care providers where Influenza-like Illnesses (ILI) are mentioned during a doctor’s visit. Since the tracking service was first introduced there has been a remarkably close correlation between the two, with Google’s predictions typically coming within 1 to 2 percent of the number of doctor’s visits where the flu bug is actually mentioned. The advantage of Google Flu Trends is that it is available about 2 weeks prior to the ILI data, giving a much needed head start for responsiveness during the height of flu season.

FluBut last year, Google’s estimates overshot actual ILI data by a substantial margin, effectively doubling the size of the predicted flu season.

Correlation is not Causation

This highlights a typical trap with big data – we tend to start following the numbers without remembering what is generating the numbers. Google measures what’s on people’s minds. ILI data measures what people are actually going to the doctor about. The two are highly correlated, but one doesn’t not necessarily cause the other. In 2013, for instance, Google speculated that increased media coverage might be the cause for the overinflated predictions. More news coverage would have spiked interest, but not actual occurrences of the flu.

Allowing for the Human Variable

In the case of Google Flu Trends, because it’s using a human behavior as a signal – in this case online searching for information – it’s particularly susceptible to network effects and information cascades. The problem with this is that these social signals are difficult to rope into an algorithm. Once they reach a tipping point, they can break out on their own with no sign of a rational foundation. Because Google tracks the human generated network effect data and not the underlying foundational data, it is vulnerable to these weird variables in human behavior.

Predicting the Unexpected

A recent article in Scientific American pointed out another issue with an over reliance on data models –  Google Flu Trends completely missed the non-seasonal H1N1 pandemic in 2009. Why? Algorithmically, Google wasn’t expecting it. In trying to eliminate noise from the model, they actually eliminated signal coming during an unexpected time. Models don’t do very well at predicting the unexpected.

Big Data Hubris

The author of the Scientific American piece, associate editor Larry Greenemeier, nailed another common symptom of our emerging crush on data analytics – big data hubris. We somehow think the quantitative black box will eliminate the need for more mundane data collection – say – actually tracking doctor’s visits for the flu. As I mentioned before, the biggest problem with this is that the more we rely on data, which often takes the form of arm’s length correlated data, the further we get from exploring causality. We start focusing on “what” and forget to ask “why.”

We should absolutely use all the data we have available. The fact is, Google Flu Trends is a very valuable tool for health care management. It provides a lot of answers to very pertinent questions. We just have to remember that it’s not the only answer.

The Psychology of Usefulness: How Online Habits are Broken

google-searchLast post, I talked about how Google became a habit – Google being the most extreme case of online loyalty based on functionality I could think of. But here’s the thing with functionally based loyalty – it’s very fickle. In the last post I explained how Charnov’s Marginal Value Theorem dictates how long animals spend foraging in a patch before moving on to the next one. I suspect the same principles apply to our judging of usefulness. We only stay loyal to functionality as long as we believe there are no more functional alternatives available to us for an acceptable investment of effort. If that functionality has become automated in the form of a habit, we may stick with it a little longer, simply because it takes our rational brain awhile to figure out there may be better options, but sooner or later it will blow the whistle and we’ll start exploring our options. Charnov’s internal algorithm will tell us it’s time to move on to the next functional “patch.”

Habits break down when there’s a shift if one of the three prerequisites: frequency, stability or acceptable outcomes.

If we stop doing something on a frequent basis, the habit will slowly decay. But because habits tend to be stored at the limbic level (in the basal ganglia), they prove to be remarkably durable. There’s a reason we say old habits die hard. Even after a long hiatus we find that habits can easily kick back in. Reduction of frequency is probably the least effective way to break a habit.

A more common cause of habitual disruption is a change in stability. Suddenly, if something significant changes in our task environment, our  “habit scripts” start running into obstacles. Think about the last time you did a significant upgrade to a program or application you use all the time. If menu options or paths to common functions change, you find yourself constantly getting frustrated because things aren’t where you expect them to be. Your habit scripts aren’t working for you anymore and you are being forced to think. That feeling of frustration is how the brain protects habits and shows how powerful our neural energy saving mode is. But, even if the task environment becomes unstable for a time, chances are the instability is temporary. The brain will soon reset its habits and we’ll be back plugging subconsciously away at our tasks. Instability does break a habit, but it just rebuilds a new one to take its place.

A more permanent form of habit disruption comes when outcomes are no longer acceptable. The brain hates these types of disruptions, because it knows that finding an alternative could require a significant investment of effort. It basically puts us back at square one. The amount of investment required is dependent on a number of things, including the scope of change required (is it just one aspect of a multi-step task or the entire procedure?), current awareness of acceptable alternatives (is a better solution near at hand or do we have to find it?), the learning curve involved (how different is the alternative from what we’re used to using), are there other adoption requirements (do we have to make an investment of resources – including time and/or money?) and how much down time will be involved in order to adopt the alternative. All these questions are the complexities that can be factors in the Marginal Value Theorem.

Now, let’s look at how each of these potential habit breakers applies to Google. First of all, frequency probably won’t be a factor because we will search more, not less, in the future.

Stability may be a more likely cause. The fact is, the act of online searching hasn’t really changed that much in the last 20 years. We still type in a query and get a list of results. If you look at Google circa 1998, it looks a little clunky and amateurish next to today’s results page, but given that 16 years have come and gone, the biggest surprise is that the search interface hasn’t changed more than it has.

Google now and then

A big reason for this is to maintain stability in the interface, so habits aren’t disrupted. The search page relies on ease of information foraging, so it’s probably the most tested piece of online real estate in history. Every pixel of what you see on Google, and, to a lesser extent, it’s competitors, has been exhaustively tested.

That has been true in the past but because of the third factor, acceptability of outcomes, it’s not likely to remain true in the future. We are now in the age of the app. Searching used to be a discrete function that was just one step of many required to complete a task. We were content to go to a search engine, retrieve information and then use that information elsewhere with other tools or applications. In our minds, we had separate chunks of online functionality that we would assemble as required to meet our end goal.

Let me give you an example. Let’s imagine we’re going to London for a vacation. In order to complete the end goal – booking flights, hotels and whatever else is required – we know we will probably have to go to many different travel sites, look up different types of information and undertake a number of actions. We expect that this will be the best path to take to our end goal. Each chunk of this “master task” may in turn be broken down into separate sub tasks. Along the way, we’ll be relying on those tools that we’re aware of and a number of stored procedures that have proven successful in the past. At the sub-task level, it’s entirely possible that some of those actions have been encoded as habits. For an example of how these tasks and stored procedures would play out in a typical search, see my previous post, A Cognitive Walkthrough of Searching.

But we have to remember that the only reason the brain is willing to go to all this work is that it believes it’s the most efficient route available to it. If there were a better alternative that would produce an acceptable outcome, the brain would take it. Our expectation of what an acceptable outcome would be would be altered, and our Marginal Value algorithm would be reset.

Up to now, functionality and information didn’t intersect too often online. There were places we went to get information, and there were places we went to do things. But from this point forward, expect those two aspects of online to overlap more and more often. Apps will retrieve information and integrate it with usefulness. The travel aggregator sites like Kayak and Expedia are an early example of this. They retrieve pricing information from vendors, user content from review sites and even some destination related information from travel sites. This ups the game in terms of what we expect from online functionality when we book a trip. Our expectation has been reset because Kayak offers a more efficient way to book travel than using search engines and independent vendor sites. That’s why we don’t immediately go to Google when we’re planning a trip.

Let’s fast-forward a few years to see how our expectations could be reset in the future. I suspect we’re not too far away from having an app where our travel preferences have been preset. This proposed app would know how we like to travel and the things we like to do when we’re on vacation. It would know the types of restaurants we like, the attractions we visit, the activities we typically do, the types of accommodation we tend to book, etc.  It would also know the sources we tend to use when qualifying our options (i.e. TripAdvisor). If we had such an app, we would simply put in the bare details of our proposed trip: departure and return dates, proposed destinations and an approximate itinerary. It would then go and assemble suggestions based on our preferences, all in one location. Booking would require a simple click, because our payment and personal information would be stored in the app. There would be no discrete steps, no hopping back and forth between sites, no cutting and pasting of information, no filling out forms with the same information multiple times. After confirmation, the entire trip and all required information would be made available on your mobile device.  And even after the initial booking, the app would continue to comb the internet for new suggestions, reviews or events that you might be interested in attending.

This “mega-app” would take the best of Kayak, TripAdvisor, Yelp, TripIt and many other sites and combine it all in one place. If you love travel as much as I do, you couldn’t wait to get your hands on such an app. And the minute you did, your brain would have reset it’s idea of what an acceptable outcome would be. There would be a cascade of broken habits and discarded procedures.

This integration of functionality and information foraging is where the web will go next. Over the next 10 years, usefulness will become the new benchmark for online loyalty. As this happens, our expectation set points will be changed over and over again. And this, more than anything, will be what impacts user loyalty in the future. This changing of expectations is the single biggest threat that Google faces.

In the next post I’ll look at what happens when our expectations get reset and we have to look at adopting a new technology.

The Psychology of Usefulness: How We Made Google a Habit

In the last two posts, I looked first at the difference between autotelic and exotelic activities, then how our brain judges the promise of usefulness. In today’s post, I want to return to the original question: How does this impact user loyalty? As we use more and more apps and destinations that rely on advertising for their revenues, this question becomes more critical for those apps and destinations.

The obvious example here is search engines, the original functional destination. Google is the king of search, but also the company most reliant on these ads. For Google, user loyalty is the difference between life and death. In 2012, Google made a shade over 50 billion dollars (give or take a few hundred million). Of this, over $43 billion came from advertising revenue (about 86%) and of that revenue, 62% came from Google’s own search destinations. That a big chunk of revenue to come from one place, so user loyalty is something that Google is paying pretty close attention to.

Now, let’s look at how durable that hold Google has on our brains is. Let’s revisit the evaluation cascade that happens in our brain each time we contemplate a task:

  • If very familiar and highly stable, we do it by habit
  • If fairly familiar but less stable, we do it by a memorized procedure with some conscious guidance
  • If new and unfamiliar, we forage for alternatives by balance effort required against

Not surprisingly, the more our brain has to be involved in judging usefulness, the less loyal we are. If you can become a habit, you are rewarded with a fairly high degree of loyalty. Luckily for Google, they fall into this category – for now. Let’s look at little more at how Google became a habit and what might have to happen for us to break this habit.

Habits depend on three things: high repetition, a stable execution environment and consistently acceptable outcomes. Google was fortunate enough to have all three factors present.

First – repetition. How many times a day do you use a search engine? For me, it’s probably somewhere between 10 and 20 times per day. And usage of search is increasing. We search more now than we did 5 years ago. If you do something that often throughout the day it wouldn’t make much sense to force your brain to actively think it’s way through that task each and every time – especially if the steps required to complete that task don’t really change that much. So, the brain, which is always looking for ways to save energy, records a “habit script” (or, to use the terminology of Ann Graybiel – “chunks”) that can play out without a lot of guidance. Searching definitely meets the requirements for the first step of forming a habit.

Second – stability. How many search engines do you use? If you’re like the majority of North Americans, you probably use Google for almost all your searches.  This introduces what we would call a stable environment. You know where to go, you know how to use it and you know how to use the output. There is a reason why Google is very cautious about changing their layout and only do so after a lot of testing. What you expect and what you get shouldn’t be too far apart. If it is, it’s called disruptive, and disruption breaks habits. This is the last thing that Google wants.

Third – acceptable outcomes. So, if stability preserves habits, why would Google change anything? Why doesn’t Google’s search experience look exactly like it did in 1998 (fun fact – if you search Google for “Google in 1998” it will show you exactly what the results page looked like)? That would truly be stable, which should keep those all important habits glued in place. Well, because expectations change. Here’s the thing about expected utility – which I talked about in the last post. Expected utility doesn’t go away when we form a habit, it just moves downstream in the process. When we do a task for the first time, or in an unstable environment, expected utility precedes our choice of alternatives. When a “habit script” or “chunk” plays out, we still need to do a quick assessment of whether we got what we expected. Habits only stay in place if the “habit script” passes this test. If we searched for “Las Vegas hotels” and Google returned results for Russian borscht, that habit wouldn’t last very long.  So, Google constantly has to maintain this delicate balance – meeting expectations without disrupting the user’s experience too much. And expectations are constantly changing.

Internet adoption over time chartWhen Google was introduced in 1998, it created a perfect storm of habit building potential. The introduction coincided with a dramatic uptick in adoption of the internet and usage of web search in particular.  In 1998, 36% of American adults were using the Internet (according to PEW). In 2000, that had climbed to 46% and by 2001 that was up to 59%. More of us were going online, and if we were going online we were also searching.  The average searches per day on Google exploded from under 10,000 in 1998 to 60 million in 2000 and 1.2 billion in 2007. Obviously, we were searching  – a lot – so the frequency of task prerequisite was well in hand.

Now – stability. In the early days of the Internet, there was little stability in our search patterns. We tended to bounce back and forth between a number of different search engines. In fact, the search engines themselves encouraged this by providing “Try your search on…” links for their competitors (an example from Google’s original page is shown below). Because our search tasks were on a number of different engines, there was no environmental stability, so no chance for the creation of a true task. The best our brains could do at this point was store a procedure that required a fair amount of conscious oversight (choosing engines and evaluating outcomes). Stability was further eroded by the fact that some engines were better at some types of searches than others. Some, like Infoseek, were better for timely searches due to their fast indexing cycles and large indexes. Some, like Yahoo, were better at canonical searches that benefited from a hierarchal directory approach. When searching in the pre-Google days, we tended to match our choice of engine to the search we were doing. This required a fairly significant degree of rational neural processing on our part, precluding the formation of a habit.

Googlebottompage1998

But Google’s use of PageRank changed the search ballgame dramatically. Their new way of determining relevancy rankings was consistently better for all types of searches than any of their competitors. As we started to use Google for more types of searches because of their superior results, we stopped using their competitors. This finally created the stability required for habit formation.

Finally, acceptable outcomes. As mentioned above, Google came out of the gate with outcomes that generally exceeded our expectations, set by the spotty results of their competitors. Now, all Google had to do to keep the newly formed habit in place was to continue to meet the user’s expectations of relevancy. Thanks to truly disruptive leap Google took with the introduction of PageRank, they had a huge advantage when it came to search results quality. Google has also done an admirable job of maintaining that quality over the past 15 years. While the gap has narrowed significantly (today, one could argue that Bing comes close on many searches and may even have a slight advantage on certain types of searches) Google has never seriously undershot the user’s expectations when it comes to providing relevant search results. Therefore, Google has never given us a reason to break our habits. This has resulted in a market share that has hovered over 60% for several years now.

When it comes to online loyalty, it’s hard to beat Google’s death grip on search traffic. But, that grip may start to loosen in the near future. In my next post, I’ll look the conditions that can break habitual loyalty, again using Google as an example. I’ll also look at how our brains decide to accept or reject new useful technologies.

The Psychology of Usefulness: How Our Brains Judge What is Useful

To-Do-ListDid you know that “task” and “tax” have the same linguistic roots? They both come from the Latin “taxare” – meaning to appraise. This could explain the lack of enthusiasm we have for both.

Tasks are what I referred to in the last post as an exotelic activity – something we have to do to reach an objective that carries no inherent reward. We do them because we have to do them, not because we want to do them.

When we undertake a task, we want to find the most efficient way to get it done. Usefulness becomes a key criterion. And when we judge usefulness, there are some time-tested procedures the brain uses.

Stored Procedures and Habits

The first question our brain asks when undertaking a task is – have we done this before? Let’s first deal with what happens if the answer is yes:

If we’ve done something before our brains – very quickly and at a subconscious level – asks a number of qualifying questions:

–       How often have we done this?

–       Does the context in which the task plays out remain fairly consistent (i.e. are we dealing with a stable environment)?

–       How successful have we been in carrying out this task in the past

If we’ve done a task a number of times in a stable environment with successful outcomes, it’s probably become a habit. The habit chunk is retrieved from the basal ganglia and plays out without much in the way of rational mediation. Our brain handles the task on autopilot.

If we have less familiarity with the task, or if there’s less stability in the environment, but have done it before we probably have stored procedures, which are set procedural alternatives. These require more in the way of conscious guidance and often have decision points where we have to determine what we do next, based on the results of the previous action.

If we’re entering new territory and can’t draw on past experience, our brains have to get ready to go to work. This is the route least preferred by our brain. It only goes here when there’s no alternative.

Judging Expected Utility and Perceived Risk

If a task requires us to go into unfamiliar territory, there are new routines that the brain must perform. Basically, the brain must place a mental bet on the best path to take, balancing a prediction of a satisfactory outcome against the resources required to complete the task. Psychologists call this “Expected Utility.”

Expected Utility is the brain’s attempt to forecast scenarios that require the balancing of risks and rewards where the outcomes are not known.  The amount of processing invested by the brain is usually tied to the size of the potential risk and reward. Low risk/reward scenarios require less rationalization. The brain drives this balance by using either positive or negative emotional valences, interpreted by us as either anticipation or anxiety. Our emotional balance correlates with the degree of risk or reward.

Expected utility is more commonly applied in financial decision and game theory. In the case of conducting a task, there is usually no monetary element to risk and reward. What we’re risking is our own resources – time and effort. Because these are long established evolved resources, it’s reasonable to assume that we have developed subconscious routines to determine how much effort to expend in return for a possible gain. This would mean that these cognitive evaluations and calculations may happen at a largely subconscious level, or at least, more subconscious than the processing that would happen in evaluating financial gambles or those involving higher degrees of risk and reward.  In that context, it might make sense to look at how we approach another required task – finding food.

Optimal Foraging and Marginal Value

Where we balance gain against expenditure of time and effort, the brain has some highly evolved routines that have developed over our history. The oldest of these would be how we forage for food. But, we also have a knack of borrowing strategies developed for other purposes and using them in new situations.

Pirolli and Card (1999) found, for instance, that we use our food foraging strategies to navigate digital information. Like food, information online tends to be “patchy” and of varying value to us. Often, just like looking for a food source, we have to forage for information by judging the quality of hyperlinks that may take us to those information sources or “patches.” Pirolli and Card called these clues to the quality of information that may lie on the other end of links information scent.

Cartoon_foraging_theoryTied with this foraging strategy is the concept of Marginal Value.  This was first proposed by Eric Charnov in 1976 as a evolved strategy for determining how much time to spend in a food patch before deciding to move on. In a situation with diminishing returns (ie depleted food supplies) the brain must balance effort expended against return. If you happen on a berry bush in the wild, with a reasonable certainty that there are other bushes nearby (perhaps you can see them just a few steps away) you have to mentally solve the following equation – how many berries can be gathered with a reasonable expenditure of effort vs. how much effort would it take to walk to the next bush and how many berries would be available there?

This is somewhat analogous to information foraging, with one key difference. Information isn’t depleted as you consume it. So the rule of diminishing returns is less relevant. But if, as I suspect, we’ve borrowed these subconscious strategies for judging usefulness – both in terms of information and functionality – in online environment, our brains may not know or care about the subtle differences in environments.

The reason why we may not be that rational in the application of these strategies in online encounters is that they play out below the threshold of consciousness. We are not constantly and consciously adjusting our marginal value algorithm or quantifiably assessing the value of an information patch. No, our brains use a quicker and more heuristic method to mediate our output of effort – emotions. Frustration and anxiety tell us it’s time to move onto the next site or application. Feelings of reward and satisfaction indicate we should stay right where we are. The remarkable thing about this is that as quick and dirty as these emotional guidelines are, if you went to the trouble of rationally quantifying the potential of all possible alternatives, using a Bayesian approach, for instance, you’d probably find you ended up in pretty much the same place. These strategies, simmering below the surface of our consciousness, are pretty damn accurate!

So, to sum up this post, when judging the most useful way to get a task done, we have an evaluation cascade that happens very quickly in our brain:

  • If a very familiar task needs to be done in a stable environment, our habits will take over and it will be executed with little or no rational thought.
  • If the task is fairly familiar but requires some conscious guidance, we’ll retrieve a stored procedure and look for successful feedback as we work through it.
  • If a task is relatively new to us, we’ll forage through alternatives for the best way to do it, using evolved biological strategies to help balance risk (in terms of expended effort) against reward.

Now, to return to our original question, how does this evaluation cascade impact long and short-term user loyalty? I’ll return to this question in my next post.

Revisiting Entertainment vs Usefulness

brain-cogsSome time ago, I did an extensive series of posts on the psychology of entertainment. My original goal, however, was to compare entertainment and usefulness in how effective they were in engendering long-term loyalty. How do our brains process both? And, to return to my original intent, in that first post almost 4 years ago, how does this impact digital trends and their staying power?

My goal is to find out why some types of entertainment have more staying power than other types. And then, once we discover the psychological underpinnings of entertainment, lets look at how that applies to some of the digital trends I disparaged: things like social networks, micro-blogging, mobile apps and online video. What role does entertainment play in online loyalty? How does it overlap with usefulness? How can digital entertainment fads survive the novelty curse and jump the chasm to a mainstream trends with legs?

In the previous set of posts, I explored the psychology of entertainment extensively, ending up with a discussion of the evolutionary purpose of entertainment. My conclusion was that entertainment lived more in the phenotype than the genotype. To save you going back to that post, I’ll quickly summarize here: the genotype refers to traits actually encoded in our genes through evolution – the hardwired blueprint of our DNA. The phenotype is the “shadow” of these genes – behaviors caused by our genetic blueprints. Genotypes are directly honed by evolution for adaptability and gene survival. Phenotypes are by-products of this process and may confer no evolutionary advantage. Our taste for high-fat foods lives in the genotype – the explosion of obesity in our society lives in the phenotype.

This brings us to the difference between entertainment and usefulness – usefulness relies on mechanisms that predominately live in the genotype.  In the most general terms, it’s the stuff we have to do to get through the day. And to understand how we approach these things on our to-do list, it’s important to understand the difference between autotelic and exotelic activities.

Autotelic activities are the things we do for the sheer pleasure of it. The activity is it’s own reward. The word autotelic is Greek for “self + goal” – or “having a purpose in and not apart from itself.” We look forward to doing autotelic things. All things that we find entertaining are autotelic by nature.

Exotelic activities are simply a necessary means to an end. They have no value in and of themselves.  They’re simply tasks – stuff on our to do list.

The brain, when approaching these two types of activities, treats them very differently. Autotelic activities fire our reward center – the nucleus accumbens. They come with a corresponding hit of dopamine, building repetitive patterns. We look forward to them because of the anticipation of the reward. They typically also engage the prefrontal medial cortex, orchestrating complex cognitive behaviors and helping define our sense of self. When we engage in an autotelic activity, there’s a lot happening in our skulls.

Exotelic activities tend to flip the brain onto its energy saving mode. Because there is little or no neurological reward in these types of activities (other than a sense of relief once they’re done) they tend to rely on the brain’s ability to store and retrieve procedures. With enough repetition, they often become habits, skipping the brain’s rational loop altogether.

In the next post, we’ll look at how the brain tends to process exotelic activities, as it provides some clues about the loyalty building abilities of useful sites or tools. We’ll also look at what happens when something is both exotelic and autotelic.

What Does Being “Online” Mean?

plugged-inFirst published October 24, 2013 in Mediapost’s Search Insider

If readers’ responses to my few columns about Google’s Glass can be considered a representative sample (which, for many reasons, it can’t, but let’s put that aside for the moment), it appears we’re circling the concept warily. There’s good reason for this. Privacy concerns aside, we’re breaking virgin territory here that may shift what it means to be online.

Up until now, the concept of online had a lot in common with our understanding of physical travel and acquisition. As Peter Pirolli and Stuart Card discovered, our virtual travels tapped into our evolved strategies for hunting and gathering. The analogy, which holds up in most instances, is that we traveled to a destination. We “went” online, to “go” to a website, where we “got” information. It was, in our minds, much like a virtual shopping trip. Our vehicle just happened to be whatever piece of technology we were using to navigate the virtual landscape of “online.”

As long as we framed our online experiences in this way, we had the comfort of knowing we were somewhat separate from whatever “online” was. Yes, it was morphing faster than we could keep up with, but it was under our control, subject to our intent. We chose when we stepped from our real lives into our virtual ones, and the boundaries between the two were fairly distinct.

There’s a certain peace of mind in this. We don’t mind the idea of online as long as it’s a resource subject to our whims. Ultimately, it’s been our choice whether we “go” online or not, just as it’s our choice to “go” to the grocery store, or the library, or our cousin’s wedding. The sphere of our lives, as defined by our consciousness, and the sphere of “online” only intersected when we decided to open the door.

As I said last week, even the act of “going” online required a number of deliberate steps on our part. We had to choose a connected device, frame our intent and set a navigation path (often through a search engine). Each of these steps reinforced our sense that we were at the wheel in this particular journey. Consider it our security blanket against a technological loss of control.

But, as our technology becomes more intimate, whether it’s Google Glass, wearable devices or implanted chips, being “online” will cease to be about “going” and will become more about “being.”  As our interface with the virtual world becomes less deliberate, the paradigm becomes less about navigating a space that’s under our control and more about being an activated node in a vast network.

Being “online” will mean being “plugged in.” The lines between “online” and “ourselves” will become blurred, perhaps invisible, as technology moves at the speed of unconscious thought. We won’t be rationally choosing destinations, applications or devices. We won’t be keying in commands or queries. We won’t even be clicking on links. All the comforting steps that currently reinforce our sense of movement through a virtual space at our pace and according to our intent will fade away. Just as a light bulb doesn’t “go” to electricity, we won’t “go” online.  We will just be plugged in.

Now, I’m not suggesting a Matrix-like loss of control. I really don’t believe we’ll become feed sacs plugged into the mother of all networks. What I am suggesting is a switch from a rather slow, deliberate interface that operates at the speed of conscious thought to a much faster interface that taps into the speed of our subconscious cognitive processing. The impulses that will control the gateway of information, communication and functionality will still come from us, but it will be operating below the threshold of our conscious awareness. The Internet will be constantly reading our minds and serving up stuff before we even “know” we want it.

That may seem like neurological semantics, but it’s a vital point to consider. Humans have been struggling for centuries with the idea that we may not be as rational as we think we are. Unless you’re a neuroscientist, psychologist or philosopher, you may not have spent a lot of time pondering the nature of consciousness, but whether we actively think about it or not, it does provide a mental underpinning to our concept of who we are.  We need to believe that we’re in constant control of our circumstances.

The newly emerging definition of what it means to be “online” may force us to explore the nature of our control at a level many of us may not be comfortable with.

Losing My Google Glass Virginity

Originally published October 17, 2013 in Mediapost’s Search Insider

Rob, I took your advice.

A few columns back, when I said Google’s Glass might not be ready for mass adoption, fellow Search Insider Rob Garner gave me this advice:“Don’t knock it until you try it.”  So, when a fellow presenter at a conference I was at last week brought along his Glass and offered me a chance to try them (Or “it”? Does anyone else find Google’s messing around with plural forms confusing and irritating?), I took him up on it. To say I jumped at it may be overstating the case – let’s just say I enthusiastically ambled to it.

I get Google Glass. I truly do. To be honest, the actual experience of using them came up a little short of my expectations, but not much. It’s impressive technology.

But here’s the problem. I’m a classic early adopter. I always look at what things will be, overlooking the limitations of what currently “is.” I can see the dots of potential extending toward a horizon of unlimited possibility, and don’t sweat the fact that those dots still have to be connected.

On that level, Google Glass is tremendously exciting, for two reasons that I’ll get to in a second. For many technologies, I’ll even connect a few dots myself, willing to trade off pain for gain. That’s what early adopters do. But not everyone is an early adopter. Even given my proclivity for nerdiness, I felt a bit like a jerk standing in a hotel lobby, wearing Glass, staring into space, my hand cupped over the built-in mike, repeating instructions until Glass understood me. I learned there’s a new label for this; for a few minutes I became a “Glasshole.”Screen-Shot-2013-05-19-at-2.09.03-AM

Sorry Rob, I still can’t see the mainstream going down this road in the near future.

But there are two massive reasons why I’m still tremendously bullish on wearable technology as a concept. One, it leverages the importance of use case in a way no previous technology has ever done. And two, it has the potential to overcome what I’ll call “rational lag time.”

The importance of use case in technology can be summed up in one word: iPad. There is absolutely no technological reason why tablets, and iPads in particular, should be as popular as they are. There is nothing in an iPad that did not exist in another form before. It’s a big iPhone, without the phone. The magic of an iPad lies in the fact that it’s a brilliant compromise: the functionality of a smartphone in a form factor that makes it just a little bit more user-friendly. And because of that, it introduced a new use case and became the “lounge” device. Unlike a smartphone, where size limits the user experience in some critical ways (primarily in input and output), tablets offer acceptable functionality in a more enjoyable form. And that is why almost 120 million tablets were sold last year, a number projected (by Gartner) to triple by 2016.

The use case of wearable technology still needs to be refined by the market, but the potential to create an addictive user experiences is exceptional. Even with Glass’ current quirks, it’s a very cool interface. Use case alone leads me to think the recent $19 billion by 2018 estimate of the size of the wearable technology market is, if anything, a bit on the conservative side.

But it’s the “rational lag time” factor that truly makes wearable technology a game changer.  Currently, all our connected technologies can’t keep up with our brains. When we decide to do something, our brains register subconscious activity in about 100 milliseconds, or about one tenth of a second. However, it takes another 500 milliseconds (half a second) before our conscious brain catches up and we become aware of our decision to act. In more complex actions, a further lag happens when we rationalize our decision and think through our possible alternatives. Finally, there’s the action lag, where we have to physically do something to act on our intention. At each stage, our brains can shut down  impulses if it feels like they require too much effort.  Humans are, neurologically speaking, rather lazy (or energy-efficient, depending on how you look at it).

So we have a sequence of potential lags before we act on our intent: Unconscious Stimulation > Conscious Awareness > Rational Deliberation > Possible Action. Our current interactions with technology live at the end of this chain. Even if we have a smartphone in our pocket, it takes several seconds before we’re actively engaging with it. While that might not seem like much, when the brain measures action in split seconds, that’s an eternity of time.

But technology has the potential to work backward along this chain. Let’s move just one step back, to rational deliberation. If we had an “always on” link where we could engage in less than one second, we could utilize technology to help us deliberate. We still have to go through the messiness of framing a request and interpreting results, but it’s a quantum step forward from where we currently are.

The greatest potential (and the greatest fear) lies one step further back – at conscious awareness. Now we’re moving from wearable technology to implantable technology. Imagine if technology could be activated at the speed of conscious thought, so the unconscious stimulation is detected and parsed and by the time our conscious brain kicks into gear, relevant information and potential actions are already gathered and waiting for us. At this point, any artifice of the interface is gone, and technology has eliminated the rational lag. This is the beginning of Kurzweil’s Singularity: the destination on a path that devices like Google Glass are starting down.

As I said, I like to look at the dots. Someone else can worry about how to connect them.