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Content Experience Content Integration

Content sources across the customer journey

Customers are always on the run, checking information, making evaluations, and tracking how well and quickly they are getting things done. This momentum — being always on and always moving — has profound implications for content strategy. The best way to gain a holistic view of what’s involved is to look at the full customer journey, and the various services needed to support that journey, whomever provides them.  At different stages, the user has different tasks, and needs content to support these tasks.  When brands examine the journey from end to end,  they often discover that they do not have some of the content needed to support many of the user’s tasks.

Content comes from many different kinds of sources.  Brands are a major creator of content, but so are individuals, communities of people, as well as governments and non-government organizations.  Content can take many forms as well: it can be articles and videos, but also items of information commonly described as data.  One shouldn’t make a artificial distinction between authored content and factual data when these resources need to be visible and are meaningful to users.

To see how to join-up different sources of content to support user journeys, let’s consider a scenario.  Neil is a 41 year American software developer, recently divorced and living by himself in Research Triangle, North Carolina.  He recently had his blood pressure checked, which was found to be a bit high.  He is told he should consider modifying his diet to reduce his blood pressure.   Neil is someone into “lifehacking” so he decides to dig deeper into the topic to find out what’s best for him.

Step one: Goal setting with personal content

Neil reviews his device’s app store to see what’s useful.  He finds a healthy living app that can track his diet and makes recommendations on how to improve it.  He enters what he eats and drinks for a week and graphs the results.  The app flags his coffee and processed food consumption as areas he should watch — processed food contains a lot of sodium.  He likes the taste and convenience of processed food, but decides he should try to cook more for himself.  He fiddles with some parameters on his healthly living app and gets some recommendations on kinds of foods he should consider eating.  He likes some recommendations, hates others, and believes others are worthy but difficult.  He sets some goals for eating, and will track these in his app.  At this  goal setting stage, the content is personal to Neil: his recommendations based on parameters he selected, his goals, and his behavioral data.

Step two: Planning using community-contributed content

Neil doesn’t particularly enjoy cooking, because in the past he’s found it time consuming, and his results have been disappointing.  He searches for a source of recipes that are easy to make and don’t sound awful.  He finds a recipe community that specializes in easy to make dishes.  Community members submit recipes they like and can vote and comment on ones they’ve tried based on taste, ease of making, ease of storing ingredients, and ease of saving leftovers.  He likes the reputational dimensions of the community: members get recognition for their submissions and the votes cast and recieved.  Neil can link his healthy living app to this community, so that he can compare his profile goals with those of other community contributors.  He scans pictures of dishes that match his criteria and notices that some are favorites of people who follow protein rich diets and avoid carbohydrates.  On closer inspection of the ingredients, he sees these dishes avoid starches.  Neil likes his carbs, so filters out these options.  He looks for people more like him who are most concerned with the sodium dimension, and looks over their favorities.  He finds a couple of cassorole dishes that sound easy to make, and easy to save as leftovers.   For planning his meals, Neil has relied on community content: what’s popluar, and with whom it is popular.  He saves these recipes to his “to try” list in his healthy living app, so he can track when he has them.

Step three: Evaluation using public content and open data

Neil has two dishes he wants to make: a tuna casserole, and a Mexican casserole.  Both use ingridents easily obtained with a long shelf life: things like cans of tuna, cans of onion soup, cans of beans, bags of chips, jars of salsa, and processed cheese.  He hates having to worry about food spoiling in the fridge.  He notices a new detail about the ingredients: he must use low sodium varieties of these ingredients if the dish will qualify as low sodium.  Neils starting to feel overwhelmed: his supermarkets seems to have endless varieties of similar items, and he finds it a pain to read the tiny nuitrial lables on products.  He’s been warned that advertised claims of “reduced salt” can be misleading.  He wants to be able to search across different brands to find which ones have the lowest sodium.  Fortunately he finds a new website that is aggregating nutritional information of food products from many brands.  Ideally the USDA would aggregate all the information from nutritional labels of food products, and make it available in an open format with a public API.  But the USDA does not offer this information itself, so instead Neil uses a website that relies of voluntary submission by vendors, or the scrapping info from their websites.  The information is useful, though incomplete.  Neil is able to search for food products such as salsa, and find candidate brands that are low sodium.  He exports this list of brands to his shopping app on his phone.  He has relied on aggregated public information to evaluate which brands are most suitable.  Third party aggregators are credible providers of such information.

Step four: Purchase selection using company content

Neil now feels ready to visit his cavernous supermarket.  He chooses to shop at a supermarket that is employing new technology that allows shoppers to use their mobile phones to navigate through the store and check inventory.  The supermarket has it’s own app that can link to Neil’s shopping list.  It tells Neil which brands it has in stock, what the prices are, and what isle they are located on.  The store only carries one brand of low sodium salsa, but has three brands of low sodium beans, and he can compare the prices on his phone before hunting for them on the shelf.  Also the app shows photos of the items, so Neil knows what to look for.  So many products look similar, so it’s important to be sure you are picking up the one you really want, and not something that’s similar but different in a critical aspect (e.g., getting the extra spicy low sodium beans, instead of unflavored low sodium ones).  For the purchase phase, Neil has relied on company provided content.  He is motivated by ease of purchase, and individual retailers are in a primary position to offer content supporting such convenience.

neils content journey

Insights and lessons

Neil’s journey illustrates three major issues audiences and brands face when integrating content from different sources:

1.  technical constraints and functional gaps that create friction

2.  fuzzy ownership of responsibilities across the customer journey

3.  balancing the financial motivations of the brand with the incentives motivating the customer

Gaps, constraints, and friction

Everything in Neil’s scenario is technically feasible, even if parts seem magical compared with today’s reality.   For the user, a journey like this is often fragemented across many separate sites and apps, which may not share content with each other.  Users often rely on different kinds of content, from different sources, at different stages.

When Neil moves between apps or sites focused on different primary tasks there is obvious potential for friction.  As a computer professional, Neil is able to take content from one task domain and use it in another, using tools like IFTTT.  Other users, however, may have to manually re-enter content from one task domain to another, unless content linking and import is built-in.  Such built-in functionlity requires common exchange formats and APIs.   There are microformats for recipes, government-mandated nutritional information follows a standardized format, and retailers track products using standardized nomenclature such as UPCs and SKUs.  But content addressing higher level tasks such as dietary goals or ease of preparation do not follow open standards, meaning the exchange of such information between applications is more difficult.  In these cases, forging partnerships to create own’s own format to exchange content may be the best option.  Obviously, any connections between task domains (sharing log-in credentials, and sharing data) will help customers carry forward their journey, and help to drive adoption of your solution.

Whose problem is it?

The scenario highlights the fuzzy boundaries surrounding who offers the right solution for Neil.  In many cases, such as outlined in this scenario, no one party will orginate all the content needed to support a complex user task journey.  From a user perspective, it may seem desirable to have a “one stop” solution where he or she can perform all the tasks.  Such an approach would eliminate hopping between applications and websites, and potentially enable users to see connections between different tasks and their associated data and content.  But it isn’t obvious that one solution can obtain all the content needed to support the user.  Typically, integrated solutions do not offer the best content available.   Rather they offer content that is easy to obtain, or content that selectively promotes the goals of the brand behind the solution.  If you want to buy a camera, reading customer reviews on the Walmart website isn’t your best source of customer evaluations — buyers can get more complete and higher quality review information from a third-party photography website.  If a customer wants recipes, your supermarket may offer some that use products that the supermarket is promoting, but these recipes are not necessarily the best ones, and will certainly represent only a small sample of what’s available.

Brands need to think about what kinds of content their customers seek and consider during their journeys, and figure out how they can be a part of the conversation.  The goal should be to make your content available at whatever stage it is needed.  Look at opportunities to incorporate outside content where appropriate.  Think about where is the main source of content relating to this user task.  Can the brand get the content itsself, or does it make sense for it to offer its content to that source?

Being helpful with your content

Jeff Bezos reportedly said why brands earn or lose customer love: “Defeating tiny guys is not cool” while “Defeating bigger, unsympathetic guys is cool.”  To earn customer love, brands also need to consider how they treat other parties’ content.  Do they seem to be freely sharing a great resource, or are they seeming to throttle choice and push their own agenda with what they present? Whether a brand chooses to incorporate other parties’ content into their solution, or offer their content to others (via an API), it needs to come across as generous, and unbiased, to earn credibility and trust.

Audiences invest time and effort evaluating content, saving content, and creating their own content, motivated by the value they derive from different content sources.  It is important to respect that effort.  Content linking and sharing is a classic example of a network effect, where the content becomes more valuable the more different task scenarios it can be used.   Brands need to consider the network effect dynamics when choosing what content to offer, and where to offer it.

There can be a natural tendancy for brands to want to only invest in content that shows immediate payoffs.  Consider the supermarket chain.  It did not choose to submit the nutritional information of its house brands to the third party website.  As a result, its house brands were not part of Neil’s consideration set.  When it created its in-store app, some members of the supermarket exective team didn’t want to include photos of the products.  They reasoned that it was an unnecessary expense.  The price and inventory information were already available in their inventory system, but that system didn’t store photographic content.  But by making the investment, they improved the customer experience, and greatly increased adoption of their app.

The supermarket executives also debated how to understand more fully what their customers wanted to buy, so they could better forecast demand.  Their prior attempt to tie their loyalty card with their own recipe app, offering coupons, didn’t result in much adoption.  They were interested in figuring out how to get people like Neil to give them his dietary goal setting information.  While this is valuable content for the supermarket chain, helping them better target ads and offers, it isn’t clear what Neil would get in return for providing this information.  More coupons?  Neil gets a clear benefit using his goals to plan his meals, but the value of providing his goals to the supermarket after he’s already decided what he wants to buy isn’t clear.  The supermarket needs to think how Neil can use this information in the context of his relationship with the supermarket, so that Neil is in charge of what he does with the information, and derives value using it.  Perhaps he could be rewarded for participating in a program to test new products that are aligned with his dietary goals.

Final thoughts

Brands, especially retail brands and service providers such as banks, hotels and airlines, are thinking more about omnichannel communication with their customers.  Customers can need help at any point, can seek content through many channels and from many sources (including those of rivals) and expect answers instantly.  A strategy that shares content across tasks is the best approach to meeting customers needs as they arise.  If customers are doing a task that involves other sources of content in addition to your own, your brand needs to figure out how customers can integrate both kinds of content to provide the level of support they increasingly expect.  Having your content play well with others is not just a nice thing to do, but a business imperative.

— Michael Andrews

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Content Effectiveness Content Experience

Seven examples of content behavior design

Content behavior design promotes the discovery of content.  It is different from information architecture, which is focused on global information organization and navigation, and on offering users tools to specify what they are seeking.  Content behavior design anticipates what content might be interesting for users, and decides what to display based on that.  Rather than assume the user is necessarily looking to find a piece of information, content design assumes that the user may not be consciously looking for a piece of information, but would be happy to have it available if it were relevant to core content she is viewing.

In some cases, content behavior design can help people discover things they were not seeking.  In other cases, additional content provides more clarity.  Effective designs give audiences more context, making the content richer.  Here are six examples to illustrate how content behavior can work for audiences.

Real time content aggregation (Kayak)

kayak flight

Many bits of information are associated with a single label (a flight number) representing a single object (a plane).  This example brings together real time information about the flight, showing information about three locations (departure, current, and arrival) and timing information about events associated with these locations.  The aggregation of many pieces of real time information makes this powerful.  Real time information is compelling because it changes and gives audiences a reason to check for updates.  One could imagine this example being even more useful if it included weather related information affecting the flight, especially any weather conditions at the arrival destination that could impact the projected arrival time.

Content about related actions (ESPN)

espn tickets

In interaction design, it is helpful to highlight a next action, instead of making the user look elsewhere for it.  In this example from ESPN, the column on the far right allows the user to order tickets for a basketball game.  But instead of simply saying “order tickets,” it provides information about how many seats are available and the costs.  Incorporating this content is successful for two reasons: 1. It gives people interested in ordering tickets an idea of their availability; and 2. It gives people not interested in attending the game in person a sense of how anticipated the game is in terms of attendance.  Based on the number of tickets sold, and the prices of tickets, do fans expect an exciting game?

Tracking components of collections (Paprika)

paprika shopping

Digital content curation is an important development.  People collect content items that have associated metadata.  As they assemble items into collections, the metadata can be combined as well.  In this example from the recipe manager app Paprika, the ingredients from two recipes are combined into one shopping list, so that the user knows how many eggs in total he needs to make both dishes.  The content is smart enough to anticipate a need of the user, and perform that task without prompting.   Another example is the app Delicious Library, which can track the replacement costs of books one owns.  Designers use content behavior for applications focused on the “quantified self”— the collection of information about yourself.  For example, a design could tell the user what night of the week she typically sleeps best.

Audience activity insights (Economist)

economist readers

What audiences are interested in is itself interesting.  The Economist has adapted the concept of a tag cloud to listen to reader comments on their articles.  The program listens for keywords, newsworthy proper nouns or significant phrases, and shows relative frequency and extent they coincide.   It’s a variation of the “most commented” article list, but shifts the focus to the discussion itself.  Audiences can see what topics specifically are being discussed, and can note any relationships between topics.  For example, Apple is being discussed in the context of China, rather than in the context of Samsung.  Users can hover over to see the actual comments.  It provides a discovery mechanism for seeing the topicality of the week’s news, and provides enough ambiguity to tempt the reader to explore more to understand why something unfamiliar is being discussed.

Data on content facets (Bloomberg)

bloomberg

Content can have many facets. Faceted navigation, which takes the user to other content related to that facet, is a well established navigation technique.  This example from Bloomberg, in contrast, brings the content to the user.  As the interview is happening, users can get more information about things mentioned in the interview.  Without leaving the interview, the user can get more context, viewing real time information about stock prices discussed, or browsing headlines about companies or industries mentioned.  The viewer can even see how often the person speaking has appeared on the show previously to get a sense of their credibility or expertise.  Even though some of this information is hidden by collapsible menus, the user does not need to request the system to pull this information – it is provided by default.

Data-driven leaderboards (IMDb)

imdb leaderboard

Lists are a helpful navigation tool, but they are more valuable when they have interesting data behind them.  Unlike tables of data, which require users to sort, leaderboards provide automatic ranking by key criteria.  In this example from IMDb, animation series and titles are ranked by user rating and gross revenues.  The ranking provides the casual viewer a chance to gauge relative popularity before clicking on one for more information, while the core fan might check the list to see if their favorite film has moved up in the rankings.

Content recommendations (Netflix)

netflix recommendation

There are growing numbers of content recommendation engines, covering articles, books, music, videos and even data.  They rely on different inputs, such as user ratings, user consumption, peer ratings, peer consumption, and imputed content similarity.  In many respects, content recommendation engines represent the holy grail of content behavior design.  The chief problem for users is understanding and trusting the algorithm.  Why am I being told I would like this?  Netflix provides a rationale, based on prior activity by the user.  It’s probably a simplification of the actual algorithm, but it provides some basis for the user to accept or reject the recommendation.  I expect recommendation engines will evolve further to provide better signals that suggestions are a good fit (no risk), and that they aren’t too narrow (the filter bubble problem).

Ideas for thinking about behavior

In choosing what content to present, it helps to ask:

  • what else might someone find helpful that is related to what is being presented?
  • what aspects of content are notable, changing, and newsworthy, and how can you highlight these aspects?
  • how can you present content elements so they are interesting, rather than simply informative?
  • if you are trying to encourage audiences to act, how can real time content to used to support that?
  • how do different audiences relate to the content, and can you provide something that appeals to different segments?
  • what content could the system automatically provide that is laborious for someone to do themselves?

Designing content behavior is central to content engagement.  Try out ideas, and test them to see what works for your audiences.

— Michael Andrews