Category Archives: Online Trends

Now, That’s a Job Description I Could Get Behind!

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

I couldn’t help but notice that last week’s column, where I railed against the marketer’s obsession with tricks, loopholes and pat sound bites got a fair number of retweets. The irony? At least a third of those retweets twisted my whole point – that six seconds (or any arbitrary length of message) isn’t the secret to getting a prospect engaged. The secret is giving them something they want to engage with.

tweet ss

As anyone who has been unfortunate to spend some time with me when I’m in particularly cynical mood about marketing can attest to, I go a little nuts with this “Top Ten Tricks” or “The Secret to…” mentality that seems pervasive in marketing. I’m pretty sure that anyone who retweeted last week’s column with a preface like “Does your advertising engage your consumer in 6 seconds or less? If not, you’re likely losing customers” didn’t bother to actually read past the first paragraph. Maybe not even the first line.

And that’s the whole problem. How can we expect marketers to build empathy, usefulness and relevance into their strategy when many of them have the attention span of a small gnat? As my friend Scott Brinker likes to say when it comes to marketer’s misbehaving, “This is why we can’t have nice things.”

Marketing – good marketing – is not easy but it’s also not a black box. It’s not about secrets or tricks or one-off tactics. It’s about really understanding your customers at an incredibly deep level and then working your ass off to create a meaningful engagement with them. Trying to reduce marketing to anything less than that is like trying to breeze your way through 50 years of marriage by following the Top 3 Tricks to get lucky this Friday night.

Again, this is about meaningful engagements. And when I say meaningful, it’s the customer that gets to decide what’s meaningful. That’s what’s potentially so exciting about breakthroughs like the Oreo Super Bowl campaign. It’s the opportunity to learn what’s meaningful to prospects and then to shift and tailor our responses in real time. Until now, marketing has been “Plan, Push and Pray.” We plan our attack, we push out our message and we pray it finds it’s target and that they respond by buying stuff. If they don’t buy stuff, something went wrong, probably in the planning stage. But that is an awfully long feedback loop.

You’ll notice something about this approach to marketing. The only role for the prospect is as a consumer. If they don’t buy, they don’t participate.  This comes as a direct result of the current job description of a marketer: Someone who gets someone else to buy stuff. But what if we rethink that description? Technology that enables real time feedback is allowing us to create an entirely new relationship with customers. What would happen if we redefined marketing along these lines: To understand the customer’s reality, focusing on those areas where we can solve their problems and improve that reality?

And as much as that sounds like a pat sound bite, if you really dig into it, it’s far from a quick fix. This is a way to make a radically different organization. And it moves marketing into a fundamentally different role. Previously, marketing got its marching orders from the CEO and CFO. Essentially, they were responsible for moving the top line ever northward. It was an internally generated mandate – to increase sales.

But what if we rethink this? What if the entire organization’s role is to constantly adapt to a dynamic environment, looking for advantageous opportunities to improve that environment? And, in this redefined vision, what if marketing’s role was to become the sense-making interface of the company? What if it was the CMO’s job was to consistently monitor the environment, create hypotheses about how to best create adaptive opportunities and then test those hypotheses in a scientific manner?

In this redefinition of the job, Big Data and Real Time Marketing take on significantly new qualities, first as a rich vein of timely information about the marketplace and secondly as a never ending series of instant field experiments to provide empirical backing to strategy.

Now, marketing’s job isn’t to sell stuff, it’s to make sense of the market and, in doing so, help define the overall strategic direction of the company. There are no short cuts, no top ten tricks, but isn’t that one hell of a job description?

The Psychology of Usefulness: The Acceptance of Technology – Part Two

In my last post, I talked about how the Theory of Reasoned Action and the original Technology Acceptance Model tried to predict both intention and usage of new technologies. As a quick recap, let’s look again at Davis and Bagozzi’s original model.

Technology_Acceptance_Model

In aiming for the simplest model possible, there was significant conflation applied to the front end of the model – with just one box representing external variables, which then led to two similarly conflated boxes: Perceived Usefulness and Perceived Ease of Use. While this simplification was admirable in the quest for parsimony, in real world situations it seemed like it went too far in this direction. There was a lot happening between the three boxes at the front of the model that demanded closer examination.

Davis indicated that there was an interesting relationship between Perceived Usefulness and Perceived Ease of Use. One of the mechanisms at play that has to be understood is self-efficacy. In understanding adoption of technology, self-efficacy is a key factor. Essential, it means that the easier a system is to use, the greater the user’s sense of efficacy. They believe they have control over what they are doing. And control, especially on a work context, is a strong motivational driver. There is an extensive body of work exploring the psychological importance of control. If we feel we’re in control, we also feel empowered to mitigate risk. The concept of self-efficacy helps to highlight the importance of the Perceived Ease of Use box. But what about the other box: Perceived Usefulness?

Davis, in his accompanying notes and research, indicated that Perceived Usefulness is a stronger indicator of intention than Perceived Ease of Use. In other words, we are willing to put up with some pain to learn a new technology if we feel it will offer a significant improvement in our ability to complete a task. This balancing equation requires two heuristic evaluations on the part of the user: the allocation of cognitive resources required to gain proficiency and the expected usefulness of the tool once proficiency is gained. This is exactly the same equation used in Charnov’s Marginal Value Theorem, applied in a different context. In optimal foraging, we (and all animals who forage) balance expenditure of resources required to reach a food patch against the expected food value to be derived from that patch. In technology adoption, we balance expenditure of resources required to master a new technology against the increased usefulness that technology offers.

In this heuristic evaluation, there are four key marketing lessons for anyone who’s business model relies on the adoption of new technology:

1)   Lessen the intimidation of the learning curve. Persuade the user (and this is a key point that I’ll return to in in point 4) that this is a reasonable investment of resources. Build a sense of perceived ease of use. Provide visible links to intuitive learning resources. Often, marketers overplay the feature benefits of their products to show how powerful they are. But, as they’re doing this, they fail to realize that this upsets the balance between perceived usefulness and perceived ease of use.

2)   Provide clear examples of perceived usefulness in terms that are immediately relevant. Remember, this is the key factor in the equation the prospect is trying to balance. The more salient you can make the perceived usefulness, the more likely the user is to adopt it, even if a learning curve is present. Ideally, get that usefulness across with very specific, industry relevant examples that allow the user to visualize usage of the technology.

3)   Remember that the user is balancing the two factors. Ease of use is great, but it can’t come at the expense of overall usefulness. In fact, in calculating the right balance (which should be done with extensive testing feedback from target customers) it should offer a significant gain in usefulness (as measured against any incumbent technologies) with a relatively manageable investment of resources.

4)   Remember that you’re talking to a user. When trying to strike the right balance, remember that you’ll probably be talking to different people as the decision progresses. For the user, the right balance between perceived usefulness and perceived ease of use must be struck. But at some point, you’ll be talking to a buyer, not a user, before the sale actually is closed. This would be one of those external variables that fall outside the scope of the Technology Adoption Model. This switching of roles from “doers” to “buyers” is dealt with extensively in my book, The BuyerSphere Project.

In the next post, I’ll talk about how the Technology Acceptance Model has been modified over the past 2 decades so it better reflects real world decision making.

Who Owns Your Data (and Who Should?)

First published January 23, 2104 in Mediapost’s Search Insider

Lock backgroundLast week, I talked about a backlash to wearable technology. Simon Jones, in his comment, pointed to a recent post where he raised the very pertinent point – your personal data has value. Today, I’d like to explore this further.

I think we’re all on the same page when we say there is a tidal wave of data that will be created in the coming decade. We use apps – which create data. We use/wear various connected personal devices – which create data. We go to online destinations – which create data. We interact with an ever-increasing number of wired “things” – which create data. We interact socially through digital channels – which create data.  We entertain ourselves with online content – which creates data. We visit a doctor and have some tests done – which creates data. We buy things, both online and off, and these actions also create data. Pretty much anything we do now, wherever we do it, leaves a data trail. And some of that data, indeed, much of it, can be intensely personal.

As I said some weeks ago, all this data is creating a eco-system that is rapidly multiplying and, in its current state, is incredibly fractured and chaotic. But, as Simon Jones rightly points out, there is significant value in that data. Marketers will pay handsomely to have access to it.

But what, or whom, will bring order to this chaotic and emerging market? The value of the data compounds quickly when it’s aggregated, filtered, cross-tabulated for correlations and then analyzed. As I said before, the captured data is its fragmented state is akin to a natural resource. To get to a more usable end state, you need to add a value layer on top of it. This value layer will provide the required additional steps to extract the full worth of that data.

So, to retrace my logic, data has value, even in it’s raw state. Data also has significant privacy implications. And right now, it’s not really clear who owns what data. To move forward into a data market that we can live with, I think we need to set some basic ground rules.

First of all, most of us who are generating data have implicitly agreed to a quid pro quo arrangement – we’ll let you collect data from us if we get an acceptable exchange of something we value. This could be functionality, monetary compensation (usually in the form of discounts and rewards), social connections or entertainment. But here’s the thing about that arrangement – up to now, we really haven’t quantified the value of our personal data. And I think it’s time we did that. We may be trading away too much for much too little.

To this point we haven’t worried much about what we traded off and to whom because any data trails we left have been so fragmented and specific to one context, But, as that data gains more depth and, more importantly, as it combines with other fragments to provide much more information about who we are, what we do, where we go, who we connect with, what we value and how we think, it becomes more and more valuable. It represents an asset for those marketers who want to persuade us, but more critically, that data -our digital DNA – becomes vitally important to us. In it lays the quantifiable footprint of our lives and, like all data, it can yield insights we may never gain elsewhere. In the right hands, it could pinpoint critical weaknesses in our behavioral patterns, red flags in our lifestyle that could develop into future health crises, financial opportunities and traps and ways to allocate time and resources more efficiently. As the digitally connected world becomes denser, deeper and more functional, that data profile will act as our key to it. All the potential of a new fully wired world will rely on our data.

There are millions of corporations that are more than happy to warehouse their respective data profiles of you and sell it back to you on demand as you need it to access their services or tools.  They will also be happy to sell it to anyone else who may need it for their own purposes. Privacy issues aside (at this point, data is commonly aggregated and anonymized) a more fundamental question remains – whose data is this? Whose data should it be? Is this the reward they reap for harvesting the data? Or because this represents you, should it remain your property, with you deciding who uses it and for what?

This represents a slippery slope we may already be starting down.  And, if you believe this is your data and should remain so, it also marks a significant change from what’s currently happening. Remember, the value is not really in the fragments. It’s in bringing it together to create a picture of who you are. And we should be asking the question – who should have the right to create that picture of you – you – or a corporate data marketplace that exists beyond your control ?

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 Inevitable Wearable Technology Backlash

First published January 16, 2014 in Mediapost’s Search Insider

piem-1024x705Okay, I’ve gone on record – I think wearable technology is a huge disruptive wave currently bearing down on us. Accept it.

And I’ve also said that stupid wearable technology is inevitable. Accept that as well.

It appears that this dam is beginning to burst.

Catharine Taylor had a humorous and totally on-point reaction to the “tech-togs” that were unveiled at CES. Her take: “Thanks but no thanks”

Maarten Albarda a similar reaction to his first go-around with Google Glass – “Huh?”

Look – don’t get me wrong. Wearable technology, together with the “web of everything,” will eventually change our lives, but most of us won’t be going willingly. We’re going to have to get through the “bubble of silliness” first. Some of this stuff will make sense and elicit a well-earned “Cool” (or “Dope” or “Sick” or what ever generational thumbs-up is appropriate). Other things will garner an equally well-earned WTF? And some will be imminently sensible but will still end up being tossed out with the bathwater anyway.

Rob Garner always says “adoption follows function” This is true, but each of us has different thresholds for what we deem to be functional. If technology starts moving that bar, we know, thanks to the work of Everett Rogers and others, that the audience’s acceptance of that will follow the inevitable bell curve. Functionality is not equal in the eyes of all beholders.

The other problem with these new interfaces with technology is that function is currently scattered around like a handful of grass clippings in the wind. Sure, there are shards of usefulness, but unless you’re willing to wear more layers of wearable tech than your average early adopting Eskimo (or, as we say here in the politically correct north – Inuit), it’s difficult to see how this can significantly improve our day-to-day lives.

The other thing we have to grapple with is what I would call the WACF – The Weird and Creepy Factor. How exactly do we feel about having the frequency of our butt imprinting our sofa, our bank balance, our blood pressure and our body fat percentage beamed up to the data center of a start up we’d never heard of before last Friday? I’m an admitted early adopter and I have to confess – I’m not ready to make that leap right now.

It’s not just the privacy of my personal data that’s holding me back, although that is certainly a concern. Part of this goes back to something I talked about a few columns back – the redefinition of what it means to “be” online rather than “go” online. With wearable technology, we’re always “on” – plugged into the network and sharing data whether we’re aware of it or not.  This requires us with a philosophical loss of control. Chances are that we haven’t given this a lot of rational consideration, but it contributes to that niggling WACF that may be keeping us from donning the latest piece of wearable tech.

Eventually, the accumulated functionality of all this new technology will overcome all these barriers to adoption, but we will all have differing thresholds marking our surrender to the inevitable.  Garner’s assertion that adoption follows function is true, but it’s true of the functional wave as a whole and in that wave there will be winners and losers. Not all functional improvements get adopted. If all adoption followed all functional improvements, I’d be using a Dvorak keyboard right now. Betamax would have become the standard for videocassettes. And we’d be conversing in Esperanto. All functional improvements – all casualties to an audience not quite ready to embrace them.

Expect more to come.

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.

Google Holds the Right Cards for a Horizontal Market

First published January 9, 2014 in Mediapost’s Search Insider

android_trhoneFunctionality builds up, then across. That was the principle of emerging markets that I talked about in last week’s column. Up – then across – breaking down siloes into a more open, competitive and transparent market. I’ll come back here in a moment.

I also talked about how Google + might be defining a new way of thinking about social networking, one free of dependence on destinations. It could create a social lens through which all our online activity passes through, adding functionality and enriching information.

Finally, this week, I read that Google is pushing hard to extend Android as the default operating system in the Open Automotive Alliance – turning cars into really big mobile devices. This builds on Android’s dominance in the smartphone market (with an 82% market share).

See a theme here?

For years, I’ve been talking about the day when search transitions from being a destination to a utility, powering apps which provide very specific functionality that far outstrips anything you could do on a “one size fits all” search portal. This was a good news/bad news scenario for Google, who was the obvious choice to provide this search grid. But, in doing so, they lose their sole right to monetize search traffic, a serious challenge to their primary income source. However, if you piggy back that search functionality onto the de facto operating system that powers all those apps, and then add a highly functional social graph, you have all the makings of a foundation that will support the ‘horizontalization” of the mobile connected market. Put this in place, and revenue opportunities will begin falling into your lap.

The writing is plainly on the wall here. The future is all about mobile connections. It is the foundation of the Web of Things, wearable technology, mobile commerce – anything and everything we see coming down the pipe.  The stakes are massive. And, as markets turn horizontal in the inevitable maturation phase to come, Google seems to be well on their way to creating the required foundations for that market.

Let’s spend a little time looking at how powerful this position might be for Google. Microsoft is still coasting on their success in creating a foundation for the desktop, 30 years later.  The fact that they still exist at all is testament to the power of Windows. But the desktop expansion that happened was reliant on just one device – the PC. And, the adoption curve for the PC took two decades to materialize, due to two things: the prerequisite of a fairly hefty investment in hardware and a relatively steep learning curve. The mobile adoption curve, already the fastest in history, has no such hurdles to clear. Relative entry price points are a fraction of what was required for PCs. Also, the learning curve is minimal. Mobile connectivity will leave the adoption curve of PCs in the dust.

In addition, an explosion of connected devices will propel the spread of mobile connectivity. This is not just about smart phones. Two of the biggest disruptive waves in the next 10 years will be wearable technologies and the Web of Things. Both of these will rely on the same foundations, an open and standardized operating system and the ability to access and share data. At the user interface level, the enhancements of powerful search technologies and social-graph enabled filters will significantly improve the functionality of these devices as they interface with the “cloud.”

In the hand that will have to inevitably be played, it seems that Google is currently holding all the right cards.

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.

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.

What’s Apple’s Plan for 2014?

First published January 2, 2014 in Mediapost’s Search Insider

apple-storeWhen new markets open, value chains first build up, then across. Someone first creates a vertically integrated experience, and then the market opens up as free competition drives efficiency. This is the challenge that currently lies ahead of Apple.

Apple has been the acknowledged master at creating seamless vertically integrated experiences. They did it with the personal computer. They did it with music. They did it with mobile. They did it with tablets. The advantage of working within a closed value chain is that you control every aspect of the experience. You can make sure that everyone plays nice with each other.

The challenge is that at some point, as adoption heats up, you simply cannot scale fast enough to meet market demand. Open competition drives horizontal competition, which drives down prices. The lack of control up and down the chain introduces some short-term user pain, but eventually the dynamics of an open market overcome this and the advantages of having several companies working on an opportunity outweigh the disadvantages.

Apple loves early markets. Or, at least, they have in the past. Under Jobs, they had a knack of creating an elegantly integrated experience that was carefully crafted from top to bottom within the walls of Cupertino. The vision and obsession with detail that defined the Jobs era was a potent combination when it came to building vertical experiences. Somehow, Apple was able to open new markets over and over again, seemingly at will. They were able to bridge Geoffrey Moore’s “Chasm” – by making new experiences painless enough for the front end of the adoption bell curve. As markets rode up the curve, markets turned from vertical to horizontal, driving a decline in margins and prices. This is where Apple tended to kick out and look for the next wave to catch.

But that was then, and this is now. As mentioned, Apple doesn’t do very well when markets turn horizontal. They depend on high margins. Only once, with the Mac, were they able to come back and stake out a respectable claim in a horizontal market. And they almost disappeared in the process. The number of dependent circumstances that would be required to repeat that trick is such that I doubt they’re eager to go down the same path with the iPhone or iPad.

In the year end summaries, many are talking about a seeming anomaly –  that despite Android’s massive market share dominance over iOS (81% vs 12.9%, according to a recent Forbes article) it’s Apple that’s ringing up the holiday sales with mobile shoppers (23% vs Android’s paltry 5%).  This becomes more understandable when you put it in the context of a vertical market that is becoming horizontal. Shopping experiences are still much less painful on iOS. And, you have a user base that is much more comfortable with mobile ecommerce because they’re on the leading edge of the adoption curve. They’ve had a mobile device for a number of years now. Android users, in general, tend to be further back on the curve. As the benefits of Darwinian competition redefine the mobile marketplace along more horizontal lines, those ecommerce numbers will revert to a more natural balance, but it will take some time.

As this inevitable change in the marketplace happens, the question then becomes, “What does Apple do next?” Can they find the next wave? And, if they do, does an Apple without Jobs still have what it takes to create the vertical experience that can open up a new market? There are plenty of opportunities – the two most notable ones being connected entertainment devices (the much-rumored new generation of Apple TV) and wearable technology (iWatches, etc).

Apple has always been known for keeping their cards glued against their chest. In 2014, it remains to be seen if they have anything amazing up their sleeve.