Category Archives: Information Seeking & Foraging

The Spreading Activation Model of Marketing

“Beatle.”

I have just primed you. Before you even finished reading the word above, you had things popping into your mind. Perhaps it was a mental image of an individual Beatle – either John, Paul, George or Ringo. Perhaps it was a snippet of song. Perhaps it was grainy black and white footage of the Ed Sullivan show appearance. But as the concept “Beatle” entered your working memory, your brain was hard at work retrieving what you believed were relevant concepts from your long-term memory. (By the way, if your reaction was “What’s a Beatle?” – substitute “Imagine Dragons.”)

1-brain-neural-network-pasiekaThat’s a working example of spreading activation. The activation of your working memory pulls associated concepts from your long-term memory to create a mental construct that creates your internal definition of whatever that first label was.

Now, an important second step may or may not happen. First, you have to decide how long you’re going to let the “Beatle” prime occupy your working memory. If it’s of fleeting interest, you’ve probably already wiped the slate clear, ready for the next thing that catches your interest. But if that prime is strong enough to establish a firm grip on your attention, then you have a choice to make. Is your internal representation complete, or do you require more information? If you require more information then you have to turn to external sources for that information.

Believe it or not, this column is not intended as a 101 primer in Cognitive Psych. But the mental gymnastics I describe are important when we think about marketing, as we go through exactly the same process when we think about potential purchases. If we can understand that process better, we gain some valuable hints about how to more effectively market in an exceedingly fluid technological environment.

Much of advertising is built on the first half of the process – building associative brand concepts and triggering the prime that retrieves those concepts into working memory. Most of what isn’t working about advertising lies on this side of the cognitive map. We’ve been overly focused on the internal activation, at the expense of the external. But thanks to an explosion of available (and objective) information we’re less reliant on using our internal knowledge when making purchase decisions. Itamar Simonson and Emanuel Rosen explain in their book “Absolute Value”: “A person’s decision to buy is affected by a mix of three related sources: The individual’s Prior preferences, beliefs, and experiences (P) Others. Other people and information services (O) and Marketers (M).”

Simonson and Rosen say that with near perfect information available for the consumer, we now rely more on (O) and less on (P) and (M). Let’s leave (M) and (O) aside for the moment and focus on the (P) in this equation. (P) represents our internal spreading activation. After we’re primed, we retrieve a representation of the product or service we’re thinking of. At this point, we make an internal calculation. We balance how confident we are that our internal representation is adequate to make a purchase against how much effort we have to expend to gather further information. This calculation is largely made subconsciously. It follows Herbert Simon’s principle of Bounded Rationality. It also depends on how much risk is involved in the purchase we’re contemplating. If all the factors dictate that we’re reasonably confident in our internal representation and the risk we’re assuming, we’ll pull out our wallets and buy. If, however, we aren’t confident, we’ll start seeking more information. And that’s where (O) and (M) come in.

Simonson and Rosen lay out a purchase behaviour continuum, from (O) Dependent to (O) Independent. It’s at the (O) Dependent end, where internal confidence in retrieved beliefs and experience is low, that buying behaviors are changing dramatically. And it’s there where conventional approaches to advertising are falling far short of the mark. They are still stuck in the mythical times of Mad Men, where marketers relied on a “Prime, Retrieve (Internal beliefs), Purchase” path. Today, it’s much more likely that the Prime and Retrieve stages will be followed by an external spreading activation. We’ll pick up that thread in next week’s Online Spin.

 

Consuming in Context

npharris-oscarsIt was interesting watching my family watch the Oscars Sunday night. Given that I’m the father of two millennials, who have paired with their own respective millennials, you can bet that it was a multi-screen affair. But to be fair, they weren’t the only ones splitting their attention amongst the TV and various mobile devices. I was also screen hopping.

As Dave Morgan pointed out last week, media usage no longer equates to media opportunity. And it’s because the nature of our engagement has changed significantly in the last decade. Unfortunately, our ad models have been unable to keep up. What is interesting is the way our consumption has evolved. Not surprisingly, technology is allowing our entertainment consumption to evolve back to its roots. We are watching our various content streams in much the same way that we interact with our world. We are consuming in context.

The old way of watching TV was very linear in nature. It was also divorced from context. We suspended engagement with our worlds so that we could focus on the flickering screen in front of us. This, of course, allowed advertisers to buy our attention in little 30-second blocks. It was the classic bait and switch technique. Get our attention with something we care about, and then slip in something the advertiser cares about.

The reason we were willing to suspend engagement with the world was that there was nothing in that world that was relevant to our current task at hand. If we were watching Three’s Company, or the Moon Landing, or a streaker running behind David Niven at the 1974 Oscar ceremony, there was nothing in our everyday world that related to any of those TV events. Nothing competed for the spotlight of our attention. We had no choice but to keep watching the TV to see what happened next.

But imagine if a nude man suddenly appeared behind Matthew McConaughey at the 2015 Oscars. We would immediately want to know more about the context of what just happened. Who was it? Why did it happen? What’s the backstory? The difference is now, we have channels at our disposal to try to find answers to those questions. Our world now includes an extended digital nervous system that allows us to gain context for the things that happen on our TV screens. And because TV no longer has exclusive control of our attention, we switch to the channel that is the best bet to find the answers we seek.

That’s how humans operate. Our lives are a constant quest to fill gaps in our knowledge and by doing so, make sense of the world around us. When we become aware of one of these gaps we immediate scan our environment to find cues of where we might find answers. Then, our senses are focused on the most promising cues. We forage for information to satiate our curiosity. A single-minded focus on one particular cue, especially one over which we have no control, is not something we evolved to do. The way we watched TV in the 60s and 70s was not natural. It was something we did because we had no option.

Our current mode of splitting attention across several screens is much closer to how humans naturally operate. We continually scan our environment, which, in this case, included various electronic interfaces to the extended virtual world, for things of interest to us. When we find one, our natural need to make sense sends us on a quest for context. As we consume, we look for this context. The diligence of our quest for that context will depend on the degree of our engagement with the task at hand. If it is slight, we’ll soon move on to the next thing. If it’s deep, we’ll dig further.

On Sunday night, the Hotchkiss family quest for context continually skipped around, looking for what other movies J.K. Simmons had acted in, watching the trailer for Whiplash, reliving the infamous Adele Dazeem moment from last year and seeing just how old Benedict Cumberbatch is (I have two daughters that are hopelessly in love, much to the chagrin of their boyfriends). As much as the advertisers on the 88th Oscars might wish otherwise, all of this was perfectly natural. Technology has finally evolved to give our brain choices in our consumption.

 

 

 

 

 

 

Why More Connectivity is Not Just More – Why More is Different

data-brain_SMEric Schmidt is predicting from Davos that the Internet will disappear. I agree. I’ve always said that Search will go under the hood, changing from a destination to a utility. Not that Mr. Schmidt or the Davos crew needs my validation. My invitation seems to have got lost in the mail.

Laurie Sullivan’s recent post goes into some of the specifics of how search will become an implicit rather than an explicit utility. Underlying this is a pretty big implication that we should be aware of – the very nature of connectivity will change. Right now, the Internet is a tool, or resource. We access it through conscious effort. It’s a “task at hand.” Our attention is focused on the Internet when we engage with it. The world described by Eric Schmidt and the rest of the panel is much, much different.   In this world, the “Internet of Things” creates a connected environment that we exist in. And this has some pretty important considerations for us.

First of all, when something becomes an environment, it surrounds us. It becomes our world as we interpret it through our assorted sensory inputs. These inputs have evolved to interpret a physical world – an environment of things. We will need help interpreting a digital world – an environment of data. Our reality, or what we perceive our reality to be, will change significantly as we introduce technologically mediated inputs into it.

Our brains were built to parse information from a physical world. We have cognitive mechanisms that evolved to do things like keep us away from physical harm. Our brains were never intended to crunch endless reams of digital data. So, we will have to rely on technology to do that for us. Right now we have an uneasy alliance between our instincts and the capabilities of machines. We are highly suspicious of technology. There is every rational reason in the world to believe that a self-driving Google car will be far safer than a two ton chunk of accelerating metal under the control of a fundamentally flawed human, but who of us are willing to give up the wheel? The fact is, however, that if we want to function in the world Schmidt hints at, we’re going to have to learn not only to trust machines, but also to rely totally on them.

The other implication is one of bandwidth. Our brains have bottlenecks. Right now, our brain together with our senses subconsciously monitor our environment and, if the situation warrants, they wake up our conscious mind for some focused and deliberate processing. The busier our environment gets, the bigger this challenge becomes. A digitally connected environment will soon exceed our brain’s ability to comprehend and process information. We will have to determine some pretty stringent filtering thresholds. And we will rely on technology to do the filtering. As I said, our physical senses were not built to filter a digital world.

It will be an odd relationship with technology that will have to develop. Even if we lower our guard on letting machines do much of our “thinking” (in terms of processing environmental inputs for us) we still have to learn how to give machines guidelines so they know what our intentions are. This raises the question, “How smart do we want machines to become?” Do we want machines that can learn about us over time, without explicit guidance from us? Are we ready for technology that guesses what we want?

One of the comments on Laurie’s post was from Jay Fredrickson, “Sign me up for this world, please. When will this happen and be fully rolled out? Ten years? 20 years?” Perhaps we should be careful what we wish for.  While this world may seem to be a step forward, we will actually be stepping over a threshold into a significantly different reality. As we step over that threshold, we will change what it means to be human. And there will be no stepping back.

Publishers as Matchmakers

gatekeeperI’m a content creator. And, in this particular case, I’ve chosen MediaPost as the distribution point for that content. If we’re exploring the role of publishing in the future, the important question to ask here is why? After all, I could publish this post in a couple clicks to my blog. And, thanks to my blogging software, it will automatically notify my followers that there’s a new post. So, what value does Mediapost add to that?

Again, we come back to signal and noise. I generate content primarily to reach both a wide and interested audience. As a digital marketing consultant, there is a financial incentive to grow my own personal brand, but to be honest, my reward is probably more tied up in the concepts of social capital and my own ego. I publish because I want to be heard. And I want to be heard by people who find my content valuable. I have almost 2000 followers between my blog, Twitter feed and other social networks, but those people already know me. Hopefully, Mediapost will introduce me to new people that don’t know me. I want Mediapost to be my matchmaker.

Now, the second question to ask is, why are you reading this post on Mediapost? While I don’t presume to be able to know your own personal intentions, I can take a pretty good shot at generalizing – you are a Mediapost reader because you find the collection of content they publish interesting. It’s certainly not the only place online you can find content about marketing and media. And, if they chose to, any of the MediaPost writers could easily publish their content on their own blogs. You have chosen MediaPost because it acts as both a convenient access point and an effective filter.

This connection between content and audience is where publishers like MediaPost add value. Because you trust MediaPost to deliver content you find interesting, it passes the first level of your filtering threshold. I, as a content creator, get the benefit of MediaPost’s halo effect. The odds are better that I can connect with new readers under the MediaPost banner than they are if you’re introduced to me through a random, unfiltered tweet or alert in your newsfeed. And here we have a potential clue in the future of revenue generation for publishers. If publishing is potentially a match making service, perhaps we need to look at other matchmakers to see how they generate revenue.

In the traditional publishing world, it would be blasphemous to suggest that content creators should be charged for access to an audience. After all, we used to get paid to generate content by the publishers. But that was then and this is now. Understand, I’m not talking about native advertising or advertorials here. In fact, it would be the publisher’s responsibility to filter out unacceptably commercial editorials. I’m talking about creating an audience market for true content generators. In this day of personal branding, audiences have value. The better the audience, the higher the value. It should be worth something to me to reach new audiences. Publishers, in turn, act as the reader’s filter, ensuring the content they provide matches the user’s interest. Again, if the match is good enough, that has value for the reader.

Of course, the problem here is quantifying value on both sides of the relationship. I would imagine that both the content creators and content consumers that are reading my suggestions are probably saying, “There is no way I would pay for that!” And, in the current state of online publishing, I wouldn’t either – as a creator nor a consumer. The value isn’t there because the match isn’t strong enough. But if publishers focused on building the best possible audience and on presenting the best possible content, it might be a different story. More importantly, it would be a revenue model that would realign publishers with their audience, rather than pit them against it.

From the reader’s perspective, if a publisher was acting as your own private information filter, and not as a platform for poorly targeted advertising, you would probably be more willing to indicate your preferences and share information. If the publisher was discriminating enough, you might even be willing to allow them to introduce very carefully targeted offers from advertiser’s, filtering down to only the offers you’re highly likely to be interested in. This provides three potential revenue sources to the publisher: content creators looking for an audience, readers looking for an effective filtering service and advertisers looking for highly targeted introductions to prospects. In the last case, the revenue should be split with the prospect, with the publisher taking a percentage for handling the introduction and the rest going to the prospect in return for agreeing to accept the advertiser’s introduction.

While radically different than today’s model, what I’ve proposed is not a new idea. It was first introduced in the book Net Worth, by John Hagel and Marc Singer. They introduced the idea in 1999. Granted, my take is less involved than theirs is, but the basic idea is the same – a shift from a relentless battering of prospects with increasingly overt advertising messages to a careful filtering and matching of interests and appropriate content. And, when you think about it, the matching of intent and content is what Google has been doing for two decades.

Disruptive innovations tend to change the ways that value is determined. They take previous areas of scarcity and change them to ones of abundance. They upend markets and alter existing balances between forces. When the markets shift to this extent, trying to stick to the old paradigm guarantees failure. The challenge is that there is no new paradigm to follow. Experimentation is the only option. And to experiment you have to be willing to explore the boundaries. The answer won’t be found in the old, familiar territory.

Same Conversation. Different Location.

online_publishing_vxwndNote: This is my first OnlineSpin column for MediaPost.

First of all, let’s get the pleasantries out of the way. I’m Gord. I’m new to Online Spin, but not to MediaPost. If you don’t know me, I have been writing over on the Search Insider side of the house for the past 10 and a half years.

Nice to meet you.

Now, on to business. Just before the switch, I took online publishing to task for sacrificing it’s ability to communication for the sake of advertising revenue. The user experience on most online publications is so littered with intrusive ads and misleading click bait that it becomes almost impossible to actually read the content. My point, which is probably obvious, is that the short-term quest for revenue is jeopardizing the long-term health of the business model.

Among the comments posted were a few asking for guidance rather than just criticism. Fair enough. It’s much easier to criticize that it is to create. So, where does the future of publishing lie?

The problem, as it is in so many other cases, is that technology has annihilated the proverbial publishing apple cart. Publishing as an industry began because of the high transactional cost of publicizing information. Information began to be stacked vertically, because that was the only cost effective way to do it. These vertical stacks of information attracted audiences because it was the only place they could get this information. Limited access points created large and loyal audiences which in turn allowed ad supported revenue models. Because transactional costs were high, information was scarce. Scarcity enabled profit.

Today, technology is, one by one, leveling the vertical stacks of information. Transactional costs of publishing have dropped to essentially zero. Yes, I’m publishing this post through a “publisher” but it would be just as easy for me to publish to my own blog. And while MediaPost’s audience is probably larger than my own bog’s, the gap between the two grows less every day. The lower transactional costs of publishing have erased the scarcity of information.

This disruptive change has flipped the publishing model on its head. The problem with information used to be that we had too little access. The problem today is that we have too much. What we need now are filters. We need a way to separate the signal from the ever-increasing noise.

Now, think of what this reversal does for revenue models of publishers. If the problem before were access, we would value any source of information that provided this access. We would be loyal to it. We would spend a significant amount of time with it. But if the problem becomes one of filtering, our loyalty level drops significantly. We just want to get to the information that is most interesting to us as quickly and efficiently as possible. If we have any allegiance to publishers at all, it is as a content filter. This is exactly why publishing empires are fragmenting into more and more specific vertical niches. We don’t need access points – we need effective filters.

Now, back to my original point. If the only way to make revenue from publishing is to introduce more noise – in the form of intrusive advertising – we quickly see the problem. We want publishers to eliminate extraneous noise and they add more. And to compound the problem, they intentionally blur the line between signal and noise in an attempt to generate more click-throughs. And, as Joe Marchese rightly points out, this vicious cycle is exacerbated by the bogus metric of “impressions” that publishers seem to have latched on to. The reader’s intent and the publisher’s intent are on a collision course with each other.

Given this, is there a way to save publishing? Perhaps, but it will be in a form much different than any we currently see. Publishing’s role may be in serving both as a filter and a matchmaker. More to come next Tuesday

The Sorry State of Online Publishing

ss-publishingDynamic tension can be a good thing. There are plenty of examples of when this is so. Online publishing isn’t one of them. The plunging transaction costs of publishing and the increasingly desperate attempts to shore up some sort of sustainable revenue model is creating a tug-of-war that’s threatening to tear apart the one person that this whole sorry mess is revolving around – the reader. Somebody better get their act together soon, because I’m one reader that’s getting sick of it.

Trying to read an article on most online is like trying to tiptoe through a cognitive minefield. The publishers have squeezed every possible advertising opportunity onto the page and in doing so, has sacrificed credibility, cohesiveness and clarity. The job of publishing is communication, but these publishers seem to think its actually sacrificing communication for revenue. Methinks if you have to attack your own business model to make a profit, you should be taking a long hard look at said model.

Either Fish or Cut Click Bait

The problem has grown so pervasive that academia is even piling on. In the past few months, a number of studies have looked at the dismal state of online publishing.

clickbaitIn the quest for page views, publishers have mastered the trick of pushing our subconscious BSO (Bright Shiny Object) buttons with clickbait. Clickbait is essentially brain porn – headlines, often misleading – that you can’t resist clicking on. The theory is more page views – more advertising opportunities. The problem is that clickbait essential derails the mind from its predetermined focus. And worse, clickbait often distracts the brain with a misleading headline the subsequent article fails to deliver on. As Jon Stewart recently told New York Magazine, “It’s like carnival barkers, and they all sit out there and go, “Come on in here and see a three-legged man!” So you walk in and it’s a guy with a crutch.”

A recent study from The Journal of Experimental Psychology showed that misleading headlines and something called “false balance” – where publishers give equal airtime to sources with very different levels of credibility – can negatively impact the reader’s ability to remember the story, create a cohesive understanding of the story and cognitively process the information. In other words, the publisher’s desperate desire to grab eyeballs gets in the way of their ability to communicate effectively.

Buzzfeed Editor-in-Chief Ben Smith has publicly gone on the record about why he doesn’t use click-bait headlines: “Here is a trade secret I’d decided a few years ago we’d be better off not revealing — clickbait stopped working around 2009.” He references Facebook engineer Khalid El-Arini in the post, saying “readers don’t want to be tricked by headlines; instead, they want to be informed by them.”

Now You Read Me, Now You Don’t

If you ever wanted to test your resolve, try getting to the end of an online article. What content there is is shoehorned into a format littered with ads and clickbait of every description. Many publishers even try to squeeze revenue from the content itself by using Text Enhance, an ad serving platform that hyperlinks keywords in the copy and shows ads if your cursor strays anywhere near these links. Users like me often use their cursor both as a place marker and a quick way to vet sources of embedded links. Text Enhance makes reading in this way an incredibly frustrating experience as it continually pops up poorly targeted ads while you try to tiptoe through the advertising landmines to piece together what the writer was originally trying to say. It turns reading content into a virtual game of “Whac-a-Mole.”

Of course, this is assuming you’ve made it past the page take-over and auto-play video ads that litter the “mind-field” between you and the content you want to access on a site like Forbes or The Atlantic. These interruptions in our intent create a negative mental framework that is compounded by having to weave through increasingly garish ad formats in order to piece together the content we’re trying to access.

A new study from Microsoft and Northwestern University shows that aggressive and annoying advertising may prop up short-term revenues, but at a long-term price that publishers should be thinking twice about paying, ““The practice of running annoying ads can cost more money than it earns, as people are more likely to abandon sites on which they are present. In addition, in the presence of annoying ads, people were less accurate in remembering what they had read. None of these effects on users is desirable from the publisher’s perspective.”

Again, we have this recurring theme about revenue getting in the way of user experience. This is a conflict from which there can be no long-term benefit. When you frustrate users, you slowly kill your revenue source. You engage in a vicious cycle from which there is no escape.

I understand that online publishers are desperate. I get that. They should be. I suspect the ad-supported business platform they’re trying to prop up is hopelessly damaged. Another will emerge to take its place. But the more they frustrate us, the faster that will happen.

 

 

Evolved Search Behaviors: Take Aways for Marketers

In the last two columns, I first looked at the origins of the original Golden Triangle, and then looked at how search behaviors have evolved in the last 9 years, according to a new eye tracking study from Mediative. In today’s column, I’ll try to pick out a few “so whats” for search marketers.

It’s not about Location, It’s About Intent

In 2005, search marketing as all about location. It was about grabbing a part of the Golden Triangle, and the higher, the better. The delta between scanning and clicks from the first organic result to the second was dramatic – by a factor of 2 to 1! Similar differences were seen in the top paid results. It’s as if, given the number of options available on the page (usually between 12 and 18, depending on the number of ads showing) searchers used position as a quick and dirty way to filter results, reasoning that the higher the result, the better match it would be to their intent.

In 2014, however, it’s a very different story. Because the first scan is now to find the most appropriate chunk, the importance of being high on the page is significantly lessened. Also, once the second step of scanning has begun, within a results chunk, there seems to be more vertical scanning within the chunk and less lateral scanning. Mediative found that in some instances, it was the third or fourth listing in a chunk that attracted the most attention, depending on content, format and user intent. For example, in the heat map shown below, the third organic result actually got as many clicks as the first, capturing 26% of all the clicks on the page and 15% of the time spent on page. The reason could be because it was the only listing that had the Google Ratings Rich Snippet because of the proper use of structured data mark up. In this case, the information scent that promised user reviews was a strong match with user intent, but you would only know this if you knew what that intent was.

Google-Ford-Fiesta

This change in user search scanning strategies makes it more important than ever to understand the most common user intents that would make them turn to a search engine. What will be the decision steps they go through and at which of those steps might they turn to a search engine? Would it be to discover a solution to an identified need, to find out more about a known solution, to help build a consideration set for direct comparisons, to look for one specific piece of information (ie a price) or to navigate to one particular destination, perhaps to order online? If you know why your prospects might use search, you’ll have a much better idea of what you need to do with your content to ensure you’re in the right place at the right time with the right content.  Nothing shows this clearer than the following comparison of heat maps. The one on the left was the heat map produced when searchers were given a scenario that required them to gather information. The one on the right resulted from a scenario where searchers had to find a site to navigate to. You can see the dramatic difference in scanning behaviors.

Intent-compared-2

If search used to be about location, location, location, it’s now about intent, intent, intent.

Organic Optimization Matters More than Ever!

Search marketers have been saying that organic optimization has been dying for at least two decades now, ever since I got into this industry. Guess what? Not only is organic optimization not dead, it’s now more important than ever! In Enquiro’s original 2005 study, the top two sponsored ads captured 14.1% of all clicks. In Mediative’s 2014 follow up, the number really didn’t change that much, edging up to 14.5% What did change was the relevance of the rest of the listings on the page. In 2005, all the organic results combined captured 56.7% of the clicks. That left about 29% of the users either going to the second page of results, launching a new search or clicking on one of the side sponsored ads (this only accounted for small fraction of the clicks). In 2014, the organic results, including all the different category “chunks,” captured 74.6% of the remaining clicks. This leaves only 11% either clicking on the side ads (again, a tiny percentage) or either going to the second page or launching a new search. That means that Google has upped their first page success rate to an impressive 90%.

First of all, that means you really need to break onto the first page of results to gain any visibility at all. If you can’t do it organically, make sure you pay for presence. But secondly, it means that of all the clicks on the page, some type of organic result is capturing 84% of them. The trick is to know which type of organic result will capture the click – and to do that you need to know the user’s intent (see above). But you also need to optimize across your entire content portfolio. With my own blog, two of the biggest traffic referrers happen to be image searches.

Left Gets to Lead

The Left side of the results page has always been important but the evolution of scanning behaviors now makes it vital. The heat map below shows just how important it is to seed the left hand of results with information scent.

Googlelefthand

Last week, I talked about how the categorization of results had caused us to adopt a two stage scanning strategy, the first to determine which “chunks” of result categories are the best match to intent, and the second to evaluated the listings in the most relevant chunks. The vertical scan down the left hand of the page is where we decide which “chunks” of results are the most promising. And, in the second scan, because of the improved relevancy, we often make the decision to click without a lot of horizontal scanning to qualify our choice. Remember, we’re only spending a little over a second scanning the result before we click. This is just enough to pick up the barest whiffs of information scent, and almost all of the scent comes from the left side of the listing. Look at the three choices above that captured the majority of scanning and clicks. The search was for “home decor store toronto.” The first popular result was a local result for the well known brand Crate and Barrel. This reinforces how important brands can be if they show up on the left side of the result set. The second popular result was a website listing for another well known brand – The Pottery Barn. The third was a link to Yelp – a directory site that offered a choice of options. In all cases, the scent found in the far left of the result was enough to capture a click. There was almost no lateral scanning to the right. When crafting titles, snippets and metadata, make sure you stack information scent to the left.

In the end, there are no magic bullets from this latest glimpse into search behaviors. It still comes down to the five foundational planks that have always underpinned good search marketing:

  1. Understand your user’s intent
  2. Provide a rich portfolio of content and functionality aligned with those intents
  3. Ensure your content appears at or near the top of search results, either through organic optimization or well run search campaigns
  4. Provide relevant information scent to capture clicks
  5. Make sure you deliver on what you promise post-click

Sure, the game is a little more complex than it was 9 years ago, but the rules haven’t changed.

Google’s Golden Triangle – Nine Years Later

Last week, I reviewed why the Golden Triangle existed in the first place. This week, we’ll look at how the scanning patterns of Google user’s has evolved in the past 9 years.

The reason I wanted to talk about Information Foraging last week is that it really sets the stage for understanding how the patterns have changed with the present Google layout. In particular, one thing was true for Google in 2005 that is no longer true in 2014 – back then, all results sets looked pretty much the same.

Consistency and Conditioning

If humans do the same thing over and over again and usually achieve the same outcome, we stop thinking about what we’re doing and we simply do it by habit. It’s called conditioning. But habitual conditioning requires consistency.

In 2005, The Google results page was a remarkably consistent environment. There was always 10 blue organic links and usually there were up to three sponsored results at the top of the page. There may also have been a few sponsored results along the right side of the page. Also, Google would put what it determined to be the most relevant results, both sponsored and organic, at the top of the page. This meant that for any given search, no matter the user intent, the top 4 results should presumably include the most relevant one or two organic results and a few hopefully relevant sponsored options for the user. If Google did it’s job well, there should be no reason to go beyond these 4 top results, at least in terms of a first click. And our original study showed that Google generally did do a pretty good job – over 80% of first clicks came from the top 4 results.

In 2014, however, we have a much different story. The 2005 Google was a one-size-fits-all solution. All results were links to a website. Now, not only do we have a variety of results, but even the results page layout varies from search to search. Google has become better at anticipating user intent and dynamically changes the layout on each search to be a better match for intent.

google 2014 big

What this means, however, is that we need to think a little more whenever we interact with a search page. Because the Google results page is no longer the same for every single search we do, we have exchanged consistency for relevancy. This means that conditioning isn’t as important a factor as it was in 2005. Now, we must adopt a two stage foraging strategy. This is shown in the heat map above. Our first foraging step is to determine what categories – or “chunks” of results – Google has decided to show on this particular results page. This is done with a vertical scan down the left side of the results set. In this scan, we’re looking for cues on what each chunk offers – typically in category headings or other quickly scanned labels. This first step is to determine which chunks are most promising in terms of information scent. Then, in the second step, we go back to the most relevant chunks and start scanning in a more deliberate fashions. Here, scanning behaviors revert to the “F” shaped scan we saw in 2005, creating a series of smaller “Golden Triangles.”

What is interesting about this is that although Google’s “chunking” of the results page forces us to scan in two separate steps, it’s actually more efficient for us. The time spent scanning each result is half of what it was in 2005, 1.2 seconds vs. 2.5 seconds. Once we find the right “chunk” of results, the results shown tend to be more relevant, increasing our confidence in choosing them.  You’ll see that the “mini” Golden Triangles have less lateral scanning than the original. We’re picking up enough scent on the left side of each result to push our “click confidence” over the required threshold.

A Richer Visual Environment

Google also offers a much more visually appealing results page than they did 9 years ago. Then, the entire results set was text based. There were no images shown. Now, depending on the search, the page can include several images, as the example below (a search for “New Orleans art galleries”) shows.

Googleimageshot

The presence of images has a dramatic impact on our foraging strategies. First of all, images can be parsed much quicker than text. We can determine the content of an image in fractions of a second, where text requires a much slower and deliberate type of mental processing. This means that our eyes are naturally drawn to images. You’ll notice that the above heat map has a light green haze over all the images shown. This is typical of the quick scan we do immediately upon page entry to determine what the images are about. Heat in an eye tracking heat map is produced by duration of foveal focus. This can be misleading when we’re dealing with images for two reasons. First, the fovea centralis is, predictably, in the center of our eye where our focus is the sharpest. We use this extensively when reading but it’s not as important when we’re glancing at an image. We can make a coarse judgement about what a picture is without focusing on it. We don’t need our fovea to know it’s a picture of a building, or a person, or a map. It’s only when we need to determine the details of a picture that we’ll recruit the fine-grained resolution of our fovea.

Our ability to quickly parse images makes it likely that they will play an important role in our initial orientation scan of the results page. We’ll quickly scan the available images looking for information scent. It the image does offer scent, it will also act as a natural entry point for further scanning. Typically, when we see a relevant image, we look in the immediate vicinity to find more reinforcing scent. We often see scanning hot spots on titles or other text adjacent to relevant images.

We Cover More Territory – But We’re Also More Efficient

So, to sum up, it appears that with our new two step foraging strategy, we’re covering more of the page, at least on our first scan, but Google is offering richer information scent, allowing us to zero in on the most promising “chunks” of information on the page. Once we find them, we are quicker to click on a promising result.

Next week, I’ll look at the implications of this new behavior on organic optimization strategies.

The Evolution of Google’s Golden Triangle

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

Why We Had a Golden Triangle in the First Place

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

Foraging for Information

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

The Area of Greatest Promise

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

Golden Triangle original

F Shaped Scanning

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

Working Memory and Chunking

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

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

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

Learning about Big Data from Big Brother

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

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

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

Owned Data vs Environmental Data

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

You Need to Make Sense of the Data – at Scale

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

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

You Have to Be Constantly Experimenting

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

You Need to Remove Human Limitations from the Equation

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

Data Needs to Be Shared

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

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