Quantitative data that provides broad, numerical insights but often lacks the contextual depth that thick data provides. Useful for capturing high-level trends and patterns, but should be complemented with thick data to gain a deeper understanding of user behavior and motivations.
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.
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 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 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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 brief overview of the main points or sections of a document or web page. Crucial for helping users quickly understand the key takeaways and decide whether to read further.
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.
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.
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.
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.
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.
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.
The technology of transmitting and understanding information through touch. Crucial for enhancing user interactions with devices and systems through tactile feedback.
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 process of making small, continuous improvements to products, services, or processes over time. Important for sustaining growth and maintaining competitiveness through ongoing improvements.
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 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 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 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 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 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.
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
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 usability evaluation method where evaluators walk through tasks to identify potential user difficulties. Essential for improving user interfaces and ensuring usability.
A set of fundamental principles and guidelines that inform and shape user research practices. Crucial for maintaining consistency and ensuring high-quality user insights.
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.
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 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.
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.
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.
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.
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.
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.
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 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.
AI systems designed to communicate with users through natural language, enabling human-like interactions. Crucial for developing advanced customer service and user engagement solutions.
A statistical rule stating that nearly all values in a normal distribution (99.7%) lie within three standard deviations (sigma) of the mean. Important for identifying outliers and understanding variability in data, aiding in quality control and performance assessment in digital product design.
The tendency for individuals to put in less effort when working in a group compared to when working alone, due to reduced accountability. Crucial for understanding group dynamics and designing systems that ensure individual accountability.
A concept in behavioral economics that describes how future benefits are perceived as less valuable than immediate ones. Important for understanding user preferences and designing experiences that account for time-based value perceptions.