Category Archives: Research

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.

Our Brain on Books

Brain-on-BooksHere’s another neuroscanning study out of Emory University showing the power of a story.

Lead researcher Gregory Burns and his team wanted to “understand how stories get into your brain, and what they do to it.” Their findings seem to indicate that stories, in this case a historical fiction novel about Pompeii, caused a number of changes in the participants brain, at least in the short term. Over time, some of these changes decayed, but more research is required to determine how long lasting the changes are.

One would expect reading to alter related parts of the brain and this was true in the Emory study. The left temporal cortex, a section of the brain that handles language reception and interpretation showed signs of heightened connectivity for a period of time after reading the novel. This is almost like the residual effects of exercise on a muscle, which responds favorably to usage.

What was interesting, however, was that the team also saw increased connectivity in the areas of the brain that control representations of sensation for the body. This relates to Antonio Damasio’s “Embodied Semantics” theory where the reading of metaphors, especially those relating specifically to tactile images, activate the same parts of the brain that control the corresponding physical activity. The Emory study (and Damasio’s work) seems to show that if you read a novel that depicts physical activity, such as running through the streets of Pompeii as Vesuvius erupts, your brain is firing the same neurons as it would if you were actually doing it!

There are a number of interesting aspects to consider here, but what struck me is the multi-prong impact a story has on us. Let’s run through them:

Narratives have been shown to be tremendously influential frameworks for us to learn and update our sense of the world, including our own belief networks. Books have been a tremendously effect agent for meme transference and propagation. The structure of a story allows us to grasp concepts quickly, but also reinforces those concepts because it engages our brain in a way that a simple recital of facts could not. We relate to protagonists and see the world through their eyes. All our socially tuned, empathetic abilities kick into action when we read a story, helping to embed new information more fully. Reading a story helps shape our world view.

Reading exercises the language centers of our brain, heightening the neural connectivity and improving the effectiveness. Neurologists call this “shadow activity” – a concept similar to muscle memory.

Reading about physical activity fires the same neurons that we would use to do the actual activity. So, if you read an action thriller, even through you’re lying flat on a sofa, your brain thinks you’re the one racing a motorcycle through the streets of Istanbul and battling your arch nemesis on the rooftops of Rome. While it might not do much to improve muscle tone, it does begin to create neural pathways. It’s the same concept of visualization used by Olympic athletes.

For Future Consideration

As we learn more about the underlying neural activity of story reading, I wonder how we can use this to benefit ourselves? The biggest question I have is if a story in written form has this capacity to impact us at all the aforementioned levels, what would  more sense-engaged media like television or video games do? If reading about a physical activity tricks the brain into firing the corresponding sensory controlling neurons, what would happen if we are simulating that activity on an action controlled gaming system like Microsoft’s X Box? My guess would be that the sensory motor connections would obviously be much more active (because we’re physically active). Unfortunately, research in the area of embodied semantics is still at an early stage, so many of the questions have yet to be answered.

However, if our stories are conveyed through a more engaging sensory experience, with full visuals and sound, do we lose some opportunity for abstract analysis? The parts of our brain we use to read depend on relatively slow processing loops. I believe much of the power of reading lies in the requirements it places on our imagination to fill in the sensory blanks. When we read about a scene in Pompeii we have to create the visuals, the soundtrack and the tactile responses. In all this required rendering, does it more fully engage our sense-making capabilities, giving us more time to interpret and absorb?

The Death and Rebirth of Google+

google_plus_logoGoogle Executive Chairman Eric Schmidt has come out with his predictions for 2014 for Bloomberg TV. Don’t expect any earth-shaking revelations here. Schmidt plays it pretty safe with his prognostications:

Mobile has won – Schmidt says everyone will have a smartphone. “The trend has been mobile was winning..it’s now won.” Less a prediction than stating the obvious.

Big Data and Machine Intelligence will be the Biggest Disruptor – Again, hardly a leap of intuitive insight. Schmidt foresees the evolution of an entirely new data marketplace and corresponding value chain. Agreed.

Gene Sequencing Has Promise in Cancer Treatments – While a little fuzzier than his other predictions, Schmidt again pounces on the obvious. If you’re looking for someone willing to bet the house on gene sequencing, try LA billionaire Patrick Soon-Shiong.

See Schmidt’s full clip:

The one thing that was interesting to me was an admission of failure with Google+:

The biggest mistake that I made was not anticipating the rise of the social networking phenomenon.  Not a mistake we’re going to make again. I guess in our defense we were busy working on many other things, but we should have been in that area and I take responsibility for that.

I always called Google+ a non-starter, despite a deceptively encouraging start. But I think it’s important to point out that we tend to judge Google+ against Facebook or other social destinations. As Google+ Vice President of Product Bradley Horowitz made clear in an interview last year with Dailytech.com, Google never saw this as a “Facebook killer.”

I think in the early going there was a lot of looking for an alternative [to Facebook, Twitter, etc.],” said Horowitz. “But I think increasingly the people who are using Google+ are the people using Google. They’re not looking for an alternative to anything, they’re looking for a better experience on Google.

social-networkAnd this highlights a fundamental change in how we think about online social activity – one that I think is more indicative of what the future holds. Social is not a destination, social is a paradigm. It’s a layer of connectedness and shared values that acts as a filter, a lens  – a way we view reality. That’s what social is in our physical world. It shapes how we view that world. And Horowitz is telling us that that’s how Google looks at social too. With the layering of social signals into our online experience, Google+ gives us an enhanced version of our online experience. It’s not about a single destination, no matter how big that destination might be. It’s about adding richness to everything we do online.

Because humans are social animals our connections and our perception of ourselves as part of an extended network literally shape every decision we make and everything we do, whether we’re conscious of the fact or not. We are, by design, part of a greater whole. But because online, social originated as distinct destinations, it was unable to impact our entire online experience. Facebook, or Pinterest, act as a social gathering place – a type of virtual town square – but social is more than that. Google+ is closer to this more holistic definition of “social.”

I’m not  sure Google+ will succeed in becoming our virtual social lens, but I do agree that as our virtual sense of social evolves, it will became less about distinct destinations and more about a dynamic paradigm that stays with us constantly, helping to shape, sharpen, enhance and define what we do online. As such, it becomes part of the new way of thinking about being online – not going to a destination but being plugged into a network.

Whom Would You Trust: A Human or an Algorithm?

First published October 31, 2013 in Mediapost’s Search Insider

I’vmindrobote been struggling with a dilemma.

Almost a year ago, I wrote a column asking if Big Data would replace strategy. That started a several-month journey for me, when I’ve been looking for a more informed answer to that query. It’s a massively important question that’s playing out in many arenas today, including medicine, education, government and, of course, finance.

In marketing, we’re well into the era of big data. Of course, it’s not just data we’re talking about. We’re talking about algorithms that use that data to make automated decisions and take action. Some time ago, MediaPost’s Steve Smith introduced us to a company called Persado, that takes an algorithmic approach to copy testing and optimization. As an ex-copywriter turned performance marketer I wasn’t sure how I felt about that. I understand the science of continuous testing but I have an emotional stake in the art of crafting an effective message. And therein lies the dilemma. Our comfort with algorithms seems to depend on the context in which we’re encountering them and the degree of automation involved.

Let me give you an example, from Ian Ayre’s book “Super Crunchers.” There’s a company called Epagogix that uses an algorithm to predict the box-office appeal of unproduced movie scripts. Producers can retain the service to help them decide which projects to fund. Epagogix will also help producers optimize their chosen scripts to improve box-office performance. The question here is, do we want an algorithm controlling the creative output of the movie industry? Would we be comfortable take humans out of the loop completely and see where the algorithm eventually takes us?

Now, you may counter that we could include feedback from audience responses. We could use social signals to continually improve the algorithm, a collaborative filtering approach that uses the power of Big Data to guide the film industry’s creative process. Humans are still in the loop in this approach, but only as an aggregated sounding board. We have removed the essentially human elements of creativity, emotion and intuition. Even with the most robust system imaginable, are you comfortable with us humans taking our hands off the wheel?

Here’s another example from Ayre’s book. There is substantial empirical evidence that shows algorithms are better at diagnosing medical conditions than clinical practitioners. In a 1989 study by Dawes, Faust and Meehl, a diagnosis algorithmic rule set was consistently more reliable than actual clinical doctors. They then tried a combination, where doctors were made aware of the outcomes of the algorithm but were the final judges. Again, doctors would have been better off going with the results of the algorithm. Their second-guessing increased their margin of error significantly.

But, even knowing this, would you be willing to rely completely on an automated algorithm the next time you need medical attention? What if there was no doctor involved at all, and you were diagnosed and treated by an algo-driven robot?

There is also mounting (albeit highly controversial) evidence showing that direct instruction produces better learning outcomes that traditional exploratory teaching methods. In direct instruction, scripted automatons could easily replace the teacher’s role. Test scores could provide self-optimizing feedback loops. Learning could be driven by algorithms and delivered at a distance. Classrooms, along with teachers, could disappear completely. Is this a school you’d sign your kid up for?

Let’s stoke the fires of this dilemma a little. In a frightening TED talk, Kevin Slavin talks about how algorithms rule the world and offers a few examples of how algorithms have gotten it wrong in the past. The pricing algorithms of Amazon priced an out-of-print book called “The Making of a Fly” at a whopping $23.6 million dollars. Surprisingly, there were no sales. And in financial markets, where we’ve largely abdicated control to algorithms, those same algorithms spun out of control in 2012 no fewer than 18,000 times. So far, these instances have been identified and corrected in milliseconds, but there’s always a Black Swan chance that one time, they’ll crash the economy just for the hell of it.

But should we humans feel too smug, let’s remember this sobering fact: 20% of all fatal diseases were misdiagnosed. In fact, misdiagnosis accounts for about one-third of all medical error. And we humans have no one but ourselves to blame but for that.

As I said – it’s a dilemma.

The Marketing Classic Few Marketers Have Ever Read

First published August 22, 2013 in Mediapost’s Search Insider

It may be the best book you’ll ever read on marketing, but you won’t find it in the marketing section of Amazon.  They have it variously filed in three different categories: Politics and Social Sciences, Technology and Text Books. The book is Everett Rogers’ “Diffusion of Innovations,” and you should add it to your reading list.

The book is a comprehensive review of how new ideas spread and take hold in our society, and although it was first written in the 60s (it’s currently in its fifthedition), the findings are as fresh and relevant as ever. Its relevance to marketing is immediate and tangible. After all, what else is marketing but promoting the  adoption and diffusion of new things?

Rogers traces almost a century of diffusion research to see how everything from new high-yield corn varieties to birth control were adopted in various cultures. While there are not a lot of examples purely from the consumer marketplace, the generalized observations beg to be applied to marketing campaigns pushing new (and hopefully improved) products.

Consider these five innovation-specific variables that affect how quickly a new idea is adopted:

1)   Relative advantage – How much of a true advantage does the new innovation offer over what is currently being used? Rogers offers an important caveat here: “The receiver’s perceptions of the attributes of an innovation – not the attributes as classified by experts or change agents, affect its rate of adoption.”

2)   Compatibility – How well does the innovation fit into the framework of the customer’s current situation? Is it an incremental innovation, easily added, or a discontinuous innovation, requiring significant pain on the part of the user to adopt?

3)   Complexity – What is the learning curve that comes bundled with the innovation? The steeper the curve, the slower the rate of adoption.

4)   Trialability – Is it possible to try the product firsthand to determine the relative advantage (see #1)?

5)   Observability – Being the herders we are, adoption is sometimes a matter of “monkey see, monkey do.”

These factors may seem fundamental, but every day new “innovative” products are turned loose on the market, there to wither and die, simply because one or several of these check boxes remain unchecked.

Rogers also spend significant time looking at the social dynamics of diffusion and adoption, including the role of early adopters, change agents, influencers, mass communication channels and interpersonal persuasion. I found amazing close correlations to the findings of my own research into buying behaviors in the B2B world.

At the risk of oversimplifying this seminal work, Rogers found that adoption balances at the intersection of risk and reward. Risk stalls adoption, reward drives it forward, and clarity of communicating this risk/reward balance in a relevant way is either the catalyst or the inhibitor that determines how steep the adoption curve is.

This is a textbook, so expect a small investment of effort to wade through the rather academic delivery, but if you persevere (and to be fair, I’ve suffered through much worse in other books) you’ll come away with perhaps the clearest summation of marketplace dynamics ever put in print.

Psychological Priming and the Path to Purchase

First published March 27, 2013 in Mediapost’s Search Insider

In marketing, I suspect we pay too much attention to the destination, and not enough to the journey. We don’t take into account the cumulative effect of the dozens of subconscious cues we encounter on the path to our ultimate purchase. We certainly don’t understand the subtle changes of direction that can result from these cues.

Search is a perfect example of this.

As search marketers, we believe that our goal is to drive a prospect to a landing page. Some of us worry about the conversion rates once a prospect gets to the landing page. But almost none of us think about the frame of mind of prospects once they reach the landing page.

“Frame” is the appropriate metaphor here, because the entire interaction will play out inside this frame. It will impact all the subsequent “downstream” behaviors. The power of priming should not be taken likely.

Here’s just one example of how priming can wield significant unconscious power over our thoughts and actions. Participants primed by exposure to a stereotypical representation of a “professor” did better on a knowledge test than those primed with a representation of a “supermodel.”

A simple exposure to a word can do the trick. It can frame an entire consumer decision path. So, if many of those paths start with a search engine, consider the influence that a simple search listing may have.

We could be primed by the position of a listing (higher listings = higher quality alternatives).  We could be primed (either negatively or positively) by an organization that dominates the listing real estate. We could be primed by words in the listing. We could be primed by an image. A lot can happen on that seemingly innocuous results page.

Of course, the results page is just one potential “priming” platform. Priming could happen on the landing page, a third-party site or the website itself. Every single touch point, whether we’re consciously interacting with it or not, has the potential to frame, or even sidetrack, our decision process.

If the path to purchase is littered with all these potential landmines (or, to take a more positive approach, “opportunities to persuade”), how do we use this knowledge to become better marketers? This does not fall into the typical purview of the average search marketer.

Personally, I’m a big fan of the qualitative approach (I know — big surprise) in helping to lay down the most persuasive path possible. Actually talking to customers, observing them as they navigate typical online paths in a usability testing session, and creating some robust scenarios to use in your own walk-throughs will yield far better results than quantitative number-crunching. Excel is not a particularly good at being empathetic.

Jakob Nielsen has said that online, branding is all about experience, not exposure. As search marketers, it’s our responsibility to ensure that we’re creating the most positive experience possible, as our prospects make their way to the final purchase.

The devil, as always, is in the details — whether we’re paying conscious attention to them or not.

Weighing Positive and Negative Impacts on Users

First published January 31, 2013 in Mediapost’s Search Insider

We humans hate loss. In fact, we seem to value losing something about twice as high as gaining something. For example, imagine I gave you a coffee cup and then offered to buy it back from you. That’s scenario 1. In scenario 2, I ask you to buy the same coffee cup from me. The price you assign to the coffee cup in the first scenario will be, on the average, about twice as much as in the second. And yes, there’s research to back this up.

When it comes to winning and losing, it’s been proven that “loss looms larger than gains.” It’s just one of the weird glitches in our logical circuitry.  We tend to be hardwired to look at glasses as half empty.

Recently, I was reviewing an academic study done in 2008, with this scintillating title: “Procedural Priming and Consumer Judgment: Effects on the Impact of Positively and Negatively Valenced Information” by Shen and Wyer. If you can get beyond the rather dry title, you find a treasure trove of tidbits to consider when crafting your online user experience.

For example, when we evaluate a product for potential purchase, we may run across both positive and negative information. The order we run into this information can have a dramatic impact on what we do downstream from that interaction. To use psychological terms, it “primes” our mental framework.  And, because we tend to focus on negatives, less favorable information has a greater impact on our decision than positive information.

But it’s not just that we pay more attention to bad news than good news. It’s that bad news can hijack the entire consideration process. According to Shen and Wyer, if we run into negative information, it can change our information-seeking strategies, leading us down further negatively biased channels to confirm the initial information we saw. Bad news tends to lead to more bad news.

Also, we can get “bad news” hangovers. If we compare negatives in one decision process, that negative mental framework can carry over to an entirely different decision that has nothing to do with the first, giving us a heightened awareness of negative information in the new situation.

Here’s another interesting finding. If we’re rushed for time, this preoccupation with the negatives will dramatically affect the decision we make. But, if we have all the time in the world, the impact is relatively insignificant. Given time, we seem to cancel out our inherently negative biases.

All this news is not bad for marketers, however. It seems that simply getting users to state their preference for one feature over another, even though they’re not actively considering purchase at that time, leads to a much greater likelihood of purchase in the future. It seems that if you can get users to compare alternatives — and, more importantly, to commit to saying they prefer one alternative over another — they clear the mental hurdle of deciding “will I buy?” and instead start considering  “what will I buy?”

Finally, there is also a recency effect, especially if prospects had ample time to consider all their alternatives. Shen and Wyer found that the last information considered seemed to have the greatest effect on the buyer.  So, if information was both positive and negative, it was good to get the least favorable information in front of the prospect early, and then move to the most favorable information. Again, this is true only if the user had plenty of time to weigh the options. If they were rushed, the opposite was true.

All in all, these are all intriguing concepts to consider when crafting an ideal online user experience. They also underscore the importance of first impressions, especially negative ones.