Summary: The easier it is to find places with good information, the less time users will spend visiting any individual website. This is one of many conclusions that follow from analyzing how people optimize their behavior in online information systems.
Information foraging is the most important concept to emerge from Human-Computer Interaction research since 1993. Developed at the Palo Alto Research Center (previously Xerox PARC) by Stuart Card, Peter Pirolli, and colleagues, information foraging uses the analogy of wild animals gathering food to analyze how humans collect information online.
To say that Web users behave like wild beasts in the jungle sounds like a joke, but there's substantial data to support this claim. Animals make decisions on where, when, and how to eat on the basis of highly optimized formulas. Not that critters run mathematical computations, but rather that suboptimal behaviors result in starvation, and thus fewer offspring that follow those behaviors in subsequent generations. After thousands of generations, optimal food-gathering behavior is all that's left.
Humans are under less evolutionary pressure to improve their Web use, but basic laziness is a human characteristic that might be survival-related (don't exert yourself unless you have to). In any case, people like to get maximum benefit for minimum effort. That's what makes information foraging a useful tool for analyzing online media.
Information Scent: Predicting a Path's Success
Information foraging's most famous concept is information scent: users estimate a given hunt's likely success from the spoor: assessing whether their path exhibits cues related to the desired outcome. Informavores will keep clicking as long as they sense (to mix metaphors) that they're "getting warmer" -- the scent must keep getting stronger and stronger, or people give up. Progress must seem rapid enough to be worth the predicted effort required to reach the destination.
The most obvious design lesson from information scent is to ensure that links and category descriptions explicitly describe what users will find at the destination. Faced with several navigation options, it's best if users can clearly identify the trail to the prey and see that other trails are devoid of anything edible.
Don't use made-up words or your own slogans as navigation options, since they don't have the scent of the sought-after item. Plain language also works best for search engine visibility: searching provides a literal match between the words in the user's mind and the words on your site.
Secondly, as users drill down the site, each page should clearly indicate that they're still on the path to the food. In other words, provide feedback about the current location and how it relates to users' tasks.
Diet Selection: What to Eat
A fox lives in a forest with two kinds of rabbits: big ones and small ones. Which should it eat? The answer is not always "the big rabbits."
Whether to eat big or small depends on how easy a rabbit is to catch. If big rabbits are very difficult to catch, the fox is better off letting them go and concentrating exclusively on hunting and eating small ones. If the fox sees a big rabbit, it should let it pass: the probability of a catch is too low to justify the energy consumed by the hunt.
The big difference between websites and rabbits is that websites want to be caught. So how can you design a site to make your content attractive to ravenous beasts?
The two main strategies are to make your content look like a nutritious meal and signal that it's an easy catch. These strategies must be used in combination: users will leave if the content is good but hard to find, or if it's easy to find but offers only empty calories.
This dual strategy is the reason I recommend that you showcase sample content on the homepage (appear nutritious) and prominently display navigation and search features (demonstrate that users can easily find what they're looking for). Diet selection also supports the traditional advice against splash screens and vacuous content. These elements convey to users that they're in for a tedious ordeal that serves up only scrawny rodents as rewards.
Patch Leaving: When to Hunt Elsewhere
Patchy environments often feature several different areas where game congregate. So where should predators hunt? In whatever patch has the most prey, of course. But after they've eaten some of that game, then what? Continue to hunt in the same patch, or move to another one? The answer depends on how far is it to the next patch.
If getting to the next patch is easy, predators are better off moving on. No need to deplete all the game in the current patch; once their next morsel becomes a bit difficult to find, they can move to richer hunting grounds. On the other hand, if it's difficult to move (say, if they have to cross a river), they're likely to hunt each patch more extensively before going to the next one.
On the Web, each site is a patch, and each site's information is its tasty venison.
Moving between sites has always been easy. But, from an information foraging perspective, it used to be best if users stayed put because the vast majority of websites were horrible and the probability that the next site would be any good was extremely low. I thus advised early website designers to follow two design strategies:
- First, convince users that the site is worthy of their attention. As I described above, this means having good information and making it easy to find.
- Second, once they arrive, make it easy for users to find even more good stuff so that they stay rather than go elsewhere. An entire movement was devoted to the idea of sticky sites and extended visits.
In the last few years, Google has reversed this equation by emphasizing quality in its sorting of search results. It is now extremely easy for users to find other good sites.
Information foraging predicts that the easier it is to find good patches, the quicker users will leave a patch. Thus, the better search engines get at highlighting quality sites, the less time users will spend on any one site.
The growth of always-on broadband connections also encourages this trend toward shorter visits. With dial-up, connecting to the Internet is somewhat difficult, and users mainly do it in big time chunks. In contrast, always-on connections encourage information snacking, where users go online briefly, looking for quick answers. The upside is that users will visit more frequently, since they have more sessions, will find you more often, and will leave other sites faster.
The patch-leaving model thus predicts that visits will become ever shorter. Google and always-on connections have changed the most fruitful design strategy to one with three components:
- Support short visits; be a snack
- Encourage users to return; use mechanisms such as newsletters as a reminder
- Emphasize search engine visibility and other ways of increasing frequent visits by addressing users' immediate needs
Better intra-site navigation and better site maps may tip the balance slightly back in favor of longer stays, but it's safest to assume that users' visits to any individual website will become ever shorter.
Informavore Navigation Behavior
Information foraging presents many interesting metaphors and mathematical models for analyzing user behavior. The most important concept is simply that of cost-benefit analysis for navigation, where users have to make tradeoffs based on two questions:
- What gain can I expect from a specific information nugget (such as a Web page)?
- What is the likely cost to discover and consume that information? (Cost is typically measured in time and effort, though it could include a monetary component in a micropayment system.)
Both questions involve estimates, which users can make either from experience or from design cues. Website designers can thus influence the analysis by designing to enhance user expectations of gains and reduce their expectations of costs. Ultimately, of course, what the site actually delivers is more important, but you'll never get experienced repeat visitors unless their first encounter is fruitful.
As the patch-leaving model demonstrates, users optimize cost-benefit relative to personal criteria and within a system that's larger than any single website. In addition to the detailed insights offered by individual models, it's healthy to remember that users are selfish, lazy, and ruthless in applying their cost-benefit analyses.