Author: Michael Andrews

Categories
Intelligent Content

Data Types and Data Action

We often think about content from a narrative perspective, and tend to overlook the important roles that data play for content consumers. Specific names or numeric figures often carry the greatest meaning for readers. Such specific factual information is data. It should be described in a way that lets people use the data effectively.

Not all data is equally useful; what matters is our ability to act on data. Some data allows you to do many different things with it, while other data is more limited. The stuff one can do with types of data is sometimes described as the computational affordances of data, or as data affordances.

The concept of affordances comes from the field of ecological psychology, and was popularized by the user experience guru Donald Norman. An affordance is a signal encoded in the appearance of an object that suggests how it can be used and what actions are possible. A door handle may suggest that is should be pushed, pulled or turned, for example. Similarly, with content we need to be able to recognize the characteristics of an item of data, to understand how it can be used.

Data types and affordances

The postal code is an important data type in many countries. Why is it so important? What can you do with a postal code? How people use postal codes provides a good illustration of data affordances in action.

Data affordances can be considered in terms of their purpose-depth, and purpose-scope, according to Luciano Floridi of the Oxford Internet Institute. Purpose-depth relates to how well the data serves its intended purpose. Purpose-scope relates to how readily the data can be repurposed for other uses. Both characteristics influence how we perceive the value of the data.

A postal code is a simplified representation of a location composed of households. Floridi notes that postal codes were developed to optimize the delivery of mail, but subsequently were adopted by other actors for other purposes, such as to allocate public spending, or calculate insurance premiums.

He states: “Ideally, high quality information… is optimally fit for the specific purpose/s for which it is elaborated (purpose–depth) and is also easily re-usable for new purpose/s (purpose–scope). However, as in the case of a tool, sometimes the better [that] some information fits its original purpose, the less likely it seems to be repurposable, and vice versa.” In short, we don’t want data to be too vague or imprecise, and we also want the data to have many ways it can be used.

Imagine if all data were simple text. That would limit what one could do with that data. Defining data types is one way that data can work harder for specific purposes, and become more desirable in various contexts.

A data type determines how an item is formatted and what values are allowed. The concept will be familiar to anyone who works with Excel spreadsheets, and notices how Excel needs to know what kind of value a cell contains.

In computer programming, data types tell a program how to assess and act on variables. Many data types relate to issues of little concern to content strategy, such as various numeric types that impact the speed and precision of calculations. However, there is a rich range of data types that provide useful information and functionality to audiences. People make decisions based on data, and how that data is characterized influences how easily they can make decisions and complete tasks.

Here are some generic data types that can be useful for audiences, each of which has different affordances:

  • Boolean (true or false)
  • Code (showing computer code to a reader, such as within the HTML code tags)
  • Currency (monetary cost or value denominated in a currency)
  • Date
  • Email address
  • Geographic coordinate
  • Number
  • Quantity (a number plus a unit type, such as 25 kilometers)
  • Record (an identifier composed of compound properties, such as 13th president of a country)
  • Telephone number
  • Temperature (similar to quantity)
  • Text – controlled vocabulary (such as the limited ranged of values available in a drop down menu)
  • Text – variable length free text
  • Time duration (number of minutes, not necessarily tied to a specific date)
  • URI or URN (authoritative resource identifier belonging to a specific namespace, such as an ISBN number)
  • URL (webpage)

Not all content management systems will provide structure for these data types out of the box, but most should be supportable with some customization. I have adapted the above list from the listing of data types supported by Semantic MediaWiki, a widely used open source wiki, and the data types common in SQL databases.

By having distinct data types with unique affordances, publishers and audiences can do more with content. The ways people can act on data are many:

  • Filter by relevant criteria: Content might use geolocation data to present a telephone number in the reader’s region
  • Start an action: Readers can click-to-call telephone numbers that conform to an international standard format
  • Sort and rank: Various data types can be used to sort items or rank them
  • Average: When using controlled vocabularies in text, the number of items with a given value can be counted or averaged
  • Sum together: Content containing quantities can be summed: for example, recipe apps allow users to add together common ingredients from different dishes to determine the total amount of an ingredient required for a meal
  • Convert: A temperature can be converted into different units depending on the reader’s preference

The choice of data type should be based on what your organization wants to do with the content, and what your audience might want to do with it. It is possible to reduce most character-based data to either a string or a number, but such simplification will reduce the range of actions possible.

Data verses Metadata

The boundary between data and metadata is often blurry. Data associated with both metadata and the content body-field have important affordances. Metadata and data together describe things mentioned within or about the content. We can act on data in the content itself, as well as act on data within metadata framing the content.

Historically, structural metadata outside the content played a prominent role indicating the organization of the content that implied what the content was about. Increasingly, meaning is being embedded with semantic markup within the content itself, and structural metadata surrounding the content may be limited. A news article may no longer indicate a location in its dateline, but may have the story location marked up within the article that is referenced by content elsewhere.

Administrative metadata, often generated by a computer and traditionally intended for internal use, may have value to audiences. Consider the humble date stamp, indicating when an article was published. By seeing a list of most recent articles, audiences can tell what’s new and what that content is about, without necessarily viewing the content itself.

Van Hooland and Verborgh ask in their recent book on linked data: “[W]here to draw the line between data and metadata. The short answer is you cannot. It is the context of the use which decides whether to considered data as metadata or not. You should also not forget that one of the basic characteristics of metadata: they are ever extensible …you can always add another layer of metadata to describe your metadata.” They point out that annotations, such as reviews of products, become content that can itself be summarized and described by other data. The number of stars a reviewer gives a product, is aggregated with the feedback of other reviewers, to produce an average rating, which is metadata about both the product and the individual reviews on which it is based.

Arguably, the rise of social interaction with nearly all facets of content merits an expansion of metadata concepts. By convention, information standards divide metadata into three categories: structural metadata, administrative metadata and descriptive metadata. But one academic body suggests a fourth type of metadata they call “use metadata,” defined as “metadata collected from or about the users themselves (e.g., user annotations, number of people accessing a particular resource).” Such metadata would blend elements of administrative and descriptive metadata relating to readers, rather than authors.

Open Data and Open Metadata

Open data is another data dimension of interest to content strategy. Often people assume open data refers to numeric data, but it is more helpful to think of open data as the re-use of facts.

Open data offers a rich range of affordances, including the ability to discover and use other people’s data, and the ability to make your data discoverable and available to others. Because of this emphasis on the exchange of data, how this data is described and specified is important. In particular, transparency and use rights issues with open data are a key concern, as administrative metadata in open data is a weakness.

Unfortunately, discussion of open data often focuses on the technical accessibility of data to systems, rather than the utility of data to end-users. There is an emphasis on data formats, but not on vocabularies to describe the data. Open data promotes the use of open formats that are non-proprietary. While important, this focus misses the criticality of having shared understandings of what the data represents.

To the content strategist, the absence of guidelines for metadata standards is a shortcoming in the open data agenda. This problem was recognized in a recent editorial in the Semantic Web Journal entitled “Five Stars of Linked Data Vocabulary Use.” Its authors note: “When working with data providers and software engineers, we often observe that they prefer to have control over their local vocabulary instead of importing a wide variety of (often under-specified, not regularly maintained) external vocabularies.” In other words, because there is not a commonly agreed and used metadata standard, people rely on proprietary ones instead, even when they publish their data openly, which has the effect of limiting the value of that data. They propose a series of criteria to encourage the publication of metadata about vocabulary used to describe data, and the provision of linkages between different vocabularies used.

Classifying Openness

Whether data is truly open depends on how freely available the data is, and whether the metadata vocabulary (markup) used to describe it is transparent. In contrast to the Open Data Five Star frameworks, I view how proprietary the data is as a decisive consideration. Data can be either open or proprietary, and the metadata used to describe the data can be based either on an open or proprietary standard. Not all data that is described as “Open” is in fact non-proprietary.

What is proprietary? For data and metadata, the criteria for what is non-proprietary can be ambiguous, unlike with creative content, where the creative commons framework governs rights for use and modifications. Modification of data and its metadata is of less concern, since such modifications can destroy the re-use value of the content. Practicality of data use and metadata visibility are the central concerns. To untangle various issues, I will present a tentative framework, recognizing that some distinctions are difficult to make. How proprietary data and metadata is often reflects how much control the body responsible for this information exerts. Generally, data and metadata standards that are collectively managed are more open than those managed by a single firm.

Data

We can grade data into three degrees, based on how much control is applied to its use:

  1. Freely available open data
  2. Published but copyrighted data
  3. Selectively disclosed data

Three criteria are relevant:

  1. Is all the data published?
  2. Does a user need to request specific data?
  3. Are there limits on how the data can be used?

If factual data is embedded within other content (for example, using RDFa markup within articles), it is possible that only the data is freely available to re-use, while the contextual content is not freely available to re-use. Factual data cannot be copyrighted in the United States, but may under certain conditions be subject to protection in the EU when a significant investment was made collecting these facts.

Rights management and rights clearance for open data are areas of ongoing (if inconclusive) deliberation among commercial and fee-funded organizations. The BBC is an organization that contributes open data for wider community use, but that generally retains the copyright on their content. More and more organizations are making their data discoverable by adopting open metadata standards, but the extent to which they sanction the re-use of that data for purposes different from it’s original intention is not always clear. In many cases, everyday practices concerning data re-use are evolving ahead of official policies defining what is permitted and not permitted.

Metadata

Metadata is either open or proprietary. Open metadata is when the structure and vocabulary that describes the data is fully published, and is available for anyone to use for their own purposes. The metadata is intended to be a standard that can be used by anyone. Ideally, they have the ability to link their own data using this metadata vocabulary to data sets elsewhere. This ability to link one’s own data distinguishes it from proprietary metadata standards.

Proprietary metadata is one where the schema is not published or is only partially published, or where the metadata restricts a person’s ability to define their own data using the vocabulary.

Examples

Freely Available Open Data

  • With Open Metadata. Open data published using a publicly available, non-proprietary markup. There are many standards organizations that are creating open metadata vocabularies. Examples include public content marked up in Schema.org, and NewsML. These are publicly available standards without restrictions on use. Some standards bodies have closed participation: Google, Yahoo, and Bing decide what vocabulary to include in Schema, for example.
  • With Proprietary Metadata. It may seem odd to publish your data openly but use proprietary markup. However, organizations may choose to use a proprietary markup if they feel a good public one is not available. Non-profit organizations might use OpenCalais, a markup service available for free, which is maintained by Reuters. Much of this markup is based on open standards, but it also uses identifiers that are specific to Reuters.

Published But Copyrighted Data

  • With Open Metadata. This situation is common with organizations that make their content available through a public API. They publish the vocabularies used to describe the data and may use common standards, but they maintain the rights to the content. Anyone wishing to use the content must agree to the terms of use for the content. An example would be NPR’s API.
  • With Proprietary Metadata. Many organizations publish content using proprietary markup to describe their data. This situation encourages web-scraping by others to unlock the data. Sometimes publishers may make their content available through an API, but they retain control over the metadata itself. Amazon’s ASIN product metadata would be an example: other parties must rely on Amazon to supply this number.

Selectively Disclosed Proprietary Data

  • With Open Metadata. Just because a firm uses a data vocabulary that’s been published and is available for others to use, it doesn’t mean that such firms are willing to share their own data. Many firms use metadata standards because it is easier and cheaper to do so, compared with developing their own. In the case of Facebook, they have published their Open Graph schema to encourage others to use it so that content can be read by Facebook applications. But Facebook retains control over the actual data generated by the markup.
  • With Proprietary Metadata. Applies to any situation where firms have limited or no incentive to share data. Customer data is often in this category.

Taking Action on Data

Try to do more with the data in your content. Think about how to enable audiences to take actions on the data, or how to have your systems take actions to spare your audiences unnecessary effort. Data needs to be designed, just like other elements of content. Making this investment will allow your organization to reuse the data in more contexts.

— Michael Andrews

Categories
Content Marketing

Content Strategy for Product Reviews

By most any measure, product reviews are one of the most important types of content.  Audiences spend serious amounts of time consulting reviews of products, relying on them to make decisions.  Some devote even more time to reviews, expressing their own views about products and services.

Despite their obvious importance, there is little consensus within content marketing precisely how product reviews matter to audiences.  Content marketers tend to focus inwardly on their own interests, rather than the audience’s.  They want to talk about their own products, and ignore the existence of competitors.  Depending on their skills and inclinations, content marketers will emphasize the role of owned media (if favoring branded content), social media (if favoring social media influence and reputation management) or promoted media (if relying on PR or promotional favors such as special access).   Yet such attempts at influencing customer opinion of products are of little value unless one first truly understands audience needs.

Brands need to embrace an audience centric perspective toward product reviews if they want to sell more online and become a trusted source of product information.  Audiences don’t just consult product reviews to evaluate products; they actively evaluate the information in those reviews to determine if relevant to their situation.  The discipline of content strategy can help brands identify the different elements audiences seek from reviews, and what information brands need to deliver.

The significance of reviews

The importance of product reviews continues to grow. For nearly every product category, people are buying more online each year.  For some categories such as travel, more than half of purchases are made online in some countries.  Even with more localized products such as groceries, online purchasing is increasing.  Buying online requires confidence that one is buying the right product or service.  Consumers are also spending more on digital products and services, which also require review information.  Even when people buy a product or service locally, they rely on online reviews to make decisions.  Time is limited, while choices proliferate.

The vast quantities of product review content need to be managed appropriately.  This content can be enhanced to make it more useful to audiences to support their buying needs.  However, brands for the most part have neglected to do this.  Most rely on a simple template of allowing their customers to rate their products with stars, and leave some comments in an open text field.  The benign neglect of product review content results in an unsatisfying customer experience — the information is not as helpful as it could be.  It hurts the brand hosting the review, because they provide information that doesn’t really answer the needs of audiences.

Product review content is a distinct genre with a long established history.  In many respects, online product reviews are less helpful than their pre-digital ancestors.  To understand the potential of the product review genre, I will draw on an extensive study of reviews by Grant Blank: Critics, Ratings, and Society: The Sociology of Reviews.  Blank’s book, which was published in 2007, barely discusses online reviews, but instead provides a very detailed examination of reviews by newspapers, magazines, and various kinds of consumer surveys.  His insights provide rich material for rethinking how online reviews should be managed.

Basic types of reviews

Blank categorizes reviews into two basic types: connoisseurial, and procedural.  They are different approaches, and each has unique strengths.

Connoisseurial reviews reflect the skills and knowledge of an individual reviewer.  For a connoisseurial review to have impact, the reviewer must have credibility with the audience.  Readers assume the reviewer knows what she is talking about because she has written other reviews on the same type of product or service, and has built a reputation as someone with deep knowledge who can be relied on.  A connoisseur’s impact is measured by how much they influence their audience, or possibly how much they influence the producer of the product.  Sometimes a reviewer’s impact is so great that they attract a regular audience of readers who may not even be looking to purchase a product, but enjoy hearing what the reviewer has to say, because they share interesting insights that add to the reader’s understanding of a topic.  Connoisseurs don’t try to review everything, only the stuff that’s most notable.

The classic kinds of connoisseurial reviewers are the great restaurant, theater and movie critics.  People like Craig Claiborne reviewing New York restaurants, or Roger Ebert reviewing movies.   Their reviews reached many people, and could have a big impact on the suppliers of the products they reviewed.  Some connoisseurial reviews are done by a corporate entity known for their expertise in an area, such as restaurant reviews by Michelin (France) or Gambero Rosso (Italy). 

In the post-mass-market, digital age, it is tempting to believe that connoisseurial reviews no longer play a big role, but this would be a mistake. Some connoisseurial reviewers started in legacy media but have moved entirely to the digital world.  People like David Pogue and Walt Mossberg reviewed technology gadgets for influential newspapers before starting their own blogs. On a smaller scale, connoisseurial reviewers are evident in many places.  In this age of self-branding, people want to show off their knowledge. Members of LinkedIn and Quora answer questions posted, often in the hope of building their reputations. Numerous platforms cater to the output of bloggers and video bloggers who comment on the offerings of fashion, technology, and products for children. Some of these bloggers and video bloggers have developed enormous followings that rival the reach enjoyed by legacy-media critics.

Procedural reviews reflect the results of tests on a product. They generally compare several similar products, and note the differences between them. The tests are meant to be transparent, and reliable, based on uniform criteria — the same test will yield the same result no matter who conducts the test.  There is an emphasis on developing data on a number of attributes of a product, and converting these data points into a numerical score that audiences will consider objective.  Because they compare many products with complex attributes, sometimes they yield surprising results.  The purpose of compiling all this detail is to support the purchase decisions of readers.

The archetypal examples of procedural reviews are the product evaluations of Consumer Reports (US), or Which? (UK.)  PC magazines did extensive procedural reviews of computer related products, with multicolumn data tables comparing features and performance results.  Among digitally native publishers, procedural reviews are less common, but some specialist sites will review products to test them for their real battery life, or their shock resistance.

While Blank considers connoisseurial and procedural reviews the two main categories of reviews, he acknowledges some hybrids that often involve surveys.  A notable example of a hybrid is a Zagat guide, which combines a procedure for reviewing restaurants with the judgments of different individuals who act as dining critics.  Zagat was the first product review to utilize public opinion surveys of customers.  Another prominent survey of critics is the Academy Awards.

Other types of hybrid review include those that that use a procedural process to evaluate products based on their historical performance.  Morningstar does this for financial products, while Consumer Reports surveys car owners to get warranty and repair data.  The popular but controversial university rankings by US News also combine survey and performance data.  Yet another kind of review-like listing involving surveys are lists that rank products according to their popularity.  Popularity reviews include the Billboard charts, the New York Times bestseller list, and box office charts for films.  The presumption of such lists is that what is most popular is what is best.

What makes a Quality Review?

Reviews that are beneficial to audiences must be credible, useful, and timely, no matter what approach is used to construct the review.

Audiences consider the credibility of reviews as essential.  Brands cannot presume that a review will be read at face value.  Audiences can spend a lot of effort unpacking the meaning of reviews.   They look for two qualities: that the reviewer is disinterested (in the sense that he doesn’t have a financial interest in the outcome of the review), and that the reviewer is knowledgeable.  Both these topics are concerns with user-generated reviews.  Audiences don’t know exactly who is making comments or writing reviews online.  There have been numerous accusations of firms either writing fake favorable reviews of their products, culling bad reviews about them, or sabotaging rivals with bad reviews.  Sometimes firms do this directly (in one widely reported case, the CEO of Whole Foods trashed a competitor anonymously) but other times firms hire surrogates to write reviews beneficial to a particular brand.  Even when audiences are convinced a review is written by a “real” customer, they may still have questions as to how much that customer knows what they are talking about, and how reliable their judgment is.

Even when the review is credible — devoid of obvious bias or misunderstanding — the review is not necessarily useful.  The utility of a review depends on the goals of the reader consulting the review.  Blank identifies two major goals audiences have for reviews: to make decisions, and for learning and enjoyment.  Some readers need very specific information to make purchase decisions; some want an overall judgment rendered, while others simply want to feel they understand what’s important about a product, and what developments are happening.  The differing goals point to different qualities in reviews: one focused on granular detail, the other highlighting big themes and trends.

Finally, audiences want to feel that reviews are up-to-date, and that the information will not be rendered obsolete soon.  They look for clues that something has changed: perhaps a slight model change, a different source now making the product, or indications that service quality has deteriorated or suddenly improved.   Audiences are sensitive to changes, and believe that reviews should indicate a consistent experience with the quality of a product.  Brands are expected to be consistent, and signs of inconsistency are worrying.  The collective body of opinion reflected through different reviews is meant to help audiences predict how a product will perform for them in the future.  One can see this phenomenon in the reviews of apps in an app store.  A new version of a popular app is released, and suddenly reviews turn negative.  Has the new version betrayed the vision of the prior versions, or is this grumbling a temporary product glitch that will be soon corrected?

While credibility, usefulness and timeliness seem like obvious standards for reviews, they can be challenging to realize under a decentralized, crowd sourced model of relying on review content that is user directed and generated.

What reviews say about products, reviewers, and readers

Reviews reflect objective and subjective qualities of products, reviewer orientation and bias, and audience preferences.  It can be hard to untangle the interplay between these elements.

About the product

The first difficulty is knowing exactly what precise product the review addresses.  Products and services are always changing, and sometimes these changes introduce uncertainties.  Have the tech specs or has the product offer changed?  Is the product now made by a different supplier, or using cheaper materials?  Has a defect been fixed, or is it a random, continuing problem?  Was the delivery experience good or bad because of time of year?  Was a hotel stay bad because of a manager who has now been replaced?  Has the priced changed, and accordingly expectations have changed as a result?

Brands sometimes make changes without changing the product name or model.  Other times, they introduce new product models for minor variations, and consumers become confused if their experience with a product they bought is relevant to the model currently for sale.  Not only do reviewers talk about past and current experiences, they may be inclined to speculate about future models or offers.

When comparing products, a difficulty can arise in deciding what features are considered essential to a product category.  Is having a choice of color an essential quality on which to judge a product?  Products with more features can appear more capable, but are not necessarily “better.”  All watches tell time, and some watches do much more than that. Figuring out which watches are comparable and should be reviewed together may involve some arbitrary decisions.

About the reviewer(s)

Readers typically know little about who the reviewer is, and what motivates them.  Many people have a suspicion of reviewers who seem overly enthusiastic or negative, which can reflect either a personality bias unrelated to the product (e.g., agreeability or snarkiness), or a naivety about what is reasonable to expect.  Positive reviews can reflect post hoc rationalizations justifying a purchase, and negative reviews can reflect buyer’s remorse.    Reviews can be as much about the reputation of the reviewer as about the reputation of the product.   In France recently, in an ultimate face-off of reputations, a reviewer of a restaurant was successfully sued for damaging the reputation of that restaurant.

In addition to whether reviewers have reasonable emotional expectations, readers wonder about the reviewer’s knowledge of the product category, and whether that knowledge is appropriate for their needs.   Reviewers may be expert users of a product or novice users, and may be either brand loyalists or first-time customers of the brand.  Each condition carries its own set of expectations.  Experts may criticize entry-level products as inferior; novice users can be wowed by mundane products, or possibly overwhelmed by them.  In some product categories, people apply different frames of reference to evaluate a product.  For some people the service at a restaurant is most important, for others, the authenticity of the food.  Different audience segments often rate products differently, applying different criteria.  When brands don’t appreciate these differences, the reviews become jumbled.  Yelp reviews tend to average three stars because everyone tends to focus on different characteristics, which all manage to cancel each other out.

About the reader

Like reviewers, readers have different priorities, which can change in different situations.  Sometimes an individual may prioritize convenience; other times he or she may prioritize price or features.  Readers will generally have more enduring preferences about products, which will shape their preferences toward reviews.  Broadly speaking, some people evaluate products (or categories of products) on a rational basis (cost/performance), and others on an emotional one (how it makes them feel).  But such distinctions are less clear than might first appear.  Most people choose clothing for emotional reasons, but will avoid a purchase if it doesn’t fulfill expected cost or performance criteria, perhaps learning that it looks great when new, but doesn’t hold up.  Some product categories, such as hiking and biking gear, appear to be about performance criteria, but these criteria are often a means of self-expression rather than utilitarian need.  When the product is truly experiential, perhaps an online course or digital music, it can be harder for individuals reviewing content to rely on explicit criteria.  Instead, they are more likely to try to compare the content with something else they have experienced previously, or to rely on the judgment of others who they feel resemble them.  In the magazine era, people who read certain magazines could be expected to judge products in similar ways, because they shared a common point of view about products, what was important and how to judge that.

Implications for digital content strategy

Audiences today face a multitude of sometimes-conflicting problems.  They may face an avalanche of product reviews about certain products such as the latest smartphone offerings.  They may have trouble comparing the usefulness and quality of different reviews of a product.  They often have trouble comparing two or more different products from different vendors, since reviews tend to focus on individual products.  They may be overwhelmed by the choice of products available, but find only some of these products are reviewed at all, and those that are reviewed have a paucity of information.  They may feel overwhelmed by apparent the irrelevance of many reviews.  They may feel many reviews seem to be more about the reviewer than the product itself.  They may feel scared to buy something when finding dramatic negative reviews, or angry when they didn’t pay attention to a negative review and later had a bad experience.  They may read many reviews but feel little wiser because the opinions seem confusing or inconclusive.

The current free-for-all in user reviews doesn’t serve brands well either.  They have little insight into how reviews are used.  They often don’t have a sound operational perspective on how to act on review information.  Some brands treat reviews as a social media channel and try to respond to comments as if they were on Facebook.  Other brands discourage reviews by asking customers to fill out private surveys.  Many Amazon vendors aggressively solicit reviews and even suggest what rating should be offered.  Those customers who do submit online reviews may not be representative of all customers.

Brands need to ask themselves some core questions.

How do we know which reviews are influential?  When ratings are aggregated, all reviews are considered equal.  But not all are equally useful to audiences.  Which specific reviews are useful and how do they influence others?  Some sites allow readers to rate reviews that are useful.   But often “useful” reviews are ones that are long, with lots of description of the product, offering information that should be in the product information, instead of offering true evaluations.

How do we improve the review experience?  It can be hard for readers to find the kind of information they seek or that reflect aspects they consider important.  They also rarely are able to discover other products they don’t already know about through reviews.

How do we encourage high quality reviews and feedback?  Too many reviews are of poor quality, or lacking essential information.  Many products aren’t reviewed at all.

Managing reviews as strategic content

Reviews are too important to be left to a junior forms designer on the UX team.  Reviews can be a strategic asset, if the right structure is in place to ensure that the content offers value.  When viewed through the lens of content strategy, there are many things that can be done to make reviews more effective.  I’ll share various ideas on how to improve the product review experience.  Not all of these ideas are appropriate for all products, and will depend on the breadth, depth and diversity of products being reviewed.  The ideas share a common theme: enrich the information by providing more explicit connections between information items.

Better product review information

Customers often comment on specific product features.  They deserve a better structure to enable them to do this.  Today reviewers are generally invited to leave comments in one big unstructured text box.  This needs to change.  One possibility would be to adopt the annotation functionality appearing on some blogging platforms.  Reviewers could provide their own comments about their experiences next to the product information describing a feature or product attribute.

Some sites elicit ratings about key product attributes, which allows these more specific ratings to be aggregated.  Potentially such attribute ratings could be compared across different products, although in practice this is rarely done.  Readers want to know how the experience of customers of one product compares with the experience of customers of another similar product, but online product reviews rarely provide this ability.  Part of the problem is that many product-oriented sites lack robust metadata to enable product comparisons; instead, they rely on highlighting what other products people looked at, which may not in fact be comparable.  Sites should combine a detailed taxonomy with database queries of product attributes and performance, to suggest what other products are most similar.  Ideally, this comparative product set could identify what attributes of are most valued, and which are the biggest concerns to buyers (durability, portability, ease of learning, etc.) Even if these attributes are not explicitly captured, they could be inferred through text analytics.

Finally, retailers and other providers of product review content need to be more proactive in managing the product architecture information. There are so many similar products: variations for different markets or buyer segments, minor product changes, white label products sold under multiple brand names.  Using the product taxonomy and product attributes and performance database, the review provider should identify similar product models that have a common basis, where possible.  Since consumer review information may be scant for a specific model, it is beneficial to highlight review information about related product models, including information about the brand’s overall reputation.

Providing context about reviewers

The use of real names is not always necessary and may not always be desirable.  But providing more context about a reviewer can help readers, and if done properly, benefits reviewers as well.  Many sites provide little incentive to reviewers to post their comments, and those that do often reward activity over quality.  Sites may acknowledge someone as a “top reviewer” because of the number of reviews posted, regardless of how relevant or useful they are.  Review providers need to move away from a social media popularity mindset, and instead think about review posting more in terms of community discussions.

The reputation of a reviewer rests on what they know (post on) and how useful their comments are to others.  The concept of reputation points used in community forums can be applied to product review forums.  If a reader deems a review useful or not useful, that reputation carries to the reviewer.  Reviewers who earn a threshold number of reputation points may receive a benefit that is unrelated to what they are reviewing: perhaps a small sum that can be donated to a cause they support.  The recognition of reputation is important for encouraging quality reviews, and helps readers evaluate reviews as well.

Readers also want to know what reviewers know about.  By correlating a person’s reviews with product categories, it is possible to provide a high level summary of what the reviewer has written about previously.  Readers could see the products a reviewer most actively reviews by brand or category.  This scent may offer the reader an indication of other content written by the reviewer they may be interested in seeing.  If the reviews are published in a community of interest focused on a product category for fans or enthusiasts, it may even make sense to allow readers to follow a reviewer who has deep expertise in a given area.

In summary, providing more context about reviewers can improve the reader’s evaluation and discovery of relevant products.

Supporting reader needs and actions

Product review content shouldn’t be considered in isolation from the product information: both influence buying decisions.  Ideally, review platforms should provide the ability for buyers to choose what criteria is important to them, and see both product information and related user comments about these attributes.  With such an approach, it may be possible to use analytics to correlate what review content a reader accessed, with the outcome of their purchase decision.  While there are many variables to track (potentially over multiple sessions), if the datasets are sufficiently large it might be possible to infer patterns.  It may then be possible to infer the impact of specific review content.  What kinds of comments, about what aspects of a product, had the biggest impact on purchases?

Conclusions

Product review content is a strategic asset, and needs to be managed as such.  Brands need to move beyond thinking about reviews as a simple popularity contest involving the awarding of stars.   Customer reviews are not just another passive KPI metric like customer satisfaction surveys: they are active content that drive customer behavior and business outcomes.  To leverage the power of this content, the content must be structured and enriched to support customer goals.

— Michael Andrews