Tag Archives: content design

How Content Can Answer Unanticipated Questions

How can publishers answer questions that audiences may have, when they don’t always know what will interest people? This is not a trick question. To be agile, publishers need to plan for flexibility.   They need to prepare content for scenarios they can’t anticipate in advance.

Content design has never been more important.  People have less time than ever to deal with unwanted content.  But content design should not be about spoon-feeding audiences answers to pre-approved questions.  Content design should instead empower audiences to consume the precise content they need.  Publishers should enable audiences to decide the answer that matches their need.  Publishers shouldn’t believe they can always anticipate what audiences need.  They can’t  always package content to match a  known need.  Recent developments in search technology are shaking up thinking about how to provide answers to audiences.

The Limitations of Questions as Templates for Content Development

Current practices presume a certain process.  We should start with a list of questions that users have, then write content answering those questions. The question will tell us what content to create. This approach, however, has limitations which may not be obvious.

I’ve long been an advocate and practitioner of user research.  It makes no sense to create content users indicate they have absolutely no interest in.  But user research is merely a starting point for considering user questions.  It should not be the final arbiter of what could be important to users.

“People are really fascinating and interesting … and weird! It’s really hard to guess their behaviors accurately. ” — Peter Koechley, Upworthy

Many user questions can’t be guessed — or discovered — in advance.  When doing user research, organizations can be over-confident about what questions they think users will have in the future.  User research probes the motivational level of interests and needs, rather than the more granular informational level of specific questions.  User research helps to  understand users, but it will simplify user needs into personas.  The diversity, and contextual complexity, that spawn the range of real word user questions gets smoothed over.  Qualitative user research data is too broad to uncover the full range of potential questions in detail.  Quantitative data analysis of past online queries can provide more granular insights, But even quantitative data won’t predict all situations, especially when novel situations arise.

Two common approaches to question-templated content development are:

  • The “top tasks” approach
  • The long-tail approach.

Some content strategists favor the top task approach  — especially those who focus on task-oriented transactional content.

Many SEOs favor the long tail approach — especially those who want to promote awareness-orientated marketing content.

The top tasks approach makes assumptions about essential user questions, based on past user behavior with a website.  An organization may decide that the top 10 search queries drive 90% of web traffic, so those 10 questions are the ones to offer answers.  Each question gets one answer.  It’s a rearview approach that assumes no curiosity on the part of audiences.  Audience needs exist only as an extension of their interaction with the organization.  All questions considered relevant relate to user tasks linked to that specific organization.

The hidden assumptions of the top tasks approach are:

  • Everyone has the same questions
  • Because everyone has the same questions, everyone should get the same answers
  • If different people start to ask different questions, publishers can ignore those questions, because they aren’t top questions.

Providing homogenized answers to homogenized questions is appealing to homogenized organizations.  Especially to  government offices, banks, or tech support units.  But cookie cutter content can seem like it’s created by a faceless organization.  Standardized answers don’t satisfy customer’s growing expectations. They expect more personalized service.

The long tail approach tries to anticipate user questions by crafting answers for many question variations.  Each variation addresses an ever narrower ranges of questions. The idea is to get an inventory of questions all kinds of people are asking, and then develop answers to all these questions, so there is something for everyone.  On the surface, this approach seems to deliver more individualized answers.  But we will see, that is not always the case.

Both the top tasks, and long tail, approaches assume that each question has one answer.  A content item exists to answer that one specific question.

In practice, the formula that one question has one answer doesn’t hold.   Different questions lead to the same content.  Type question variations on Google, and Google rewards you with the same links going to the same content.  Not all question variations are substantially different.  If you type “How to fly a kite” in Google, you can see related questions such as “How to fly a kite step-by-step” or “How to fly a kite by yourself”.  You’ll also find “long tail” questions such as “How to fly a kite with little wind” or even more optimistically, “How to fly a kite with no wind”.

The notion of a related search is vague.  It could be a search query that is essentially equivalent to another, but phrased differently.  It could be question that implies distinctions or details that may not be present in the information or that may not even be crucial.  Suppose we imagine content addressing “How to fly a kite for firefighters” and another on “Easy steps to kite flying for bus drivers”.  We’d likely find the essence of this long tail content is no different from the more general answer.  The idea that long tail content is necessarily more relevant is fiction.

The other characteristic of question-templated content is that the questions and answers are pre-assembled and frozen.  If we phrase a question differently, such as “What’s different about kite flying for bus drivers?”, we aren’t likely to get an answer.  At most, we’ll get content talking about kite flying that for some reason mentions bus drivers.  The content creator decides what content the reader will get, instead of the reader deciding.

Content design should be built on a foundation of compositional content.  What content is assembled and delivered can be based on the specific question asked.  Suppose you want to ask “How to tell someone to ‘go fly a kite’ ”?  When decomposed, the question reveals two distinct sub-questions.  One sub-question concerns how to deliver a message in general, covering tone or medium.  The other sub-question concerns what message alternatives are available about a specific issue — in this example, the desire to get someone else to change their behavior.

In principle, machines can assemble an answer to such a complex question, even though no person has created an answer to that specific question already.   The machine would draw on two components.  One would component address points to make about an issue; and the other component would address ways to deliver those points.

A compositional topic could be rich in variations that would yield different answers.  It could address: “How to tell a colleague…” or “How to tell a nosy relative…,” or whomever.  The answer could include components about the general aspects of the issue, which could be supplemented with some advice specific to the question variation.

For those familiar with structured content, the use of components to create content variations will seem familiar.  The difference here is that users initiate the assembly of components in novel configurations.  We don’t know in advance what the user wants, so we therefore have to provide them with the raw material to supply the answer to their unknown query.

Information Generates Questions

Part of the reason people can be unpredictable in their questions is that their interests and understanding evolve over time.  Sometimes the facts of a situation can change as well.

Laura E. Davis, digital news director of USC’s Annenberg Media Center, recently wrote about “Writing answers before you know the question.”   Her question flips the assumption that most writers have: that writers know reader questions ahead of time, and the task of the writer is to provide answers to them.  Most writers expect that information presented will follow the questions audiences ask.  But the reverse is also true. Information, or the expectation of information, sparks questions.  Sometimes writers will never have thought of the questions their readers might have.

Davis cites several trends that are making audience questions less predictable.  Audiences are becoming more conversational in how they access content.  Questions can unfold in a conversation, without knowing where they may lead.  Events can unfold quickly, and not conform to a tidy summary answer. These issues gain importance as conversational interfaces become more common.  “As we move forward, more and more, we’ll be writing answers before we know the question.”

In conversation, questions and answers flow spontaneously.  How can content become more spontaneous?  How can content prepare for a “zero UI” future, as Davis puts it?  We’ll look at two approaches, metadata and machine reading, which publishers can combine to offer laser precision in answers.

‘Literate machines’ will provide dynamic answers

Historically, questions asked online were answered by a list of hyperlinks.  Even today, many chatbots provide an answer by pointing to a hyperlink of content the reader must read.   When a computer points a user to a document title (in the form of a hyperlink), it generally is pointing the user to pre-assembled content.  Pre-assembled content runs a high risk of not being exactly what the user is looking for.

Yet the more recent trend is to provide answers directly, instead of answering queries by providing links to documents.  Everyone is familiar with Google’s instant answers. This approach is being adopted most of the other major tech companies as well.  How answers are being delivered is transforming quickly.

Advances in semantic technology and AI are allowing both questions and answers to become more iterative, and fluid.  Users may not consider a single answer to a question they pose as complete. They may want several pieces of information to give them a complete understanding.  To give users complete answers, machines stitch together several fragments from different source.  Audiences can ask clarifying or follow up questions to fill out their knowledge, and contextual answers will appear.

Semantic metadata facilitates machine discovery and understanding of information.  Metadata is powerful because it can relate information from different sources. Publishers can include their information as part of a relevant answer to a user query.  For example, suppose a user asks “What local cinemas are showing films made before 1960 this evening?”  There may not be a single item of content providing that answer.  But metadata from different content can assemble an answer.  The listings of local cinemas can be combined with data about films from a film encyclopedia (to filter by year).  The ability of metadata to assemble information from many sources upends the expectation of some publishers, who believe they must provide comprehensive information about topics to answer any audience question.  Instead, their goal should be to focus on providing comprehensive information that they are uniquely positioned to offer, and to link through metadata to other sources that provide related information that might arise in a question asked by users.

The question in this example may seem arbitrary — and it is.  Why would someone want to watch films made before 1960?  What special about 1960?  Why not 1965?  Or 1950?  Because the question, seen from the outside, seems arbitrary, no one will create content specifically to answer this question.  The variations in how the question could be framed are limitless.  Which is why metadata is powerful in providing answers to questions that may be infrequently asked, or have never been asked previously.  Just because a question is novel does not mean it is unimportant.

Given the quantity of content that’s created, someone may have written content that provides part of an answer to a question.  But that answer could be buried within a larger discussion that isn’t the focus of the user’s question.  If you are curious where a new film start grew up, there might not be specific content answering that question.  But he or she may have mentioned it in passing during an interview about their latest film.  How might you locate that information without reading various interviews in full?

Machine reading comprehension (MRC) is an emerging technique that promises to transform how content is used.  Its premise is simple but awe inspiring.  Machines can read texts just like humans do, and understand what the text means.  They can do this at incredible speeds, so that can locate specific statements quickly, interpreting what the statement means, relating it to questions or statement made elsewhere.   Machine reading does not require structure, but it presumably benefits from having structure.

Amy Webb at NYU demonstrated how machine reading comprehension works in a recent presentation (here at minute 34) . Reading a book, MRC can extract the meaning.  Yes, someday soon computers will be able to speed-read War and Peace and be able to tell us what the novel is about (beyond the obvious, that it’s about Russia.)

slide with text
Slide from Amy Webb presentation on machine reading comprehension (MRC) at ONA17 conference.

MRC has been a keen research focus of many firms developing audio interfaces.  Audioburst is a new service that digests the transcripts of audio interviews.  Users can ask Alexa a question about a news topic, and Alexa can query Audioburst to find snippets of content relevant to the query, and will combine and play back different audio clips from different radio programs related to the question.

Microsoft has been at the forefront of MRC research.   I want to highlight some of their work because they are combining MRC with semantic metadata in products that are widely used.

“We’re trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it’s written or orally.” — Kaheer Suleman of Microsoft

Microsoft notes: “Machine reading comprehension systems also could help people more easily find the information they need in car manuals or dense tax code documents.”

MRC is being used in Microsoft products such as Cortana (the voice assistant similar to Alexa or Siri), and Bing (the search engine that competes with Google).

A recent news article states: “Microsoft’s virtual assistant Cortana will get an upgrade as well, allowing it to make use of machine reading comprehension to summarize search results. ”

Earlier this month, Bing announced it would use MRC: “Bing’s comparison answers understand entities, their aspects, and using machine reading comprehension, reads the web to save you time combing through numerous dense documents.”

screenshot of Bing blog post on MRC
How Bing uses machine reading to provide multifaceted answers based on text from different sources


For Bing users this means:

  • “If there are different authoritative perspectives on a topic, such as benefits vs drawbacks, Bing will aggregate the two viewpoints from reputable sources”
  • “If there are multiple ways to answer a question, you’ll get a carousel of intelligent answers.”
  • “If you need help figuring out the right question to ask, Bing will help you with clarifying questions.”

As the Microsoft examples highlight, the notion that there is only one best answer to a question is no longer a given.  People want different perspectives, and different levels of detail.  Literate machines can help people retrieve answers that match their interests.


Information-rationing is not in the best interests of content consumers.  Content strategists have long warned of the dangers of providing too much information.  But too much information isn’t necessarily the problem.  No one complains about Wikipedia having too much information.

My advice to content creators is this.  If you have unique information to share, you should publish it.  Even if you’re not sure whether users have a pre-existing need to look for that information, it could be valuable.  Self-censorship does not make sense.  At the same time, content creators should not feel they must create a complete or definitive presentation of a topic.  Increasingly, machines will be able to stitch together information from different sources for the benefit of users.  Content creators should focus on what they know best.  Duplicating information that exists elsewhere benefits no one.

We can’t predict what information people will need in the future. Content that is information-rich is worthwhile content.  We need to make such information accessible, so audience can retrieve it when it is be needed.  We need to help make machines literate.

— Michael Andrews

What is Content Design?

The growing interest in content design is a welcome development.  Such interest recognizes that content decisions can’t be separated from the context in which the content will be used.  Consideration of content design corrects two common misperceptions: the notion that content presentation is simply visual styling, and the belief that because content may need to exist in many contexts, the context in which content is displayed becomes irrelevant.  Direct collaboration between writers and UI designers is now encouraged.  Content must fit the design where it appears — and conversely, UI designs must support the content displayed.  Content has no impact independently of a container or interaction platform for which it has been designed, and is being relied upon by users.  Content depends on context.  And context frames the content experience.

Yet content design is more than a collaborative attitude. What content design actually entails is still not well understood. Content design requires all involved to consider how different elements should work together as a system.

“Content and Design Are Inseparable Work Partners”  — Jared Spool

Current Definitions of Content Design

There is no single accepted definition of content design.  Two meanings are in use, both of which are incomplete.

The first emphasizes layout and UI decisions relating to the presentation of content.  It looks at such questions as will the text fit on the screen, or how to show and hide information.  The layout perspective of content design is sometimes referred to as the application of content patterns.

The second, popularized by the Government Digital Service (GDS) in Britain, focuses on whether the words being presented in an article support the tasks that users are trying to accomplish.  The GDS instructs: “know your users’ needs and design your content around them” and talks about “designing by writing great content.”  The GDS’ emphasis on words reflects the fixed character of their content types —a stock of 40 formats.  These structures provide ready templates for inserting content, but don’t give content creators a voice in how or what to present apart from wording.

Content design encompasses much more than wording and layout.

The design of content, including printed media, has always involved layout and wording, and the interaction between the two. Comprehensive content design today goes further by considering behavior: the behavior of the content, and the behavior of users interacting with the content.  It designs content as a dynamic resource.  It evaluates and positions content within a stream of continuous interaction.

Most discussion of content design approaches content from a “one size fits all” perspective.  What’s missing in current discussions is how to design content that can serve multiple needs.  User needs are neither fixed, nor uniform.  Designs must be able to accommodate diverse needs.  Formulaic templates generally fall short of doing this.  Content must be supported by structures that are sophisticated enough to accommodate different scenarios of use.

Breaking Free from the Static Content Paradigm

Content creators typically think about content in terms of topics.  Topics are monolithic.  They are meant to be solid: to provide the answers to questions the audience has. In an ideal scenario, the content presented on the topic perfectly matches the goals of the audience.

The problem with topics is that they too often reflect a publisher-centric view of the world.  Publishers know people are seeking information about certain topics — their web logs tell them this.  They know the key information they need to provide on the topic.  They strive to provide succinct answers relating to the topic.  But they don’t consider the wide variation of user needs relating to the topic.  They can’t imagine that numerous people all reading the same content might want slightly different things.

Consider the many factors that can influence what people want and expect from content:

  • Their path of arrival — where they have come from and what they’ve seen already
  • Their prior knowledge of the topic
  • Their goals or motivations that brought them to the content
  • The potential actions they might want to take after they’ve seen the content

Some people are viewing the content to metaphorically “kick the tires,” while others approach the content motivated to take action.  Some people will choose to take action after seeing the content, but others will defer action.  People may visit the content with one goal, and after viewing the content have a different goal. Regardless of the intended purpose of the content, people are prone to redefine their goals, because their decisions always involve more than what is presented on the screen.

In the future, content might be able to adjust automatically to accommodate differences in user familiarity and intent.  Until that day arrives (if it ever does), creators of content need to produce content that addresses a multitude of users with slightly varying needs.  This marks the essence of content design: to create units of content that can address diverse needs successfully.

A common example of content involving diverse needs relates to product comparison.  Many people share a common task of comparing similar products.  But they may differ in what precisely they are most interested in:

  • What’s available?
  • What’s best?
  • What are the tradeoffs between products?
  • What options are available?
  • How to configure product options and prices?
  • How to save options for use later?
  • How to buy a specific configuration?

A single item of content providing a product comparison may need to support many different purposes, and accommodate people with different knowledge and interests.  That is the challenge of content design.

Aspects of Content Design

How does one create content structures that respond to the diverse needs of users in different scenarios? Content design needs to think beyond words and static informational elements.  When designs include features and dynamic information, content can accomplish more.  The goal is to build choice into the content, so that different people can take away different information from the same item of content.

Design of Content Features

A feature in content is any structural element of the content that is generated by code.  Much template-driven content, in contrast, renders the structure fixed, and makes the representation static.  Content features can make content more “app-like” — exhibiting behaviors such as updating automatically, and offering interactivity.  Designing content features involves asking how functionality can change the representation of content to deliver additional value to audiences and the business.  Features can provide for different views of content, with different levels of detail or different perspectives.

Consider a simple content design decision: should certain information be presented as a list, in a table, or as a graph?  Each of these options are structures.  The same content can be presented in all three structures.  Each structure has benefits. Graphs are easy to scan, tables allow more exact information, while lists are better for screen readers.  The “right” choice may depend on the expected context of use — assuming only one exists.  But it is also possible that the same content could be delivered in all three structures, which could be used by different users in different contexts.

Design of Data-driven Information

Many content features depend on data-driven information.  Instead of considering content as static — only reflecting what was known at the time it was published — content can be designed to incorporate information about activities related to the content that have happened after publication of the article.

Algorithmically-generated information is increasingly common.  A major goal is to harvest behavioral data that might be informative to audiences, and use that data to manage and prioritize the display of information.  Doing this successfully requires the designer to think in terms of a system of inter-relationships between activities, needs, and behavioral scenarios.

Features and data can be tools to solve problems that words and layout alone can’t address.  Both these aspects involve loosening the control over what the audience sees and notices.  Features and data can enrich the content experience.  They can provide different points of interest, so that different people can choose to focus on what elements of information interest them the most.   Features and data can make the content more flexible in supporting various goals by offering users more choice.

Content Design in the Wild

Real-world examples provide the best way to see the possibilities of content design, and the challenges involved.

Amazon is famous for both the depth of its product information, and its use of data.  Product reviews on Amazon are sometimes vital to the success of a product.  Many people read Amazon product reviews, even if they’ve no intention of buying the product from Amazon.  And people who have not bought the product are allowed to leave reviews, and often do.

Amazon’s product reviews illustrate different aspects of content design.  The reviews are enriched with various features and data that let people scan and filter the content according to their priorities.  But simply adding features and data does not automatically result in a good design.

Below is a recent screenshot of reviews for a book on Amazon.  It illustrates some of the many layers of information available.  There are ratings of books, comments on the books, identification of the reviewers, and reactions to the ratings.  Seemingly, everything that might be useful has been included.

Design of product review information for a book
Design of product review information for a book

The design is sophisticated on many levels. But instead of providing clear answers for users trying to evaluate the suitability of a book, the design raises various questions.  Consider the information conveyed:

  • The book attracted three reviews
  • All three reviewers rated the book highly, either four or five stars
  • All the reviewers left a short comment
  • Some 19 people provided feedback on the reviews
  • Only one person found the reviews helpful; the other 18 found the reviews unhelpful

Perhaps the most puzzling element is the heading: “Most Helpful Customer Reviews.”  Clearly people did not find the reviews helpful, but the page indicates the opposite.

This example illustrates some important aspects of content design.  First, different elements of content can be inter-dependent.  The heading depends on the feedback on reviews, and the feedback on reviews depend on the reviews themselves. Second, because the content is dynamic, what gets displayed is subject to a wide range of inputs that can change over time.  Whether what’s display makes sense to audiences will depend on the design’s capacity to adapt to different scenarios in a meaningful way. Content design depends on a system of interactions.

Content Design as Problem Solving

Content design is most effective when treated as the exploration of user problems, rather than as the fulfillment of user tasks.  Amazon’s design checks the box in terms of providing information that can be consulted as part of a purchase decision.  A purely functional perspective would break tasks into user stories: “Customer reads reviews”, etc.  But tasks have a tendency to make the content interaction too generic. The design exploration needs to come before writing the stories, rather than the reverse. The design needs to consider various problems the user may encounter.  Clearly the example we are critiquing did not consider all these possibilities.

An examination of the content as presented in the design suggests the source of problems readers of the reviews encountered.  They did not find the comments helpful.  The comments are short, and vague as to what would justify the high rating.  A likely reason the comments are vague is that the purchasers of the product were not the true endusers of the product, so they refrained from evaluating the qualities of the product, and commented on their purchase experience instead.  The algorithms that prioritize the reviews don’t have a meaningful subroutine for dealing with cases where all the reviews are rated as unhelpful.

 Critique as the Exploration of Questions

Critiquing the design of content allows content creators to consider the interaction of content as seen from the audience perspective.  As different scenarios are applied to various content elements, the critique can ask more fundamental questions about audience expectations, and in so doing, reconsider design assumptions.

Suppose we shift the discussion away from the minutiae of screen elements to consider the people involved.  The issue is not necessarily whether a specific book is sold.  The lifetime value of customers shopping on Amazon is far more important.  And here, the content design is failing in a big way.

Customers want to know if a book, which they can’t look at physically or in extensive detail, is really what they want to purchase.  Amazon counts on customers to give other customers confidence that what they purchase is what they want.  Returned merchandise is a lose-lose proposition for everyone.  Most customers who leave reviews do so voluntarily, without direct benefit — that is what makes their reviews credible.   So we have buyers of a book altruistically offering their opinion about the product.  They have taken the trouble to log-in and provide a review, with the expectation the review will be published, and the hope it will be helpful to others.  Instead, potential buyers of the book are dinging the reviews.  The people who have volunteered their time to help others are being criticized, while people who are interested in buying the book are unhappy they can’t get reliable information.  Through poor content design, Amazon is alienating two important customer constituencies at once: loyal customers who provide reviews on which Amazon depends, and potential buyers considering a product.

How did this happen, and how can it be fixed?  Amazon has talented employees, and unrivaled data analytics.  Despite those enviable resources, the design of the product review information nonetheless has issues.  Issues of this sort don’t lend themselves to A/B testing, or quick fixes, because of the interdependencies involved.  One could deploy a quick fix such as changing the heading if no helpful reviews exist, but the core problems would remain.  Indeed, the tendency in agile IT practices to apply incremental changes to designs is often a source of content design problems, rather than a means of resolving them.  Such patchwork changes mean that elements are considered in isolation, rather than as part of a system involving interdependencies.

Many sophisticated content designs such as the product review pages evolve over time.  No one person is directing the design: different people work on the design at different stages, sometimes over the course of years.  Paradoxically, even though the process is trumpeted as being agile, it can emulate some of the worst aspects of a “design by committee” approach where everyone leaves their fingerprints on the design, but no holistic concept is maintained.

News reports indicate Amazon has been concerned with managing review contributors.  Amazon wants to attract known reviewers, and has instituted a program called Vine that provides incentives to approved reviewers.  At the same time, it wants to discourage reviewers who are paid by outside parties, and has sued people it believes provide fake reviews.  To address the issue of review veracity, reviews use badges indicating the reviewer’s status as being a verified purchaser, a top reviewer, or a Vine reviewer.  The feedback concerning whether a review is helpful is probably also linked to goals of being able to distinguish real reviews from fake ones.  It would appear that the issue of preventing fake reviews has become conflated with the issue of providing helpful reviews, when in reality they are separate issues.  The example clearly shows that real reviews are not necessarily helpful reviews.

The content design should support valid business goals, but it needs to make sure that doing so doesn’t work at cross-purposes with the goals of audiences using the design.  Letting customers criticize other customers may support the management of review content, but in some cases it may do so at the cost of customer satisfaction.

A critique of the design also brings into focus the fact that the review content involves two distinct user segments: the readers of reviews, and the writers of reviews.  The behavior of each affects the other.  The success of the content depends on meeting the needs of both.

The design must look beyond the stated problem of how to present review information.  It must also solve second-order problems.  How to encourage useful reviews?  What to do when there are no useful reviews?  Many critical design issues may be lurking behind the assumptions of the “happy path” scenario.

Re-examining Assumptions

A comprehensive content design process keeps in mind the full range of (sometimes competing) goals the design needs to fulfill, and the range of scenarios in which the design must accommodate.  From these vantage points, it can test assumptions about how a design solution performs in different situations and against different objectives.

When applied to the example of product reviews, different vantage points raise different core questions.   Let’s focus on the issue of encouraging helpful reviews, given its pivotal leverage.  The issue involves many dimensions.

Who is the audience for the reviews: other customers, or the seller or maker of the product?  Who do the reviewers imagine is seeing their content, and what do they imagine is being done with that information?  What are the expectations of reviewers, and how can the content be designed to match their expectations — or to reset them?

What are the reviewers supposed to be rating?  Are they rating the product, or rating Amazon?  When the product is flawed, who does the reviewer hold accountable, and is that communicated clearly?  Do raters or readers of ratings want finer distinctions, or not?  How does the content design influence these expectations?

What do the providers of feedback on reviews expect will be done with their feedback?  Do they expect it to be used by Amazon, by other customers, or be seen and considered by the reviewer evaluated?  How does the content design communicate these dimensions?

What is a helpful review, according to available evidence?  What do customers believe is a helpful review?  Is “most helpful” the best metric?  Suppose long reviews are more likely to be considered helpful reviews. Is “most detailed” a better way to rank reviews?

What kinds of detail are expected in the review comments?  What kinds of statements do people object to?  How does the content design impact the quality of the comments?

What information is not being presented?  Should Amazon include information about number of returns?  Should people returning items provide comments that show up in the product reviews?

There are of course many more questions that could be posed.  The current design reflects a comment moderation structure, complete with a “report abuse” link.  The policing of comments, and voting on reviews hits on extrinsic motivators — people seeking status from positive feedback, or skirting negative feedback. But it doesn’t do much to address intrinsic motivators to participate and contribute.  A fun exercise to shift perspective would be to try imagining how to design the reviews to rank-order them according to their sincerity. Because people can be so different in what they seek in product information, it is always valuable to ask what different people care about most, and never to assume to know the answer to that with certainty.

Designing Experiences, Not Tasks

Tasks are a starting point for thinking about content design, but are not sufficient for developing a successful design.  Tasks tend to simplify activities, without giving sufficient attention to contextual issues or alternative scenarios.  A task-orientation tends to make assumptions about user motivations to do things.

Content design is stronger when content is considered experientially.  Trust is a vitally important factor for content, but it is difficult to reduce into a task.  Part of what makes trust so hard is that it is subjective.  Different people value different factors when assessing trustworthiness — rationality or emotiveness, thoroughness or clarity.  For that reason, content designs often need to provide a range of information and detail.

Designing for experiences frees us from thinking about user content needs as being uniform. Instead of focusing only on what people are doing (or what we want them to be doing), the experiential perspective focuses on why people may want to take action, or not.

People expect a richly-layered content experience, able to meet varied and changing needs.  Delivering this vision entails creating a dynamic ecosystem that provides the right kinds of details. The details must be coordinated so that they are meaningful in combination. Content becomes a living entity, powered by many inputs.  Dynamic content, properly designed, can provide people with positive and confidence-inducing experiences. Unless people feel comfortable with the information they view, they are reluctant to take action.  Experience may seem intangible, and thus inconsequential.  But the content experience has real-world consequences: it impacts behavior.

— Michael Andrews