Pendo Framework
The Pendo framework is a prioritisation and effort framework developed by the company Pendo. It is designed to help product and design teams make data-driven decisions about where to allocate their resources, particularly for user research and design efforts. The Pendo framework is a valuable tool for product teams to align on the required effort for a given project, set realistic expectations, and ensure that resources are being directed toward the work that will provide the most value to both the users and the business. The core of this framework is a quadrant system with two key dimensions:
Problem Clarity: How well is the problem or user need understood? Is it clearly defined, or is it vague and requires more discovery?
Risk: What is the risk associated with getting the solution wrong? Is it a high-stakes change (e.g., a core feature of the product) or a low-risk one (e.g., a minor UI tweak)?
By plotting a project or feature on this matrix, the framework suggests the appropriate level of effort and type of research needed:
High Problem Clarity & Low Risk: "Ship it and Measure." This quadrant is for projects where the problem is well-understood and the stakes are low. The best approach is to quickly build the solution and use product analytics to measure its impact and make adjustments.
High Problem Clarity & High Risk: "Design Heavy." When the problem is clear but the risk of failure is high, the framework advises a significant investment in design and evaluative testing (e.g., usability testing). This ensures the solution is robust and effective before a high-cost release.
Low Problem Clarity & Low Risk: "Research Light." In cases where the problem isn't fully defined but the risk is low, teams should do some quick, targeted research to gain clarity. This could involve surveys or leveraging existing data without a huge investment.
Low Problem Clarity & High Risk: "Research Heavy." This is the most complex quadrant. When both the problem is unclear and the risk is high, a significant investment in deep, foundational user research is necessary to understand the problem space before a solution can even be designed.
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