A method used to create detailed narratives of potential future events to explore and understand possible outcomes and inform decision-making. Essential for strategic planning and anticipating the impact of different decisions or changes.
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.
A motivational theory suggesting that individuals are motivated to act based on the expected outcomes of their actions and the attractiveness of those outcomes. Important for understanding motivation and behavior, distinct from decision-making under uncertainty.
A systematic process for determining and addressing needs or gaps between current conditions and desired outcomes. Important for identifying user requirements and guiding the development of digital products that meet those needs.
A behavioral economics model that explains decision-making as a conflict between a present-oriented "doer" and a future-oriented "planner". Useful for understanding user decision-making and designing interventions that balance short-term and long-term goals.
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 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.
A cognitive bias where people give greater weight to outcomes that are certain compared to those that are merely probable. Important for designers to consider how users weigh certain outcomes more heavily in their decision-making.
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 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.
A strategic planning tool that focuses on outcomes and objectives rather than specific features, allowing for flexibility in achieving goals. Important for maintaining strategic focus and adaptability in product development.
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 concise statement of what the team aims to achieve during a sprint, providing direction and a shared understanding of the sprint's purpose. Crucial for ensuring team alignment and focus on the most important outcomes during a sprint.
A cognitive bias where people perceive an outcome as certain while it is actually uncertain, based on how information is presented. Crucial for understanding and mitigating biased user decision-making.
A cognitive bias where people attribute greater value to outcomes that required significant effort to achieve. Useful for designing experiences that recognize and reward user effort and persistence.
CSM (Customer Success Management) is a business methodology focused on ensuring customers achieve their desired outcomes while using a product or service. Crucial for driving customer retention and satisfaction.
A product development methodology that emphasizes shaping work before starting it, fixing time and team size but leaving scope flexible to ensure high-quality outcomes. Crucial for managing product development efficiently and delivering high-quality results within constraints.
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.
Time to Value (TTV) is a metric that measures the time it takes for a customer to realize the value of a product or service after purchase. Crucial for optimizing customer satisfaction and improving business outcomes.
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 phenomenon where individuals' expectations about a situation influence their actual experience of that situation. Useful for understanding the influence of expectations on outcomes.
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.
The process of evaluating the impact and success of a feature after its release, based on predefined metrics and user feedback. Crucial for understanding the effectiveness of features and informing future development.
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 study of how people make choices about what and how much to do at various points in time, often involving trade-offs between costs and benefits occurring at different times. Crucial for designing systems that account for delayed gratification and long-term planning.
A meeting held at the end of a project or development cycle, also known as a "post-mortem," to review what went well, what didn't, and how processes can be improved in the future. Crucial for continuous improvement and learning from past experiences to enhance future projects.
The tendency for individuals to continue a behavior or endeavor as a result of previously invested resources (time, money, or effort) rather than future potential benefits. Important for understanding decision-making biases and designing systems that help users avoid irrational persistence.
The tendency to overestimate the duration or intensity of the emotional impact of future events. Important for understanding user expectations and satisfaction.
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 practice of organizing the context in which people make decisions to influence the outcomes, often used to nudge users towards certain behaviors. Crucial for designing user experiences that guide decision-making and improve outcomes.
A cognitive bias where individuals give stronger weight to payoffs that are closer to the present time compared to those in the future. Important for understanding user time-related decision-making and designing systems that encourage long-term thinking.
The use of data, algorithms, and machine learning to recommend actions that can achieve desired outcomes. Essential for optimizing decision-making and implementing effective strategies.
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.
An organization that applies behavioral science to policy and practice to improve public services and outcomes. Important for understanding practical applications of behavioral science in policy and public services.
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.
An inference method used in AI and expert systems where reasoning starts from the goal and works backward to determine the necessary conditions. Important for developing intelligent systems that can solve complex problems by working from desired outcomes.
A cognitive bias that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
The combined efforts of humans and AI systems to achieve better outcomes than either could alone. Important for leveraging the strengths of both humans and AI in various tasks.
A set of fundamental principles and guidelines that inform and shape marketing practices. Crucial for maintaining consistency and ensuring high-quality marketing outcomes.
A cross-functional team that is given the autonomy, resources, and authority to make decisions and take ownership of the product's success, focusing on solving user problems and achieving business outcomes. Important for fostering innovation, accountability, and agility, leading to more effective product development and higher user satisfaction.
A cross-functional team focused on solving customer problems and achieving business outcomes, with a strong emphasis on discovery, experimentation, and continuous improvement. Crucial for creating valuable and innovative products that meet user needs and drive business success through iterative development and close collaboration with stakeholders.
A strategic plan that outlines the goals, milestones, and steps needed to deliver a product that achieves desired outcomes incrementally, providing a clear path forward. Essential for guiding product development and ensuring alignment with strategic objectives.
A document that provides a high-level overview of a product, including its objectives, target market, key features, and requirements, used to guide development efforts. Essential for ensuring that all stakeholders have a clear and consistent understanding of the product.
A principle stating that as investment in a single area increases, the rate of return on that investment eventually decreases. Important for understanding and optimizing resource allocation in product design and development.
A cognitive bias where individuals overlook or underestimate the cost of opportunities they forego when making decisions. Crucial for understanding user decision-making behavior and designing systems that highlight opportunity costs.
Cost Per Action (CPA) is an online advertising pricing model where the advertiser pays for a specified action, such as a sale or registration. This model is crucial for optimizing ad spend and measuring marketing effectiveness.
Capability, Opportunity, Motivation (COM...) is a framework for understanding Behavior (àB). Important for designing interventions that effectively change user behavior.
Activities that give the appearance of innovation but do not produce tangible results. Important for recognizing and avoiding ineffective innovation efforts.
A strategic planning technique that uses visual maps to align activities with business goals and user needs. Essential for ensuring that development efforts are aligned with strategic objectives.
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.
A theory suggesting that information processed at a deeper, more meaningful level is better remembered than information processed at a shallow level. Crucial for designing educational and informational content that enhances retention and understanding.
The process of identifying, assessing, and controlling dependencies between tasks or projects to minimize risks and ensure smooth project execution. Crucial for effective project management and delivery.
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.
Goal-Question-Metrics (GQM) is a framework for defining and interpreting software metrics by identifying goals, formulating questions to determine if the goals are met, and applying metrics to answer those questions. This framework is essential for measuring and improving software quality and performance.
A psychological state where individuals feel as though the success and well-being of a project or task is their personal responsibility, akin to having an "owner's mentality.". Essential for fostering accountability, motivation, and proactive engagement within a product design team.
The process of managing multiple related projects in a coordinated way to achieve strategic business objectives. Crucial for ensuring alignment and efficiency across multiple projects to achieve broader goals.
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.
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.
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 approach that involves meaningful analysis of information, leading to better understanding and retention. Crucial for designing educational and informational content that promotes deep engagement and learning.