A metric that measures how engaged users are with a product, often based on usage frequency, feature adoption, and user feedback. Crucial for assessing user satisfaction and identifying areas for improvement in the product experience.
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.
Monthly Recurring Revenue (MRR) is a metric that quantifies the predictable revenue generated each month from customers. This metric is crucial for SaaS companies to track financial health and growth.
The ability of consumers to remember a brand when prompted by a product category. Crucial for understanding brand strength and effectiveness in marketing.
The set of shared values, practices, and goals that characterize a startup company. Important for fostering innovation, agility, and a collaborative environment within product design teams.
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.
The Project Management Body of Knowledge (PMBOK) is a comprehensive set of guidelines, best practices, and standards for project management. Essential for ensuring consistency and excellence in managing projects across various industries.
A structured communication technique originally developed as a systematic, interactive forecasting method which relies on a panel of experts. Important for gathering expert opinions and making informed decisions.
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 role responsible for ensuring that products and services are delivered efficiently, on time, and within budget. Crucial for managing project timelines, resources, and stakeholder expectations.
The process of quickly creating a preliminary version of a product to test and validate ideas before full-scale development. Important for validating design concepts and gathering user feedback early.
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.
Program Increment (PI) Planning is a cadence-based event that serves as the heartbeat of the Agile Release Train, aligning teams on goals and priorities for the next increment. Crucial for aligning teams, setting goals, and planning work.
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 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.
The belief in one's ability to create change through creativity, often leading to more innovative and effective problem-solving. Essential for fostering a mindset of innovation and creativity in design.
Performance and Accountability Reporting (PAR) is a comprehensive document that outlines an organization's performance in achieving its goals and its accountability in managing resources. This report is essential for transparency, governance, and continuous improvement.
The tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
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 method used in AI and machine learning to ensure prompts and inputs are designed to produce the desired outcomes. Essential for improving the accuracy and relevance of AI responses.
The use of software tools to run tests on code automatically, ensuring functionality and identifying defects without manual intervention. Crucial for maintaining high code quality and efficiency in the development process.
A professional responsible for planning, executing, and closing projects, ensuring they are completed on time, within scope, and on budget. Crucial for managing project activities and ensuring successful delivery of project goals.
A cognitive bias where a person's subjective confidence in their judgments is greater than their objective accuracy. Crucial for understanding user decision-making and designing systems that account for overconfidence.
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.
Artificial Superintelligence (ASI) is a hypothetical AI that surpasses human intelligence and capability in all areas. Important for understanding the potential future impacts and ethical considerations of AI development.
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 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.
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.
The degree to which a product satisfies strong market demand, often considered a key indicator of a product's potential for success. Essential for validating the viability of a product in the market and guiding strategic decisions.
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.
Emotional states where individuals are calm and rational, often contrasted with hot states where emotions run high. Important for understanding decision-making processes and designing experiences that accommodate both states.
Guidelines and principles designed to ensure that AI systems are developed and used in a manner that is ethical and responsible. Crucial for building trust and ensuring the responsible use of AI technologies.
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 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.
New Product Development (NPD) is the complete process of bringing a new product to market, from idea generation to commercialization. Essential for companies to innovate, stay competitive, and meet evolving customer needs through a structured approach to creating and launching new offerings.
The study of how people interact with their environment and products, aiming to improve comfort, efficiency, and safety. Crucial for designing user-friendly and safe products and workspaces.
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.
Total Addressable Market (TAM) represents the total revenue opportunity available if a product or service achieves 100% market share. Essential for understanding the full potential of a market.
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 principle that states tasks always take longer than expected, even when considering Hofstadter's Law itself. Important for setting realistic project timelines and managing expectations in digital product development.
The principle that the more a metric is used to make decisions, the more it will be subject to corruption and distort the processes it is intended to monitor. Important for understanding the limitations and potential distortions of metrics in design and evaluation.
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.
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
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.
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.
The process by which a measure or metric comes to replace the underlying objective it is intended to represent, leading to distorted decision-making. Important for ensuring that metrics accurately reflect true objectives and designing systems that prevent metric manipulation.
Portfolio Management is the process of overseeing and coordinating an organization's collection of products to achieve strategic objectives. Crucial for balancing resources, maximizing ROI, and aligning products with business goals.
Web Content Accessibility Guidelines (WCAG) are a set of guidelines developed by WAI to make web content more accessible. Essential for ensuring that websites are usable by individuals with disabilities, thereby promoting inclusivity and compliance with accessibility standards.
A type of software testing that ensures that recent changes have not adversely affected existing features. Essential for maintaining software quality and reliability.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments. Crucial for ensuring the reliability, scalability, and performance of AI systems throughout their lifecycle, bridging the gap between model development and operational implementation.
The accumulated consequences of poor design decisions, which can hinder future development and usability. Crucial for understanding and addressing the long-term impact of design choices.
The speed at which leads move through the sales funnel. Crucial for understanding and optimizing the sales process.
A cognitive bias where individuals evaluate outcomes relative to a reference point rather than on an absolute scale. Essential for understanding decision-making and consumer behavior.
The percentage of visitors to a website who navigate away from the site after viewing only one page. Important for understanding user engagement and the effectiveness of a website's content and design.
The error of making decisions based solely on quantitative observations and ignoring all other factors. Important for ensuring a holistic approach to decision-making.
The systematic identification, analysis, planning, and implementation of actions designed to engage and influence stakeholders in a project. Crucial for maintaining positive relationships and ensuring stakeholder support throughout the project lifecycle.
Model-Based Systems Engineering (MBSE) is a methodology that uses visual modeling to support system requirements, design, analysis, and validation activities throughout the development lifecycle. Essential for managing complex systems, improving communication among stakeholders, and enhancing the overall quality and efficiency of systems engineering processes.
An environment that replicates the production environment, used for final testing before deployment. Crucial for ensuring that digital products are thoroughly tested and perform as expected before going live.
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 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.