A decision-making strategy that involves choosing an option that meets the minimum requirements rather than seeking the optimal solution, balancing effort and outcome. Important for designing user experiences that accommodate decision-making under constraints.
Zero Moment of Truth (ZMOT) is a concept in marketing that refers to the point in the buying cycle when the consumer researches a product before the seller even knows they exist. Crucial for understanding consumer behavior and optimizing marketing strategies to influence decision-making at this early stage.
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 overestimate the accuracy of their judgments, especially when they have a lot of information. Important for understanding and mitigating overconfidence in user decision-making.
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 cognitive bias where people disproportionately prefer smaller, immediate rewards over larger, later rewards. Important for understanding and designing around user decision-making and reward structures.
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 phenomenon where having too many options leads to anxiety and difficulty making a decision, reducing overall satisfaction. Important for designing user experiences that balance choice and simplicity to enhance satisfaction.
A statistical technique that uses random sampling and statistical modeling to estimate mathematical functions and simulate systems. Useful for risk assessment, decision-making, and performance optimization in digital product design.
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.
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.
Business Rules Engine (BRE) is a software system that executes one or more business rules in a runtime production environment. Crucial for automating decision-making processes and ensuring consistency and compliance in digital products.
The process of using statistical analysis and modeling to explore and interpret business data to make informed decisions. Essential for improving business performance, identifying opportunities for growth, and driving strategic planning.
Cost of Delay (CoD) is a metric that quantifies the economic impact of delaying a project, feature, or task. Important for making informed decisions about project prioritization and resource allocation.
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.
The tendency for people to overestimate their ability to control events. Important for understanding user behavior and designing experiences that manage expectations.
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 strategic approach where multiple potential solutions are tested to identify the most promising one. Crucial for innovation and reducing risk in 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 cognitive bias where people focus on the most noticeable or prominent information while ignoring less conspicuous details. Important for understanding user decision-making and ensuring balanced presentation of information.
The process of gathering and analyzing information about competitors to inform business strategy and decision-making. Essential for understanding market positioning and developing effective competitive strategies.
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.
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 phenomenon where the winner of an auction tends to overpay due to emotional competition, leading to a less favorable outcome than anticipated. Important for understanding decision-making biases and designing systems that mitigate overbidding risks.
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.
The practice of using data analytics and metrics to make informed decisions, focusing on measurable outcomes and efficiency rather than intuition or traditional methods. Important for optimizing design processes, improving product performance, and making data-driven decisions that enhance user experience and business success.
The interpretation of historical data to identify trends and patterns. Important for understanding past performance and informing future decision-making.
The process of integrating knowledge into computer systems to solve complex problems, often used in AI development. Important for developing intelligent systems that can perform complex tasks and support decision-making in digital products.
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.
The practice of setting defaults in decision environments to influence outcomes, often used in behavioral economics and design. Crucial for creating user experiences that encourage beneficial behaviors through preselected options.
The practice of measuring and analyzing data about digital product adoption, usage, and performance to inform business decisions. Crucial for making data-driven decisions that improve product performance and user satisfaction.
A strategic framework that designs user experiences to guide behavior and decisions towards desired outcomes. Crucial for creating effective and ethical influence in digital interfaces.
The implied cost of additional rework caused by choosing an easy or limited solution now instead of using a better approach that would take longer. Essential for understanding and managing the long-term impacts of short-term technical decisions.
A usability testing method where users interact with a system they believe to be autonomous, but which is actually operated by a human. Essential for testing concepts and interactions before full development.
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.
Information Visualization (InfoVis) is the study and practice of visual representations of abstract data to reinforce human cognition. Crucial for transforming complex data into intuitive visual formats, enabling faster insights and better decision-making.
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.
Garbage In-Garbage Out (GIGO) is a principle stating that the quality of output is determined by the quality of the input, especially in computing and data processing. Crucial for ensuring accurate and reliable data inputs in design and decision-making processes.
A research approach that starts with a theory or hypothesis and uses data to test it, often moving from general to specific. Essential for validating theories and making informed decisions based on data.
The systematic investigation of competitor activities, products, and strategies to gain insights and inform decision-making. Crucial for staying competitive and improving product and service offerings.
A problem-solving method that explores all possible solutions by examining the structure and relationships of different variables. Useful for generating innovative design solutions and exploring a wide range of possibilities in digital product development.
A statistical method used to identify underlying relationships between variables by grouping them into factors. Crucial for simplifying data and identifying key variables in research.
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.
The risk that the product will not be financially or strategically sustainable for the business, potentially leading to a lack of support or profitability. Essential for ensuring that the product aligns with business goals and can be maintained and supported long-term.
The practicality of implementing a solution based on technical constraints and capabilities. Crucial for evaluating the viability of design and development projects.
Integrated Business Planning (IBP) is a process that aligns strategic, operational, and financial planning to optimize business performance. It ensures cohesive and efficient planning across all functions.
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.
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 tendency to give more weight to negative experiences or information than positive ones. Crucial for understanding user behavior and designing systems that balance positive and negative feedback.
A principle stating that 80% of effects come from 20% of causes, often used to prioritize tasks and identify key areas of focus. Essential for prioritizing tasks and focusing efforts on the most impactful areas.
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.
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.
Joint Application Development (JAD) is a collaborative approach to gathering requirements and designing solutions in software development projects. It facilitates rapid decision-making and consensus-building by bringing together key stakeholders, including users, developers, and project managers, in structured workshop sessions.
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.
Proof of Concept (PoC) is a demonstration, usually in the form of a prototype or pilot project, to verify that a concept or theory has practical potential. Crucial for validating ideas, demonstrating feasibility, and securing support for further development in product design and innovation processes.
Business Process Automation (BPA) refers to the use of technology to automate complex business processes. Essential for streamlining operations, reducing manual effort, and increasing efficiency in recurring tasks.
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.
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.
A statistical phenomenon where a large number of hypotheses are tested, increasing the chance of a rare event being observed. Crucial for understanding and avoiding false positives in data analysis.
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.