Berry Picking

What is Berry Picking?

Berry Picking is an information-seeking model developed by Marcia Bates that describes how users naturally search for information in an evolving, non-linear fashion. Unlike traditional search models that assume users have a fixed query and seek a complete answer set, berry picking recognizes that users gather bits of information incrementally, modifying their search as they learn, similar to picking berries from different bushes.

In this model, users start with an initial query but continuously evolve their search as they encounter new information. Each piece of information they find may lead them in new directions, change their understanding of their information need, or prompt them to refine their search terms. The search is not a single, linear process but rather a journey where the destination may change as users discover new "berries" of information along the way.

Why is Understanding Berry Picking Important?

Understanding Berry Picking is important because it reflects how users actually search for information in the real world, which is rarely a straightforward, linear process. This model helps designers create more intuitive search and navigation systems that support users' natural information-seeking behaviors, rather than forcing them into rigid, predefined paths.

By recognizing that users' queries evolve as they gather information, designers can create interfaces that facilitate discovery, provide contextual recommendations, and support users in refining their searches. This leads to more satisfying user experiences and more successful information-finding journeys. Understanding berry picking also helps explain why traditional search metrics like "time to completion" may not always be the best measure of search success.

How to Design for Berry Picking Behavior?

To design for berry picking behavior, provide clear contextual navigation that shows users where they are and where they can go next, implement related content suggestions that help users discover relevant information they might not have explicitly searched for, design search systems that support query refinement and evolution, and create interfaces that help users keep track of information they've already found.

Additional strategies include supporting browsing alongside searching, as many users prefer to explore rather than search directly, designing for information scent by providing previews and summaries that help users decide which paths to follow, implementing features that allow users to save and organize information they find valuable, and conducting user research to understand the specific information-seeking patterns of your target audience. Remember that the goal is to support users' natural exploration process, not to force them into a predetermined path.