Comment validez-vous vos hypothèses avant et après l’itération?

Assumptions are beliefs or hypotheses that you have about your users or the problem you're addressing. They may stem from experience, intuition, or early research, but they are not verifiable facts. If these assumptions are not confirmed, they might misguide design decisions, resulting in solutions that fall short of expectations. Validating assumptions is crucial in user experience design because it reduces uncertainty, saves resources, and ensures that your solution genuinely meets user wants and expectations. Through research and testing, you can transform assumptions into insights, ensuring that your design decisions are based on genuine user behavior and evidence.

Before making changes, test your assumptions by talking to users, running surveys, or creating simple prototypes to see if your idea solves a real problem. After making updates, check if they worked by measuring key metrics like engagement or sales and gathering user feedback. If the results don’t match your expectations, adjust and test again. The goal is to validate quickly, learn from real data, and keep improving.

To validate assumptions before iterating, start by conducting user research such as interviews, surveys, or usability testing to gather real feedback and identify pain points. Use prototypes or wireframes to test concepts early and confirm they meet user needs. After iterating, validate by measuring user interactions through analytics, A/B testing, or further usability sessions to ensure changes improve the experience and solve the intended problems effectively.

To validate assumptions, first identify them. The Assumption Mapping Canvas is a useful tool for sorting assumptions by relevance and uncertainty. The canvas is divided into four sections: Critical Unknowns, Critical Knowns, Uncritical Unknowns, and Uncritical Knowns. Begin by writing your assumptions on sticky notes or cards, then arranging them in the appropriate quadrant. Concentrate on Critical Unknowns these are the most unpredictable and have the most impact on your project. Validating them early can decrease risk and help guide design decisions, whereas Uncritical Knowns can frequently be safely accepted or deprioritized.

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Assumption Mapping Canvas could be a good tool for that! It allows to sort by relevance and uncertainty. There are various templates that can be found as a starting point (eg Notion).

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Before iterating on design solutions, validate your Critical Unknowns with the Lean Experiment Canvas. This tool allows you to structure experiments to evaluate assumptions and reduce risk. The canvas has six sections: assumption, hypothesis, experiment, success criteria, results, and learnings. Define the premise, develop a testable hypothesis, design an experiment, establish clear success criteria, collect data, and extract insights. Surveys, interviews, usability tests, landing pages, and prototypes are all examples of effective experiments. Validating assumptions early ensures that your revisions are based on real user behavior and facts, not speculation.

After you've proven your assumptions, iterate on your design with the Build-Measure-Learn Feedback Loop. This loop promotes continual learning and improvement through three steps: build, measure, and learn. Begin by creating a minimum viable product (MVP) that reflects the validated assumptions. Next, evaluate its effectiveness based on user input, behavior, and pertinent metrics. Then, assess the data to learn what works and what needs to be changed. Use these insights to improve your design, generate new hypotheses, and repeat the process. This iterative method ensures that your product improves based on actual consumer needs and proof.

Even after iterating on your design solutions, confirming your assumptions is critical, as they can change over time or across contexts. The Validation Board is an effective tool for managing this process. It contains three crucial columns: assumption, validation, and pivot or persevere. First, note your present assumption. Then, execute experiments and record the validation outcomes. Finally, determine whether to pivot change your assumption or solution in response to unfavorable feedback or to persevere move forward with confidence based on good validation. This organized method ensures that your design decisions are based on data and genuine user behavior.

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