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)
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)
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)
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)
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)
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)
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)
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