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 attractive as an ideal. But much recent discussion of the approach is short on specifics. Karen McGrane, who introduced the concept several years ago to the wider content strategy community, recently wrote: “I am really, really annoyed with hearing adaptive solutions presented as some kind of magical panacea.” We need less discussion about adaptive content as an abstract concept, and more focus on how it is implemented. The critical question is not, “Why adaptive content?” but “How?” Until we understand more of the how, its value can’t be judged.
What Adaptive Content Means
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)
- Layout decisions
- Style conditions
- 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:
- Server-side Adaptation
- Variant Selection
- Structural Transformation
- Media Adaptation
- Using Meta-information
- Client-side Adaptation
- Image Resizing
- Font Substitution
- Contextual Selection
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.
Constraints can be technical or human:
- Technical constraints: device capabilities, network connection, ability to offer desired content
- 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.
— Michael Andrews