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.
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
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.
A statistical technique that uses several explanatory variables to predict the outcome of a response variable, extending simple linear regression to include multiple input variables. Crucial for analyzing complex relationships in digital product data.
The use of statistical techniques and algorithms to analyze historical data and make predictions about future outcomes. Important for optimizing marketing strategies and anticipating customer needs.
The process of making predictions about future trends based on current and historical data. Useful for anticipating user needs and market trends to inform design decisions.
The process of predicting future customer demand using historical data and other information. Crucial for optimizing inventory levels, production schedules, and supply chain management.
Also known as the 68-95-99.7 Rule, it states that for a normal distribution, nearly all data will fall within three standard deviations of the mean. Important for understanding the distribution of data and making predictions about data behavior in digital product design.
The systematic computational analysis of data or statistics to understand and improve business performance. Essential for data-driven decision making in design, product management, and marketing.
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 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 mathematical framework used to analyze strategic interactions where the outcomes depend on the actions of multiple decision-makers. Useful for designing systems and processes that involve competitive or cooperative interactions.
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 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.
The study of the principles that govern human behavior, including how people respond to stimuli and learn from their environment. Crucial for designing user experiences that anticipate and influence user behavior.
Recency, Frequency, Monetary (RFM) analysis is a marketing technique used to evaluate and segment customers based on their purchasing behavior. Essential for targeting high-value customers and optimizing marketing strategies.
A theory in economics that models how rational individuals make decisions under risk by maximizing the expected utility of their choices. Essential for understanding decision-making under risk.
A digital replica of a physical entity, used to simulate, analyze, and optimize real-world operations. Essential for improving operational efficiency and decision-making.
The process of estimating future sales based on historical data, trends, and market analysis. Crucial for setting realistic sales targets and planning resources effectively.
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.
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.
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 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.
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 method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
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.
Behavioral Science (BeSci) is the study of human behavior through systematic analysis and investigation. Essential for understanding and influencing user behavior in design and product development.
Market Requirements Document (MRD) is a comprehensive document that outlines the market's needs, target audience, and business objectives for a product. It serves as a crucial tool for aligning product development efforts with market demands and business goals, ensuring that the final product meets customer needs and achieves market success.
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.
Return on Advertising Spend (ROAS) measures the revenue generated for every dollar spent on advertising. Essential for assessing the effectiveness and profitability of marketing campaigns.
The loss of customers over a specific period, also known as customer churn. Important for understanding and addressing customer retention issues.
The study of complex systems and how interactions within these systems give rise to collective behaviors. Useful for understanding and managing the complexity in design processes and systems.
The reduction in sales of a company's existing products due to the introduction of a new product by the same company. Crucial for understanding product strategy and market impacts of new product introductions.
A theory that explains how individuals determine the causes of behavior and events, including the distinction between internal and external attributions. Crucial for understanding user behavior and designing experiences that address both internal and external factors.
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.
Data points that represent an individual's, team's, or company's performance in the sales process. Essential for tracking progress, identifying issues, and optimizing sales strategies.
Measurements that track the effectiveness of each stage of the funnel, such as conversion rates and drop-off points. Crucial for identifying areas of improvement in the customer journey.
The percentage of customers who stop using a product or service during a specific time period. Essential for understanding customer retention and identifying areas for improvement.
The rate at which customers stop using a product or service, often used as a metric to measure customer retention. Crucial for understanding customer behavior and improving retention strategies.
A strategic research process that involves evaluating competitors' products, services, and market positions to identify opportunities and threats. Essential for informing product strategy, differentiating offerings, and gaining a competitive advantage in the market.
The process of identifying and assessing the influence and interest of various stakeholders in a project, to prioritize engagement and communication strategies. Crucial for effectively managing stakeholder relationships and ensuring project success.
The study of how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
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 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.
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.
Net Promoter Score (NPS) is a metric used to measure customer loyalty and satisfaction based on their likelihood to recommend a product or service to others. Crucial for gauging overall customer sentiment and predicting business growth through customer advocacy.
A scheduling term that indicates a delay in the project timeline that cannot be recovered. Important for identifying and addressing potential project delays, ensuring timely delivery of digital products.
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.
Customer Relationship Management (CRM) is a strategy for managing an organization's relationships and interactions with current and potential customers. Essential for improving business relationships and driving sales growth.
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 dynamic systems that are highly sensitive to initial conditions, leading to unpredictable behavior. Important for recognizing and managing unpredictable elements in design and development processes.
A metric that shows the revenue that a company can expect to receive annually from its customers for subscriptions or services. Essential for understanding business performance and growth potential.
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.
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 process of designing, developing, and managing tools and techniques for measuring performance and collecting data. Essential for monitoring and improving system performance and user experience.
The competitive advantage gained by the initial significant occupant of a market segment, which can lead to brand recognition and customer loyalty. Important for understanding the benefits and risks of being an early entrant in a new market.
Needs and expectations that are not explicitly stated by users but are inferred from their behavior and context. Crucial for identifying and addressing unarticulated user needs.
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.
The speed at which leads move through the sales funnel. Crucial for understanding and optimizing the sales process.
The approach a company takes to manage and market its portfolio of products, ensuring each product supports the overall business strategy. Important for optimizing the range of products offered to maximize market reach and profitability.