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.
Web users behaving like beasts in a jungle? There is ample data to support this claim. Animals make decisions on where, when, and how to eat on the basis of highly optimized formulas that have been developed over generations as behaviors that result in starvation are discarded. 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": Figuratively speaking, users estimate their 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 that they're "getting warmer"the scent must keep getting stronger and stronger or they will give up. Their progress must seem rapid enough to be worth the effort required to reach their goal.
Diet Selection: What Sites to Visit
A fox lives in a forest with big rabbits and small rabbits. Which should it eat? This is a question of diet selection, and 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 on the small onesthe probability of a catch is too low to justify the energy consumed by the hunt.
The big difference between Web sites and rabbits is that Web sites want to be caughtbig ones as well as small ones. So how can you design a site that will attract 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 together: Users will leave if the content is good but hard to find or if it's easy to find but not satisfying.
This dual strategy is the reason that Jakob's book with Marie Tahir, Homepage Usability: 50 Websites Deconstructed (New Riders Publishing, 2002), recommended showcasing sample content on the homepage (appear nutritious) and prominently display navigation and Search features (be an easy catch). Diet selection also supports our 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 Abandonment: When to Hunt Elsewhere
Grazing environments often feature several different areas where game congregates. So where should predators hunt? In whatever patch has the most prey, of course. And after predators have eaten some of that game, should they 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; when it becomes a bit difficult to find their next prey, they can move to richer hunting grounds. On the other hand, if it's difficult to movesay, if they have to cross a riverpredators are more likely to hunt each patch more extensively before going to the next.
For informavores, each site is a patch and each site's information is their prey. Moving between sites has always been easy, but for information foraging, it used to be best if users stayed put. The vast majority of sites were horrible and the probability that the next site would be better was extremely low.
We used to advise Web site designers to follow two strategies: Convince users that the site is worthy of their attention (by having good information and making it easy to find), and make it easy for users to find even more good stuff once they arrive so that they don't go elsewhere. An entire movement was devoted to the idea of "sticky sites" and extended visits.
An entire movement was devoted to the idea of "sticky sites" and extended visits. Improved search engines have reversed this equation.
In recent years, highly improved search engines have reversed this equation by emphasizing quality in their 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. This theoretical prediction was amply confirmed by the empirical data we collected for this book: People left the sites they found useless within less than two minutes.
The growth of always-on broadband connections also encourages this trend toward shorter visits. With dial-up, connecting to the Internet was somewhat difficult, and users mainly did it in big time chunks. In contrast, always-on connections encourage "information snacking": brief online searches for quick answers. The upside of this trend is that users will visit the Web more frequently and therefore find you more often, and will leave other sites faster.
Better intra-site navigation and 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 Web site will become ever shorter. This prediction is supported by our empirical data showing that high-experience users spent less time on pages than the low-experience users.
Informavore Navigation Behavior
Information foraging presents many interesting metaphors and mathematical models for analyzing user behavior. The most important is that of cost-benefit analysis for navigation. Users make tradeoffs based on two questions:
Both questions involve estimates, which users make from either experience or design cues. Web site designers can thus influence users' decisions by designing to enhance their expectations of gains and reduce their expectations of costs. Ultimately, of course, what a site actually delivers is most important, but it will never get experienced repeat visitors unless their first encounter is fruitful.
Users optimize cost-benefit relative to personal criteria and within a system that's larger than any single Web site. It's helpful to remember that they are selfish, lazy, and ruthless in applying their cost-benefit analyses.