A recent post on Google’s webmaster blog illustrates how metadata needs to address both the structure of web content, and the meaning of that content.
People who work in SEO talk about structured data a lot, while those who work in content strategy talk about structured content. These topics are obviously related, but the terminology used by each party obscures how each topic relates to the other. My take: both structured data and structured content are different dimensions of metadata. Structured data is generally descriptive metadata identifying entities discussed in the content. Structured content provides the foundation for structural metadata that indicates the logic and organization of the content. Both descriptive and structural metadata are important in content, and they should ideally be integrated together.
The Google blog advises publishers to include structured data in their content. The below screenshot shows how this advice is presented.
The advice presented follows a pattern:
Advice to follow
Best practices to implement advice (shown in green)
Actions not to do (shown in pink)
Some other items of advice in the post include another element:
Practices to avoid when implementing advice (shown in yellow)
We can see that the post follows good structure that is easy to scan and understand, and provides a foundation to reuse the information in other contexts. Now, let’s look at the post’s source code. This is where we’d expect to see the structured data associated with the content.
Disappointingly, no structured data is associated with the specific items of advice. The details of the advice are marked up with “class” attributes intended to style the content, but not to identify the meaning of the content. The only structured data on the page relates to the blog post in general (such as its author).
Imagine how the content could be reused if structured data identified the meaning of the advice. Someone might type a search looking for tips on “mistakes when using schema.org,” “why use schema.org,” or “schema.org best practices” and get specific bullets of content relating to their query.
In this example, the post’s author has done nothing wrong, though an opportunity has been missed nonetheless. Currently, schema.org doesn’t have any entity types that address advice statements that would contain sub-elements such as Rationale, Do, Avoid, and Don’t. The closest types are related to Questions and Answers, which are slightly different in their structure.
Because the structured data used in SEO, particularly schema.org, tends to focus on descriptive metadata, it has less coverage of other dimensions of metadata such as structural metadata indicating the role of content elements, or technical, administrative and rights metadata. All these kinds of metadata are important to address, to allow content to be shared and reused across different platforms and in different contexts. Fortunately, schema.org has been evolving quickly, and its coverage is improving every month. This expansion will allow for genuinely integrated metadata that indicates both the meaning and the structure of the content.
Metadata is a rich and important topic for everyone concerned with content published on the web. If you are interested in learning more about the many dimensions of metadata, you may be interested in my forthcoming book, Metadata Basics for Web Content, which will be available in early 2017 from XML Press.
One of the central challenges of content strategy is tracking all the content being created. So much content is available about so many different things. If you’ve ever done a content inventory, you know that different URLs may refer to the same content. It’s even possible for the same content to exist with two different titles. And sometimes it isn’t clear if two items of content are talking about the same thing, or simply talking about things that sound similar.
Identifiers are the solution to this chaos. Identifiers are alphanumeric strings associated with an item. They don’t seem very exciting, but they will play an increasingly important role in content moving forward. We are finding that relying on titles and URLs to identify content is not enough. We need something more robust.
It’s hard to relate to something as abstract as an alphanumeric string. Fortunately, some real world examples point to how identifiers can support content. Real world identifiers show how they can indicate such important things as:
The provenance of an item
A persistent way to refer to something
Whether something is unique or a copy
A way to listen to changes about something described.
Who Moved My Cheese?
One basic need is to know where content comes from. There is much pilfering of content online these days: it’s become a big industry to rip off other people’s content and republish it as one’s own.
The problem of impostors and lookalikes is not limited to web content. People who produce cheese worry about the confusion that can arise from similar looking and sounding products. Parmigiano Reggiano is a famous Italian cheese, colloquially known in English as parmesan. It can be very expensive: a wheel of Parmigiano Reggiano typically weighs 38 kilos and will cost several hundred dollars. Parmigiano Reggiano is similar to other another Italian cheese called Gran Padano, and is the original inspiration for various cheeses called parmesan made outside Italy. The makers of Parmigiano Reggiano work to distinguish their cheese from the rest through identifiers. Each cheese house (caseificio) has a unique number that they apply to the outside rind of a cheese wheel, together with the month and year of production. These identifiers let the consumer know the provenance of the cheese.
At the supermarket it can be hard to figure out where products come from. Online it can be hard to know where content comes from. Increasingly people get content not from the producer, but indirectly through a channel like Facebook. As content gets promoted and aggregated across a growing range of platforms and channels, the provenance of the content will be increasingly important to track. Content requires identifiers that can reveal the originator of the content. The Federal Trade Commission issued guidance recently rejecting vague statements that content is “sponsored”. Publishers need a process that can track and identify who that sponsor is.
Another challenge for content arises when it is remixed. Titles and URLs are designed to identify pages, not content components that might show up in a multitude of delivered content.
The challenge of remixed content is similar to a situation facing trial lawyers. As part of the pretrial discovery process, lawyers collect volumes of information. This information needs to be shared between opposing parties, and may not have any intrinsic order to it. Lawyers solved how to identify all these random bits with something called a Bates number. Originally a Bates number was produced by an elaborate mechanical ink stamp, that would sequentially number each page of any documentation with a unique alphanumeric string. Today, lawyers will scan documents into PDFs, which can render Bates numbers for each page automatically.
The elegance of the Bates number is that it provides a persistent identifier for a piece of information that is independent of its source and its context. No matter how different items of content are shuffled around, a specific item can be located by any party according to its unique Bates number.
Having persistent identifiers for content components is valuable when content is assembled from different components, and components are reused in many contexts.
In the Matrix
Another inevitable dimension of content is that there can be many versions of a content item. Sometimes this is unintended: organizations have generated duplicate content. But other times organizations have purposefully made different versions of the same underlying content to meet slightly different needs. Either way, it can be hard to sort out what is master content, and what is the derivative.
Distinguishing what’s the original content is an old problem. Enthusiasts of early jazz recordings faced this problem when they wanted to trace the recordings of a famous musician such as Louis Armstrong. Early recordings on 78 records didn’t supply much information about the full orchestra. And sometimes the masters of these recordings were rented to other record companies, who released the recording on their own label. Licensees even sometimes put false information on record labels to disguise that they were re-releasing an existing recording (done sometimes to get around labor contracts). To complicate matters even more, the same artist might release several versions of the same tune. Jazz is after all about improvisation, and each different version can be interesting in its own right. So even knowing the song title and the artist wasn’t sufficient to know if the recording was unique or not.
Fans who developed discographies of early jazz found a key to solving the problem of unreliable information on the labels on records. They tracked recordings according to their matrix number. Each matrix used to press records contained a hand inscribed number indicating the master recording. No matter who subsequently used the master to release the recording, the same number was stamped into the record. As a result, one could see that a French record was the same recording as an American one, because they shared the same matrix number, while two records with the same title and performers were in fact different recordings.
Content variation is a phenomenon driven by the desire of audiences to have choice. People want versions of content that match their needs: that are shorter or longer depending on their interests, or are formatted for a larger or smaller screen depending on their device. To track all these variations, organizations need identifiers that can let them know how content is being repurposed, and where.
Broadcast radio stations often identify themselves by number. They broadcast at a certain frequency, and use that frequency as an identifier: “101.3 FM” or whatever. RFID is a different kind of radio broadcast, one specifically designed to identify objects. Identifiers have morphed into stickers that we can listen to.
Last year I visited an exhibit at Expo Milan featuring an MIT prototype of the supermarket of the future. The premise of the exhibit was that RFID tags can track produce and other food items, to give consumers information about where the products are from, when they were harvested, how they were shipped, and so forth. What’s intriguing about this vision is that products can now have biographies. No longer does one need to talk about the product generically. One can now talk about a specific instance of the product: this orange, or this batch of pesto. Products now have real stories that can be told.
RFID allows us to listen to things: to know what’s been going on with them. We are starting to move toward creating specific content that tells stories about specific instances of items. To do this, we will need the ability to be very specific about what we refer to.
Identifiers give us the ability to make statements about things. They allow us to distinguish what specifically we are saying, and about what specifically we are making a statement. That capability will be important as content and products become more varied and customized. Identifiers support accountability in the face of growing complexity.
Publishers want their content to be appropriate for their audiences. They need to know when it is appropriate to adapt their content to specific situations.
Until recently, publishers presumed audiences would adapt to their content. They supplied the same content to everyone, and people were expected to find what interested them in that content. In some circumstances, they created different versions of the same content targeted for different segments of readers, perhaps people in different countries. But audiences still needed to find what was relevant to them in that version.
What happens if we reverse the equation, so that the content adapts to the individual, rather than the individual adapting to the content? On an intuitive level it sounds great, but how is it done in practice? Does it now mean everyone is not getting the same content?
Discussion of adaptive content has increased noticeably in the past year. The motivation behind adaptive content is to give people precisely what they want, when they want it, how they want it. Marketers imagine if their brand that can satisfy the egocentric needs of their customers, they will cement their relationship with them.
Adaptive content is difficult to define precisely. It has various properties, a number of which are also associated with other content concepts, such as personalization, dynamic content, and intelligent content. Those who discuss adaptive content may emphasize different aspects of it. Perhaps the biggest difference is between those who emphasize the production side of adaptive content (What do producers need to do to deliver content adaptively?) and those who talk about the consumption side (Why do consumers care and what do they notice that’s different?)
Adaptive content is a topic of growing interest in large part due to the smartphone. The significance of the smartphone goes beyond the difference between a smaller touchscreen and a larger screen with a keyboard. Smartphones are used in diverse situations and offer many capabilities. They have cameras, microphones, GPS, a unique ID tied to an individual, and sensors such as gyroscopes. These features can capture different information to support interaction with content and influence what content is provided to the user. They’ve changed our assumptions about when and where users might need information. We can no longer assume users will be making a simple explicit request, and getting content matching that request.
The adjective adaptive implies the user can somehow direct the content. An adaptive approach involves various possibilities. It’s an approach in the early stage of its adoption. Its benefits and limitations at this point aren’t yet well understood.
I’ll pass here on trying to define precisely what adaptive content is. Others such as Karen McGrane, Joe Goliner and Noz Urbina have valuable things to say on this topic. I want to focus on what is genuinely useful in the approach. Understanding in more detail what adaptive content could represent helps us assess both its application, and the effort involved.
For me, the core idea of adaptive content is that content variations are available to provide a better, more relevant experience for users. The key phrases are content variations (production side) and experiences (consumption side).
Many discussions of adaptive content look at the numerous variables relating to people, devices, locations, and so forth. The number of permutations can seem enormous, and would imply a need for omniscient engineering.
It may be more valuable to focus on variations, which links the content to scenarios of use, and to whom is responsible for it.
Two key questions of adaptive content are:
How much variation is necessary?
How much variation is possible?
The first question speaks to what audiences need, and the second to what businesses can realistically do to meet those needs.
One point needs clarifying. Adaptive content is not about mind-reading. There is a big push in the world of big data around predictive analytics. While predictive analytics might occasionally play a role in determining what content variation to show, it generally will not. In most cases the intent and needs of the individual user will be clear, and conjecture isn’t necessary.
Examples of Adaptive Content
The best way to illustrate content variation is through examples, looking at use cases where individuals receive different variations of content depending on their situation. These examples may not be relevant to all organizations, but they offer alternative perspectives on the value of content adaption. We might even consider these as adaptive archetypes.
One popular archetype is context aware content. The best known example is the card UI provided by Google. A Google card might combine information relating to time, location, and the user’s calendar with status information from elsewhere. The context is often event-focused. Different people receive different variations of structured information. People know the structure of the information they will receive, but not the precise information they will be getting.
A related archetype is situationally aware content. Here, the context is not predefined, but is fluid. The situation is defined by preferences set by the user relating to variables in their environment. Wearable devices may offer situationally aware content. You may be a work and can’t watch a football match, but perhaps your wrist will buzz when your team scores a goal. The focus is less on the structure of the information, and more on what specific content to receive, and how to receive it. In the future, wearables may have sensors that trigger health advice, possibly on a different platform. So we have a possibility of trans-device content.
Another kind of adaptive content is omnichannel content, a favorite of the retail sector. Macy’s, the U.S. department store chain, needs to adapt content to various shopping scenarios. Some people will go to the store to browse, but others want to know what’s available before going to the store. A shopper may be looking for a sweater that’s been advertised in a specific color and size, and wants to know if it is in stock at her local store. The content needs to display the stock availability of the item according to location. There will be countless variations of content about the sweater depending on the size, color and store location.
A different sort of adaptive content is possible in e-learning. Pearson, a large educational publisher, provides students with materials that adapt to their understanding of subject matter. It compares what learning outcomes they need to achieve for different proficiencies with the student’s mastery of these topics, and provides an individualized learning path based on their knowledge of concepts. Each student will see a different sequence of content, and different students may see different content items. This is an example of outcome driven content variation based on inference.
In some of these examples, users imagine they are getting unique content. But we are discussing content for an audience of many people, not personal information such as your fitness tracker information. Individuals may just be seeing a variation tailored to them, and others matching their circumstances will see similar variations.
Back to the Future: Adaptive Content’s Origins
Adaptive content may seem like a new approach, but much of the thinking around it has been years in the making. The W3C defined core aspects of adaptive content over ten years ago, in 2004. The proliferation of internet-connected devices with different characteristics and purposes has been evident for a long while, and with that, questions about how to provide content to increasingly diverse users.
The W3C uses the phrase “content adaptation” rather than “adaptive content,” but the two terms refer to the same general topic. Here’s the W3C definition:
“Content Adaptation is a process that based on factors such as the capabilities of the displaying device or network, or the user’s preferences, adapts the content that has been requested to provide an optimized user experience. This adaptation can occur in a number of places in the content delivery chain: the author may make choices when writing the content, or intermediary automated content transformation proxies could adapt the content based on heuristics and knowledge of the user, or the adaptation could occur within the browser itself.”
This definition is slightly different from how adaptive content is commonly discussed. Yet it highlights some important issues. First, there are technical considerations (hardware and network) but also human considerations (preferences). The goal is to deliver a good user experience, not conversions or network optimization. And there are multiple ways to accomplish this: through content planning, technical transformation of content based according to specific user needs, and using browser technology.
Over a decade ago a W3C working group documented issues relating to device independent-content: How to provide different versions of the same core content, irrespective of platform. They looked at the relationship between what is created and what is presented, and also the different dimensions of how content is received and manipulated by users. A major focus is what they call the delivery context.
The W3C working group believed that users will often need to interact with units of content that are different from the units created by authors. Authors may create larger content units that are broken down when presented to users (the perceivable unit). The decomposition approach contrasts with the infinite scrolling people commonly experience these days, regardless of device. The notion of decomposition also contrasts with some newer ideas of writing small atomic units of content, although the W3C also considered the possibility of aggregating units of content.
The most significant idea was the possibility of variations in content created. Users weren’t just seeing different presentational views of a single version of content, they were seeing different variants.
The W3C considered how the delivery context shapes the user’s focus of attention: what users notice, and how they need to interact. They noted interaction might not only be visual, but also gestural or based on speech. They considered adaptation preferences — how the user indicates they want to experience the content, such as alert preferences. And they reviewed the impacts of application personalization — things likes settings for video playback, or whether sound or location tracking is on. These variables are already important considerations for content on smartphones.
The delivery context is often overlooked. Some recent adaptive content discussions have focused on predicting implicit user desires and delivering variations based on those predictions. But the other, less explored aspect of adaptive content is making sure users can get content that matches their explicit preferences — especially when they don’t want to use a feature. Many applications assume users will use certain features: to take a selfie, use beacons, talk to a virtual assistant, or something else that designers think would be fun. A growing number of applications assume people will use their smartphones to do things, including producing content such as bar code IDs or social media check-ins, for use by the brand. Except it might not be fun for everyone. Content needs to adapt to when people opt-out of such experiences.
Adaptive Content Delivery
Before the rise of today’s popular techniques like AJAX, responsive web design and APIs, the W3C identified different techniques that can enable content adaptation. They identified different processes to support content adaption, and listed various client and server side processors to deliver the content. While the specific recommendation details are dated, the range of approaches remains interesting because they are not limited by current conceptions about how content is delivered.
Adaption Processes refer to how to change the content itself. Examples the working group identified included:
Selection via URL redirect
In-document Decision Tags (conditional or switch selection)
Adaptation via Substitution
Adaptation via Transformation
Many of these techniques involved markup and other instructions embedded in the content. A tremendous amount of variation is possible using these techniques in combination.
Adaptation Processors, in the W3C working group’s terminology, refer to the technical means for enabling content adaptation — from the server side, client side, or some combination. The working group identified:
While most of the client-side adaptation techniques focused on alternative renderings of content, the server side techniques focused more on generating substantive variations in the content. For example, one possibility mentioned for structural transformation is providing auto-summarization of content.
Much of the substantive variation in content needs to come from the server side. Server-side data repositories are becoming more flexible delivering mixed types of content from different sources. The lagginess of server-provided content should improve with true 4G network speeds. The other major server-side factor, which was not mentioned at all by the working group, is the use of analytics data to shape the content adaptation. Using data to guide the display of content has been a significant transformation in the past five years. Tracking user behavior over time can provide useful information for providing the right content variant, as the Pearson example shows.
The tools available to adapt content vary in what they accomplish and the effort they entail. Server side approaches will generally be more complex to do, though they can potentially offer the most value if they provide content that would otherwise be unavailable or not accessed. We can see this with Macy’s approach. Having specific inventory information could be a decisive factor for a person making a purchase. It is an example where the content variation is both high value to the user, and high value to the brand.
Design Parameters for Adaptive Content
What should publishers focus on, given that there are many approaches to adapting content? Adaptive content can be challenging to implement, given the many factors that influence its success.
The success of adaptive content depends on the alignment of three factors:
The profile of the individual user
The opportunity that a variation offers the user and the brand
The constraints on the ability to execute the variation in a manner that offers value to both parties
The individual user profile is a mix of their current and past behavior (typically clicks, perhaps purchases), together with any preferences they have provided (opt-ins, default settings, etc.) Brands with loyalty programs may have a range of indicators about a user. A person who is a frequent patron of a hotel would expect content more adapted to their needs than someone who doesn’t use the hotel often. This suggests that the opportunity to implement adaptive content is strongest in cases where a relationship already exists. Adaptive content may be more effective at keeping a customer than it is at creating one.
The opportunities for content variations will often relate to timing and location: when and where users most need specific content. It may be based on the need variations of different segments. Location and segmentation could even be related in the case of regional segments.
Human constraints: motivation to engage, attention and distraction
Sometimes constraints interact. Many retailers show an option to pick up merchandise at the nearest store, but not everyone lives near a store. That information, while useful to those near stores, may seem punishing to those far away. Ideally, the adaptation needs to account for the possibility that not everyone can take advantage of the variant content, so that the content can “gracefully degrade” to a state where the variant is not in the foreground.
A critical implementation dimension involves timing: how anticipatory the adaptation is. Some adaptations are real-time, responding to uncertain user interactions. Other are event-triggered, where the event is already known and being monitored. Still others involve scripts based on knowable interaction pathways. Here adaptive content overlaps with dynamic content (user-initiated requests) and some forms of personalization (remembering information across sessions.)
Content adapts to what is known within different time horizons:
Path-based adaptation, which serves different variations according either to prior actions from past sessions, or the immediate preceding actions of the current session
Forecast-based adaptation, which serves variations based on known variables such as calendar information or stages of a lifecycle
Real-time adaptation, which provides variations based on matching current behaviors with user profiles or task outcome goals.
Real-time adaptation is a data and algorithmically intensive approach. It requires fast decisions using multiple variables, some of which may lack data. The more inputs into the decision, and the more outputs of the decision (different content variations), the more challenging it is. A widely encountered example of real time adaptation are ad exchanges, where display ads are shown according to user profile characteristics and advertiser bids. An impressive amount of computing power is marshaled to deliver display ads, a cost justified by the big stakes involved.
When is adaptation appropriate?
If done properly adaptive content can benefit audiences. So should brands implement adaptive content? The answer depends on many factors. Brands need to evaluate how important content variants are to the audience, and to the brand. Brands need to understand how much complexity is involved: the inputs needed to decide on the variant, and the number of variants needed to deliver the expected experience.
Adaptive content will often have the strongest business case when supporting transactions, such as sales. The stronger the business rationale, the larger the potential investment and sophistication.
Adaptive content encompasses a range of approaches. Not all require state-of-the-art back-end systems. Some implementations may be small enhancements that improve the experience of using content without involving complex implementations.
What’s appropriate depends on user needs analysis, an assessment of available technical capabilities, and a development of a business case.