The risk that users will find the product difficult or confusing to use, preventing them from effectively utilizing its features. Crucial for making sure the product is user-friendly and intuitive, enhancing the user experience and adoption.
User Experience (UX) refers to the overall experience of a person using a product, system, or service, encompassing all aspects of the end-user's interaction. Crucial for creating products that are not only functional but also enjoyable, efficient, and satisfying to use.
User-Centered Design (UCD) is an iterative design approach that focuses on understanding users' needs, preferences, and limitations throughout the design process. Crucial for creating products that are intuitive, efficient, and satisfying for the intended users.
A cognitive bias where the pain of losing is psychologically more powerful than the pleasure of gaining. Important for designing user experiences that account for and mitigate loss aversion.
The theory that people adjust their behavior in response to the perceived level of risk, often taking more risks when they feel more protected. Important for designing safety features and understanding behavior changes in response to risk perception.
A cognitive bias where individuals tend to avoid risks when they perceive potential losses more acutely than potential gains. Important for understanding decision-making behavior in users and designing systems that mitigate risk aversion.
A cognitive bias where people assume others share the same beliefs, values, or preferences as themselves. Important for helping designers avoid projecting their own biases and assumptions onto users during research and design.
A theory that emphasizes the role of emotions in risk perception and decision-making, where feelings about risk often diverge from cognitive assessments. Important for designing systems that account for emotional responses to risk and improve decision-making.
A cognitive bias that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
A cognitive bias that causes people to believe they are less likely to experience negative events and more likely to experience positive events than others. Crucial for understanding user risk perception and designing systems that account for unrealistic optimism.
The tendency to believe that things will always function the way they normally have, often leading to underestimation of disaster risks. Important for understanding risk perception and designing systems that effectively communicate potential changes.
A cognitive bias where people seek out more information than is needed to make a decision, often leading to analysis paralysis. Crucial for designing decision-making processes that avoid information overload for users.
A dark pattern where availability is falsely limited to pressure users into making a purchase. Awareness of this deceptive practice is important to provide honest information about product availability.
A behavioral economic theory that describes how people choose between probabilistic alternatives that involve risk, where the probabilities of outcomes are known. Crucial for understanding decision-making under risk and designing systems that align with user behavior.
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
The process of testing product ideas and assumptions with real customers to ensure they meet market needs. Essential for reducing risk and ensuring product-market fit.
Emotional states where individuals are calm and rational, often contrasted with hot states where emotions run high. Important for understanding decision-making processes and designing experiences that accommodate both states.
A cognitive bias where people overestimate the importance of information that is readily available. Essential for designers to understand and mitigate how easily accessible information can disproportionately influence decisions.
The tendency to overestimate how much our future preferences and behaviors will align with our current preferences and behaviors. Important for understanding user behavior and designing experiences that account for changes over time.
A cognitive bias where decision-making is affected by the lack of information or uncertainty. Important for understanding and mitigating user decision-making biases due to uncertainty or lack of information.
A Japanese term for "mistake-proofing," referring to any mechanism or process that helps prevent errors by design. Crucial for designing systems and processes that minimize the risk of human error.
A deployment strategy where a new version is released to a small subset of users to detect any issues before a full rollout. Crucial for minimizing risk and ensuring the stability of digital products during updates and deployments.
A decision-making paradox that shows people's preferences can violate the expected utility theory, highlighting irrational behavior. Important for understanding inconsistencies in user decision-making and designing better user experiences.
A cognitive bias where people judge the likelihood of an event based on its relative size rather than absolute probability. Important for understanding user decision-making biases and designing systems that present information accurately.
The study of how people interact with their environment and products, aiming to improve comfort, efficiency, and safety. Crucial for designing user-friendly and safe products and workspaces.
A deployment strategy that reduces downtime and risk by running two identical production environments, switching traffic between them. Crucial for ensuring seamless updates and minimizing disruptions in digital product deployment.
Minimum Viable Product (MVP) is a version of a product with just enough features to be usable by early customers who can then provide feedback for future product development. Essential for validating product ideas quickly and cost-effectively, allowing teams to learn about customer needs without fully developing the product.
Net Promoter Score (NPS) is a metric used to measure customer loyalty and satisfaction based on their likelihood to recommend a product or service to others. Crucial for gauging overall customer sentiment and predicting business growth through customer advocacy.
A cognitive bias where people underestimate the complexity and challenges involved in scaling systems, processes, or businesses. Important for understanding the difficulties of scaling and designing systems that address these challenges.
Know Your Customer (KYC) is a process used by businesses to verify the identity of their clients and assess potential risks of illegal intentions for the business relationship. Essential for preventing fraud, money laundering, and terrorist financing, particularly in financial services, while also ensuring compliance with regulatory requirements and building trust with customers.
A technique used in software development to enable or disable features in a production environment without deploying new code, allowing for controlled feature rollouts. Essential for managing feature releases and testing in live environments.
Proof of Concept (PoC) is a demonstration, usually in the form of a prototype or pilot project, to verify that a concept or theory has practical potential. Crucial for validating ideas, demonstrating feasibility, and securing support for further development in product design and innovation processes.
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
The accumulated consequences of poor design decisions, which can hinder future development and usability. Crucial for understanding and addressing the long-term impact of design choices.
Return on Investment (ROI) is a performance measure used to evaluate the efficiency or profitability of an investment or compare the efficiency of different investments. Crucial for assessing the financial effectiveness of business decisions, projects, or initiatives.
A cognitive bias where people ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.