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Content Engineering Intelligent Content

Defining Meaning in a Post-Document World

Digital content is undergoing a metamorphosis. It is no longer about fixed documents. But neither is it just a collection of data. It is something in-between, yet we haven’t developed a vivid and shared way to conceive and discuss precisely what that is. We see evidence of this confusion in the vocabulary used to describe content meaning. We talk about content as structurally rich, as semantic, as containing structured data. Behind these labels are deeper convictions: whether content is fundamentally about documents or data.

Content has evolved into a complex experience, composed of many different pieces. We need new labels to express what these pieces mean.

“The moment it all changed for me was the moment when Google Maps first appeared. Because it was a software application—not a set of webpages, not a few clever dynamic calls, but almost aggressively anti-document. It allowed for zooming and panning, but it was once again opaque. And suddenly it became clear that the manifest destiny of the web was not accessibility. What was it? Then the people who advocated for a semantically structured web began to split off from the mainstream and the standards stopped coming so quickly.” — Paul Ford in the Manual

In the traditional world of documents, meaning is conveyed through document-centric metadata. Publishers govern the document with administrative metadata, situate sections of the document using structural metadata, and identify and classify the document with descriptive metadata. As long as we considered digital content as web pages, we could think of them as documents, and could rely on legacy concepts to express the meaning of the content.

But web pages should be more than documents. Documents are unwieldy. The World Wide Web’s creator, Tim Berners-Lee, started agitating for “Raw data now!” Developers considered web pages as “unstructured data” and advocated the creation and collection of structured data that machines could use. What is valuable in content got redefined as data that could be placed in a database table or graph. Where documents deliver a complete package of meaning, data structures define meaning on a more granular level as discrete facts. Meaningful data can be extracted, and inserted into apps when in a structured format. In the paradigm of structured data, the meaning of an entity should be available outside of a context in which it was associated. Rather than define what the parts of documents mean, structured data focuses on what fragments of information mean independently of context.

Promoters of structured data see possibilities to create new content by recombining fragments of information. Information boxes, maps, and charts are content forms that can dynamically refresh with structured data. These are clearly important developments. But these non-narrative content types are not the only forms of content reuse.

The Unique Needs of Component Content

A new form of content emerged that was neither a document nor a data element: the content component. In HTML5, component level content might be sections of text, videos, images and perhaps tables.[1] These items have meaning to humans like documents, but unlike documents, they can be recombined in different ways, and so carry meaning outside the context of a document, much the way structured data does.

Component content needs various kinds of descriptions to be used effectively. Traditional document metadata (administrative, structural, and descriptive) are useful for content components. It is also useful to know what specific entities are mentioned within a component; structured data is also nice to have. But content components have further needs. If we are moving around discrete components that carry meaning to audiences, we want to understand what specific meaning is involved, so we match up the components with each other appropriately. The component-specific metadata addresses the purpose of the component.

Component metadata allows content to be adaptable: to match the needs of the user according to the specific circumstances they are in. We don’t have well-accepted terms to describe this metadata, so its importance tends to get overlooked. Various kinds of component metadata can characterize the purpose of a component. Though metadata relating to these facets aren’t yet well-established, there are signs of interest as content creators think about how to curate an experience for audiences using different content components.

Contextual metadata indicates the context in which a component should be used. This might be the device the component is optimized for, the geolocation it is intended for, the specific audience variation, or the intended sequencing of the component relative to other components.

Performance metadata addresses the intended lifecycle of the component. It indicates whether the component is meant to be evergreen, seasonal or ephemeral, and if it has a mass or niche use. It helps authors answer how the component should be used, and what kind of lifting it is expected to do.

Sentiment metadata describes the mood or the metaphor associated with the component. It answers what kind of impression on the audience the component is expected to make.

We can see how component metadata can matter by looking at a fairly simple example: using a photographic image. We might use different images together with the same general content according to different circumstances. Different images might express different metaphors presented to different audience segments. We might want to restrict the use of certain images to ensure they are not overused. We need to have different image sizes to optimize the display of the image on different devices. While structured data specialists might be preoccupied with what entities are shown in an image, in this example we don’t really care about who the models are appearing in the stock image. We are more concerned about the implicit meaning of the image in different contexts, rather than its explicit meaning.

The Challenges of Context-free Metadata

Metadata has a problem: it hasn’t yet evolved to address the changing context in which a content component might appear. We still talk about metadata as appearing in the head of a document, or in the body of a document, without considering that the body of the document is changing. We run the risk that the head and the body get out of alignment.

The rise of component variation is a key feature of the approach that’s commonly referred to as intelligent content. Intelligent content, according to Ann Rockley’s definition, involves structurally rich and semantically categorized content. Intelligent content is focused on making content components interchangeable.

Discussions of intelligent content rarely get too explicit about what metadata is needed. Marcia Riefer Johnston addressed the topic in an article entitled Intelligent Content: What Does ‘Semantically Categorized’ Mean? She says: “Semantic categories enable content managers to organize digital information in nearly limitless ways.” It’s a promising vision, but we still don’t have a sense of where the semantic categories come from, and what precisely they consist of. The inspiration for intelligent content, DITA, is an XML-based approach that allows publishers to choose their own metadata. DITA is a document-centric way of managing content, and accordingly assumes that the basic structure of the document is fixed, and only specific elements can be changed within that stable structure. Intelligent content, in contrast, suggests a post-document paradigm. Again, we don’t get a sense of what structurally rich means outside of a fixed document structure. How can one piece together items in “limitless ways?” What is the glue making sure these pieces fit together appropriately?

Content intelligence involves not only how components are interchangeable, but also how they are interoperable — intelligible to others. Intelligent content discussions often take a walled-garden approach. They focus on the desirability of publishers providing different combinations of content, but don’t discuss how these components might be discovered by audiences.[2] Intelligent content discussions tend to assume that the audience discovers the publisher (or that the publisher identifies the audience via targeting), and then the publisher assembles the right content for the audience. But the process could be reversed, where the audience discovers the content first, prior to any assembly by the publisher. How do the principles of semantically categorized and structurally rich content relate to SEO or Linked Data? Here, we start to see the collision between the document-centric view of content and the structured data view of it. Does intelligent content require publisher-defined and controlled metadata to provide its capabilities, or can it utilize existing, commonly used metadata vocabularies to achieve these goals?

Document-centric Thinking Hurts Metadata Description

Content components already exist in the wild. Publishers are recombining components all the time, even if they don’t have a robust process governing this. Whether or not publishers talk about intelligent content, the post-document era has already started.

But we continue to talk about web pages as enduring entities that we can describe. We see this in discussions of metadata. Two styles of metadata compete with each other: metadata in the document head of a page, and metadata that is in-line, in the body of a page. Both these styles assume there is a stable, persistent page to describe. Both approaches fail because this assumption isn’t true in many cases.

The first approach involves putting descriptive metadata outside of the content. On a web page, it involves putting the description in the head, rather than the body. This is a classic document-centric style. It is similar to how librarians catalog books: the description of the book is on a card (or in a database) that is separate from the actual book. Books are fixed content, so this approach works fine.

The second approach involves putting the description in the body of the text. Think of it as an annotation. It is most commonly done to identify entities mentioned in the text. It is similar to an index of a book. As long as the content of the book doesn’t change, the index should be stable.

Yet web pages aren’t books. They change all the time. There may be no web page: just a wrapper for presenting a stream of content. What do we need to describe here, and how do we need to do that?

Structured Data’s Lost Bearings

When people want to identify entities mentioned in content, they need a way to associate a description of the entity with the content where it appears. Entity-centric metadata is often called structured data, a confusing term given the existence of other similar sounding terms such as structured content, and semantic structure. While structured data was originally a term used by data architects, the SEO community uses it to refer more specifically to search-engine markup using vocabulary standards such as Schema.org. The structure referred to in the term “structured data” is the structure of the vocabulary indicating the relationships associated with the description. It doesn’t refer to the structure of the content, and here is where problems arise.

While structured data excels at describing entities, it struggles to locate these entities in the content. The question SEO consultants wrestle with is what precisely to index: a web page, or a sentence fragment where the item is mentioned? There are two rival approaches for doing this. One can index entities appearing on a web page using a format called JSON-LD, which is typically placed in the document head of the page (though it does not have to be). Or one can index entities where they appear in the content using a format called RDFa, which are placed in-line in the body of the HTML markup.

Both these approaches presume that the content itself is stable. But content changes continually, and both approaches founder because they are based on a page-centric view of content instead of a component-centric view.

Disemboweled Data

First, consider the use of RDFa to describe the entities mentioned in the sentence. The metadata is embedded in the body of the page: it’s embodied metadata. It’s an appealing approach: one just needs to annotate what these entities are, so a search engine can identify them. But embedded in-line metadata turns out to be rather fragile. Such annotation works only so far as every relevant associated entity is explicitly mentioned in the text. And if the text mentions several different kinds of entities in a single paragraph, the markup gets complicated, because one needs to disambiguate the different entities so as not to confuse the search robots.

The big trouble starts when one changes the wording of texts containing embedded structured data. The entities mentioned change, which has a cascading impact on how the metadata used to describe these entities must be presented. What seemed a unified description of related entities can become disemboweled with even a minor change in a sentence. The structured data didn’t have a stable context with which to associate itself.

Decapitated data

Given the hassles of RDFa, many SEO consultants lately are promoting the virtues of putting the structured data in the head of a page using JSON-LD. The head of the description is separate from the body of the content, much like the library catalog card describing a book is separate from the book and its contents. The description is separate from the context in which it appears.

Supporters of JSON-LD note that the markup is simpler than RDFa, and less prone to glitchiness. That is true. But the cost of this approach is that the structured data looses its context. It too is fragile, in some ways more so than RDFa.

Putting data in the document head, outside of the body of the content, is to decapitate the data. We now have data that is vaguely associated with a page, though we don’t know exactly how. Consider Paul Ford’s recent 32,000-word article for Business Week on programming. He mentioned countless entities in the article, all of which would be placed in the head. You might know the entity was mentioned somewhere, but you can’t be sure where.

What's efficient for one party may not be for another.  (original image via Wikipedia)
What’s efficient for one party may not be so for another. (original image via Wikipedia)

With decapitated data, we risk having the description of the content get out of alignment with what the content is actually discussing. Since the data is not associated with a context, it can be hard to see that the data is wrong. You might revise the content, adding and deleting entities, and not revise the document head data accurately.

The management problem becomes greater when one thinks about content as components rather than pages. We want to change content components, but the metadata is tied to a page, rather than a component. So every variation of a page requires a new JSON-LD profile in the document head that will match the contents of the variation. As a practical matter this approach is untenable. A dynamically-generated page might have dozens or hundreds of variations based on different combinations of components.

Structured data largely exists to serve the needs of search engines. Its practices tend to define content in terms of web pages. Structured data can describe a rendered page, but isn’t geared to describe content components independently of a rendered page. To indicate the main theme of a piece of content, Schema.org offers a tag called “main content of page”, reflecting an expectation that there is one webpage with an overriding theme. Even if a webpage exists for a desktop browser, it may be a series of short sections when viewed on a mobile device, and won’t have a single persistent “main content” theme. Current structured data practices don’t focus on how to describe entities in unbundled content — entities associated with discrete components such as a section of text. Each reuse of content involves a re-creation of structured data in the document head.

It is important not to confuse structured data with structured content. Structured data needs to work in concert with structured content delivered through content management systems, instead of operating independently of it.

When structured data gets separated from the content it represents, it creates confusion for content teams about what’s important. Decapitated data can foster an attitude that audience-facing content is a second class citizen. One presentation on the benefits of JSON-LD for SEO advised: “Keep the Data and Presentation layer separate.” Content in HTML gets reduced to presentation: a mere decoration. Such advocates talk about supplying a data “payload” to Google. It is true that structured data can be used in apps, but some structured data advocates create a false dichotomy between web pages and data-centric apps, because they are stuck in a paradigm that content equals web pages.

This perspective can lead to content reductionism: only the facts mentioned in the content matter. The primary goal is to free the facts from the content, so the facts can be used elsewhere by Google and others. Content-free data works fine for discussing commodities such as gas prices. But for topics that matter most to people, having context around the data is important. Decapitated data doesn’t support context: it works against it, by making it harder to provide more contextually appropriate information. Either the information is hanked out of its context entirely, or the reader is forced to locate it within the body of the content on her own.

The ultimate failure of decapitated data occurs when the data bears no relationship to the content. This is a known bug of the approach, and one no one seems to have a solution for. According to the W3C, “it is more difficult for search engines to verify that the JSON-LD structured data is consistent with the visible human-readable information.” When what’s important gets defined as what’s put in a payload for Google, the temptation exists to load things in the document head that aren’t discussed. Just as black hat operators loaded fake keywords in the document head of the meta description years ago to game search engines, there exists a real possibility that once JSON-LD becomes more popular, unscrupulous operators will put black hat structured data in the document head that’s unrelated to the content. No one, not least the people who have been developing the JSON-LD format, wants to see this happen.

Unbundling Meaning for Unbundled Content

The intelligent content approach stresses the importance of unbundling content. The web page as a unit of content is dying. Unbundled content can adapt to the display and interactive needs of mobile devices, and allow for content customization.

Metadata needs to describe content components, not just pages of content. Some of this metadata will describe the purpose of the component. Other metadata will describe the entities discussed in the component.

There are arguments whether to annotate entities in content with metadata, or whether to re-create the entities in a supplemental file. Part of the debate concerns the effort involved: the effort for inputting the content structure, verses the effort involved re-entering the data described by the structure. One expert, Alex Miłowski at the University of California Berkeley, suggests a hybrid approach could be most efficient and accurate. Regardless of format, structured content will be more precise and accurate if it refers to a reusable content component, rather than a changeable sentence or changeable web page.[3] Components are swappable and connectable by design. They are units of communication expressing a unified purpose, which can be described in an integrated way with less worry that something will change that will render the description inaccurate. It is easier to verify the accuracy of the structured data when it is closely associated with the content. Since content components are designed for reuse, one can reuse the structured data linked to the component.

While the idea of content components is not new, it still is not widely embraced as the default way of thinking about content. People still think about pages, or fragments. Even content strategists talk suggestively about chunks of content, instead of trying to define what a chunk would be in practice. As a first step, I would like to see discussion of chunks disappear, to be replaced by discussion of components. Thinking about reusable components does not preclude the reuse of more granular elements such as variables and standardized copy. But the concept of a component provides a way to discuss pieces of content based around a common theme.

Components need to be defined as units to manage internally in content management systems before they will be recognized as a unit that matters externally. A section of content in HTML may not map to standard templates in a CMS right now, but that can change — if we define a component as a section. A section of content in HTML may not mean much to a search engine right now, but that can change — if search engines perceive such a unit as having a coherent meaning. The case for both intelligent content and semantic search will be more compelling if we can make such changes.

Final note

More dialog is needed between the semantic search community and the intelligent content community about how to integrate each approach. Both these approaches involve significant complexity, and understanding by each side of the other seems limited. I’ve discovered that some ideas about structured data and the semantic representation of entities have political sensitivities and a stormy past, which can make exploration of these topics challenging for outsiders. In this post I have questioned a current idea in structured data best practice, separating data from content, even though this practice wasn’t common a year ago, or even widely practical. Practices used in semantic search (such as favored formats and vocabulary terms) seem to fluctuate noticeably, compared to the long established principles guiding content strategy. The cause of structured data will benefit when it is discussed in the wider context of content production, management and governance, instead of in isolation from these issues. For its part, content strategy should become more specific with how to implement principles, especially as adaptive content becomes more common. I foresee possibilities to refine concepts in intelligent content through dialog with semantic search experts.

— Michael Andrews


  1. I am merely suggesting kinds of HTML structures that correspond to content components, rather than attempting to provide a formal definition. HTML5 has its quirks and nuances, and the topic deserves a wider discussion.  ↩
  2. A notable exception is Joe Pairman’s article, “Connecting with real-world entities: is structured content missing a trick?”.  ↩
  3. Embedding JSON-LD in components seems like it could offer benefits, though I hesitate to suggest casually standards on such a multifaceted issue. I don’t want the merits of a particular solution to detract attention from a thorough examination of the core issues associated with the problem.  ↩
Categories
Content Efficiency

Content Strategy Formation and Competitive Advantage

How does an organization form a content strategy?  How does it know what it should be doing differently with its content?  Content strategy can be more than just a program to improve the performance of content.  It can be a part of business strategy and help to inform how a firm should compete.

As content becomes an increasingly important element in business, it moves beyond being a support activity like human resources or accounting. It becomes a core activity, and joins the ranks of cost, technology, service, logistics and design as a potential source of differentiation and competitive advantage.  By competitive advantage, I am not referring to simply outperforming a competitor at a given time, perhaps following a content refresh in response to an assessment of competitor content.  I am referring to a more systematic approach to finding profitable opportunities relating to content that competitors aren’t pursuing.

Many content strategists associate strategy with planning and process.  A number of popular definitions of content strategy mention planning as the engine driving content strategy.  You need a plan: you can’t just wing it.  Yet in business theory, the notion that strategy is synonymous with planning has become dated. The business strategy guru Henry Mintzberg wrote an influential book in the 1990s on the decline of strategic planning.  He considered the centrality of objectives and programs in strategic planning as a frozen perspective, placing an unrealistic emphasis on control. Elaborate planning has fallen out of favor as businesses confront an increasingly unpredictable environment.

Michael Porter, another business strategy guru, criticized the view of strategy as “benchmarking and adopting best practices.” He argued that strategy should be based on delivering something others can’t.

Planning and process are used to execute a strategy, but they don’t define a strategy.

Popular Perspectives on Strategy Today

Many content strategists present content strategy in terms of a circular diagram.  It starts with discovery and planning, proceeds to creating content, and then moves to assessment before starting a new cycle of discovery and planning.  Many more steps may be involved in this cycle, with more specific descriptions, but the basic pattern draws on classic Plan-Do-Check-Act process for process improvement developed in the 1950s by W. Edwards Deming.  The image is so generic that it’s become a standard PowerPoint template that countless people fill in for business review meetings, a sort of mental comfort food.  No one will disagree with a circular diagram: it doesn’t say where you are going, so there’s nothing controversial about it.

Content strategists also emphasize an organization’s mission and values.  Strategy is more than mission and values.  One needs these things, and it’s important that all content conforms to them.  But values by themselves won’t suggest how to proceed into an unknown future.

Most discussions of content strategy don’t talk about how strategy is formed.  They reference the need to establish goals and to have content strategy reflect those goals, but don’t discuss how decisions are made, and what criteria are used to establish goals.

Three popular ideas color discussions about strategy. Because they are so familiar, people rarely question their limitations.

The first idea is optimization.  Optimization assumes analytics will tell you where you should be heading.  If you apply best practices, you can incrementally improve performance.  It assumes what you are currently doing is basically sound; it just needs tweaking. Sometimes this concept is referred to as performance-based strategy.  But as mentioned earlier, following best practices isn’t a genuine strategy.  By definition, best practices are the same tactics that countless others are using.  With its focus on incremental improvement, optimization can result in a blinkered perspective, where brands myopically follow the same basic course of action even as the operating environment shifts dramatically.  Optimization doesn’t tell us what we should be doing differently with our content, except in the most limited manner.

The second idea is growth hacking.  Here, the approach is trial and error. Firms try something that’s been done by someone else, and sees if it works for them.  If not, they try something different.  Many start ups embrace this approach.  They have a core idea, but have no idea how to make money from it, so they keep trying different things, pivoting along the way.  At its worst, growth hacking provides customers with an exhausting stream of alpha release products based on the hunches of alpha males.  User needs are stress-tested rather than solicited as design inputs.  When big organizations try this approach at scale, it can result in wild gyrations, and can hurt the brand’s standing and customer retention, as high profile initiatives are suddenly and publicly abandoned when they don’t produce their expected magic.

The third idea is goal-setting.  The necessity of having a vision and goals seems self-evident.  Airport kiosks are full of books promising a better tomorrow by setting goals.  TED talks exhort us to ask big questions, and be driven by big ideas.  Why are we here?  What do we want to become?  A goal fetish, however, can generate vague, wishful thinking, along the lines of “we want to be awesome so we can help our customers be awesome.”  Unless the goal is viable, building a strategy off it is pointless.

Goal Viability: the Critical Success Factor

Finding the right goals is key: goals that are both achievable and have competitive impact.  If your goals are tired, or ill- defined, pursuing them won’t result in a big difference.  Tired goals are those that reflexively follow past practices, target obviously achievable outcomes, or simply imitate what others are doing.  Ill-defined goals are those that vague and aspirational without sufficient consideration of constraints and tangible outcomes.  Promising goals, in contrast, blend both realism and imagination.

Conventional thinking about strategy is anchored in the notion that goals shape the strategy, which is the foundation for the plan (Goals > Strategy > Plan).  Strategy based on goals communicates the idea that “failure is not an option,” since the goal is not questioned once selected. Consequently, the goals are often either safe or fuzzy, since no one wants to fail.  There is plenty of need in our personal lives for both safe and fuzzy goals: to exercise three times a week, or to try to be a better parent.  But large organizations face different needs: to find goals that can transform their practices when there’s no obvious script to follow.  They need to change, but don’t know exactly what are the right changes to make.

Rather than have goals determine strategy, it may be more insightful to reverse the process.  We need to create a strategy that can identify viable goals we can plan around.   In this revised formulation, the strategy drives the discovery of goals (Strategy > Goals > Plan).  We then stop thinking about strategy as a declaration, and start thinking of it as a discovery process.  Strategy becomes a way of finding viable goals to pursue.

Viewing strategy as a way to choose goals means the emphasis is on making appropriate decisions.   Strategy is ultimately about making the right choices.  We need a framework for making decisions.

Two themes dominate recent strategic business thinking: the rapid and unexpected changes that can occur outside of organizations from technology, competitors, and consumers, and the vast volumes of data being generated that are difficult to interpret.  These themes impact all areas of business, the field of content included.  In 2011, the World Economic Forum (the Davos conference for global CEOs) sponsored a review of the Future of Content to try to make sense of some of these changes.  The review examined the need for organizations to adopt “transformative business models” to address changes in the content landscape.

Content strategy can draw on recent thinking in business strategy, particularly ideas relating to options thinking and hypothesis testing.  These tools can help organizations answer what they should be doing differently with their content.   Strategy should generate interesting and worthwhile options to pursue.  Options need to be tested for their viability.  Viable options can be put into the plan, after which they are executed and optimized.  In this formulation, the front-end of strategy formation involves the discovery of viable goals, and the back-end involves the testing, selection and implementation of these goals into plans.

“One of the toughest strategy challenges is still the creation of options—creating them is the black box of strategy. It’s easy to write ‘diverge’ on the strategy-process map, but it’s darned difficult to create truly innovative strategy options.”  Dan Simpson (Clorox) in the McKinsey Quarterly

Goal-finding: Generating Options

Three approaches can generate innovative content strategy goals that can be evaluated for their viability.  These are:

  1. Dilemma exploration
  2. Hidden value opportunities
  3. Refinement of beliefs concerning differentiation

Each of these approaches can identify and develop specific goals that seem viable — levers that provide leverage.  Dilemma exploration looks at where to put emphasis. Hidden value exploration looks at opportunities to offer things differently.  Belief refinement is about tightening up beliefs about the capabilities of the brand, and behavior of audiences, so that goals are more specific and potentially achievable.  All three approaches help brands to develop fresh ideas that might become candidate goals.  Candidate goals can then be expressed as hypotheses that can be tested to see if they hold.

Strategy can be a discovery process focused on developing and selecting viable goals
Strategy can be a discovery process focused on developing and selecting viable goals

Dilemma Exploration

Dilemmas are about choices, where two or more options seem desirable.  Strategy is similarly about choices: what to emphasize, to the exclusion of something else.

Organizations face resource trade-offs.  The choices they make when allocating resources can impact their overall effectiveness with content.  While it is easy to allocate content spending in direct proportion to revenues from lines of business or customer segments, doing so might overlook the possibility that a different mix might yield a higher overall impact from content.

Some trade-offs are global ones relating to approach, such as whether to emphasize:

  • Breadth of content, or more limited but highly produced content
  • Targeted content addressing specific niches, or content with wide appeal
  • Succinct, compact content, or expansive content using rich media

Brands need to know where to spend their money.  Let’s imagine that 25% of a budget were devoted to discretionary spending on content: some forms of content receive special emphasis, with the intention that such content would be unique and distinguished from the general content offered by competitors.  What should that emphasis be?  Is it better to do a few splashy things that will get the attention of a particular group, or to try to broaden the reach by creating content more targeted to various specific interests?  For example video is more expensive to produce than written content.  It might yield higher engagement from people not ready to buy, compared with those doing serious comparison shopping, who are reading detailed specs.  Does the attraction of video outweigh the thoroughness of detailed product information?

The interesting thing about such dilemmas is that there are answers, but they are not obvious.  The answers are situationally dependent.  No one can know in advance what the best choice will be, because of the many variables.  There is no best practice that everyone in the industry is following, so there is an opportunity to make a choice that is different from one’s competitors, and potentially benefit from this choice.   As soon as conventional wisdom takes hold about what’s the best approach, the competitive advantage disappears — unless conventional wisdom is wrong and one tacks differently.  Dilemmas therefore are a rich area to explore: decisions with two or more tempting choices that sound promising, but only one of which will yield the biggest overall payoff in terms of value for spending.

Trade-offs also exist concerning whom to target, and which lines of business to emphasize.  This is especially urgent for areas of emerging interest that look promising, but where no reliable information is available.  For example, firms may need to decide whether it is better to emphasize:

  • Segment A (single millennials who travel) or segment B (home-oriented millennial families)
  • Product C (cashless payments) or product D (social lending)
  • Platform E (Apple watch) or platform F (large wall public displays)
  • Marketing theme G (the future) or marketing theme H (nostalgia)

Many marketing campaigns are pitched around a tidy story about how various choices will synergistically work together to yield a perfect outcome — without addressing missed opportunities.  Campaigns may fail or succeed without any clear understanding as to why, and with no learning that can be leveraged later.

All well-considered alternatives offer some value, so it is important to understand the potential value of each. The benefit of dilemma exploration is to determine which alternative provides the most leverage.  Brands may be tempted to try to do everything to some degree, but that would provide no emphasis, and would simply dissipate efforts.  Unfortunately, trying all options at once won’t work.  Dilemma exploration is unlike the superficial comparisons of options done in much A/B testing.  A/B tests generally compare only minor differences, rather than more fundamental differences in emphasis.  Exploring options associated with a dilemma can entail a small special project.  Test an option by making a guess as how big an impact it might offer, and comparing the actual result.  This provides a baseline to know if the option performs better or worse then expected.  Rotate options to try different possibilities and develop a comparison between them.

Discovery of Hidden Value

Hidden value exists when the brand and audience both derive value from a change.  Such value can be discovered when one questions the assumptions embedded in the existing brand-audience value exchange.

Start by asking a probing question: What does the customer want from us that we aren’t providing?  The answer to this question, if grounded in user research and customer feedback, can uncover unmet needs.

Next, respond to each customer “ask” with a question from the brand: What does the brand want from the customer?  To be insightful, the question should be answered candidly, revealing both ideal outcomes and feared ones.

An example will illustrate how hidden value discovery can be applied to content.  Suppose your customer insights indicate that customers are frustrated by your complex terms and conditions.  You benchmarked your terms and conditions, and found them no more onerous than your competitors.  Nonetheless, customers want more clarity in the terms and conditions.  While not at a disadvantage, the brand isn’t using terms and conditions as a competitive advantage either.

When the question is turned on the brand — what it wants from the customer — two themes emerge.  The brand is concerned about possible legal actions from customers, or bad PR if they seem to over promise.  The wordy and weasely terms and conditions are a way to discourage too much attention to what is promised.  The brand’s ask of consumers is: Don’t sue the brand, and don’t create negative PR.

Once the needs of both sides are explicit, one can see common ground that adds value to both parties.  Simpler, clearer terms and conditions would benefit customers, who will then trust the brand more.  Such trust could also benefit the brand, by encouraging more sales.  The brand could feel confident simplifying terms and conditions if it improved its risk management, perhaps by assigning warranty fulfillment to a third party or improving communication regarding scheduled maintenance.  The simplified terms would then be a competitive advantage.

“Framing questions is the other tough challenge, and it’s one of the most important yet under appreciated parts of strategy development. Questions are the lens by which problems are defined and addressed. Generating great answers to bad questions is all too common and not all that helpful in strategy.” Dan Simpson (Clorox) in the McKinsey Quarterly

Beliefs about Differentiation

Everyone wants to be different and special: brands and consumers alike.  Differentiation is a major motivation in strategy. Companies want a competitive advantage compared with their peers, and content needs to stand out in some special way for it to get noticed by audiences.  Differentiation attempts to address two issues simultaneously: things that a company can do that will benefit them but that their competitors are not doing, and things that audiences want but are not getting from the industry segment.

How can the brand be more relevant to customers than other brands?  Unlike the optimization approach, differentiation  does not simply ask how to become better than one has done in the past: it asks how to be better than anyone else.

Three core questions are at the heart of differentiation:

  • Why this?  What’s really unique about the product or service, and in what ways is existing product discourse commodified?
  • Why us?  How do people compare vendors and brands, and where are these factors being addressed inadequately?
  • Why now?  How might content influence the readiness of the customer to take action?

The product and vendor questions are familiar to those involved with market positioning.  Because of their familiarity, it takes a special effort to break free of routine points of comparison: features, benefits, and likability.  Last weekend I walked by a shop in Rome I assumed was a jewelry store.  Something was intriguing about it, so I stepped inside, and realized it was a pastry shop.  The shop had redefined pasty as jewelry: precious and regal, simultaneously reframing my conception of the product, and what a pastry vendor can be.

Brands less often think about how to position their communication with customers to bridge the gap between the customer’s readiness to act, with the brand’s readiness to meet pre-purchase and post-purchase customer needs.  Most brands behave by assuming customers will decide when they decide; the brand keeps badgering them in the meantime to stay top of mind, without probing into how customers decide.  But content has tremendous potential to close the gap between customer readiness and action. It can simplify choices, help customers evolve their preferences over time through dynamic customization, and address buyer concerns about risks and future needs for change.

Refining Beliefs and Testing Hypotheses

Being different can involve having a different set of beliefs from the rest of the field.  We embrace various beliefs about what differences make a difference.  Perhaps beliefs about what makes a company successful: Companies that do certain actions achieve outcomes as a result.  Or beliefs about how customers and audiences behave: Audiences that receive content with certain characteristics will behave in a certain way.

Beliefs about both industry behavior and audience behavior can be expressed as hypotheses that are testable.  With a hypothesis, it becomes possible to refine ideas and determine what precisely might be successful.  Consider the area of content marketing.  Content marketing is common: many brands are doing it.  At the same time, there is widespread debate about how effective content marketing actually is.  Anecdotal evidence suggests that some firms in some sectors can benefit using content marketing with some customer segments.  But there is no consensus that simply doing content marketing is valuable — it may be a waste of money, or even counterproductive if it prompts segments to opt out.

Beliefs many times reflect a hidden goal or wish the believer desires.  Consider some beliefs brands may hold:

  • Customers who share content from a brand are likely to become repeat buyers
  • Our brand can create content for customers that will encourage them to identify with us
  • Our brand can gain new customers by producing non-sales oriented content
  • People who do not typically use our brand may be willing to read helpful content from us if it was available
  • People want to be entertained, and will think highly of us if we provide them with content they enjoy
  • Storytelling is the most effective way to reach customers
  • People expect brands to provide advice about daily life issues, and want such information from our brand

These beliefs vary in their generality and plausibility.  Some may be true in some circumstances, but deserve to be teased apart to appreciate different dimensions involved.  Sweeping generalizations are rarely true in all cases. Some beliefs involve a leap in logic, and should be unpacked to identify intermediate causal dimensions.  Some beliefs may raise a “so what?” response: they sound good, but it is not explicitly clear what the broader benefit is.  Umbrella beliefs about firms and customers can be compared with available cross-industry evidence to see what general patterns and special circumstances may apply, if solid data exists at all.  People can falsely assume their beliefs about one industry or segment are valid for different sector or segment.  People may miss the possibilities of borrowing ideas from a seemingly unrelated area.  Once beliefs are expressed as a statement specific to a brand, or to an audience segment, they can actually be tested.

This kind of rigorous examination of beliefs helps find the kernels of truth in various beliefs that can be usefully developed into hypotheses that can be tested.  Once several plausible beliefs about industry behavior and audience behavior are identified and woven together, the brand has a unique proposition that it can explore.  Examples might be:

  • If our brand creates stories about fun romantic holidays that prospects like (hypothesis 1: brand can successfully create fun romantic holiday stories), they will book more travel services with us (hypothesis 2: bookings influenced by stories).
  • Millennials need, but can’t find, information about their long term disability risks (hypothesis 1: unserved need for content on millennial long term disability risks exists).  If we provide them with relevant information (hypothesis 2: target millennials, and see if they find information relevant), we will make gains with the millennial demographic selling insurance related to this.  (hypothesis 3: some sales activity results).

In the second example, we still see signs that wishful thinking might be at work, but we don’t know for sure.  If people can’t find content that doesn’t exist, that doesn’t mean it is needed or wanted.  What’s seen as relevant information may depend on the segment.  Insurers might consider information relevant to a segment, but the target fails to be interested by it.  Perhaps the insight gained from the hypothesis testing is that targeting millennials is not productive, but targeting their parents about the financial risks of a long term disability to their twenty-something year old children is effective.  In this fictitious example, the pursuit of a hypothesis leads to a genuinely novel goal to execute.

Goals Worth Pursuing

I’ve presented a range of approaches on how to identify fresh ideas that could have strategic value.  There is no need to pursue all these approaches at once, but each might be useful at different times when reviewing high level content goals.  By having tools that invite questions, the development of goals can happen continually, rather than being tied to an event trigger such as a content audit or redesign.

The process for testing and evaluating hypotheses is similar to processes used when monitoring analytics and optimizing content.  Unlike with optimization, a specific answer is sought.  One is looking for confirmation of an effect, rather than just trying to improve what’s happening.  In this sense it resembles the experimentation of growth hacking, although it is focused on innovative ideas screened based on their suitability to the challenge, rather than on copying and trying out marketing tactics widely used by others to see if they fit the problem.  Since the option was chosen because it looked promising, it should show some confirmation that it’s a viable goal, even if it has room to improve.  Testing a hypothesis triggers a decision: whether to keep the candidate goal and try to develop it further, or drop it and try a different candidate.

One benefit of having strategy centered on the discovery of viable goals is that it produces many candidate goals.  The brand can avoid the temptation to make a big bet on one audacious goal.  There are many possible goals worth pursuing, and that encourages creativity and experimentation.

Getting Strategic with Content: the Ultimate Goal for Content Maturity

Many organizations are still trying to close the gap between their current operations, and known best practices.  They are playing catch up, and haven’t yet reached the level of maturity with their content to focus on doing things differently, with the intention of out competing their peers.  They are understandably focused on improving their operations so they can execute plans and goals effectively.

But as content strategy takes root in organizations, and as processes and planning improve, the work of content strategy will be less reactive to fixing quality and operational problems, and more proactive, searching for ways to offer greater value to the brand and its customers.  Firms will stop thinking of content as a commodity to cost-manage, and think of it as a product with defined value.  The evolution of content strategy from process improvement to innovation would thus resemble the evolution of product manufactures from their past focus on total quality process improvement as the central competitive concern, toward considering design and innovation as contributing sources of competitive advantage.

All firms, no matter how mature their content operations, face the challenge of uncertainty.  They face resource dilemmas, and make decisions based on faulty assumptions.  In this respect, all firms need a way to work with imperfect information.  They can’t just follow the example of others.  They need their strategy to empower them to choose goals that meet their specific needs.

— Michael Andrews