A theoretical approach that focuses on observable behaviors and dismisses internal processes, emphasizing the role of environmental factors in shaping behavior. Foundational for understanding how external factors influence user behavior and for designing behavior-based interventions.
The study of the principles that govern human behavior, including how people respond to stimuli and learn from their environment. Crucial for designing user experiences that anticipate and influence user behavior.
Practical applications of behavioral science to understand and influence human behavior in various contexts. Crucial for applying scientific insights to design and improve user experiences and outcomes.
Behavioral Science (BeSci) is the study of human behavior through systematic analysis and investigation. Essential for understanding and influencing user behavior in design and product development.
A systematic evaluation of behaviors within an organization or process to identify areas for improvement and ensure alignment with goals. Crucial for understanding and improving user behaviors and organizational processes.
The evaluation of products based on their ability to influence and shape user behavior. Useful for assessing how well a product guides and influences user actions and decisions.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
The study of strategic decision making, incorporating psychological insights into traditional game theory models. Useful for understanding complex user interactions and designing systems that account for strategic behavior.
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 research method that involves observing subjects in their natural environment. Crucial for gathering authentic data and insights into real-world behaviors and interactions.
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 research method where participants take photographs of their activities, environments, or interactions to provide insights into their behaviors and experiences. Important for gaining in-depth, visual insights into user contexts and behaviors.
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.
The study of psychology as it relates to the economic decision-making processes of individuals and institutions. Essential for understanding and influencing user decision-making and behavior in economic contexts.
The study of how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
The process of collecting, analyzing, and reporting aggregate data about which pages a website visitor visits and in what order. Essential for understanding user behavior and improving website navigation and content.
A behavioral economics concept where people categorize and treat money differently depending on its source or intended use. Crucial for understanding financial behavior and designing systems that align with users' mental accounting practices.
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 theory that explains how individuals determine the causes of behavior and events, including the distinction between internal and external attributions. Crucial for understanding user behavior and designing experiences that address both internal and external factors.
A theoretical concept in economics that portrays humans as rational and self-interested agents who aim to maximize their utility. Important for understanding economic decision-making and designing systems that align with rational behavior.
The reduction of restraint in behavior, often due to the absence of social cues, which can lead to impulsive actions and emotional outbursts. Important for understanding user behavior in online and anonymous contexts.
A concept in transactional analysis that describes three different aspects of the self: Parent, Adult, and Child, each influencing behavior and communication. Important for designing communication strategies and interfaces that resonate with different user states.
The use of data from digital devices to measure and understand individual behavior and health patterns. Crucial for developing personalized user experiences and health interventions.
A data visualization technique that shows the intensity of data points with varying colors, often used to represent user interactions on a website. Essential for understanding user behavior and identifying areas of interest or concern in digital product interfaces.
Qualitative data that provides insights into the context and human aspects behind quantitative data. Crucial for gaining deep insights into user behaviors and motivations.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
A cognitive bias that causes people to attribute their own actions to situational factors while attributing others' actions to their character. Essential for helping designers recognize their own situational influences on interpreting user behavior and feedback.
A decision-making strategy where individuals allocate resources proportionally to the probability of an outcome occurring, rather than optimizing the most likely outcome. Important for understanding decision-making behaviors and designing systems that guide better resource allocation.
A theory in economics that models how rational individuals make decisions under risk by maximizing the expected utility of their choices. Essential for understanding decision-making under risk.
A qualitative research method that studies people in their natural environments to understand their behaviors, cultures, and experiences. Crucial for gaining deep insights into user behaviors and contexts.
The tendency for individuals to continue a behavior or endeavor as a result of previously invested resources (time, money, or effort) rather than future potential benefits. Important for understanding decision-making biases and designing systems that help users avoid irrational persistence.
The study of complex systems and how interactions within these systems give rise to collective behaviors. Useful for understanding and managing the complexity in design processes and systems.
The process of understanding user behaviors, needs, and motivations through various qualitative and quantitative methods. Essential for designing user-centered products and ensuring they meet actual user needs.
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 collaborative tool used to visualize what a user thinks, feels, says, and does to better understand their experiences and needs. Essential for gaining deep insights into user behavior and guiding design decisions.
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 statistical measure that quantifies the amount of variation or dispersion of a set of data values. Essential for understanding data spread and variability, which helps in making informed decisions in product design and analysis.
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
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 systematic computational analysis of data or statistics to understand and improve business performance. Essential for data-driven decision making in design, product management, and marketing.
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.
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
A tree-like model of decisions and their possible consequences, used in data mining and machine learning for both classification and regression tasks. Valuable for creating interpretable models in digital product design and user behavior analysis.
The study of social relationships, structures, and processes. Important for understanding the impact of social dynamics on user behavior and designing for social interactions.
A type of data visualization that uses dots to represent values for two different numeric variables, plotted along two axes. Essential for identifying relationships, patterns, and outliers in datasets used in digital product design and analysis.
A field research method where researchers observe and interview users in their natural environment to understand their tasks and challenges. Crucial for gaining authentic insights into user behavior and needs.
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.
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 system that suggests products, services, or content to users based on their preferences and behavior. Essential for personalizing user experiences and increasing engagement and conversion rates.
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.
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.
Representativeness is a heuristic in decision-making where individuals judge the probability of an event based on how much it resembles a typical case. Crucial for understanding biases in human judgment and improving decision-making processes.
The value or satisfaction derived from a decision, influencing the choices people make. Crucial for understanding user preferences and designing experiences that maximize satisfaction.
The study of how individuals make choices among alternatives and the principles that guide these choices. Important for designing decision-making processes and interfaces that help users make informed choices.
A cognitive bias where individuals strengthen their beliefs when presented with evidence that contradicts them. Important for understanding user resistance to change and designing strategies to address and mitigate this bias.
The spread and pattern of data values in a dataset, often visualized through graphs or statistical measures. Critical for understanding the characteristics of data and informing appropriate analysis techniques in digital product development.
A mathematical framework used to analyze strategic interactions where the outcomes depend on the actions of multiple decision-makers. Useful for designing systems and processes that involve competitive or cooperative interactions.
The rate at which customers stop using a product or service, often used as a metric to measure customer retention. Crucial for understanding customer behavior and improving retention strategies.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
The change in opinions or behavior that occurs when individuals conform to the information provided by others. Important for understanding social dynamics and designing systems that leverage social proof and peer influence.