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 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.
A logical fallacy in which it is assumed that qualities of one thing are inherently qualities of another, due to an irrelevant association. Important for avoiding incorrect associations in user research and data interpretation.
A document that defines the functionality, behavior, and features of a system or component. Important for providing clear requirements and expectations for product design and development teams, ensuring alignment and successful project outcomes.
The ability to identify and interpret patterns in data, often used in machine learning and cognitive psychology. Crucial for designing systems that leverage pattern recognition for predictive analytics and user interactions.
A tool used during brainstorming sessions to prompt and inspire creative thinking, often containing questions, scenarios, or constraints. Useful for facilitating structured ideation sessions and sparking new ideas.
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 emotional attachment an employee feels toward their organization, which influences their desire to stay. Useful for understanding employee retention and motivation in organizational design and management.
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.
Systematic errors in AI models that arise from the data or algorithms used, leading to poor outcomes. Important for ensuring fairness and accuracy in AI systems.
An economic theory that explains why some necessities, such as water, are less expensive than non-essentials, like diamonds, despite their greater utility. Useful for understanding consumer behavior and designing pricing strategies.
The tendency for individuals to favor information that aligns with their existing beliefs and to avoid information that contradicts them. Crucial for understanding how users engage with content and designing systems that present balanced perspectives.
Internet of Things (IoT) refers to a network of interconnected physical devices embedded with electronics, software, sensors, and network connectivity, enabling them to collect and exchange data. Essential for creating smart, responsive environments and improving efficiency across various industries by enabling real-time monitoring, analysis, and automation.
A theoretical framework in economics that assumes individuals act rationally and seek to maximize utility, used to predict economic behavior and outcomes. Important for understanding traditional economic theories and designing systems that account for rational decision-making.
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.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
The process of combining different systems or components in a way that ensures they work together smoothly and efficiently without disruptions. Essential for providing a cohesive user experience and ensuring the reliability of complex systems.
A behavior where users repeatedly bounce back and forth between a search engine results page and individual search results. Important for identifying issues in search result relevancy and user satisfaction.
Lifetime Value (LTV) is a metric that estimates the total revenue a business can expect from a single customer account throughout their relationship. Crucial for informing customer acquisition strategies, retention efforts, and overall business planning by providing insights into long-term customer profitability.
A cognitive bias where people ignore general statistical information in favor of specific information. Critical for designers to use general statistical information to improve decision-making accuracy and avoid bias.
A cognitive bias where individuals underestimate the time, costs, and risks of future actions while overestimating the benefits. Important for realistic project planning and setting achievable goals for designers.
A theory that a person's behavior is influenced by and influences personal factors and the environment, creating a continuous loop of interaction between these elements. Important for understanding how behavior, personal factors, and environmental contexts dynamically interact to shape user experiences and outcomes.
The level of awareness or popularity a product or brand has among consumers. Essential for understanding brand perception and guiding marketing and product design strategies to enhance visibility and user adoption.
A concept that humans make decisions within the limits of their knowledge, cognitive capacity, and available time, leading to satisficing rather than optimal solutions. Crucial for designing systems and processes that account for human cognitive limitations and decision-making processes.
The time it takes for a webpage to load and become interactive, impacting user experience and search engine rankings. Essential for improving user satisfaction and SEO performance.
A distributed version control system for tracking changes in source code during software development. Essential for collaborative development and managing codebase evolution in digital product design.
A usability evaluation method where evaluators walk through tasks to identify potential user difficulties. Essential for improving user interfaces and ensuring usability.
The practice of linking one page of a website to another page on the same website, improving navigation, user experience, and SEO. Essential for enhancing website structure, user engagement, and search engine optimization.
The process by which search engines organize and store web content to facilitate fast and accurate information retrieval. Crucial for understanding how search engines work and ensuring that web content is accessible and searchable.
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.
A metric that predicts how well a specific page will rank on search engine result pages (SERPs). Important for understanding and improving a webpage's search engine performance.
The technology of transmitting and understanding information through touch. Crucial for enhancing user interactions with devices and systems through tactile feedback.
The process of making small, continuous improvements to products, services, or processes over time. Important for sustaining growth and maintaining competitiveness through ongoing improvements.
The use of touch sensations to communicate information to users, often through vibrations or other tactile responses in devices. Essential for enhancing user interaction and providing sensory feedback.
A set of fundamental principles and guidelines that inform and shape user research practices. Crucial for maintaining consistency and ensuring high-quality user insights.
The practice of managing and resolving incidents that disrupt normal operations, ensuring minimal impact on business activities. Essential for maintaining service reliability and managing operational disruptions effectively.
A statistical distribution where most occurrences take place near the mean, and fewer occurrences happen as you move further from the mean, forming a bell curve. Crucial for data analysis and understanding variability in user behavior and responses.
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.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
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.
A navigation system that groups related links or content into clusters for easier access. Important for enhancing user experience by simplifying access to related information.
A persuasion strategy that involves getting a person to agree to a small request to increase the likelihood of agreeing to a larger request later. Crucial for building user commitment and enhancing marketing and sales strategies.
A quick and often temporary fix applied to a software product to address an urgent issue without going through the full development cycle. Essential for maintaining the stability and functionality of digital products in the face of critical issues.
The path or sequence of actions users follow based on information scent to find their desired information. Crucial for understanding user behavior and optimizing content discovery paths.
Marketing Qualified Lead (MQL) is a prospective customer who has shown interest in a company's product or service and meets specific criteria indicating a higher likelihood of becoming a customer. Essential for prioritizing leads and optimizing the efficiency of sales and marketing efforts by focusing resources on prospects most likely to convert.
A statistical method that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Essential for predicting outcomes and understanding relationships between variables in digital product design and 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.
The practice of identifying and analyzing search terms that users enter into search engines, used to inform content strategy and SEO. Essential for understanding user intent and optimizing content to meet search demand.
The use of AI and advanced analytics to divide users into meaningful segments based on behavior and characteristics. Crucial for personalized marketing and improving user experience.
Getting Things Done (GTD) is a productivity methodology that emphasizes capturing tasks, organizing them, and taking action. Essential for improving personal and team productivity and task management.
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.
Moment of Truth (MoT) refers to any instance where a customer interacts with a brand, product, or service in a way that leaves a significant impression. Crucial for identifying key touchpoints in the customer journey and optimizing them to enhance overall user experience and brand perception.
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.
The cognitive bias where people treat a set of items as more significant when they are perceived as a cohesive group. Important for understanding user perception and decision-making.
An economic approach that treats human attention as a scarce commodity, focusing on capturing and retaining user attention. Crucial for understanding user engagement and designing products that effectively capture and retain attention.
A research method where participants record their activities, experiences, and thoughts over a period of time, providing insights into their behaviors and needs. Important for gaining in-depth, longitudinal insights into user experiences.
A document that provides a high-level overview of a product, including its objectives, target market, key features, and requirements, used to guide development efforts. Essential for ensuring that all stakeholders have a clear and consistent understanding of the product.
Products are individual items or services designed to meet specific customer needs, while programs are collections of related projects and products managed together to achieve broader strategic goals. Essential for understanding the different scopes and objectives involved, helping to manage and align efforts effectively within an organization.
A cognitive bias where people perceive past events as having been more predictable than they actually were. Important for understanding and mitigating biases in user feedback and decision-making.