Tag Archives: curation

Content Strategy Innovation: Emerging Practices

What new practices will forward-looking publishers start to implement in the next few years? Digital content is in a constant state of change. Are current practices up to the task?

Various professions are actively developing new computer-based practices to address high volume content. Journalists are under pressure to produce greater quantities of content with fewer resources, and to make this content even more relevant. Organizations focused on vast quantities of historical content, such as museums and scholars, have been developing new approaches to extract value from all this material. These practices may not be ones that content strategists are familiar with, but should be.

Ten years ago, online content was largely about web pages. Today it includes mobile apps, tablets, even self published ebooks that live in the cloud, and new channels are around the corner. Even though we now accept that the channels for content are always changing, we still consider content as primarily the responsibility of an individual author. We should expand our thinking to include ways to use computer-augmented authoring and analysis.

It may seem hasty to talk about new practices, when many organizations struggle to implement proven good practices. As powerful as current content strategy practices are, they do not address many important issues organizations face with their content. It’s essential to develop new practices, not just advocate well-established ones. It is complacent to dismiss change by believing that the future cannot be predicted, so we can worry about it when it arrives.

We shouldn’t be defined by current tools and short-term thinking. As Jonathon Colman recently wrote in CCO magazine: “What I fear about our future, however, is that we get so caught up with the technologies, tools and tactics of our trade that we reassign our thinking from the long term to the short. We start thinking and strategizing in ever shorter cycles: months instead of years, campaigns instead of life cycles, individual infographics instead of brands they represent.”

Fortunately, content strategy can draw upon the deep experience of other disciplines concerned with content. To quote William Gibson: “The future is already here — it’s just not very evenly distributed.” I want to highlight some promising approaches being developed by colleagues in other fields.

The pressures for innovation

The pressures for innovation in content strategy come from audiences, and from within organizations. Audience expectations show no sign of diminishing — consumers everywhere are becoming more demanding. They are fickle and individualistic, and don’t want canned servings of content. They desire diversity in content at the same time they complain of too much information (TMI). They expect personalization but don’t want to relinquish control. They want to be enthusiastic about what they view, but can easily react with skepticism and impatience.

Organizations of all kinds are struggling to get their content affairs in order. They are trying to bring process and predictability to the creation and delivery of their content. Much of this effort focuses on people and processes. But approaches that are primarily labor-intensive will not ultimately provide the capability to satisfy escalating customer demands.

Future ready: beyond structure and modularity

Content strategy recommends being future-ready. Generally this means applying structure and modularity to one’s content, so it can be ready for whatever new channel emerges. While these concepts are still not widely implemented, the concepts themselves are already old, having been a recommended best practice since the early 2000s (see for example, the first edition of Anne Rockley’s Managing Enterprise Content, published in 2002). Adoption of structure and modularity has been slow to take hold due to the immaturity of standards and tools. But it does seem that structure and modularity is now crossing the chasm from being a specialized technical communications practice towards mainstream acceptance. While it can be easy to become preoccupied by the implementation of current practices, content strategy shouldn’t stop thinking about what new practices are needed.

The content must be future-ready: able to adapt to future requirements. Equally importantly, one’s content strategy must be future-forward, anticipating these requirements, not just reacting to them. When discussing the value of “intelligent content,” the content strategy discipline has largely focused on one part of the equation: the markup of content, and how rules should govern what content is displayed. It has generally avoided more algorithmic issues. To realize the full possibilities of intelligent content, content strategy will need to move beyond markup and into the areas of queries and text and data analysis. These areas are rich with possibilities to add value for audiences, and enable brands to offer better experiences.

Emerging practices

Content strategy can learn much from other content-intensive professions, especially developments coming from certain areas of journalism (data journalism and algorithmic journalism), the cultural sector (known as GLAMs), and computer-oriented humanities research (digital humanities).

These disciplines offer four approaches that could help various organizations with their content strategy:

  1. Data as Content
  2. Bespoke content
  3. Semantic curation
  4. Awareness of meaning

Data as Content

Savvy journalists are aware that there can be engaging stories hidden in data. Data is solid and concrete compared to anecdotes. Data can be visual and interactive. Data is happening all the time: the story it tells is alive, always changing. The fascination of data is evident in the growing trend to monitor and track one’s own data: the so-called quantified self. We gain a perspective on our exercise or eating we might not otherwise see. The possibility for content strategy is to look not just at “me data” but also “we data”: data about our community. There are numerous quality of life indicators relating to communities we identify with. We already track data about communities of interest: the performance of our favorite sports team, or the rankings of the university we attended. But data can provide stories about much more.

Data journalists think about sources of data as potential story material. How do the property values of our local neighborhood compare with other neighborhoods? If you adjust these findings for the quality of schools, or average commute time, how does it compare then? Journalists curate interesting data, and think of ways to present it that is interesting to audiences. Audiences can query the data to find exactly what interest them.

Brands can adopt the techniques of data journalism, and use data as the basis of content. Brands can tell the story of you, the customer. For example, looking at their data, what do they notice about changes in customer needs and preferences? People are often interested in how their perspectives and behavior compare with others. They want insights into emerging trends. By offering visual data that can be explored thematically, customers can understand more, and deepen their relationship to a brand. The aggregation of different kinds of customer data (even what colors are most popular in what parts of the country) can provide an interesting way to tie together an egocentric angle (reader as protagonist) with a brand centric story (what the brand does to serve the customer). Data about such attributes can humanize activities than might otherwise appear opaque.

I can imagine data storytelling being used in B2B content marketing, where demonstrating engagement is a pressing need. There are opportunities to provide customers with useful insights, by sharing data about order and servicing trends for product categories. Providing data about the sentiment of fellow customers can strengthen one’s identification as a customer of the brand. Obviously this information would need to be anonymized, and not disclose proprietary data.

Bespoke Content

Bespoke content represents the ultimate goal of personalization. It is content made to order: for a person, or to fit a specific moment in time. The tools to create bespoke content are emerging from another area of journalism: robot journalism.

In robot journalism, software takes on writing tasks. Where data journalism uses data to tell stories with interactive charts and tables, robot journalism writes stories algorithmically from data. The notion that computers might write content may be hard to accept. Many content strategists come from a background in writing, and may equate writing quality with writing style. But when we view writing through the lens of audience value, relevance is the most important factor. Robot journalism can provide highly customized and personalized content.

Organizations such as the Associated Press are using robot journalism to write brief stories about sports, weather and financial news.

The process behind algorithmic writing involves:

  1. Take in data related to a topic
  2. Compute what is “newsworthy” about that data
  3. Decide how to characterize the significance of an event
  4. Place event in context of specific interests of an audience segment
  5. Convert information into narrative text

Good candidates for robot journalism are topics involving status-based, customer-specific information that is best presented in a narrative form.  A simple example of an algorithmically authored narrative using customer and brand data might be as follows:
“Your [car model] was last service on [date] by [dealer]. Driving in your region involves higher than average [behavior: e.g., stop and go traffic} that can accelerate wear on {function: e.g., brakes}. According to your driving history, we recommend you service [function] by [this date]. It will cost [$]. Available times are: [dates] at [nearest location].”

Although conditional content has been used in DITA-described technical communications for some time, robot journalism takes conditional content a couple steps further by incorporating live data, and by auto-creating the sentence clauses used in narrative descriptions, rather than simply substituting a limited number of text variables such as a product model name.

The approach can also be used for micro-segments, such as product loyalists who have bought three or more of a product over the past twelve months. A short narrative could be constructed to share the significance of something newsworthy relating to the product. A wine enthusiast might get a short narrative forecasting the quality of the newest vintage for a region she enjoys wine from.

Writing such bespoke narratives manually would be prohibitively expensive. Robot journalism approaches will enable brands to offer customized and personalized narrative content in a cost-effective way and at a large scale.

Semantic Curation

Today multiple issues hinder content curation. Some curation is done well, but is labor intensive, so is done on a limited scale that only touches a small portion of content. Attempts to automate curation are often clumsy. Much curation today is reactive to popularity, rather than choosing what’s significant in some specific way. We end up with lists of “top,” “favorite” or “trending” items that don’t have much meaning to audiences: they seem rather arbitrary, and are often predictable.

True curation aides discovery of content not known to a reader that reflects their individual interests. Semantic curation empowers individuals to find the best content that matches their interests. By semantic, I mean using linked data. And leading the way in developing semantic curation is a community with deep experience in curation: galleries, libraries, archives, and museums (GLAM).

GLAMs have been pioneers developing metadata, and as a result, have been some of the first to experience the pain of locked up metadata. Despite the richness of their descriptions of content, these descriptions didn’t match the descriptions developed by others. It is hard to pair together the content from different sources when their metadata descriptions don’t match. So GLAMs have turned to linked open data to describe their content. It is opening up a new world of curation.

The development of open cultural data is a significant departure from proprietary formats for metadata. When all cultural institutions describe their content holdings in the same way, it becomes possible to find connections between related items that are in different places. For GLAMs, it is opening access to digital collections. For audiences, it enables bottom up curation. Individuals can express what kind of content they are interested in, and find this content regardless of what source has the content. Unlike with a search engine, the seeker of content can be very specific. They may seek paintings by artists from a certain country who depicted women during a certain time period. No matter what physical collection such painting belong to, the content seeker can access the content. They can access any content, not just a small set of content selected by a curator.

The potential to expand such interest-driven, bottom-up curation beyond the cultural sector is enormous. While the work involved in creating open metadata standards is far from trivial, significant progress is being achieved to describe all kinds of content in a linked manner. The BBC has been exemplary in providing content curated using linked data on topics from animals to sports.

Awareness of Meaning

Content analytics today are not very smart. They show activity, but tell us little about the meaning of content. We can track content by the section on a website where it appears, the broad topic it is classified under, or perhaps the page title, but not by what specifically is discussed in an article. When we don’t understand what our content is actually about, what it says specifically, it is hard to know how it is performing.

This problem is well known to people working with social media content. It helps little to know that people are discussing an article. It is far more important to know what precisely they are saying about it.

As Hemann and Burbary note in their recent book, Digital Marketing Analytics: “There is not currently any pieces of marketing analytics software that can do as good job as a human at… classifying the social data collected into meaningful information.” People must manually apply tags to social content in their social listening tool for later analysis. This is labor intensive, and often means that only some of the content gets analyzed. The problem is largely the same for brand created content: CMSs don’t generate tags automatically based on the meaning of the text, so tagging must be done manually, and is often not very specific.

Again, the innovation is coming from outside the disciplines of content management and marketing. Scholars working in the field of digital humanities (DH) have been working at ways to query and tag large bodies of textual content to enable deeper analysis. Some the techniques are quite sophisticated, and rely on widely available open source tools. It is surprising these techniques haven’t been applied more frequently to consumer content.
DH techniques examine large sets of digital content to learn what these sets are about, without actually reading the content. Perhaps the most famous example of such techniques is Google’s Ngram Viewer, which can find the frequency of different phrases over time in books to learn what idioms are popular, or how famous different people are over time. (You can learn about the origins and applications of Ngram Viewer in the book Uncharted.)

Employing diverse methods, the techniques are often referred to as text analytics. Two leading approaches to text analytics are topic modeling, and corpus linguistics. Topic modeling allows users to find themes in large bodies of text, by identifying key nouns that when discussed together signal the presence of a specific topic. Corpus linguistics can identify phrases that are significant, that are used more frequently than would be expected.

Text analytics can be useful for many content activities. It can be used in content auditing, to learn what specific topics has a brand been publishing about, or to learn more about how the brand’s voice is appearing in the actual content. These same approaches can be used for social media analysis. Topic modeling also can be used to auto categorize content for audiences, to provide audiences with richer and more detailed navigation.

A complex machine is not necessarily an intelligent one.  (author photo)
A complex machine is not necessarily an intelligent one. (author photo)

The Opportunities Ahead

This quick tour of emerging practices suggests that it is possible to apply a more algorithmic approach to content to improve the audience experience. Unfortunately, I see few signs that CMS vendors are focused on these opportunities. They seem beholden to the existing paradigm of content management, where individual writers are responsible for curating, tagging and producing nearly all content. It’s an approach that doesn’t scale readily, and severely limits an organization’s capacity to deliver content that’s tailored to the interests of audiences.

It is a mistake to assume that greater use of technology necessarily results in greater complexity for authors. Some new practices need to be performed by specialists, rather than foisted on non-specialist authors who already are busy. When implemented properly, with a user-centric design, new practices should reduce the amount of manual labor required of authors, so they can focus on the creative aspects of content that machines are not able to do. As the value of content becomes understood, organizations will realize they face a productivity bottleneck, where it becomes difficult to deliver sophisticated content they aspire to with existing staff levels. The most successful publishers will be ones that adopt new practices that deliver more value without needing to add to their headcount.

Noz Urbina notes the importance of planning for change early if organizations hope to adapt to market changes. “I fear communicators are in a vicious cycle today. As the change in our market accelerates, the longer we avoid taking on revolutionary changes in search of simple short-term incremental changes, the bigger our long-term risk. Short term simple can be medium-long term awful. The risk increases with every delay that in 2 years’ time, management or the market will push us to deliver something in a matter of months that would have needed a 3-7 year transition process to prepare for. This is a current reality for many organisations for whom I have worked.”

The best approach is to learn about practices that are on the horizon, and to think about how they might be useful to your organization. Consider a small scale project to experiment and pilot an approach to learn more what’s involved, and what benefits it might offer. Very small teams are doing many interesting content innovations, often as a side project.

—Michael Andrews

Putting choice into personalization

In previous posts, I noted that the deluge of content makes personalization a necessity for individuals, but that big data approaches that aggregate segment data can’t deliver personalization successfully.  People are moving away from hunting for content, to expecting it to be delivered to them through a feed.  Brands need to offer content that reflects the actual interests of individuals.

Fortunately, there is growing evidence that some content providers are considering individual needs, not just averaged needs.  Most content providers continue to assume they know what individuals want more than individuals themselves.  But some recent services are giving individuals a chance to express their interests directly, instead of hoping that big data will be smart enough to do that on its own.  The paradigm emerging will involve computers responding to your needs, so you can train the provider to give you what you really want.

Why precision matters again

For content to have value, it can’t be treated as a commodity.

Accessing content through a service such as DIALOG could cost $200 an hour when I started working professionally with online content in the 1980s.  Due to the cost, precision was important.  One would create an SDI (selective dissemination of information) query profile to deliver content on topics that one was specifically interested in, without having to spend much time online.  SDI was effective for someone familiar with how to construct complex Boolean queries, but was not a viable option for untrained users.

When the Worldwide Web made information free, content publishers focused on getting the most traffic, either through search engine rankings, or later, social media.  The needs of specific individuals became secondary to the rankings of traffic.  If a person didn’t find what she wanted, she could spend more time “surfing” for it.  Over time surfing lost its luster, as most individuals would only look at content results that were most easily accessible.  Content providers correctly noted that individuals didn’t want to spend effort trying to “pull” content, so they offered more channels that “push” content to people, based on big data.  But the move to pushing content has not reduced the effort for individuals, because they still find themselves having to filter through extraneous content.  The cost to individuals of “free” content is that their attention is depleted and time wasted.

Now more genuine personalization is becoming a reality, thanks to the rise of mobile and tablet apps that are centered on personal usage, and cloud-based data management.

The rise of personal curation

In recent years, content services and applications have appeared that enable individuals to curate content themselves, so that their feed of content matches their specific interests.  The Pandora music service was one of the first major examples.  Tim Westergreen of Pandora told the late Bill Moggeridge: “We learned that because of Pandora’s personalization capabilities, it causes people to interact with it a lot.  You get rewarded for going in and thumbing songs, engaging with the listening.  And as a result people come back steadily, about six times an hour, to do something: whether it’s to create a new station, thumb a song, skip a song, add a new artist, or find out about an artist they’ve heard but don’t know.” (Moggeridge, Designing Media, p. 145)  Pandora’s thumbs up or down approach has been used 35 billion times, which provides a lot of feedback.

A notable approach to personalization comes from Trapit, a consumer content discovery iPad app that was briefly available before it shut down earlier this month.  “Trapit’s AI-driven approach goes completely counter to the dominant trend in news curation today, which emphasizes the power of social networking and collaborative filtering” one news story explained. “You can also train Trapit manually by clicking on the thumbs-up or thumbs-down buttons—and the more you do this, the faster the software will learn your preferences.”  Commenting on the end of consumer service, Trapit’s CEO noted “We challenged this belief — our mantra: ‘You are not the crowd.’ We are all individuals with our own beliefs, tastes, and principles.”

Most recently, National Public Radio (NPR), a leader in content innovation, is preparing a new personalization app.  NPR hopes to present its content “to people in different ways so people can pick and choose based on what they’re doing.”  An innovation will be a DVR-like feature to enable time shifting, so that the stream of content can be paused and picked up when the individual wants to use it.

While these examples differ in their specifics, they are part of a growing wave of personalization efforts that give individuals genuine choice over what content they receive.

Feedback is the basis of choice

Content providers that tout the powers of big data presume to know the best interests of the audience.  To some ordinary individuals, this presumption may feel like a rationalization for collecting all the data involved.  Many platforms have business drivers that involve getting users to make recommendations or expand their range of activity, and as a result, they promote doing these things in the name of the self interest of the users.

Even if big data is not as magical as it is presented, it has a role in personalization provided it is coupled with data on the choices made by individuals.  But among big data’s promoters, the concept of soliciting individual input to shape content personalization is widely resisted.  I have seen a range of objections, most of which are unconvincing.  I’ll paraphrase some objections I’ve seen content providers make:

  • viewers don’t know what they really want, and they say they want more than they use
  • viewers don’t want the burden of having to articulate what they want
  • providing feedback is kludgy and ruins the user experience
  • viewer preferences aren’t reliable indicators of what they actually use
  • machine learning can tell people things they don’t realize they would like
  • viewer feedback is unnecessary, because social recommendations provide the same data

Objections like these treat the viewer as lazy and lacking self-awareness, and the data-rich content provider as wise and concerned.   I don’t want to underplay the limitations of individuals to state unambiguously what they want.  We are human, after all.  But the bigger risk here is of devaluing individuals by not asking them to express their choices.  And some of the problems cited reflect old or poorly done implementations of content choice, not current best practices.

In general, intelligent data should make it easier for people to express their interests, and be aware of what they want.  Even the basic act of declaring topics of interest is made easier through linked data, such as used in Google’s knowledge graph, and as a result, people don’t need to be as precise or complete in saying what they want.

The range of individual signals of content preferences available now to content providers is unprecedented, thanks to the app economy.  There are three main areas an individual can express what the want to see:

  1. the specific interests they declare
  2. feedback on what they see
  3. how they manage defaults, such as links to other services

Many content apps now let individuals choose what topics or themes they’d like to follow.  It may involve creating your own magazine or radio station, then indicating a mix of topics, artists, or sources of interest.  These can be changed at any point if they aren’t serving the individual’s needs.  But selections become richer by the micro feedback on specific content items.  Examples of such micro feedback include:

  • mute or skip
  • reorder prioritization of content streams
  • likes /  dislikes ( or more like this / less like this)
  • now /  later prioritization (viewed now verses read later)
  • most saved articles or videos

These user signals, by themselves, aren’t sufficient to find all pertinent content, and need to be combined with convential secondary data found through social, segment and collective usage.    Incorporating user signals fine-tunes the individual relevance of the content.  Sometimes the relevance to individuals can be about the qualities of the content, rather than whether the content is on-topic.  People interested in the same topic can differ about tastes, such as the content’s style (way content is presented), point of view a topic, and specific themes addressed.  These subtle dimensions are hard for individuals to articulate, but easy for individuals to notice and to react to.  By listening to what individuals say about these dimensions, brands can learn much about their emotional preferences.

How brands can benefit

In the early days of the Web, the concept of “intelligent agents” was a popular approach researchers hoped would help individuals find what they wanted.  In a representative article called “How to Personalize the Web,”  IBM researchers expressed optimism that “agents can personalize otherwise impersonal computational systems.”    Interest in agents faded because most users at the time were anonymous, and no one could figure out how to profit from agents when content was treated as a disposable commodity. Today content is king, and individuals consume their media on their personal devices.

The rise of streaming content, and the desire to control the fire hose it offers, has renewed attention to the need for reader-defined discovery and filtering of content.  Brands can capitalize on this.

Agents are making a stealth reemergence in the form of content personal aggregation apps.  As people aggregate content based on their own interests, they make statements about their preferences that can be used to offer content that matches their preferences.  Brands are also aggregating content through curation.  Such content curation can be an effective approach for connecting with audiences, but it is often based on hunches and crude analytics.  Insights into the actual interest of individuals, what they feel about content as expressed through their micro feedback, would be more effective.

The other promising area for micro feedback is in the area of discovery.  Content providers and consumers both recognize the discovering new content that one wasn’t consciously seeking is difficult to do well.  Big data can potentially offer some insights, but people want to feel they, not the machine, are driving the discovery.  Showing new things to people who have not shown prior interest in something is risky, and involves a lot of trial and error that looks clumsy to people.  People may push back on being typecast, or feel that such content is reflecting the provider’s interests, rather than their own.  It violates the idea that the individual has control over the content they view.  So brands that present discovery well, by introducing serendipity in a measured way that doesn’t seem forced, will earn credibility with audiences.  Individuals want the same choice and control.  Providing opportunities for micro feedback on suggestions is doubly important.

The convergence of curation, discovery and personalization presents many opportunities for brands.  An obvious opportunity would be to offer apps that focus on specific topics of interest to customers, and enable individuals to curate content from different sources, including the brand’s.  Such deep knowledge of a person’s interests is highly valuable for brands.  They can learn much about their customers at the same time they make their customers feel valued as individuals.  By putting choice at the center of the experience, the brand makes their customer the hero.

— Michael Andrews