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 use of HTML tags to convey the meaning of content on web pages, improving accessibility and search engine optimization. Essential for creating accessible and SEO-friendly web content.
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.
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
Plan, Do, Check, and Act (PDCA) is a four-step management method used for continuous improvement of processes and products. Essential for implementing and maintaining continuous improvement in business and design processes.
A psychological phenomenon where individuals are perceived as more likable if they make a mistake, provided they are generally competent. Important for understanding human perception and leveraging relatability in marketing and leadership.
A project or venture that starts from scratch, with no constraints imposed by prior work, enabling innovation and flexibility in development. Essential for recognizing opportunities for innovation and fresh development in business initiatives.
The use of technology to perform repetitive tasks or processes in a workflow, liberating skilled experts from tedious activities and empowering them to focus on higher-order problem-solving and creative tasks. Crucial for streamlining operations, reducing human error, and enhancing the overall efficiency and innovation capacity of product design teams.
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.
A recommendation system technique that suggests items similar to those a user has shown interest in, based on item features. Important for providing personalized recommendations and improving user satisfaction.
The process of tracking and managing potential customers from initial contact through to sale. Important for ensuring that leads are properly engaged and converted.
A web-based interface that allows customers to find information and perform tasks without needing assistance from a customer service representative. Essential for improving customer experience and reducing support costs.
The main brand in a brand architecture that houses sub-brands or extensions. Crucial for providing overarching brand identity and consistency across sub-brands.
The extent to which a measure represents all facets of a given construct, ensuring the content covers all relevant aspects. Important for ensuring that assessments and content accurately reflect the intended subject matter.
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.
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.
The process of investigating and experimenting with new technologies to understand their potential applications and benefits. Essential for innovation and staying ahead in a rapidly changing technological landscape.
Actions, messages, or visuals that do not align with the established brand identity and values. Important for identifying and correcting deviations from brand standards.
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.
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 strategy or plan that outlines how a company will launch a product to market, including target audience, marketing tactics, and sales strategy. Essential for successfully launching products and capturing market share.
The experience of noticing something for the first time and then frequently encountering it shortly after, also known as frequency illusion. Important for understanding user perception and cognitive biases in information processing.
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.
The study of psychology as it relates to the economic decision-making processes of individuals and institutions. Essential for understanding and influencing user decision-making and behavior in economic contexts.
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 organizational structure and dynamics of teams within a company, designed to enhance collaboration and delivery. Important for optimizing team performance and project outcomes.
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.
Trust, Risk, and Security Management (TRiSM) is a framework for managing the trust, risk, and security of AI systems to ensure they are safe, reliable, and ethical. Essential for ensuring the responsible deployment and management of AI technologies.
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.
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.
Numeronym for the word "Interoperability" (I + 14 letters + Y), the ability of different systems, devices, or applications to work together and exchange information effectively without compatibility issues. Crucial for ensuring compatibility and integration between systems.
Product Strategy is a framework that outlines how a product will achieve its business goals and satisfy customer needs. Crucial for guiding product development, prioritizing features, and aligning the team around a clear vision.
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 structured classification of risks into categories, helping organizations identify, assess, and manage different types of risks. Important for understanding and managing risks effectively within an organization.
Numeronym for the word "Personalization" (P + 13 letters + N), tailoring a product, service, or experience to meet the individual preferences, needs, or behaviors of each user. Important for enhancing user satisfaction and 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.
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 blend of physical and digital experiences to create a cohesive user experience. Important for integrating online and offline customer interactions.
A cognitive bias where individuals evaluate the value of bundled items differently than they would if the items were evaluated separately. Important for understanding user behavior and designing effective product bundles and pricing strategies.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
A mental shortcut that relies on immediate examples that come to mind when evaluating a specific topic, concept, method, or decision. Crucial for understanding how people make decisions and the biases that influence their choices.
The practice of quickly testing and iterating on ideas to validate assumptions and learn from user feedback in a short time frame. Essential for agile development and making data-driven decisions efficiently.
The SEO value or authority passed from one website to another through hyperlinks, influencing the search engine ranking of the linked site. Important for understanding and leveraging the impact of links on SEO performance.
The process of testing product ideas and assumptions with real customers to ensure they meet market needs. Essential for reducing risk and ensuring product-market fit.
Anchoring (also known as Focalism) is a cognitive bias where individuals rely heavily on the first piece of information (the "anchor") when making decisions. Crucial for understanding and mitigating initial information's impact on user decision-making processes.
A guided, interactive overlay that introduces users to features or tasks within an application. Crucial for onboarding new users and enhancing user understanding of complex features.
The underlying goal or motivation behind a user's search query, crucial for understanding and optimizing content to meet user needs and improve SEO. Essential for creating content that aligns with user needs and improving search engine rankings.
AI systems designed to communicate with users through natural language, enabling human-like interactions. Crucial for developing advanced customer service and user engagement solutions.
The strategic promotion, placement, and persuasive presentation of digital products or services within an online platform to maximize sales, engagement, and user satisfaction. Important for optimizing the visibility, appeal, and persuasive impact of digital offerings, enhancing user experience, and driving conversions in online environments.
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 Japanese term meaning "the real place," used in Lean management to describe the place where value is created. Important for understanding the actual processes and identifying areas for improvement.
A design pattern that combines human and machine intelligence to enhance decision-making and problem-solving. Important for leveraging AI to support and amplify human capabilities.
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.
A concept in behavioral economics that describes how future benefits are perceived as less valuable than immediate ones. Important for understanding user preferences and designing experiences that account for time-based value perceptions.
An AI model that has been pre-trained on a large dataset and can be fine-tuned for specific tasks. Essential for developing state-of-the-art NLP applications.
The percentage of times a keyword appears in a text relative to the total number of words, used to evaluate the relevance and optimization of a webpage for specific search terms. Important for optimizing content for search engines without overstuffing keywords.
The tendency for individuals to put in less effort when working in a group compared to when working alone, due to reduced accountability. Crucial for understanding group dynamics and designing systems that ensure individual accountability.
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.
The process of turning potential customers into paying customers, often measured by the conversion rate. Essential for understanding and optimizing the customer journey.
A psychological phenomenon where a person who has done a favor for someone is more likely to do another favor for that person than if they had received a favor from them. Useful for building positive relationships and encouraging cooperative behavior in design and user interactions.