Intentionality Bias
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
Recency, Frequency, Monetary (RFM) analysis is a marketing technique used to evaluate and segment customers based on their purchasing behavior. Essential for targeting high-value customers and optimizing marketing strategies.
A framework suggesting there are two systems of thinking: System 1 (fast, automatic) and System 2 (slow, deliberate), influencing decision-making and behavior. Crucial for understanding how users process information and make decisions.
The process of predicting how one will feel in the future, which often involves biases and inaccuracies. Important for understanding user behavior and decision-making, aiding in the design of better user experiences.
A mental shortcut where current emotions influence decisions, often bypassing logic and reasoning. Important for understanding how emotions impact user decisions, aiding in more effective design and marketing.
The mistaken belief that a person who has experienced success in a random event has a higher probability of further success in additional attempts. Crucial for understanding and designing around user decision-making biases.
A cognitive bias where individuals interpret others' behaviors as having hostile intent, even when the behavior is ambiguous or benign. Important for understanding user interactions and designing experiences that mitigate negative interpretations.
A psychological theory proposed by Abraham Maslow that outlines a five-tier model of human needs, ranging from basic physiological needs to self-actualization. Crucial for designing products and services that address various levels of user needs.
The phenomenon where people follow the direction of another person's gaze, influencing their attention and behavior. Important for understanding visual attention and designing more effective visual cues in interfaces.
The study of social relationships, structures, and processes. Important for understanding the impact of social dynamics on user behavior and designing for social interactions.
The process of dividing a broad consumer or business market into sub-groups of consumers based on shared characteristics, needs, or behaviors. Important for tailoring marketing strategies and product offerings to specific customer groups.
A decision-making strategy where individuals are prompted to make a choice rather than defaulting to a pre-set option. Useful for increasing user engagement and ensuring intentional decision-making.
A cognitive bias where people judge the likelihood of an event based on the size of its category rather than its actual probability. Crucial for designers to understand how category size influences user perception and decision-making processes.
A user research technique where participants organize information into categories to inform information architecture and design. Essential for creating intuitive information architectures and improving user experience.
A framework for understanding what drives individuals to act, involving theories such as Maslow's hierarchy of needs. Important for designing products and experiences that align with users' intrinsic and extrinsic motivations.
A theory suggesting that information processed at a deeper, more meaningful level is better remembered than information processed at a shallow level. Crucial for designing educational and informational content that enhances retention and understanding.
The tendency for people's perception to be affected by their recurring thoughts at the time. Important for understanding how current thoughts influence user perception and decision-making.
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.
An SEO issue that occurs when multiple pages on the same website target the same keyword, causing them to compete against each other and potentially harming search rankings. Important for optimizing SEO strategy and ensuring that each page targets unique keywords effectively.
A phenomenon where the winner of an auction tends to overpay due to emotional competition, leading to a less favorable outcome than anticipated. Important for understanding decision-making biases and designing systems that mitigate overbidding risks.
The practice of two websites agreeing to link to each other's content, often used to build relationships and improve SEO. Important for understanding link-building strategies and their impact on SEO.
A cognitive approach that involves meaningful analysis of information, leading to better understanding and retention. Crucial for designing educational and informational content that promotes deep engagement and learning.
A phenomenon where the probability of recalling an item from a list depends on the length of the list. Important for understanding memory processes and designing effective information presentation.
A cognitive bias where people ascribe more value to things merely because they own them. Useful for understanding user attachment and designing persuasive experiences.
The application of neuroscience principles to marketing, aiming to understand consumer behavior and improve marketing strategies. Important for creating more effective and engaging marketing campaigns.
A cognitive bias where individuals better remember the most recent information they have encountered, influencing decision-making and memory recall. Important for designing user experiences that leverage or mitigate the impact of recent information.
Interaction Design (IxD) focuses on creating engaging interfaces with well-thought-out behaviors. Crucial for ensuring intuitive and effective user interactions.
A cognitive bias where people prefer the option that seems to eliminate risk entirely, even if another option offers a greater overall benefit. Important for understanding decision-making and designing risk communication for users.
A model by Don Norman outlining the cognitive steps users take when interacting with a system: goal formation, planning, specifying, performing, perceiving, interpreting, and comparing. Important for designing user-friendly and effective products by understanding and supporting user behavior at each stage.
A detailed description of a system's behavior as it responds to a request from one of its stakeholders, often used to capture functional requirements. Essential for understanding and documenting how users will interact with a system to achieve their goals.
The enhancement or diminishment of perception, cognition, or related performance as a result of exposure to a stimulus of greater or lesser value in the same dimension. Useful for designing interfaces that leverage contrasting elements to guide user attention and behavior.
A cognitive bias where people tend to remember the first and last items in a series better than those in the middle, impacting recall and memory. Crucial for designing information presentation to optimize user memory and recall.
The tendency for people to overestimate their ability to control events. Important for understanding user behavior and designing experiences that manage expectations.
A set of metadata standards used to describe digital resources, facilitating their discovery and management. Important for ensuring effective organization and retrieval of digital assets in product design and development.
A heuristic where individuals evenly distribute resources across all options, regardless of their specific needs or potential. Useful for understanding and designing around simplistic decision-making strategies.
A research technique that explores the context in which users interact with a product, service, or environment to understand their needs and behaviors. Crucial for gaining deep insights into user contexts and designing more relevant solutions.
A cognitive bias where people prefer a greater variety of options when making simultaneous choices compared to sequential choices. Important for designers to consider user preferences for variety when designing choice architectures and product offerings.
A cognitive bias where people favor members of their own group over those in other groups. Important for designing inclusive and equitable experiences for users.
The style and attitude of the communication in a product, reflecting the brand's personality and affecting how messages are perceived by users. Important for creating a consistent and engaging user experience that aligns with the brand identity.
A framework for designing habit-forming products that includes four phases: Trigger, Action, Variable Reward, and Investment. Crucial for creating engaging and sticky user experiences.
A method of categorizing information in more than one way to enhance findability and user experience. Crucial for improving navigation, search, and overall usability of complex information systems.
The practice of presenting information in a way that is clear, accessible, and useful to the user. Essential for creating effective and user-friendly interfaces and communications.
A concept describing how motivation fluctuates over time, influenced by various factors such as goals, rewards, and external circumstances. Crucial for designing systems that align with users' motivational states to maximize engagement and productivity.
Measurements that track the effectiveness of each stage of the funnel, such as conversion rates and drop-off points. Crucial for identifying areas of improvement in the customer journey.
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.
The number of pixels per inch (PPI) on a display, affecting the sharpness and clarity of visual elements. Crucial for optimizing visual content for different devices.
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.
An experimental design where different groups of participants are exposed to different conditions, allowing for comparison between groups. Important for understanding and applying different experimental designs in user research.
A logical fallacy where people assume that specific conditions are more probable than a single general one. Important for understanding and addressing cognitive biases in user behavior.
Numeronym for the word "Observability" (O + 11 letters + N), the ability to observe the internal states of a system based on its external outputs, facilitating troubleshooting and performance optimization. Crucial for monitoring and understanding system performance and behavior.
The percentage of users who start but do not complete a desired action, such as completing a form or purchasing a product. Important for identifying issues in user flows and improving conversion rates.
A cognitive bias where individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
A set of cognitive processes that include working memory, flexible thinking, and self-control, crucial for planning, decision-making, and behavior regulation. Crucial for designing interfaces and experiences that support users' cognitive abilities.
The tendency for individuals to recall information that is consistent with their current mood. Important for understanding how mood affects memory and designing experiences that account for emotional states.
The tendency to cling to one's beliefs even in the face of contradictory evidence. Important for understanding resistance to change and designing interventions that address this bias.
A research method that involves repeated observations of the same variables over a period of time. Crucial for understanding changes and developments over time.
A statistical method used to predict a binary outcome based on prior observations, modeling the probability of an event as a function of independent variables. Essential for predicting categorical outcomes in digital product analysis and user behavior modeling.
The economic theory that suggests limited availability of a resource increases its value, influencing decision-making and behavior. Important for creating urgency and increasing perceived value in marketing.
A cognitive bias where individuals or organizations continue to invest in a failing project or decision due to the amount of resources already committed. Important for designers to recognize and mitigate their own risks of continuing unsuccessful initiatives.
ARIA attributes that define additional characteristics of elements, such as roles and relationships. Important for enhancing the accessibility and usability of web applications.