Today, our online galaxy is more than our website. It’s also our presence on a collection of social sites, on a collection of mobile sites and applications, and perhaps even in digital signage and kiosks. What’s more, people want content from us constantly. Almost daily, you can find a new statistic showing that people in the U.S. and around the world spend more time with web content now than ever. This constant hunger for content creates a powerful demand, a force that we are, indeed, reckoning with.

While replete with potential, this complex digital galaxy introduces new challenges. One challenge has become particularly tricky: content credibility. My company, Content Science, recently surveyed 800 Americans, and found that even though 79% are using web content a lot more now than five years ago, 65% of them think web content is “hit or miss” or “unreliable.” (Figure 1) Of respondents, 63% reported that their trust in web content is the same or less than it was five years ago. (Results were similar for the same survey with 800 British participants, as well.)

People turn more often to digital content but don’t necessarily trust it. Why? One reason is that people judge the credibility of content by the credibility of its source. Let’s take a closer look at the role of source in perceived credibility.

Anyone can be a content source

The situation has become much more complicated.

Qui-Gon Jinn, Star Wars Episode I: The Phantom Menace

Not long ago (though it feels far, far away), our sources of content were limited. Before the web, the main source was the media. The distinctions between news, features, opinion, entertainment, and advertising were clear, too. You wouldn’t mistake a news report for an opinion / editorial piece. How things have changed.

Online, any person or organization can be a source of content. Any individual can launch a blog or a YouTube account and start posting content. Any company or organization can have a website (or five or ten), blog (or three or four), social account, or other means of publishing content online. The media are no longer the only sources of content.

Those complications are only the beginning. We live in an age of APIs. Techniques such as aggregation, curation, and mashing up bring together multiple sources of content into a new source. For example, Sharecare answers health questions by curating content from media personalities, academic institutions, doctors and clinicians, and nonprofit organizations.

The good news? My survey suggests people are open to a brave new world of content sources. For example, my survey asked people to rank potential sources of content in the order they trust them. Non-media sources such as brands, nonprofits, and government agencies performed well. Niche media such as WebMD also performed better than big media. Figure 2 shows an example of how people in the U.S. ranked sources of finance content.

The challenge? When anyone and everyone can be a source, distinguishing which sources are credible becomes harder for users. That distinction also becomes more urgent. Let’s delve into why and what to do about it.

Not everyone is a credible content source

The ability to speak does not make you intelligent.

Qui-Gon Jinn, Star Wars Episode I: The Phantom Menace

Online, there is no shortage of content. The quantity is there; often, the quality is not. Our users are well aware of this situation. How do they cope? By spending a lot of time considering, and reconsidering, specific content sources. In both my survey and observational testing with users, this need to verify content sources drives much user behavior, especially for content discovery. For example, source credibility affects which search results users view. Users will skip results that don’t seem credible. Skipping search results is only the beginning. My team at Content Science is finding that source credibility affects all kinds of content discovery behavior.[1]

What does this mean for you? Support your users’ behavior as they consider the source. Become a master of credibility. No matter what kind of source you are, you will better reach and influence users when they gain confidence in your credibility quickly. That takes more than applying a few journalistic rules to your content: most of us who work in digital content today are not journalists, and we’re not working for media sources. To see how to establish credibility, let’s walk through four key insights about content sources.

1. A credible source is easy to identify

Sounds simple, right? But a lot of content sources are getting this wrong. I asked survey participants to view and rate the credibility of content from a range of sources for health, travel, and finance. (I included samples from big media, niche media, government, and more.) Then, I asked participants why they gave the rating. Among samples rated not credible, the number two reason was confusion about the source. Figure 3 shows an example of results for travel web content.

Now, being easy to identify isn’t as simple as it sounds. It’s more than saying who you are, splashing your logo, or having the right colors. To be quick for users to identify, you need a distinct tone across your digital touchpoints. Tone is your verbal imprint. When your content sounds like you, you reassure users that you are speaking. You immediately give users confidence that you are the source. The more distinct your tone is, the more credible you are. So, every organization needs a recognizable tone.

Today’s digital universe makes tone complicated. Take a curation situation, such as Sharecare mentioned earlier. Sharecare collects other people’s credible voices but it doesn’t have a distinct tone. Even subtle flourishes of perspective in introducing other voices could help Sharecare become easier to identify. For instance, imagine if Sharecare introduced the answers with a statement like We carefully selected these answers from trusted sources to share with you. Now, imagine peppering other screens with statements or labels that similarly tie in the Sharecare brand. A more distinct tone would imbue the Sharecare experience.

Another complication for tone is the many jobs your content has to do—across a myriad of platforms and formats. Trying to come up with an end-all, be-all tone doesn’t work. What does work is defining a core tone and then defining facets to support different jobs, such as marketing or customer support. Figure 4 shows an example model for defining a core tone (in this case for a digital product) and then slight variations for other functions.

With this approach, you balance the need to have a distinct tone with the need to vary it slightly for a specific job. Make your content easy to identify by crafting a distinct tone across your touchpoints.

2. A credible source offers useful content.

Credibility requires substance. Past digital credibility studies have focused on the role of design and format, so I felt higher than an Ewok in the treetops when I discovered that content usefulness has an influence.[2] In my survey, I asked participants to explain why they rated content samples as credible. The number one reason across travel, finance, and health was knowing and trusting the source. The number two reason? The content seemed useful. Figure 5 shows the results for travel web content in the U.S.

It’s hard to fake being useful. You have to know what you’re doing, from your strategy all the way through your execution. But when useful content is so important to your credibility, it’s hard to justify anything less.

How do you make content useful? The short answer is that you make content useful when you craft it with users’ real needs, decisions, and questions in mind. Let me share an example from PubMedHealth. (Figure 6) This page brings together content explaining what a health condition is and what to do about it. Every bit of content here is relevant to the condition and common questions about it. The content on the right side, especially, guides the user into more pertinent content.

Now, if you’re a startup or simply new to being a content source, such as a retailer exploring branded content, take special note. This insight and the next one should give you hope. You can be a new source AND a credible source. You don’t have to go to the dark side and flout content quality or credibility.

3. A new source can become credible by being easy to verify and being recommended.

When users are unfamiliar with you as a content source, they look for mentions of you. They try to verify what you say. Let’s look at some proof. In my survey, I asked people about how they verify content. Of the options we gave, most participants said they search for other sources that corroborate the content. (Figure 7)

If you’re offering useful content, people will be more likely to notice, cite, mention, or curate it. That means that when users go to verify you as a source, they will be able to do so quickly.

I know what you might be thinking. That’s what people say about their behavior. Is that what they really do? Our observational testing suggests yes. While trying to find the answer to a question about travel vaccinations, one test participant said, “I noticed that all roads point to CDC (The Center for Disease Control and Prevention)…most articles about this [topic] refer back to CDC. So, I’m just going straight to CDC now.”

The trick to being easy to verify is that you can’t completely control it, you can only influence it. How? By making your useful content easy to find and easy for other people to share or reference. As a simple example, CDC makes social sharing available on every content-rich page of its mobile site and website.

Many Americans might not realize the CDC covers travel vaccinations, but most of them know the CDC. The CDC was ranked the most trusted government agency in a 2009 Gallup poll. What if you’re not simply new as a source for a topic, but new as a source, period? Being recommended will go a long way with users. I asked survey participants to rank features of credibility. Recommendations ranked highly, as you can see in Figure 8.

If you try to fake recommendations and mentions, you won’t make it as a credible source. Think for a moment about Luke Skywalker’s path to becoming a Jedi. Did he skip mastering the Force and learn lightsaber skills? No. He started with mastering the Force. In the same way, you can’t skip offering useful content and expect to earn meaningful recommendations or mentions.

Let’s take Cerner Corporation as an example. Cerner launched a blog on thought leadership for the health industry. As one instance, Cerner explained the meaning of changes in ICD-10 codes in a series of posts. (That might sound like Huttese to you, but to the public and private health industry, those codes are important.) The content of the post was so handy, that respected trade publications such as Investor’s Business Daily cited the post. So, like Cerner, you can’t control whether anyone mentions or recommends you. But, you can influence that behavior by offering useful content.

These insights will take you far in mastering credibility. When you try to establish credibility with a diverse group of users, you will appreciate this last insight.

4. A credible source means different things to different people—so test.

Obi-Wan Kenobi once wisely said Many of the truths that we cling to depend on our point of view. The same is true for credibility. Insights one-three run true for most everyone. But, other aspects of credibility are more relative. In my survey, we found some notable differences between the U.K. and U.S. results. For example, in the U.S., companies and brands fared reasonably well as credible content sources. Personalities did not. The opposite was true in the U.K.

My team also wielded some masterful analysis on our survey data. They found statistically significant differences in some results by age. For example, take a look at Figure 7 again. In the U.S., people aged 55-64 were more likely than other age groups to rank endorsements highly. If you want to be a trusted source of content to people in that age range, you would want to secure and show endorsements.

So, if you’re trying to reach users in different regions or of different ages, don’t assume they judge credibility exactly the same way. How can you be sure your approach to being credible will work? By testing it. Testing with representative users doesn’t have to be laborious or outrageously expensive. When you think about the risks you take by not testing, the investment of time and resources seems small. What will be big is the influence you gain as a credible content source.

Be credible or be forgettable

Do or do not. There is no try.

Yoda, Star Wars Episode V: The Empire Strikes Back

Constant content demand makes our digital galaxy complicated. The possibilities are awe-inspiring, but they also cause costly credibility problems. The only way to make the force of content demand work for you, not against you, is to master credibility. I predict that if you apply these research-based insights, you will not simply survive amidst these complexities—you (and your users) will thrive.

References

1. The full Content Science study of content and credibility is at content-science.com/the-study. Special thanks to Michael Driscoll, Lisa Clark, and our advisory partners for their contributions.↩
2. For a detailed look at the role of design (not content) in web credibility, don’t miss the work of B.J. Fogg at Stanford Persuasive Technology Laboratory captology.stanford.edu.↩

For me, it’s a recollection of sitting atop a washing machine in the mud room of my family’s first home as my parents painted the walls a pale yellow to the sound of Steely Dan’s Can’t Buy A Thrill. To this day, I can’t listen to Elliott Randall’s guitar solo on Reelin’ in the Years without recalling the smell of fresh paint and the image of my chubby toddler legs banging away against sheet metal in time with the music. The triggers, purely sensory, never fail to make me smile.

No matter what your earliest recollection is, chances are it’s not a memory that’s tied to language. We’re wired to recognize movement and sound before we ever start to process language: anyone who watches an infant’s interactions with the world can see they are guided largely by embedded behaviors and sensory inputs, which in turn become part of an individual’s embodied cognition.

Embodied cognition is the idea that the body influences the mind. It’s essentially the belief that our ability to gain knowledge, comprehend concepts, remember, judge, and problem solve are not confined to the brain. In this framework, cognition then is influenced, if not determined, by our experiences with the physical world. This is why we say something is “beyond us” when we want to express something we don’t understand: we connect the physical nature of distance with the mental feeling of uncertainty to illustrate our point.

The ways in which we express ourselves via language provide insights about the associations we make between physical sensations and mental experience—for example, when we’ve had a “heavy day,” or when someone is being “soft” while delivering punishment to someone for whom they care deeply. In this light, metaphors are not simply a matter of language: they are literally the vehicle by which we understand the physical world around us.
The study of embodied cognition through metaphors is well documented. These works have led to the formation of new branches of psychology, sociology, and neuroscience and have helped to further everything from artificial intelligence, to robotics, to computer science.

But how can these concepts help further content work?

Exploiting embodied knowledge

As children, we repeatedly link bodily experiences with abstract concepts. This leads to embodied knowledge, because our motor functions develop first, resulting in implicit understanding, in bodily terms, of something that would normally be difficult to comprehend.

Here’s an example. My 10-month-old son spends a great deal of time crawling toward the things he wants (a squeaking giraffe, my iPhone or food). His early experiences of reaching a goal through movement is paving the way for his later understanding of how to reach a more abstract goal. He’ll come to understand that “destinations” may be achieved through the metaphorical movement along a path. One day he may complete his first brain surgery and recall the process and schooling, thinking “look how far I’ve come.”

In essence, we come to understand abstractions through metaphorical extensions of something physical. When we do so, we create systems of understanding that are grounded in physical experiences.

This is where it gets interesting for us—the people who develop content and systems. If we understand how the process of embodiment occurs, we can begin to adapt the systems, content, and digital (or physical) environments we create to either support the construction of meaning or tap into embodied knowledge. One of the biggest challenges in understanding user behavior is that so much of their decision making happens before they even stop to think about the choice. Research in embodied cognition is encouraging in that it offers us a look behind the curtain at what began in the limbic system and grew into drivers that influence judgment, decision making, and human emotion.

Think of the advantages we could have in designing learning programs, educational software, or even games for children with the power of embodied cognition and metaphor at the core of development.

An example: if we were creating a section of our webpage that was instructing someone on how to change the oil on a motorcycle, one might think to take pictures of the different parts and describe the steps. Alternatively, we might create a video that demonstrates the process and lists the steps in text.

If we truly want to be effective, we’d consider the underlying mechanisms that occur in changing a motorcycle’s oil, to support the interplay of action, cognition, and the environment to enforce a commitment to embodiment. To do this, we might create an interactive diagram that allows users to physically use the mouse or their hands to interact with the drain plug, oil filter, and other key elements. As we physically manipulate the parts, we are manipulating our mental models of the motorcycle in the world.

This is precisely why motion-based gaming consoles have become popular, for children and adults. Traditional console games are more difficult for those who didn’t grow up with video games because they don’t require us to use our entire bodies as a physical control. Platforms like the Nintendo Wii became popular because they force us to use our embodied cognition to perform tasks. In doing so, the learning curve for these games is much smaller than that of controller-based titles. In fact, motion-based gaming is so natural that it’s used to rehabilitate major injuries, help treat Parkinson’s disease, and train surgeons.

When we realize that ideas are, at least in part, shaped by the body and its interaction with the world, we’ve found the sweet spot for content work. We also open worlds of opportunity for creating work that can resonate, educate, and engage with audiences that we may never otherwise reach.

The importance of the physical world

Just as important as the body’s connection to metaphor is its connection to the physical world.

If we know something as simple as location, we understand the local weather, whether it’s an urban or rural setting, and what time of day it is. Why do these things matter?

Consider just one variable: temperature. If a user were to access our content while working in a country experiencing a sub-zero freeze, we can use a content management system and multi-variant testing to adapt our tone and imagery to give him a feeling of warmth. We know from studies of embodied cognition that if we can evoke a sense of physical warmth within our users, they will, in turn, think warmly of us, our product, or service.

Marketers have been doing this for years. Campbell’s Soup has spent years and millions of dollars trying to divine the perfect amount of steam to display on its labels. They’ve been able to sell more soup by simply removing a spoon and adding more steam to the pictures they’ve chosen for their in-store displays.[1]

The increased amount of steam reinforces the embodied knowledge tied to our metaphors for warmth. (It also triggers several actions in the brain, but that is for another column.) Several members of a shopper panel exposed to the steamier images reportedly put more soup in their basket than they intended to buy.

These findings are both wonderful and scary: embodied cognition implies that if we’re truly interested in changing someone’s behaviors or capacity to learn, we don’t need users to consciously change their attitudes. We need only make a change to their environment, via music, imagery, color variation, or even copy tone. This means that the work we do, the things we make, and all the choices that lead to a system’s creation are far from neutral. As we pioneer new techniques in publishing, those who understand the ways in which cognition is rooted in embodied actions can achieve greater understanding from their audiences. With that power comes great responsibility.

References

1. The Emotional Quotient of Soup Shopping Wall Street Journal 17 Feb 2010

The details of his essay came to mind when I heard the news of Havel’s death last December. In a year largely defined by acts of courage and power from the powerless, it carried added poignancy. In the year’s wave of popular uprisings, we saw an even more potent mode of public defiance: rather than removing official messages from public view to register their discontent, dissenters published their own competing views and forms of evidence.

In the unruly public arena of social media, once-marginalized activists can subject those used to exercising tight image controls to new levels of scrutiny. For those living in countries where official violence, coercion, deception, and intimidation are common, social media tools have provided an outlet for dissenting voices. The people of Tunisia and Egypt in particular used them to escape state censorship, to call domestic and international attention to injustices, to enable large-scale anonymous communications, and to offer individuals with opposing political viewpoints a neutral space in which to work together.

Throughout these events, many groups and individuals used social media tools to publish material specifically intended to provoke strong reactions and deliver conclusive evidence. For those of us who create and moderate public platforms, the past year has provided both a stern test of usage policies and powerful examples of the kind of ethical, cultural, and political quandaries we can expect to arise in this new landscape.

Separating truth from fiction

Authoritarian regimes have long recognized the importance of the media in projecting a sense of calm and order through which political spin or selective ignorance can be applied to help extinguish threats to this perception. But, like all sources of information, state-controlled media lives and dies by its credibility. Through the internet and satellite television, alternative and independent sources have been able to contradict official reports to such an extent that the level of influence some regimes retain has begun to erode.

Consider the immediate aftermath of the incident that appeared to touch off last year’s protests across the Arab world. In December 2010, a 26-year-old Tunisian fruit vendor called Mohamed Bouazizi set himself on fire after experiencing repeated humiliation at the hands of local police and government officials. His act of self-immolation led to large public demonstrations in and around the central town of Sidi Bouzid, but the Tunisian government’s response was to attempt to contain the story through traditional media channels, strict controls on internet access, and harsh penalties for propagating anti-government sentiments.

From Facebook to Al Jazeera

In the absence of social networks, it’s likely that the government’s strategy would have prevented wider public attention to the origin and severity of these localized protests^[1]. It was partly through Facebook, which had evaded censorship by virtue of its popularity^[1] and perceived innocence^[2], that news of the protests and the story of Mohamed Bouazizi began to spread.

Pro-democracy activists, who had long suffered imprisonment and torture for speaking out against government abuses, won a further victory over the censors when pan-Arab media network Al Jazeera broadcast footage of violent clashes with police, which had been captured by protesters and circulated via Facebook. In Al Jazeera, the new movement found a trusted source accessible to the Tunisian population and outside the control of their government^[3][4]. Now an even greater share of the public were able to make up their own minds on what they saw in this unedited, untempered footage; they weren’t being told how to interpret it.

What followed, as news of Bouazizi’s death filtered through, was a collective release of pent-up energy as huge numbers of people took to the streets and public squares in a display of unity and defiance^[5]. After weeks of mounting pressure interspersed with high-ranking government sackings and announcements of price cuts and fresh elections^[7] Tunisian president Ben Ali fled the country.

 For the Tunisian people, online content—in the form of words and images circulated by individuals using social media platforms—was more credible than their submissive and mendacious domestic media^[8]. Social networks offered Tunisians a chance to democratize the news cycle; to decide for themselves which stories and events were important and relevant.

The responsibilities of citizen journalism

Unsurprisingly, post-revolutionary Tunisia continues to mistrust traditional media filters^[1]. Instead, many Tunisians rely on citizen journalism to uphold the principles of free speech, while working to build a free and fair press. But with this trust in citizen journalists comes responsibility. When thoughts of impact and timeliness take precedence over journalistic practices such as fact-checking and source protection, we all risk becoming unwitting sources of distorted information.

Self-broadcasting services like Twitter lend themselves to reporting unfolding events, but these uses require us to consider the implications of our subjective contributions and interpretations. What’s the difference between impassioned and destructive forms of protest, between shots fired in anger and celebration, or between a legitimate exposure and a cynical attack? Our contributions are influenced by our motives, persuasions, and feelings, and our assumptions are shaped by our past experiences, cultural knowledge, and biases. These factors add to the colorful mosaic of social-media reporting, but we also need to consider how others may interpret it.

Balancing impact with responsibility

The events that led to neighboring Egypt’s own revolution shared with Tunisia a tragic coincidence: Egypt too saw a surge of public anger following the death of a young businessman. But here the similarities end. While the cause of Mohamed Bouazizi’s demise was clear, there were deep-rooted suspicions of foul play following the 2010 death of Khaled Said. Two state autopsies concluded that Said had asphyxiated after swallowing a packet of illegal drugs, but witnesses claimed he’d been beaten to death after being abducted from an internet café by two plainclothes policemen^[9]. It later emerged that Said had been targeted after posting a video online showing two policemen dividing up the spoils of a drug haul^[10].

Within days, a Facebook group dedicated to Said’s memory was anonymously created. The page, “We are all Khaled Said,” published mobile-phone images taken from the morgue showing the full extent of his injuries. This material, coupled with video evidence uploaded to YouTube, contradicted initial police and autopsy reports^[11].

Such was the public pressure created by the weight of evidence available online for hundreds of thousands to see—and the series of organized street protests that followed in Said’s hometown of Alexandria and ten other cities including Egypt’s capital, Cairo—that the authorities yielded and arrested the two officers^[11].

Moderation: how much is too much?

The publication of graphic evidence of Khaled Said’s manner of death—and the effects of its publication—raises several points about ethical journalistic practice. Traditional news agencies have long accepted their responsibility to consider the legal, ethical, and political implications of material they publish or screen^[12]. TV news anchors forewarn us of potentially unsettling scenes, and when documents containing sensitive information are handed to a newspaper, they must be subjected to thorough editing and clearance processes before they can be released, to protect the identities of those mentioned^[13].

Journalists also follow organizational guidelines before reproducing material uploaded to public social media accounts or personal website. The BBC’s Editorial Guidelines and Reuters’ Handbook of Journalism highlight some of the privacy, copyright, and safety implications of re-using material from social media. BBC journalists, for example, should consider the “impact our re-use of a picture to a much wider audience may have on those in the picture, their family or firends (sic)—particularly when they are grieving or distressed,” and “whether the individuals in the picture are likely to have consented—either explicitly or tacitly—to its publication and public accessibility.” Reuters expects their staff to “seek to find and seek permission from the originator of the material, as we would do for any third-party material accessed in any other way” and to consider the “safety of our journalists on the ground and the risk of reprisals against them, especially (but not only) if the material were to prove bogus.” However a “strong public interest reason” or the “‘news value’ of an item” might offer sufficient justification for a BBC or Reuters journalist respectively to re-use material without prior permission.

In comparison, individuals face far less scrutiny when they publish potentially controversial content to the internet. Whether they do depends mainly on their judgment—or, if it has already been published, that of a moderator who can vet or remove it. But even if such material is swiftly removed for contravening a host’s policies, its controversial nature may ensure that it continues to burn brightly in hundreds or thousands of cached or deliberately saved copies.

Publishing platforms, of course, try to reduce the amount of time certain kinds of content remains live by posting clear guidelines on what constitutes appropriate and acceptable use, establishing a culture of self-moderation, allowing community members to notify moderators of potential violations, and setting up automated alerts for new content that receives a sudden surge of viewers. But are there exceptions to the rules?

In the case of the Egyptian uprisings, visitors to the “We are all Khaled Said” Facebook group saw potentially distressing images and footage, but it was only through the publication of that evidence that the official cause of death was comprehensively rubbished. Simply put: without this material being brought to public attention, the arrests might never have happened.

Despite the arrests, the group’s administrator—later revealed to be Google executive Wael Ghonim—pressed ahead with further details about other cases of police abuse and torture and, having been encouraged by the events in Tunisia, worked with other activist groups to publicize planned demonstrations that ended Egyptian president Hosni Mubarak’s 30-year rule. Who’s to say how much of this momentum could have been lost by earlier intervention by well-meaning platform moderators?

Preserving the rights of users

Prior to his ousting, Hosni Mubarak’s method of suppressing traditional opposition was archetypal of many autocratic regimes: disrupt or remove mass communication channels. This policy was ruthlessly applied to television and print media^[8], while the internet got off lightly by comparison because of its perceived lack of a leadership structure^[2].

In reality it was precisely this decentralized network that helped bring together the more secular leftists and conservative Islamists^[14]. Bound by their shared hunger for democracy, these young activists joined forces within the neutral spaces offered by social media platforms and seized momentum from other comparatively disconnected parties^[11] led by elders more than twice their age^[15].

In a bid to counter the threat of a new, technically skillful generation of opposition, Egypt shut down their entire internet and cellphone network for five days^[16]. This blunt and ultimately futile act served only to make an already perilous position worse, as many Egyptians openly lamented their true access to, and participation in, the wider world^[16]. Households previously content with following events online were suddenly devoid of information, and took to the streets to find out what was going on. Needless to say, Mubarak’s presidency didn’t last long after.

Government intervention

The Egyptian internet shutdown shined a spotlight on the measures autocratic governments take to control access to information, domestically and beyond their borders.

Of course, it would be naïve to suggest that governmental suspicion of social networking tools is limited to authoritarian states. At the height of the riots in England last August, British Prime Minister David Cameron argued that Facebook, Twitter, and Research in Motion (the maker of BlackBerry smartphones) should take more responsibility for content posted on their networks. He later added that the government would look to ban people from major social networks if they were suspected of inciting organized violence. In response, a spokesperson from Facebook told the Guardian that it had already actively removed several “credible threats of violence” related to the riots. But given that much of the planning for these disturbances took place within the private, encrypted BlackBerry Messenger service, how far might such measures impose on a citizen’s right to secure communications^[17]?

By way of comparison, broadcasters regularly resist governmental and police pressure to hand over unused footage of public disturbances, even though it may contain evidence that could lead to positive identifications, arrests, and convictions. In their roles as independent observers, they argue that police access to private content risks reducing the press to police evidence gatherers^[18] and threatens the safety of and trust in their journalists^[19]. For the British government to have pressed further with their plans, perhaps by passing emergency legislation granting the police powers to trawl through millions of privately-held messages or suspend a potential rioter’s account, would represent a huge shift in policy. Not only might it cause the government to stray into the territory marked “free speech,” it might put them in a difficult position when trying to convince authoritarian regimes to ease access restrictions on their own networks.

Making a difference

Last year proved that when enough people determined to improve conditions in their country have access to safe, stable, robust communication platforms, free from politically-motivated censorship, remarkable things can happen.

And though not all of us enjoy unbridled access to the internet, we’re edging closer. It’s no longer as easy for organizations or governments to control the flow of information. Nor can unscrupulous media sectors rely on false premises becoming received wisdom, or decide for us who or what is worthy of our attention—eyewitnesses wielding mobile phones have largely put paid to that.

Furthermore, the internet is nothing if not resourceful. For instance, despite the limited cellphone service during Egypt’s communications blackout, a speak-to-tweet service that combined elements of Google, Twitter, and SayNow, a voice messaging social media platform, allowed Egyptians to continue telling the story that led to their revolution^[20].

Of course, even a revolution is just part of the longer process of change. In the power vacuum that follows the collapse of a stifling regime, the unity that bonds a nation in crisis can be lost in the subsequent scramble for power^[21]. Once more, the internet offers an opportunity to remove divisions and encourage unified action. Efforts are underway to build new technologies to help implement more democratic systems in Tunisia and Egypt—even including a crowdsourcing platform to negotiate new constitutions^[22].

Even at their most rough, angular, and controversial, online communication technology, tools, and platforms have shown the potential to shift the balance of power to a nation’s people. And we, the people who will shape the intelligent content and communication platforms of tomorrow, can play an important role in safeguarding this power. There are few social, educational, and environmental challenges that can’t be solved when motivated, passionate minds gain access to resources and data; when valuable information can be quickly created, combined, and shared; when collaboration and debate continues long after publishing; when we’re prepared to challenge our beliefs and policies; and when responsible governance and reporting means acting in the interests of the many and not the few. The internet’s role in this year of political upheaval serves as a reminder that our capacity to create, invent, and improvise is just as important as our capacity to observe, analyze, and control.

References

1. Peter Beaumont “The truth about Twitter, Facebook and the uprisings in the Arab world” The Guardian 25 Feb 2011 ↩
2. Mishal Husain “How Facebook Changed the World: The Arab Spring” — Episode 1 BBC Television ↩
3. Nadia Marzouki “From People to Citizens in Tunisia” Middle East Report Summer 2011 ↩
4. Robert F. Worth and David D. Kirkpatrick “Seizing a Moment, Al Jazeera Galvanizes Arab Frustration” The New York Times 27 Jan 2011 ↩
5. “US summons Tunisia ambassador over handling of protests” BBC News 7 Jan 2011 ↩
6. Thomas L. Friedman “China, Twitter and 20-Year-Olds vs. the Pyramids” The New York Times Feb 5 2011 ↩
7. Peter Walker and agencies “Tunisian president declares state of emergency and sacks government” The Guardian 14 Jan 2011 ↩
8. Christopher Walker and Robert W. Orttung “Lies and Videotape” The New York Times 22 Apr 2011 ↩
9. Jack Shenker “Mohamed ElBaradei joins Egyptian sit-in over police death case” The Guardian 25 Jun 2011 ↩
10. “Egyptian policemen charged over Khaled Said death” BBC News 7 Jul 2010 ↩
11. Jennifer Preston “Movement Began with Outrage and a Facebook Page That Gave It an Outlet” The New York Times 5 Feb 2011 ↩
12. Bill Kovach and Tom Rosenstiel “The Elements of Journalism: What Newspeople Should Know and the Public Should Expect” ISBN-10: 0307346706 ↩
13. Chris Elliott “Opendoor” The Guardian 9 Aug 2010 ↩
14. Michael Slackman “Bullets Stall Youthful Push for Arab Spring” The New York Times 17 March 2011 ↩
15. David D. Kirkpatrick and Mona El-Naggar “Protest’s Old Guard Falls Behind the Young” The New York Times 31 Jan 2011 ↩
16. James Glanz and John Markoff “Egypt Leaders Found ‘Off’ Switch for Internet” The New York Times 15 Feb 2011 ↩
17. Josh Halliday “David Cameron considers banning suspected rioters from social media” The Guardian 11 Aug 2011 ↩
18. Josh Halliday “News channels granted legal challenge to police call for Dale Farm footage” The Guardian 16 Mar 2012 ↩
19. Josh Halliday “ITN to fight police pressure to hand over public unrest footage” The Guardian 6 Mar 2012 ↩
20. Christine Hauser “New Services Lets Voices from Egypt Be Heard” The New York Times 1 Feb 2011 ↩
21. Nicholas D. Kristof “Democracy Is Messy” The New York Times 30 March 2011 ↩
22. Neal Ungerleider “Hackers for Egypt Advocate for a Better Democracy Through Technology” Fast Company May 26 2011 ↩

Book reviewing, after all, requires a special sort of skill. You need to identify what the book is about; place it in a context; and assess the author’s intentions and how successful the author is in achieving them. A reviewer reads a book closely to understand the plotting and characters and style, but a reviewer also reads beyond the book, identifying the conversations it’s in with other books, recent (or long past) history, and cultural trends. The book reviewer takes these two readings and crafts a story about them that will be meaningful to a listening (or reading) public.

Another way to look at book reviewing is that a book reviewer is a content auditor. The book reviewer’s job is not simply to offer a thumbs up or a thumbs down, but to examine the book’s qualities and draw out evidence that communicates why it is or isn’t worth reading, what it does or doesn’t offer, whether the content does or does not deliver on what the title and back cover copy promises.

What is a content audit?

As a content strategist, I help my clients develop plans for how they create, manage, and govern content. This planning needs to start somewhere—and it almost always starts with a content audit.

A content audit can do many different things. It can look closely at navigation, or voice/tone, or microcopy used with the calls to action. It can look more broadly at content across a single content type (like product copy), or the organization of content, or the depth and quality of content. Or it can do all these things. Its purpose is to tease out what is and isn’t working and inform the thinking behind what the plan for creating, managing, and governing content ought to be. An audit is primarily a strategic document; it helps answers the question, “is the right stuff there?” against a set of criteria like user goals, brand position, and communication objectives.

Comparing what book reviewing teaches to content auditing

Book reviewers analyze the stuff that’s there against the stuff that could be there and the stuff that ought to be there. The reviewer analyzes a book against the author’s intention and ambition, and the world of possible options, namely, what other writers have achieved with similar materials. In the process, the book reviewer considers critical questions about the book: does the author deliver on the promise of the book’s premise? Do the chapters build on one another? How clear is the style? How well supported is the argument? How compelling is the story or narrative?

To a content strategist, this will sound quite similar to what goes into performing a content audit. The content strategist examines a website’s objectives against how well it achieves them, how appropriately it communicates a message to its audience, the logic of its architecture, and the story it tells.

The book reviewer has a second, equally important task on which the success of the analysis depends: communicating her findings. She can do that playfully, seriously, enthusiastically—tone is at the reviewer’s discretion—as long as she does it with clarity. Those listening to, or reading, the review, need to understand the rationale, and come away with the information that tells them whether the book is relevant to them or not (or whether it’s worth finishing the review).

Similarly, the content audit is always for an audience. Above all, it needs to be clear, although it ought to be well-supported, logical, and actionable, too.

There is one major difference between the content audit and the book review, however; reviewers deliver their message directly to citizens—to potential readers. The content audit is delivered to a client. As such, the content auditor has an opportunity that a book reviewer can only dream of: direct, meaningful input into creating a more useful, more relevant, and more meaningful experience—a better product.

Content auditing lessons from book reviewing

As content strategists, then, we can apply the practices of a book reviewer to our audits. By taking lessons from the book reviewer—or any reviewer, really—we can better refine and target our audits.

1. Identify “what it’s about”

At the core of the book review, the reviewer articulates what the book is about: the idea that motivates it. Similarly, an interactive experience is always about something. It’s not simply about getting people to buy a thing, participate in a thing, or understand a thing; it’s the brand or organization’s unique approach to that thing. For example, a content audit that finds a brand or organization whose stated vision is “inspiring the world” talking mostly about its internal processes will have usefully identified a discrepancy between what it wants to be and what it is.

2. Understand the context

All books live in a wider context. So do all interactive experiences. The context for an interactive experience doesn’t come from the site itself; it comes from what others are doing in the category and out of category, as well as all the information about the organization, its audience, and its objectives. Audits are best begun by collecting information through stakeholder interviews; downloading all available strategic marketing documents, user research, and analytics; reviewing what competitors and best-in-class experiences consist of; and, often, by talking to the target audience.

The context, after all, is not the thing itself; it’s the thing in relationship to everything going on around it—and all the circumstances that inform it. That’s why the most useful audit will consider the business, competitors, audience behaviors, and trends across the internet—what’s outside the “text” as well as what’s in it.

3. Identify intentions

Authors have an intention; they’re telling a story or proving a point in a way that’s specific to that book. An interactive experience ought to have an intention, too. As a content auditor, you’re working to figure out what it is about—or what it wants to be about, as suggested by user research, strategy, and analytics—and its context. Once you’ve done that, the job of combing through the experience and figuring out how well it measures up to its ambitions begins.

As content strategists, we’re looking for how the brand or organization wants to manifest itself—its vision for itself. We’re looking at what people are coming to the site for—the information they need and the experience they’re seeking. We’re doing a close reading of subheads, imagery, text blocks, and microcopy to see how they match the brand or organization’s vision, its users needs, and its objectives. We’re looking at how content lives across the brand or organization’s content properties—on Facebook, Twitter, YouTube, in brochures and other print materials—to see whether its values and personality are appropriately expressed, because the risks of a mismatch between what its content says, and how its audiences interpret it, can be significant.

Example: A brand about affordable shopping throws up a Pinterest board about “Gifts for Her” where a red high-heeled shoe sits next to a bakeware set. The idea of the board is not necessarily a bad idea in and of itself—obviously, “she” needs “gifts”—but this particular configuration of objects risks communicating a secondary, sexist message; in other words, this board is conflicted on what it’s about. Our job is to see that conflict and help resolve it.

The value of an audit

An audit is strategic—and subjective. It’s a point-of-view that’s informed by expertise, research, and, wherever possible, metrics. The expertise is part of the craft of content strategy. We ground that expertise not simply in a close reading of content, but in how we apply our understanding of user needs and business objectives to our reading of content.

A book review and a content audit ultimately serve a similar purpose: they make people’s experience a little better, expanding their vision of the possible. They do this by telling a story about something that exists today. Where the book reviewer’s job is to entertain and connect an object’s value with those who will most appreciate it, helping them decide whether to make a commitment to a book later on, the content auditor’s job is to craft a story that offers an actionable vision between where an object is now, and where it could be, if it were to more fully meet its audience’s desired experience.

Of course, there are plenty of steps from the auditing to the creation. But every user experience has to start somewhere.

This doesn’t just affect how you write and which types of logos, ethos, and pathos you use. Perhaps more importantly, it defines what you write. When you learn what your audience needs to know, it simplifies the problem of what content to create and when to create it.

Most approaches to understanding digital audiences center upon learning about visitors to your site through web analytics, site search analytics, user research, or user surveys. These ways of learning your audience are of course important. But they limit your understanding of the audience to people who have found your site. What about all those people who haven’t found your site yet but who are in your target audience?

Outside-in content strategy

In marketing, we call these people prospects. If you market just to the people who already know about your site, you are marketing to your existing customers, rather than prospects. The only way to grow your business is to market to prospects and entice them to become customers.

To address this need in my work at IBM, I’ve developed a methodology called “outside-in content strategy,” in which you analyze what information your audience needs by looking at the words and phrases they query in search engines, and then you build content to suit their needs.

How much do we know about our audience based on their keyword usage? Not enough to create a content plan without prep work. You still have to build fictional personae—based on known traits or audience patterns—and ask yourself what those people are searching for before you start doing the keyword research. For example, we use personae for IT managers and Chief Whatever Officers (CXOs) a lot at ibm.com. Once you have detailed personae, you’d be surprised at how much you can learn about their information needs by doing deep keyword research.

When you understand audience information needs, you can add a keyword column to your content audit, uncovering the gaps and overlaps in your existing content footprint by keyword. Then you can build an editorial calendar around what your audience is looking for through search. This helps us with the whole content lifecycle—creation, curation, updating, and retirement—though for now, I’ll just focus on creation.

Step-by-step

At IBM we prioritize content creation by looking for the top audience pain points for which we do not have content. We identify those pain points through keyword research. We don’t just analyze audience keyword usage for the topics they’re looking for, but also for the tasks they want to complete. Audiences are getting increasingly sophisticated in their search query grammar. We mine this grammar to learn what their top tasks are when they land on our pages from search. That helps us create user experiences they will be inclined to engage in.

Okay, so that last bit might seem like a whirlwind of activities with which you’re not entirely comfortable. Allow me to break it down into a step-by-step process.

1. Build your personae

Ask yourself who you want to attract to your site. What would an ideal prospect look like, act like, and think like? What are their needs? How does your organization attempt to satisfy these needs? These are tough questions that might force you to think about the nature of your job and its role in your organization. Chances are, if you haven’t already done this thinking, you are missing the mark with your content.

The goal is to give life to the people you’re trying to communicate with so that you can better serve them. Give your personae names, interests, and pain points. Go beyond mere job roles to get inside their heads. You probably need to read what they read to learn their particular subset of the English language.

2. Develop a cloud of seed words

If you know your target audience well enough to construct fictional personae, you will know a rough landscape of keywords they search on. Start with nouns or topics. Pick 10 of the most important nouns for your personae. Ignore brand names; focus on big-picture concepts. For example, if the topic is smarter water management, keywords might include waste-water treatment, water quality monitoring, water conservation, etc.

3. Perform keyword research on these nouns

Keyword research is a long recursive process. I primarily use five free tools to do it, but there are expensive packages that combine insights from the five tools I use into convenient analysis. (For a good list, see Chapter 4 of Audience Relevance and Search: Targeting Web Audiences with Relevant Content.) The tools are as follows:

A. Google Adwords:

This basic tool gives you local and global demand for your words and tens of related words. I recommend putting your seed words in one at a time, outputting the results to Excel, and removing duplicates. If nothing else, this tool will tell you what not to use, and it should give you a good cloud of words to look for.

One often missed step here is removing false positives. To do this, you have to click the words in AdWords, which will take you to their search engine results pages (SERPs). You will know at a glance if the SERP is relevant to your target audience or not. If it isn’t, delete the word from your spreadsheet.

B. Google Insights:

This tool will show trending data on two or three strings of words that AdWords identified as having high demand. This helps you see longer term how the words are doing and what media events have affected demand.

C. Google Autocomplete:

At some point, you have to start typing the words into Google and seeing what related phrases come up in the drop down. Many of these will not be in AdWords for some reason. It’s important to see these phrases because they represent the grammar of actual queries. Your end goal is to learn the query grammar of your target audience and deduce content plans from it.

D. Google Search:

When you type these queries into Google, click the results for all the sites that rank on the first page for that word and take notes about the content you find on their sites. Obviously, you don’t want to copy them, but there is no substitute for this kind of competitive analysis. Note pages for high demand words that are relevant to your target audience but appear to have low competition. These are your top opportunities to serve your audience’s needs in ways that are not currently served.

E. Wikipedia article statistics:

Perform keyword searches in Wikipedia with your words and click on the article statistics links for each of your words. Here you will see trends and gain all kinds of semantic information about the relationship between your words and synonyms, antonyms, and other linguistic weirdness. Note this weirdness in your Excel spreadsheet. It will come in handy later. In particular, look for verbs and articles in the query logs. These can tell you not only the topics your audience is interested in, but the tasks they need to accomplish on your site.

This process is recursive because it involves a lot of checking and rechecking with the same tools, as you gradually reduce the number of common queries you want to focus on to a manageable set—the most relevant to your target audience.

4. Audit your content

There are several great books on how to audit content, so I won’t belabor the point—two favorites are Content Strategy for the Web, (2nd edition) and The Elements of Content Strategy—but for large organizations such as IBM, auditing is a bigger challenge than any of the books I’ve read make it out to be. It’s easy enough to search for content on your site related to the keywords you have identified for your target audience and catalog it according to some simple criteria. I mean, it’s not easy to do—it’s tedious and time consuming. But it’s not complicated. What is complicated is to create an audience-centric content audit.

If you start with your personae and filter the content in your audit for audience needs and pain points, you might find that your audit missed the mark. You might find the content that surfaces on a simple search is not for your target audience. It might be nominally relevant to them, but it doesn’t serve their core information needs. When you start to discover these insights, you know you are on the right track. Log these gaps for later analysis.

For example, say your site has 20 digital assets (pages, PDFs, videos, podcasts, etc.) related to the word “cloud.” Perhaps you already have 10 product pages, with four apparent duplicates, six support pages with two apparent duplicates and some how-to technical documentation about your company’s cloud offerings. What you lack is information related to common queries, such as “what is cloud?” or “define cloud.” When you look at your keyword spreadsheet, you’ll see these are very popular queries. Perhaps they type these queries a lot because your target audience seems to be confused about the concept. Perhaps each company that offers cloud solutions defines it differently. For whatever reason, it’s clear that your target audience needs this information and you should note in your audit the relative priority of providing it clearly.

5. Create a content plan

A content plan inputs all the gaps and overlaps in your audience-specific content audit, and uses business rules to output a list of strategic priorities for content. Not all of them will be the highest demand words identified in your keyword research. Perhaps you want to establish a niche that only addresses a portion of the pain points your personae have. If you start with the words they use, you’ll develop content that they can connect with regardless of your business strategy.

Note that the content plan is not limited to web pages or even digital assets. In a perfect world, your keyword research informs all the ways in which you communicate with your target audience. This is especially important for any social content in your plan. This needs to be in synch with site assets so that social can be an additional referring engine for your site.

Case study: IBM Smarter Water Management

IBM transformed its brand starting in 2008 with a concept called Smarter Planet. It started as a pure branding play for IBM—emphasizing its unique position to help large organizations solve the big problems in the world: population growth, food scarcity, clean water supplies, energy, etc. The challenge for content strategists associated with this effort is to help our audience bridge the gap between the language they use to describe their problems and Smarter Planet branding.

As a content strategist and search specialist for the project, I pulled the keyword research for the topic “smarter water” and this is what I found in Google Trends: below is a Google trends snippet of the term “water management” versus “smarter water.” Index of 1 = 200,000 queries per month world wide. Note that smarter water had insignificant demand even after we ran advertising around the term.

As you can see, “smarter water” (the blue graph) had very little search demand. This is no surprise—the only people searching on the term were those who had first seen IBM’s advertising. (I’m sure the editing types reading this will also cringe at the very idea of “smarter water.”)

As the graph shows, “water management” (the red graph) had a long history of good search demand—about 200,000 queries per month at the time I pulled the data (it now has 450,000 queries per month). There were other good candidates to consider, such as “water conservation” (200,000), and “waste water treatment” (650,000). But these were only relevant to a subset of the content we would develop. Smarter Water Management is about all the activities our target audience engages in to provide clean water for their constituents.

Ultimately, we built the experience from the URL up using the term “water management” instead of “smarter water.” We bridged the gap on the site by calling the page “smarter water management,” which made the brand people happy while helping to turn our web property into one of the most successful in ibm.com.

The first success was evident in search: we become the first commercial site on the first page in Google for the term water management (a page dominated by groups like the UN). This increased our organic search traffic for the page by 800 times overnight.

But we didn’t just drive more traffic to the page. Our deep keyword research around the core term water management helped us find and tell good stories about our efforts to help people more intelligently manage water. When we started listening to social media using the words our target audience used (in this case, primarily NGO and governmental operations managers, but also concerned citizens), we learned a lot about how we could help them solve their problems. This made the content rich with case studies, fascinating facts and figures, and ways concerned citizens could get involved to help their communities improve water quality and abundance.

The seeds of our keyword research also grew into fruitful efforts outside of our ibm.com property. In our companion blog, we referenced lots of rich video content, such as the “mad scientist” who placed sensors up and down the Hudson River watershed and hooked them together into a system of data points to analyze as a whole. The system then identified points of impact to river quality that we could help alleviate.

Even our Think Exhibit—a museum celebrating IBM’s centennial year (2011) in Lincoln Center—featured content along a massive wall of interconnected displays demonstrating how much water leaks out of New York City’s water supply every year. Hint: it’s a lot, but we’re helping the city find and stop the leaks.

The right keyword research can inform the entire enterprise of content around a topic of interest to your company and its audiences. Keyword research is not just for search anymore, it’s a valuable tool for the whole of content strategy and beyond.

Postscript: not just site analytics

Some people limit the concept of intelligent content to the information we can gather from our existing users on our site. This includes site search analytics, referral and click-stream data, user surveys and studies, etc. This is all very valuable. But it only measures content effectiveness for your existing audience. If you want to get a sense of what content will be effective for audiences that don’t even know about your site, you have to do external keyword research to learn it.

For example, when we launched Smarter Planet, not many people knew IBM was engaged with NGOs and governments to help build smarter water management systems around the world. Nobody went to ibm.com looking for answers to these problems. So how could we learn to serve their needs better with site analytics if they never came to our site? When we did the external keyword research, using the steps outlined in the step-by-step section, and learned how they talked about their problems, we developed a content strategy for them. Because we built content to answer their questions and help them solve their problems, we gained a new engaged audience.

Our task now is to replicate this method for all the audiences around the world who might need IBM’s help to solve their problems. To help educate the thousands of content creators, editors, and strategists in the company, three of us co-wrote a book, Audience Relevance and Search: Targeting Web Audiences with Relevant Content, to define a method for learning what’s relevant to the prospective audience through keyword research and social media listening.

The method has been particularly popular within IBM for two reasons. The first is that our audience members are low on time and need our content to get to the point as concisely and clearly as possible. The same is true for most people online.[1] The second is that we know site visitors typically use search to find content related to their interests, and ignore everything else. On ibm.com, more than 96% of our primary target audiences frequently or always uses search to find what they’re looking for.[2]

Transforming large organizations takes time. To replicate this model across marketing at IBM could take years. But our efforts are already paying off. Hundreds of IBMers are now coming to my team for advice on how to use the language of clients and prospects to:

• Listen for social signals and better understand prospect needs and pain points
• Name and brand products in ways that better adhere to the way prospects think
• Write messaging guides for all assets that align with the way prospects think
• Write content that is immediately understandable to prospects
• Name anchor text and navigation elements with the words prospects recognize at a glance
• Design pages that are relevant at a glance to what prospects are looking for

All these practices depend on good keyword research. We are building a master keyword database that can help practitioners create more relevant experiences for clients and prospects.

References

1. Nielsen, 2010 ↩
2. TechTarget, 2010 ↩

I spent the next 15 minutes with the iPad (all while my daughter squealed, “Lemme see! Lemme see! Does it have Paaaaaac-Man!?”). I completed a lengthy questionnaire which led to a few recommended products and a couple of videos of outdoorsy models romping through a meadow. It wasn’t a great experience, but it was a valiant attempt. Retail content ain’t as easy as it used to be.

During the last year or so, retailers have slammed headfirst into the future of customer communications. Tried-and-true tricks are not working anymore, and there’s no single, clear path forward. Other industries aren’t far behind. Luckily some retail trends are emerging that most businesses can learn from. And, lucky for us, content is at the epicenter of all of them.

Why should you care about retail?

While there’s no proof, I’d guess the first content strategists were either retailers, clerics, or politicians (high fives to you, Aristotle). All three groups know the fundamental rule of a successful content strategy: the strategy serves the organization, but the content serves the user.

The retail industry has always been good at balancing business and user needs. They pioneered techniques such as market research, market segmentation, and choice modelling—all ways for businesses to learn about their target consumers. Then, they used that knowledge to create an impressive suite of content tools: enticing the consumer with brand campaigns, creating urgency with newspaper ads, increasing “basket size” with persuasive in-store signage and product placement, etc. How many times have you gone to Target for toilet paper and come out $200 poorer? Or, ran to the store to get something that’s 50% off, “while quantities last”?

The internet rattled retail a bit. I distinctly remember sitting in the Chief Marketing Officers office at a major retailer’s headquarters (circa 1997ish) and listening to him say, “People will never actually buy clothes on the internet. They need to feel the fabric. They need to try things on.” But, within a year or two Amazon perfected the trifecta of product descriptions, user reviews, and personalized recommendations—and a new normal was established. Retail again took the lead on user research and online segmentation, and this time they realized a few disconcerting characteristics about online consumers: they can compare prices and products easily; they aren’t fooled by product placement tactics; and they share experiences—good or bad—with the “community.” But the economy was good and online sales rose steadily. So, c’est la vie.

Like a lot of organizations, most retailers kept online and traditional media in different departments. It wasn’t perfect, but it was good enough. It’s the way things had always been, and anyway, in-store customers and online customers act differently, right?

The rise of the omni-channel business

But while retailers were busy churning out their last few seasonal collections, a shift occurred: consumers in all retail settings started acting like online consumers. That’s right folks, people are bringing their online content-craving, community-consulting, deal-demanding, bullshit-detecting behaviors to a store near you.

For years, pundits have been predicting the advent of omni-channel marketing—where customers want to glide between communication channels (such as websites, print materials, in-store interactions, etc.) with the greatest of ease. Obviously, that time is now. And, the business/user balance is seriously out of whack.

How did this happen? It’s easy to point to mobile devices—smart phones and tablets—as the change makers. They’re definitely causing a ruckus. But there’s more at play:

• The recession made consumers more careful about how they spend their money and retailers more desperate—so savvy shoppers now expect a deal every time. (“No discount? No worries, I’ve got a Groupon for your competitor.”)
• An anti-business sentiment (think Occupy Wall Street) made people skeptical of large retail organizations. (“No Wal-Mart in my neighborhood! They kill the mom-and-pop stores!”)
• Social and environmental concerns have made consumers seek products and organizations that reflect their values. (“I know Whole Foods is expensive, but the food is all natural.”)

In short, online culture has become the mainstream culture. Which means the customers want content—lots of it, new kinds of it, across dozens of communications channels, accessible at all times—before they even consider buying a product. Yipes.

What should you do about it?

So how can retailers—and businesses of all kinds—use content to meet the demands of an online-influenced user? Here are a few ways to get started:

1. Feed the need: facilitate the customer’s research

In online culture, people want a lot of information before they make a decision. In the retail world, studies show that 74 percent of people conduct online research before making an in-store purchase. So, make it easy. Be as helpful as possible. If they’re shopping by price, give ’em a competitive comparison (Flo over at progressive.com will be so proud). If they have a gluten allergy, give them an app that lets them know if your muffins pass muster.

You won’t always be cheaper than the competitor or fulfill the customer’s product need, but you will gain the customer’s trust—which will make them more likely to support your business in the future. In Edelman’s 2011 Trust Barometer study, almost two-thirds of people said “transparent and honest business practices” were most important to a company’s reputation.

Of course, creating all of this helpful content is not easy. You’ll have to do a significant amount of research to know what your customers want to know—not to mention collecting all of the information you need to answer their questions. It’s not cheap and it’s time-consuming, but it’s worth it.

Jack Abraham of eBay Local says, “More transparency by retailers will provide more choice for consumers. Shoppers feel more comfortable making a purchase when they feel they have all the information they crave.”

2. Show off your do-gooding with good content

Customer research is not just about price and product specs. There’s mounting evidence that people want to support businesses that share their values. A survey conducted by Do Well Do Good, LLC says 88% of consumers think that companies should try to accomplish their business goals while trying to improve society and the environment.

Consumers put their money where their mouths are, too. They’ll pay extra for products or services that align with their beliefs, such as fair trade coffee or LEED-certified products. And, they’ll boycott businesses they don’t like (just ask Rush Limbaugh’s sponsors). A 2010 study published in Texas A&M-Corpus Christi’s SAM Advanced Management Journal found a “statistically significant positive relationship” between companies that do good and those that do well.

As a result, retailers are going to have to get more comfortable creating content that answers questions about formerly taboo topics, such as: how are products made? Are employees treated ethically? Are green materials used? What’s the profit margin on a product? What politicians does the company support? Are there women or minorities in leadership positions?

Content strategies need to make room for this kind of content—finding a time and place to show it appropriately—whether do-gooding is the whole story (Seventh Generation) or slightly more conspicuous (Starbucks’ “responsibility” tab ).

3. Be totally “flawsome”

All this transparency can be a little uncomfortable. After all, isn’t it common sense that content should always portray the business in the best possible light? Not anymore. Today’s savvy content users don’t want perfection, they want the truth. For example, in retail:

• Of consumers, 68% trust reviews more when they see both good and bad scores, while 30% suspect censorship or faked reviews if there aren’t any negative comments or reviews.
• Shoppers who go out of their way to read bad reviews convert 67% more than the average consumer[1].

The smartypantses at trendwatching.com call this being “flawsome.” They say, “In 2012 consumers won’t expect brands to be flawless; they will even embrace brands that are FLAWSOME…Brands that are honest about their flaws, that show some empathy, generosity, humility, flexibility, maturity, humor, and dare we say it, some character and humanity.”

If you make a mistake, apologize for it. If you haven’t seen Johnson & Johnson’s apology video for discontinuing o.b. tampons, check it out. And check out the reaction it got.

Your company is going to make mistakes. So, put a content workflow in place to make amends with consumers quickly. And, when apologies are necessary, don’t just issue a crummy old press release. Put some muscle into your content. When was the last time a regular ad campaign got you this kind of loyalty?

(NOTE: Since this article was written, the trendwatching.com team has come out with their April 2012 Trend Briefing which touches on many of the concepts in this article and includes dozens of excellent “flawsome” content examples.)

4. Become an omni-channel team

An omni-channel approach requires that the content on all of the channels is coordinated, connected, and consistent. The path of a single purchase might look like this:

• Do research on the web
• Go to store number one to see the product in person, pick up a brochure
• Double-check the price comparison on your phone
• Buy the product from another store’s website (it was cheaper)
• Exchange the product at the store number two’s bricks-and-mortar location (it was damaged)
• Talk about the whole experience on Facebook or Twitter

And, accommodating that channel surfing isn’t easy. Seamless content requires seamless internal teams—everyone who works on content at your organization needs to work together. Yes, that means that the web team and traditional marketing need to merge or at least snuggle up real close. Sayonara, internal channel siloes.

5. Alright already, go mobile

Mobile content is a critical piece of any multi-channel content strategy. It acts as the glue, keeping the web and the store content connected. According to Colliers International’s U.S. Retail Highlights: 2012 Outlook report, mobile commerce increased 90% from 2010 to 2011. And, they expect it to increase by nearly 40% again (up to $10 billion) in 2012. So, yeah, you need to start working that mobile content yesterday.

Now, mobile content doesn’t necessarily mean creating a fancy app. Depending on the content you have, an optimized version of your website will work just as well. For example, nobody wants to download an app just to get your store hours and locations—they can get that from your website.

If you’re going to create an app, make it a unique part of the customer experience, offering content they can’t get anywhere else. Maybe it helps the consumer locate a product in a large store or find out if a fabulous dress is available in the right size. These exclusively mobile conveniences can help your brand become the consumers’ brand of choice.

6. Communicate to your employees, too

Creating a lot of excellent content for your customers is important. But, what about content for the people who serve your customers? With all this information about your products, corporate responsibility initiatives, and flawsome mistakes in the public eye, your employees need to be in the loop.

Make sure your employees, particularly your customer-service personnel, know as much or more about your organization than your customers do. Institute a corporate education program to support your new content initiatives. (Yes, you understood me correctly: create some content about your content. Seriously, it works.)

Start experimenting now

Let’s face it, we’re in uncharted territory. In this new reality where online behaviors reign, there’s still a lot to be learned. But one thing is sure, the time for waiting and watching is over. Customers are expecting you to act, and the risk of doing nothing is worse than the risk of failing. (Remember, customers like to see a flaw here and there.)

Start experimenting. As the old adage says, “Today’s mistakes are tomorrow’s innovations.” Whether or not you’re a retailer, the time to invest in some new content initiatives is now.

P.S. If you happen to be a retailer who targets busy moms—forget about the models traipsing through meadows. Put some Pac-Man on that iPad. I bet in-store sales would skyrocket.

References

1. Reevoo.com, January 2012 ↩

Calling Lapham’s Quarterly a “magazine” describes its form, but not its essence. It’s a collection of excerpts leavened with a few original pieces, all assembled around a central theme, bound in white covers, and delivered to homes and bookstores four times a year. The result falls somewhere between an academic anthology and a quote-book, but more charming and readable than either of those forms. Founded by former Harper’s editor Lewis Lapham in 2007, Lapham’s Quarterly has produced a bookshelf’s length of printed collections and also lives a variegated life online via a website that publishes dozens of excerpts from each issue, a Tumblr, a Twitter account, and a digital facscimile edition. We asked Michelle to tell us a bit about the editorial process of the magazine, and to give us a peek inside her particular section of the LQ brainhive.

In the past five or six years, we’ve seen an explosion in history-based curation and blogging: Letters of Note, Shorpy, Tweets of Old, The Public Domain Review, Paleofuture, The Retronaut, World War II in real time, the NYT’s Disunion blog, all of which package history into new capsules. Perhaps we should add a third act to Karl Marx’s maxim about history: that it repeats itself first as tragedy, then as farce, and then as content.

We—all of us doing this work—come to un-bury content, from bookshelves, from libraries, from course packets, from notebooks, from letters, from tweets, from stacks of old magazines, from new editorials, from Pulitzer-Prize winning biographies, from dime novel, from commercials, from sheets of papyrus, from engravings, from broadsheets, from scribblings in a margin. And we come to un-bury it here: in print, online, everywhere that will have it.

When we do this at Lapham’s Quarterly, we start with an organizing principle, a theme: love, crime, religion, travel, nature, work. Something simple and stubbornly universal. If it didn’t exist in ancient Mesopotamia, it won’t exist in the magazine. If the readings we choose don’t have at their core something unmistakably human, they won’t resonate.

The list

Then we begin to dig. This requires several distinct and necessary kinds of people: academics, editors, specialists, and interns. It’s essential to have people who know which writers are dangerously overexposed (Shakespeare for one, Thoreau perhaps, and David Foster Wallace is nearing a lifetime ban.) and which have yet to see the light of day. Then we start a list.

Here’s just a fraction of the books, magazines, and ideas I looked at for our most recent issue, “Means of Communication”:

• Nancy Mitford, Noblesse Oblige
• 88 Charing Cross Road
• “Jabberwocky,”  by Lewis Carroll
• Sapir-Whorf hypothesis (might be hard to find a compelling passage, but it’s a pivotal text)
• Used Orwell’s “Politics of the English Language” already? (yes, but maybe a different part?)
• The Elements of Style, Strunk & White
• The Professor and the Madman, Simon Winchester
• After Babel by George Steiner
• Transcript from Sophie Scholl and the White Rose about phamplets
• “Talk this Way” The New Yorker (on dialect coaches)
• Pygmalion, George Bernard Shaw
• Cratylus, Plato
• Roland Barthes
• “Who’s on First?” Abbott & Costello
• The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the Internet
• Tweets from Tahrir
• The F-Word

Everyone has a list, and then we meet with our editorial board to make an even bigger list, a massive centuries-spanning list. Then we split it and search it, following footnotes and translations back to a primary source. At last, we find the passages we’ve been looking for, congrats. But is it good enough?

The excerpts

Not all history is a tasty morsel. Walter Benjamin will never parse at 500 words—nor did he ever expect to. Every bit of content we excerpt has to have a beginning, middle, and end. It has to read coherently on its own, without insider knowledge of who slept with whom in ancient Greece.

And then there’s the question of familiarity. Even if you remember reading a selection in the sixth grade, and even if every one you know read it in the sixth grade, it’s simply not fair to assume the reader will know it. These un-buried bits of historical content aren’t here to make us feel stupid, they’re here to remind us of something we never knew.

Order

Once we find the excerpts, we line our selections up in a table of contents and search out chronological holes and imbalances in gender and diversity. Once the list is set from ancient to modern, it gets seriously, and thoughtfully, messed up.

Disorder

And the final step, the one that un-buries history the most, is to put the whole thing online in a piecemeal collage, one that can be exploded, rearranged, reallocated, contrasted, collected, and isolated.

This work begins with a reordering of the table of contents according to subtle themes, subtext, length considerations, date spread, and of course, humor. And it doesn’t end with the publication of the magazine. Disorder is essential in getting this magazine online and into your feeds.

As the online editor, I sometimes feel like my job is to make something beautiful, just to hack it apart for kindling. Here’s the way I (mostly) think about it instead: any link to a fragment of LQ is a breadcrumb that can bring you back to the whole. Every magazine wants to lead you back to the mothership, but when you finally pick up an issue of Lapham’s Quarterly, what you have isn’t the end of your own curation and the beginning of our vision. It’s the start of a new reading in a closed-off sphere that also resembles the web you came from: a rabbit hole of thought that you’ll gladly fall into.

As an industry—or really, as a loose collection of sibling fields—we’re trying a lot of new things, and also producing heaps of commentary analyzing, anatomizing, and second-guessing each new move. The conversations around “the future of _____” (news, books, content, reading, the web) are dense, interconnected, and often inspiring, but much of the real work of making new things still happens a few layers deeper, in private conversations, on small teams, and in closed conference rooms. It’s our goal, in this issue, to ferret out and expose some of the things we each do privately that could be more widely useful, and to introduce some of the systems and people at the center of our ongoing collective project of sensemaking.

It’s an article of faith at Contents that we all need to get smarter, in practical and immediate ways. We need to know more about our readers. We need to better understand the systems that let them find and use the things we publish. We need better ways of interpreting large quantities of information, and of translating machine-friendly data (big and otherwise) into narratives and insights that humans can use.

We also need to make better and more efficient use of the resources available to us, even when they come from outside of the subfields and niches in which we’re most comfortable. This issue centers on many senses and kinds of “intelligence” that radiate from a range of perspectives and specialties in Contentland. We’ll talk about editorial processes and philosophies, about ways to gain a deeper understanding of our readers’ needs and language, about the design of smarter systems, and quite a lot more.

See you in the comments, and on the tweets.

— Erin

“To thrive amid unprecedented amounts of novelty, we must shift from being mere seekers of the new to being connoisseurs of it.”

New: Understanding Our Need for Novelty and Change, Winifred Gallagher

“all mankind is of one author, and is one volume; when one man dies, one chapter is not torn out of the book, but translated into a better language…”

Meditation XVII, John Donne

“Aristotle also regards things—non-linguistic, non-psychological, non-propositional entities—as first principles. We come to know, e.g., that there are four elements, and this proposition that we know is a first principle; but the four elements themselves are also first principles and are prior and better known by nature. Actually existing things are first principles because they explain other things, and our knowledge of the world requires us to know the explanatory relations in it. To have scientific knowledge (epistêmê) about birds is to be able to explain why birds are as they are and behave as they do. The things and processes that explain others are basic and fundamental; when we have found them, we have found the first principles of birds.”

Aristotle’s First Principles, Terence Irwin

The computer systems that increasingly mediate our communication can either facilitate or hinder the flow of information. As professional communicators, we need to understand how well the content in the system keeps the conversation flowing, and how these communications are taking place.

First, a bit of linguistics

Written words are made up of symbols which—in many writing systems—represent sounds. The symbols combine to form words that represent concepts. The words combine to form sentences and paragraphs that describe more complex concepts, including some that are kind of meta, like this one.

As babies we experience this in reverse order: we form basic concepts (hunger, discomfort, desire), then learn that certain sounds correspond to those concepts. We learn to mimic the sounds so we can communicate to others, and then we learn the symbols. Meanwhile, we’re developing more complex concepts, and we study spelling and grammar to help us express those ideas clearly. In a few short years, we develop from burbling infants to speaking toddlers, and from there to learning a complex, nuanced communication system.

Contrast this learning process with the way we program computers: we use a limited language with rigid syntax to designate a set of commands and responses. The magic of human communication is that it isn’t merely a conduit for information. We often want to amuse, inspire, surprise, or persuade. Abstraction and ambiguity—we have many ways of describing a single concept—give us the flexibility to communicate artfully. This enables a rich culture of literature, art, philosophy, and politics. Ambiguity is a critical aspect of that which makes us human.

Interacting with machines

And we also make tools. As our tools have become more complex, we’ve made them mechanical, then automated them, then computerized them. Along the way, they’ve become increasingly “intelligent,” by which we mean they can take in anticipated bits of information and make preprogrammed decisions. A quick tour of science fiction makes clear our mixed longing for and fear of the day when machines are intelligent enough to learn new information and take un-anticipated actions.

Speculative fiction aside, no machines yet parse ambiguity as well as we do. But we’re no longer just communicating through machines, we’re increasingly communicating to machines. Often that means adopting new modes of behavior.

For example, most of us have gotten used to finding everything we can imagine through a simple web search. But let’s say you love text adventure games and you want to find out if the very first one, Adventure, is available for iPhone. You search Google for “original adventure game for iphone” and it returns a whole lot of links that touch on one or more of those ideas, but not what you were actually seeking. So you reword your request in search engine terms, adding more specific words in the hopes that it will make the results more relevant. In our dealings with machines, we make numerous minute adjustments in communication so that they’ll give us the response we need. But we’re always looking for ways to design computerized systems so that they understand us better.

Miscommunication is part of communication

Communication between people is often more complex than “message sent” and “message received.” Take this IM my favorite nerd sent me about an online video:

“Kal Penn is in a webvertisment for Rayman Origins where he is playing a kid in Rayman Origins.”

Our brains fill in missing bits of information and decipher made-up words like “webvertisement,” even if we’ve never seen them before. Here’s how I interpreted that statement:

[The actor] Kal Penn is … playing [the role of] a kid in [some entertainment product called] Rayman Origins.

A reasonable interpretation, though a little strange since Kal Penn is a grown man. But the next message was completely unintelligible in that context:

“The stakes? The kid’s mom. Kal’s going on a date with the kid’s mom.”

If I had realized that Rayman Origins was a video game, I might have understood that this ambiguous description actually meant:

Kal Penn is … playing [against] a kid in [a video game called] Rayman Origins.

As the conversation developed, I was able to get more information, ask questions, confirm the context, and revise my interpretation of the original statement until I was confident that I accurately understood it. This process of disambiguation—so natural in human communication—is just as important in our interactions with computers, but they have to be designed to communicate that way.

Computers haven’t traditionally operated well in the realm of ambiguity and miscommunication. But now we have machines like IBM’s Watson, the computer that competed in Jeopardy! against two of the show’s most successful contestants, and beat them. It was able to answer some pretty complex—and yes, ambiguous—questions in order to win the game. So does that mean the age of intelligent machines is here? Not quite yet.

Ambiguity is the major obstacle to communicating with machines as easily as we communicate with other people. Let’s consider a few examples to understand how and why ambiguity is so challenging for machines—and how we’re working to overcome these individual challenges. After that, we’ll take a closer look at major developments in the attempt to disambiguate our communication so that machines can understand us (and we them) in elegant, gratifying ways.

Lack of specificity

As with the example of the IM conversation, sometimes someone just doesn’t give us enough information to understand what they’re saying. In a conversation, we can ask for more details or for clarification for as long as we need to, limited only by our patience.

In the same situation, a computer system generally does one of two things. It makes a guess with the information it has, as in the Google example; or it may just break down, as when the extension is missing from a file name and your PC can’t open it.

Good usability design will avoid sending people down a dead end, so these days, your computer is more likely to open a dialogue box asking you what program you’d like to use to open an unidentifiable file type—essentially, it is asking for clarification. If a system fails to process ambiguous information, it should fail elegantly and give the user a way to continue forward.

This is something we need to keep in mind as we create interactive sites and products. A witty 404 page, for example, may keep people from feeling frustrated with your brand, but it would be more satisfying if you offered them some possible reasons for the misstep and some corrective suggestions to set them back on the path towards their goal.

Homonyms

Homonyms are sets of words that are spelled the same, but have different meanings, like blackberry, the fruit and Blackberry, the mobile device. If you start a sentence with “She shot him…” it’s going to mean something different if you finish it with “a glance” or with “in the knee.”

Wikipedia has developed a very manual solution to this type of ambiguity: disambiguation pages. On these pages, people can list every possible usage of a word or name, with some context that helps people distinguish between them. There are currently over 200,000 disambiguation pages on Wikipedia. I admit, I’ve created a few myself. The intelligence in these pages is only limited by the contributions of the Wikipedia community, since anyone can add a new page, a new word usage, or clarifying information at any time.

If homonyms are a serious concern in the body of content you’re working with, it’s best to work with a content tagging system that assigns each tag a unique ID. That way, behind the scenes, blackberry-the-fruit is identified to your system as “4865,” Blackberry-the-device is identified as “8943,” and the content they’re each associated with will remain distinct. When a user does a search for “blackberry” on your site, you can give them a raw set of results that match the string of text, but you can also give them the option to filter their results by the particular blackberry they’re looking for.

Variety

Just as some words represent many meanings, we also have many ways of saying the same thing. This is fine for people, but machines have a harder time recognizing when two non-identical sets of symbols represent the same entity.

At one point in my career, I worked on an entertainment website. We set out to normalize the free-form keywords in several years’ worth of articles and replace them with a controlled vocabulary of people, movies, albums, etc. In one extreme example, we discovered that the name of the movie “Star Wars: Episode I—The Phantom Menace” had been expressed in the following ways in the keyword field:

• Episode 1
• Episode I
• Phantom Menace
• Star Wars Episode I The Phantom Menace
• Star Wars Episode I: The Phantom Menace
• Star Wars prequel
• Star Wars: Episode 1 — The Phantom Menace
• Star Wars: Episode i — the Phantom Menace
• Star Wars: Episode I: The Phantom Menace
• Star Wars: Episode I — The Phantom Menace
• Star Wars: Episode I–The Phantom Menance
• Star Wars: Episode One — The Phantom Menace
• Star Wars: The Phantom Menace
• Star Wars: The Phantom Menace — Episode I
• The Phantom Menace
• The Phanton Menace

Some of those are misspellings, but many are perfectly legitimate. And while it might be annoying that the editors used so many variations of the name, a reader could also have used any one of these variations to search for content. And if they had, they would have gotten different results each time. In this case, disambiguation is needed to accurately search across variations in terms and provide a consistent experience.

Many controlled vocabulary systems and search engines allow for the inclusion of synonyms, alternate spellings, and misspellings. Take a look at your search logs to see what terms people are searching for and not finding—and if they match something you do cover on your site, add them into the index as alternate versions of the appropriate term.

Intention

When you give your friend some news and she responds, “You’ve got to be kidding me!” you have a pretty good idea if she’s pleasantly surprised or unpleasantly shocked, based on the tone of her voice. We demand a lot of our words and often use the same phrases to express a whole range of circumstances. In person, we have tone of voice, body language, and context to help us interpret the meaning of the words.

We don’t have all of those cues on the internet. So, when sending an email or instant message, or posting something to a social media site, we sometimes attempt to clarify by using emoticons, extra punctuation (!!!), ALL CAPS, or (in extreme cases) <sarcasm></sarcasm> tags around a potentially ambiguous phrase.

Automated sentiment analysis is a growing industry in our communication landscape, where everyone who has an opinion also has a way to express it online. We have access to a huge amount of data, but we need accurate tools that can interpret the intention of what people are saying. Keep an eye on the convergence of semantic technologies and analytics for more developments in this area.

Unreliable narrators

If you’re of a certain age, you may remember the scene in My So-Called Life when teen protagonist Angela Chase told us in voice-over how her dreamy crush, Jordan Catalano, was “always closing his eyes, like it hurts to look at things.” A quick shot of Jordan leaning on his locker, whipping out the eye drops, gave us a different perspective on the source of his eye discomfort. Angela’s failure to accurately portray the situation tells us more about her than it does about Jordan.

Machines have the potential to be more accurate narrators than humans. After all, they have access to (and reliable recollection of) a lot more data than we do, and with the right framework they can identify connections that we would never have thought of. It’s hard to make the case that you’re sticking to your workout regimen when you check in to your gym and Foursquare says “Welcome back! You haven’t been here in 3 months!”

So why aren’t sites more effectively personalized? We have the ability to know what the users of our site like—sometimes better than they do themselves. The question we should always be asking ourselves is: what information have we gathered that we can use to create a better experience and make our content more useful?

Now let’s take a look at some of the approaches we’ve developing to help disambiguate communication between people and machines.

Where we’re headed

If we want to communicate better with our machines, we have two choices. We can learn to think and express ourselves more like machines do. Or we can design technology systems that think more like us.  One way of doing the latter is to make the content smarter—to pack it with rich metadata so that it contains information that will help automated systems interpret it better. The other way is to make the systems themselves smarter—program them to look for increasingly subtle cues about the meaning of any content it receives. Some interesting development is already taking place in both of these approaches.

Making the content smarter

In order to make the content itself more easily interpretable, there’s a growing need for content creators to add metadata to their content. This is information that isn’t necessarily visible to the public, but adds structure and meaning to the content so that it can be delivered in a variety of flexible, dynamic, and contextually meaningful ways. Metadata is very useful for text-based content, but indispensible for images, video, and other rich media content, much of which is not directly searchable. When we search flickr.com for interesting photos, we rely on the titles, descriptions, and tags that people have provided. Sometimes we may also use system-provided metadata like the photo date or camera type.

As content strategists (or co-conspirators from other disciplines), we need to help our content creators by identifying and implementing the most effective content structures for their needs. There are many useful standards to choose from (last year I gave a talk called “Make Your Content Nimble” which referenced many of them). And new ones are emerging all the time. Then we need to point our content creators towards viable sources for adding metadata to their content—whether that’s in-house resources, hired guns, automation, or crowdsourcing. In-house and hired guns are manual, time consuming, and expensive approaches, and just may not be viable for large volumes of content. So let’s take a look at the other two alternatives.

Automation

Some semantic technology tools take in unstructured content and identify key names and concepts in the text. This process is called “entity extraction,” and it uses some pretty sophisticated natural language processing to determine the meaning and classification of the words it identifies. As a result, it doesn’t just tell you “related terms,” it tells you whether the terms are people, places, businesses, products, dates, concepts, etc.

Some services take it a step further and automatically find other useful resources that can be incorporated into the content. Zemanta, for example. I put a draft of this article into the live demo on their site. It analyzed the text and suggested a bunch of tags (Rayman Origins, Kal Penn, Video game, iPhone, Ubisoft, Xbox 360, Wii, Games). Some of those terms weren’t mentioned in the draft, and while they may not be appropriate in this article, they would probably be appropriate if I were actually writing an article about Rayman Origins. In addition to tags, Zemanta’s demo suggested some inline links to a variety of sites (for named entities like Kal Penn, but also for concepts like “ambiguity” and “miscommunication”), a bunch of usable images that were either public domain or creative commons licensed, and a handful of related articles, with the option to identify my preferred sources.

Zemanta is a tool that’s mainly aimed at bloggers, but there are others designed to be incorporated into major publishing platforms, and even to perform entity extraction on huge archives of existing content. A service called Calais, owned by Reuters, also has a web demo available (viewer.opencalais.com). The metadata this demo generates isn’t as publish-ready, but it provides more insight into how it’s structuring the extracted entities.

If you’re working with an organization with massive amounts of existing content, automation may be the best option to get a baseline of metadata pretty quickly. It may not be cheap, and it may not be as accurate or complete as if it were manually tagged by experts, but it will add a level of contextual information that makes the content much more valuable.

Crowdsourcing

Individual content creators are getting used to tagging their own content, and some larger organizations have employed crowdsourcing to enlist their audience to help out with the work. On flickr.com, for example, depending on how permissions are set, you can tag other people’s photos. The Commons is a huge public archive of images, submitted to flickr.com by libraries and other institutions from all over the world. The public is invited to browse the photos and add tags to them, making them more findable and useful to future visitors to the collection.

Crowdsourcing approaches can also be used in targeted ways to augment automated processes. A company called Metaweb has built Freebase, a database of knowledge similar to Wikipedia but much more structured. It takes sources of information, including Wikipedia, and uses a mix of automated and manual means to transform it into consistently structured data. They developed some “data games” to allow the public to help them fill in the gaps when the automated process isn’t able to extract all of the data it needs. One such game, the “Genderizer,” asks readers to look at short passages about real people, fictional people, or biological organisms and indicate whether the entity mentioned is male, female, or other.  For example:

Soanya Ahmad

Soanya Ahmad (born October 5, 1983) is a photographer and sailor, holding the current women’s world record for the longest time spent non-stop at sea.

While an automated system may not necessarily be able to make the connection between “holding [a] women’s world record” and being female, a person can read that sentence and quickly come to the correct conclusion. This kind of game, though not quite as fun as Angry Birds, can be strangely addictive—one user has racked up nearly 40,000 responses. And it spreads out the work of generating a lot of useful disambiguation data.

Crowdsourcing, while being an inexpensive source of human mind power, has a number of drawbacks that may make it impractical for many projects. Take these into account when considering this approach:

• Quality control. The data provided by users will not be as accurate, complete, or consistent as it would be if created by trained experts. Even if your domain doesn’t require specific expertise, people think differently and will apply different lenses to the content. Alternatively, you can limit the exercise, as in the Freebase example, and you’ll get less variance by offering pre-defined choices than by having freefrom data entry. You can also have multiple people evaluate the same set of data and take only the best or most common responses.
• Speed. Since you won’t have a dedicated set of contributors, it could take a long time for the data you need to trickle in. Most people who actively contribute at first will eventually drop off. This means that if you have a very large body of content, you’ll have to keep getting the word out and recruiting new contributors to the project.
• Incentive. Just because people can contribute doesn’t mean that they will. I’ve been in brainstorming meetings where someone suggested adding user tagging capabilities and I was the only one to ask “Why would people want to do that?” Sometimes there’s an inherent incentive. For example, the writer of a popular webcomic called Diesel Sweeties asked readers to help transcribe each comic so they could be text searchable. His dedicated fans made quick work of the 1500+ comics that were already in the archive. But would casual readers of a major publication have the passion to do the same?

If these factors don’t pose a problem for your project, then crowdsourcing might be an excellent way to get some badly needed metadata for your content.

Making our tools smarter

In a utopian world of human-machine communications, we would continue making content as we always have, and make requests as we naturally would, and computerized systems would just understand us. We may never get to that ideal state, but we’re working to make significant improvements. There have been notable developments in the areas of recommendations, searching, voice recognition, and machine intelligence.

Recommendations

In person-to-person interactions, our friends and acquaintances sometimes suggest things they think we would like. They base these recommendations on things they’ve enjoyed and filter them through their accumulated knowledge of what we like. It’s a guess, and the recommendations are not always correct, but it’s a pretty sophisticated activity that takes into account a lot of nuanced knowledge about our personalities and preferences.

As we conduct more of our purchasing and content consumption activities online, sites and systems have collected a lot of information about our tastes and habits, and many of them attempt to make us into repeat customers by suggesting other things we might want to purchase or consume. For many people, the first time they encountered a recommendation engine was probably on Amazon.com. When you’re looking at a page about a product and it tells you “People who bought this also bought…” that’s a pretty blunt instrument, but sometimes it leads you to discover things that are also in your area of interest.

Netflix famously offered a $1 Million prize to the person or group that could help improve their recommendation algorithm by 10%. The algorithm not only suggests movies that you might like based on previous choices and ratings, but indicates how likely you are to enjoy any given movie.  After nearly three years, two teams combined and won the prize in a photo finish against another combined team. A second round of the contest was cancelled in March 2010 due to legal concerns over privacy. (Some people just don’t want the world to know how much they’re going to like “My Little Pony: Friendship Is Magic.”)

Other sites that aren’t directly selling something, but want to keep you engaged and build page views—many online newspapers, for example—also try to capture your attention by offering other content you might like. But they often simply offer more content of the same type, more content from the same section, or the most popular items on the site. If that’s not good enough for Amazon or Netflix, why should it be good enough for sites like the New York Times or the Huffington Post?

It may be beneficial to incorporate more targeted recommendations into publishing sites, but I haven’t seen anyone do it yet. Traditionally, one obstacle has been that sites like the New York Times didn’t have access to as much data about a person’s consumption habits as sites like Amazon or Netflix. But that’s changing with deeper social integration. Now, if you sign in with Facebook, the Times will have a better idea what articles you like, share, and comment on. So why aren’t they making use of that data to provide their readers a more engaging experience?

Searching

Most people use Google to search for things online. I do too, and have done for a long time. But a few years ago I started noticing that the results had become, well, kind of crappy. A lot of the results I was getting for any given query would be link farms, sketchy rip-offs of original content, or otherwise not-very-useful pages. I thought, “Well, they’ve done it. The rampant proliferation of junk content has overwhelmed Google. And now it’s broken.” But I adjusted my search behavior and kept using it anyway, and then… it got better again. I don’t know exactly when. I don’t even remember noticing that it was better until I was writing this piece and I harkened back to those dark times.

According to a Wired article called “How Google’s Algorithm Rules the Web,” Google’s primary competitive advantage is that their search algorithm is engineered in such a way that they can constantly tweak it. They collect massive amounts of data about what people search for and what they do when they get the results: not only what users click on, but whether they change a word in the query and try again, or scrap it and start over. Collecting data on the results that didn’t work for people—and what they did to remedy their query—points Google towards areas where they could potentially improve the algorithm.

In the two years since that article was written, one can assume that Google has made thousands of minor adjustments to the algorithm, in addition to the major initiatives we’ve heard about. While Google tends to keep their detailed plans pretty close to the vest, here are a few examples of recent activities and the possible impact they could have:

The “content farm killer”

• A new version of the algorithm, dubbed Google Panda, dealt head-on with the crappy content problem I mentioned earlier. This upset the apple cart for a lot of content farms (and perhaps a few innocent bystanders caught in the crossfire), but there was a significant improvement in the quality of search results.

Structured data

• Google bought Freebase, the database of knowledge mentioned earlier. This massive collection of relationship-rich data about people, places and things will most likely be used to improve the accuracy of search results as well as display useful information directly in the results.
• A year later Google, Bing, and Yahoo! announced that they would be supporting a structured data standard called schema.org. This move could inspire many content publishers to add semantic markup to their web pages, making all of that content that much easier for machines to find and interpret.

Vertical search

• Just last fall they purchased Zagat, which will likely be used to feed local review content directly into search results. Ratings, reviews, and other Zagat data could show up instantly when you search for the name of a restaurant.
• At the same time, they launched Google Flight Search, and soon included that data in search results. If you search for “NYC to London” you’ll see actual flight information along with the usual list of links. [At the time of this writing, they’ve gotten some pushback on this feature, so they’re currently only displaying flight schedules, not prices or links to purchase tickets].

Human validation of relevance

• When a 125-page “content quality” rating guide—detailed guidelines Google employs to test and evaluate the search algorithm—was leaked to the Web, SEO specialists immediately tried to mine the document for exploitable tips. But there were no secrets there. In fact, it’s probably more useful to writers, editors, and content strategists who just want to create quality content. Also, it’s interesting to note that, with all the data available to them, Google also manually evaluates the effectiveness of their algorithm, because people bring a nuance to judging relevance that machines just can’t match.

Social search

• As I write this, Google has just announced “Search plus Your World,” their latest foray into Social Search. It allows users to toggle between global results and results from within your social network (your own content as well as that shared by your contacts). This could be useful for certain kinds of queries, since it incorporates the intelligence of people who, ostensibly, share your interests.

Even though Google is making constant improvements in an effort to become all things to all people, for some, it never will be. Around 2008, interest in “semantic search engines” intensified—perhaps in reaction to the Crappy Age of mainstream search engines, when Google and its direct competitors just weren’t performing the way we needed them to.

Semantic search engines vary, but the general premise is that they use semantic technology to provide more accurate results through a better understanding of context and meaning. Some use natural language processing to better understand the intention of the queries. Others unlock relationships in the data to provide more nuanced results. Many have a lot of promise, but also major shortcomings: Often they’re designed to work with a certain subset of data, not the entire web. Some are designed to be used for a particular vertical—for example, legal, medical, or financial research. On top of that, there’s almost no way for them to wrest market share from the major players, though they’re sometimes acquired, as when semantic search engine Powerset was acquired by Microsoft before they relaunched their search as Bing and branded it a “decision engine.”

As content creators, or creators of digital experiences that serve up content, it’s important for us to keep track of these developments in search. SEO is one area of immediate impact, and the community of SEO professionals tends to be the quickest to analyze the implications of any and every movement in the search world. But the rest of us (content strategists, copywriters, developers, and designers) are probably lagging behind. Google has shown that they will continue to lead the way when it comes to connecting people with content, and we need to make sure that our content is formed, formatted, and delivered in a way that allows Google and others to tap into what it has to offer.

Devices you can talk to

The iPhone 4S, released late last year, included a highly anticipated new feature called Siri that allows users to speak directly to their smartphones. Give Siri a command, or ask her a question, and “she” will respond. This functionality incorporates two major accomplishments in the realm of computerized disambiguation. First, it uses voice recognition to discern the words you’re saying to it. Then it interprets the meaning of those words so that it can take action or provide you information. I don’t have an iPhone, so I set out to talk to some colleagues that do and find out what they think of the experience. Most of the people I spoke to that have the feature don’t use it because, after trying it, they don’t really like it—not a good sign. Eventually I found a colleague, Razorfish’s Jakes Keyes, who does use Siri and I sat down with him to test it out and hear about his impressions.

First he told me that he had to learn to speak very clearly to it, and he quickly stopped asking it the kinds of things it’s not good at handling. It can give you directions to Rhode Island, but isn’t so good at giving directions to a downtown restaurant called “Corton” (because it didn’t recognize the name). It’s good at conversions and measurements—like how many pints are in a gallon. It’s not as good at answering cultural questions, especially those involving names.

As a test, we asked it “What’s Tom Cruise’s middle name?” It repeatedly interpreted this question as “What time cruises middle name?” After half a dozen tries I finally over-enunciated the word “TOM” to a comical degree and, to our surprise, it displayed the text “Thomas Cruise Mapother IV.” Finally an answer! But we weren’t even sure if Siri had interpreted our question accurately, so we had no way to evaluate the credibility of this answer. We had to check IMDb.com before we believed it. A person responding to this question might have said, “Tom Cruise the actor? Did you know that Cruise actually is his middle name? His real name is Thomas Cruise Mapother IV.” But Siri didn’t give us enough context to feel confident that we were actually talking about the same topic.

Jake felt that Siri works best if you talk to it using natural language (“When is Hugo playing?” as opposed to “Movie times Hugo”), but it still seems to have three areas where communication regularly breaks down, even in the brief time we were talking and testing it out:

1. Sometimes it doesn’t properly identify the words you said. I tried asking it “When was Matt Damon born?” and it alternately interpreted the word “born” as “by” or “boren” (!?) On top of that, its general inability to understand non-US English speakers is already becoming a well-known issue. The voice recognition on my Android’s Google search was much more accurate, at least with phrases involving celebrity names.
2. It may not interpret your question properly. Sometimes Siri understood the words I was saying, but still didn’t seem to understand the nature of the question.
3. It may not have the information you seek, or be able to take the action on your query. It seems that its data sources are a lot more limited than I had originally anticipated. I was surprised how often an interaction ended with Siri offering to do a web search.

Jake summed it up like this: “When people first pick it up, they think they can ask anything they want and it will give them an answer. The reality is it only really becomes useful and satisfying when you start to ask it things that it’s capable of doing.” What does he use Siri for most? Setting a timer when he’s cooking and his hands are messy.

While this kind of functionality might not be quite ready for broad adoption, engineers will continue to improve it, and people will want to use it. But let’s not put voice recognition in every electronic device we own and have homes full of jabbering gadgets. Let’s think about the situations when it would be most useful for people to communicate with their devices verbally and focus on those experiences.

Machines that understand you

And that brings us back to Watson, the astonishingly successful Jeopardy!-playing computer. Let’s be clear—Watson’s amazing performance did not include voice recognition. It didn’t see or hear the clues; they were texted to the machine. What Watson did do was interpret a natural language query, parse through a tremendous amount of information to come up with a range of responses, and evaluate its confidence in the possible responses. All in a matter of roughly three seconds.

The confidence-evaluation part is a fascinating development. After coming up with many possible interpretations of a clue, Watson uses thousands of algorithms to find possible solutions. Then it assesses the likely accuracy of those responses based on how many algorithms pointed to each one. And it only presses the buzzer if the top answer surpasses a certain threshold of surety. But this isn’t the same as reasoning. Going back to the Kal Penn video example, Watson might have simultaneously come up with all the possible interpretations of the statement, but it wouldn’t have been able to progressively absorb information and adjust its understanding. This shortcoming yielded some humorously incorrect responses from Watson during the game; one of which resulted in Alex Trebeck scolding, “No, Ken said that.” Turns out that Watson wasn’t programmed to “listen” to and consider its competitors’ answers.

Since Watson was designed to succeed at playing a specific game, it has some skills that don’t necessarily carry over to other pursuits—for example: betting strategically, determining whether to buzz in, guessing where to find the Daily Double, and giving the answer in the form of a question. But its ability to analyze unstructured information and assign a confidence level to the answer—even when the question involved complex logic and enigmatic references—could be applied to many areas of knowledge and problem solving. And that’s what IBM seems to be planning. Add progressive reasoning, and Watson might really start to function like a human mind. But currently Watson’s hardware and software “brain” resides in 90 servers, processing massive amounts of data in parallel. So it’s going to be a while before Watson is available on your smart phone, telling you Tom Cruise’s middle name.

In the meantime, the lesson of credibility is an important one. As we learned from Siri, it’s not enough to just give users a free-floating response or a piece of content. If you conduct a query and receive a link to an untitled file that says “Download this” would you do it? In order for people to trust the information that they get from machines, we need to give them enough information to feel confident that what they’re getting is accurate and appropriate. It’s all part of the give and take of a conversation.

Areas for further inquiry

We need a combination of approaches if we want our machines to understand us better—to make the content smarter at the same time that we make our tools smarter. When we’re trying to communicate with machines, we can’t let the interaction end with “Let’s agree to disagree.” We need to at least get to “Let’s agree not to let communication break down even though I don’t understand you yet.”

As content strategists and content-first designers, we need to expand our horizons and look to other fields for inspiration and direction. We have a lot to learn from the study of cognitive psychology, developmental psychology, and linguistics (especially logic and semantics). We should also be attentive to developments in the practices of Human-Computer Interaction (HCI), Natural Language Processing (NLP), and Semantic Technology. There are a lot of different people tackling these problems from different angles, and if we want our content to be a dynamic part of the conversation, we need to get involved.

In conclusion

Content strategists think a lot about messaging from brands to people, people to people, or even people to brands, with digital devices as a conduit. But as people move into an increasingly “connected” lifestyle, perhaps the lesson of disambiguation is that we need to shift our focus from content to communication. Not just communication between people, through machines, but also from person to machine and from machine back to person.

Sometimes we’ll need to think about augmenting the content itself—data structure, data quality, and data sourcing. Other times we’ll need to think about the systems that touch our content and how it moves through them.  We have to take the capabilities of these systems into account when employing content-first design. Or, perhaps, communication-first design: designing experiences that allow us to communicate with our devices as seamlessly as we communicate with each other.