Category Archives: Personalization

Improving content discovery through typologies

Brands face a challenge: how to improve the content discovery process. They want to offer fresh, interesting content to audiences, but aren’t sure what an individual might like. The individual may also not be sure: they have a hard time specifying which content seems interesting, and which kind seems dull. Fortunately, content discovery can be improved. Brands can use the concept of typologies to improve the relevance of content they recommend.

Why content discovery is an issue

General interest content has grown dramatically. Audiences seek content to relax with, and make them feel better informed. Most general interest content is content people want to use, rather than need to use. Brands hope to create sticky content that audiences like and share. But brands can’t rely solely on social media referrals to position interesting content in front of audiences. Audiences are flooded with content that is pushed at them, including from their social contacts, but only a fraction of that content really resonates.

General interest content can be tricky to recommend. What one person finds interesting about a topic will be different from another person. Two people both like stories about food, but one person wants to know what’s new, while another wants to improve his cooking knowledge. People, and content systems, tend to think about content in terms of topics, but for general interest topics, just naming the topic of interest isn’t enough. People have trouble saying what exactly they find interesting. Subconsciously, they search for ”stories about gardening that aren’t boring.” They don’t want any story about gardening, but aren’t prepared to limit the content by type of plant.

What’s the opportunity?

Marketing and other forms of branded content make extensive use of general interest content. Presented in the right way, it attracts a diverse audience. But general interest content needs to be distinctive enough to stand out, to make an impression on audiences. Such content needs to be differentiated, not simply good.

Audiences find distinctive content more relevant. Brands benefit when they offer more relevant suggestions to their audiences. Better content recommendations increase the usage of brand content, resulting in happier, more loyal audiences.

To improve the relevance of recommendations, brands should focus on defining the elements that make their content distinctive. Typologies are a tool that can enable that.

What’s a typology?

Typology is a term not often used in content strategy — but should be. Sometimes content strategists talk about content types to refer to a content format with a regular structure such as a press release. I am using typology in a different sense, to refer to the qualities of content, not its structural elements. Typologies are a well-established approach used in the social sciences “A typology is generally multidimensional and conceptual” with a goal of reducing complexity by identifying similarities, notes Kenneth Bailey in his book, Typologies and Taxonomies: An Introduction to Classification Techniques. Archeologists use typologies to characterize items they unearth, looking at the qualities of artifacts to determine commonalities among these items. Psychologists rely on typology to map distinct personality types based on different dimensions, such as whether a person’s social orientation tends toward extraversion or introversion.

Typologies examine the attributes or dimensions of stuff, seeking to determine the most important dimensions that form the essence of something. For each separate dimension, two or more values are possible. The goal of a typology is to find patterns, to examine which values tend to occur in which dimensions for which items. Not all combinations of dimensions and values are important. Some combinations are more common, and seem familiar to people. We all instinctively recognize different styles of music, but don’t generally think of these styles in terms of their individual dimensions, such as tempo, rhythm, mood, instrumentation, loudness, and so on. Sometimes we don’t even have a label in our minds for the music we like, we just know we like certain music that has certain qualities.

Typologies serve a different role than taxonomies, the standard way to categorize content. Taxonomies are hierarchical and generally focused on concrete attributes (nouns), aiming for precise specificity. In contrast to the specificity and literalism of taxonomies, typologies focus on qualities (adjectives and concepts), and seek to make generalizations based on these qualities.

illustration of urns to show relationship to typology
All are urns, but what distinctions matter to their users: style, symbolism, status? (image courtesy Getty open images)

What typologies reveal in content

To develop a typology for content, one needs to think like the audience. The easiest way to do that is to talk to them. Brands can ask their audiences what content on a general topic they like, and what they don’t like. Ask fans what they most like about certain content. Ask them what they don’t like about other content that is on the same topic. Assuming the content is accurate and free of defects, the feedback should yield insights into the emotional qualities of content different audiences most value.

When doing this research, listen for when someone mentions dimensions such as the style of the content, its perspective or point of view, its approach to help, and the kind of occasion it would be viewed. These factors are dimensions you should consider including in your content typology.

Another way to get insights into these dimensions is by looking at specific content that is popular with a specific segment, and what you know about that segment’s lives and values. If a segment with an especially busy lifestyle likes certain content, then it may offer a clue that other people with busy lives might consider the content as time-saving. You can validate that assumption in user research.

How to develop and use a typology

Let’s look at how to characterize content according to content dimensions. I’ve developed an illustrative list of content dimensions, based on a review of some leading examples of branded content, as identified by Kapost (mostly B2B), as well as some typical B2C content. I also summarize how these qualities can vary.

Dimension Value 1 (No value for dimension) Value 2
Educational value Practical N/A Expertise and thought leadership
Curation style What’s new and notable N/A What experts say
Forum approach Peer-to-peer discussion N/A Ask an expert
How news reported Surprise: you didn’t think this could happen N/A or neutral What you suspected is true
Trendiness Trend embracing Neutral Fad-wary
Attitude toward social change Advocacy, trying to make change happen Neutral Adaptation — how to deal with change
Content personality We are just like you Neutral People look to us for authoritative advice

This is just a sample of dimensions and is by no means a complete list. I’ve included just two values (plus the empty value of not applicable) for simplicity, and some dimensions may not be applicable to your content. You’ll want to find the dimensions and values most relevant to your own content, by identifying content items with distinctive qualities.

Suppose a nonprofit needs to address several audiences, who may view a range of content depending on their interests at a given time. The nonprofit has three different core topics they address. Some of the content is meant to help people take action in their personal lives. Other content is intended to catalyze collective action. Some content is meant to build community discussion and solidarity around deeply held perspectives, while other content needs to get people aware of new issues. Using a typology, the organization might classify one piece of content as “practical advice for people having to deal with [topic x].” Another content item may be “breaking advocacy news on [topic x].” Even though both items of content address the same broad topic, they do so in different ways. By recommending an article about a topic that has similar qualities, instead of any article about that topic, the brand can improve the likelihood audiences view recommended content.

A content typology will be used to develop a audience-responsive recommendation engine. The closer the match between the qualities of the current content, and recommended content, the more likely the recommendation will be relevant.

Who is using content typologies?

Content typologies work behind the scenes, so it is not obvious to audiences when they are used. But in general, few brands use content typologies. At most, they focus on one quality of content only, and consider that quality a unique category. They might classify content that uses anecdotes as a feature, and place it in a category called “feature article.” Or they rely too heavily on audience segmentation, and categorize their content by audience segment, making broad assumptions about the qualities each segment wants. They haven’t yet made the effort to characterize their content according to multiple, distinctive qualities. As a result, discovery is hindered, because audiences can’t see content outside the narrow category in which the content was placed.

One notable brand using typologies is Netflix. Netflix has developed a very rich and detailed typology of film genres generated through the tagging of film attributes, looking at everything from how funny the film is, the personality of the audience to which the film might appeal, to the qualities of a lead actor or actress in the film. Netflix uses these taggings, together with extensive data analytics, to make recommendations of other films it believes are of a similar type.

Netflix’s typology is impressive in its sophistication, and the scope of content it covers. Fortunately, most organizations have far simpler content to characterize, and can use a simple system to do that. A content typology need not be complex, and a recommendation engine can use simple rules to improve relevance.

Making content emotionally intelligent

Intelligent content is “structurally-rich and semantically categorized that is, therefore, automatically discoverable,” according to Ann Rockley in the Language of Content Strategy. Structure is key to discoverability. But most of the focus of intelligent content thus far has been on factual details, rather than the essence of the content, its rhetorical intentions and its appeal.

Discoverability needs to include desirability. Categories need to include the distinctive qualities that matter to audiences, not just topics. Fully intelligent content will be content that is emotionally intelligent, self-aware of how it presents itself to audiences. Content typologies can provide additional metadata that improves content relevance.

— 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