A method in natural language processing where multiple prompts are linked to generate more complex and contextually accurate responses. Essential for enhancing the capability and accuracy of AI models in digital products that rely on natural language understanding.
A design flaw where users mistakenly believe they have reached the end of the content due to a misleading visual cue. Crucial for ensuring content is properly signposted to avoid user confusion and ensure thorough exploration.
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 comprehensive list of all content within a system, used to manage and optimize content. Essential for organizing, auditing, and improving content strategy.
A cognitive bias where individuals believe that past random events affect the probabilities of future random events. Important for designers to understand user decision-making biases related to randomness.
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 marketing strategy that involves releasing a product to a limited audience to evaluate its market performance before a full-scale launch. Important for assessing market response, identifying potential issues, and refining digital products before a wider release.
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.
A range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter. Essential for making inferences about population parameters and understanding the precision of estimates in product design analysis.
A bias that occurs when researchers' expectations influence the outcome of a study. Crucial for designing research methods that ensure objectivity and reliability.
The process of comparing design metrics to historical performance, competitive standards, or industry best practices to identify areas for improvement. Crucial for measuring progress, improving practice maturity, and evaluating competitive differentiation.
A risk management model that illustrates how multiple layers of defense (like slices of Swiss cheese) can prevent failures, despite each layer having its own weaknesses. Crucial for understanding and mitigating risks in complex systems.
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.
The percentage of users who continue to use a product or service over a specified period, indicating user loyalty and engagement. Essential for assessing the effectiveness of user retention strategies and improving user experience.
Natural Language Processing (NLP) is a field of AI focused on the interaction between computers and humans using natural language. Essential for developing applications like chatbots, language translation, and sentiment analysis.
The process of combining multiple products or product lines into a single offering to streamline operations and reduce complexity. Useful for optimizing product portfolios and improving operational efficiency.
A metric that predicts how well a website will rank on search engine result pages (SERPs), based on factors like backlink quality and quantity. Important for understanding and improving a website's search engine performance.
A self-reinforcing process in which a collective belief gains more plausibility through its increasing repetition in public discourse. Important for understanding how information spreads and influences public perception.
The speed at which users start using a new product, typically measured as a percentage of the target market over a specific period. Essential for evaluating the success of a product launch and planning subsequent strategies.
Voice User Interface (VUI) is a system that allows users to interact with a device or software using voice commands. Essential for creating hands-free, intuitive user experiences.
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.
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 unit of measure used in Agile project management to estimate the relative effort required to complete a user story or task. Crucial for planning and managing workload within Agile teams.
A component in neural networks that allows the model to focus on specific parts of the input, improving performance. Essential for developing advanced AI models, particularly in natural language processing.
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.
A statistical theory that states that the distribution of sample means approximates a normal distribution as the sample size becomes larger, regardless of the population's distribution. Important for making inferences about population parameters and ensuring the validity of statistical tests in digital product design.
Average Revenue Per Account (ARPA) is a metric used to measure the average revenue generated per user or account. Crucial for understanding and optimizing revenue streams in subscription-based businesses.
A strategy used to determine the proportion of various SMEs needed to support a pipeline of work. Important for optimizing resource allocation, enhancing efficiency, and ensuring teams have the appropriate support based on design demand and complexity.
The risk that the product being developed will not deliver sufficient value to the users, meaning it won't meet their needs or solve their problems. Critical for ensuring the product will be desirable and valuable to the users, which is essential for its success.
ARIA attributes that describe the current state of an element, such as whether it is selected or expanded. Crucial for providing context and improving navigation for users with disabilities.
A form of regression analysis where the relationship between the independent variable and the dependent variable is modeled as an nth degree polynomial. Useful for modeling non-linear relationships in digital product data analysis.
A phenomenon where the success or failure of a design or business outcome is influenced by external factors beyond the control of the decision-makers, akin to serendipity. Important for recognizing and accounting for external influences in performance evaluations to ensure fair assessments and informed decisions.
ARIA attributes that define additional characteristics of elements, such as roles and relationships. Important for enhancing the accessibility and usability of web applications.
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.
An experimental design where subjects are paired based on certain characteristics, and then one is assigned to the treatment and the other to the control group. Important for reducing variability and improving the accuracy of experimental results.
A consensus-building technique where participants show their level of agreement or support by raising zero to five fingers. Useful for quickly gauging team agreement and making collaborative decisions in product design and development meetings.
Quality Function Deployment (QFD) is a method used to transform customer needs into engineering characteristics for a product or service. Essential for ensuring that customer requirements are systematically incorporated into the design and development process.
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 graphical representation of the distribution of numerical data, typically showing the frequency of data points in successive intervals. Important for analyzing and interpreting data distributions, aiding in decision-making and optimization in product design.
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 search method that seeks to improve search accuracy by understanding the contextual meaning of terms in a query rather than just matching keywords. Important for understanding modern search algorithms and optimizing content accordingly.
The percentage of users who take a specific action that signifies they are engaging with a product or service. Important for measuring user engagement and the effectiveness of onboarding processes.
The ability of consumers to remember a brand when prompted by a product category. Crucial for understanding brand strength and effectiveness in marketing.
A metric used to evaluate the trustworthiness of a website based on the quality of links pointing to it, often used in SEO. Crucial for improving website credibility and search engine rankings.
Large Language Model (LLM) is an advanced artificial intelligence system trained on vast amounts of text data to understand and generate human-like text. Essential for natural language processing tasks, content generation, and enhancing human-computer interactions across various applications in product design and development.
A cognitive bias where people overestimate the probability of success for difficult tasks and underestimate it for easy tasks. Useful for designers to understand user confidence and design
Acquisition, Activation, Retention, Referral, and Revenue (AARRR) is a metrics framework for assessing user engagement and business performance. Important for product managers to understand customer lifecycle and optimize business growth.
Data points that differ significantly from other observations and may indicate variability in a measurement, experimental errors, or novelty. Crucial for identifying anomalies and ensuring the accuracy and reliability of data in digital product design.
A usability test to see what impression users get within the first 10 seconds of interacting with a product or page. Important for designers to quickly gauge initial user impressions and improve immediate engagement.
Numeronym for the word "Localization" (L + 10 letters + N), adapting a product or content to meet the language, cultural, and regional preferences of a specific target market. Essential for ensuring product relevance in different regions.
The process of examining large and varied data sets to uncover hidden patterns, correlations, and insights. Important for making informed business decisions and identifying opportunities for innovation and growth.
A focus on the results or benefits of a project rather than the activities or deliverables produced. Crucial for ensuring that efforts are aligned with achieving meaningful results.
A cognitive bias where people tend to believe that others are more affected by media messages and persuasive communications than they are themselves. Important for understanding media influence and designing communications that account for this bias in user perception.
Business Intelligence (BI) encompasses technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. Crucial for making data-driven decisions and improving business performance.
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 use of algorithms to generate new data samples that resemble a training dataset, often used in AI for creating realistic outputs. Important for developing creative and innovative solutions in digital product design, such as content generation and simulation.
A visual tool used to map out the components and features of a product, showing how they relate to each other and to the overall product vision. Useful for visualizing and planning product development, ensuring all elements are aligned with the product vision.
Zone of Proximal Development (ZPD) is a concept in educational psychology that describes the difference between what a learner can do independently and what they can achieve with guidance and support. Crucial for designing effective educational experiences and scaffolding that promote optimal learning and skill development.
Key Performance Indicators (KPIs) are quantifiable measures used to evaluate the success of an organization, employee, or project in meeting objectives for performance. Essential for tracking progress, making informed decisions, and aligning efforts with strategic goals across various business functions, including product design and development.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.