An experimental design where subjects are paired based on certain characteristics, and then one is assigned to the treatment and the other to the control group. Important for reducing variability and improving the accuracy of experimental results.
The process of designing and refining prompts to elicit accurate and relevant responses from AI models. Crucial for optimizing the performance of AI applications.
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.
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.
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 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.
The process of optimizing content and website structure to improve visibility and ranking in voice search results. Important for adapting to the growing use of voice search and ensuring content is accessible to voice queries.
A type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Crucial for developing intelligent systems that can make data-driven decisions.
Specific and less common keyword phrases that visitors are more likely to use when they are closer to making a purchase or when using voice search. Important for targeting niche markets and improving SEO with highly specific search terms.
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.
The study of the interplay between individuals and their surroundings, including built environments and natural settings. Essential for designing spaces that enhance well-being and productivity.
The study of the relationships between people, practices, values, and technologies within an information environment. Helps in understanding and designing systems that are sustainable and adaptive to human and environmental changes.
A performance testing method that evaluates the system's behavior and stability over an extended period under a high load. Essential for identifying memory leaks and ensuring the reliability and performance of digital products under prolonged use.
A test proposed by Alan Turing to determine if a machine's behavior is indistinguishable from that of a human. Important for evaluating the intelligence of AI systems.
A pricing strategy that offers a middle option with substantial value at a moderate price, often perceived as the best deal by users. Useful for driving sales by presenting a balanced choice that appears more attractive relative to higher and lower-priced options.
A type of data visualization that uses dots to represent values for two different numeric variables, plotted along two axes. Essential for identifying relationships, patterns, and outliers in datasets used in digital product design and analysis.
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 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.
A method in natural language processing where multiple prompts are linked to generate more complex and contextually accurate responses. Essential for enhancing the capability and accuracy of AI models in digital products that rely on natural language understanding.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
A product that significantly changes the market or industry by introducing innovative features or a new business model. Important for understanding market dynamics and identifying opportunities for innovation.
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.
A tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
Specific conditions that must be met for a product or feature to be considered complete and satisfactory. Essential for product managers and developers to ensure clarity and alignment on what constitutes "done".
Search Engine Optimization (SEO) is the process of improving a website's visibility and ranking in organic search engine results. Essential for attracting more traffic and enhancing the online presence of a website.
The process of assigning target keywords to specific pages on a website to optimize each page for relevant search terms and improve overall SEO strategy. Crucial for creating a structured and effective SEO strategy.
The origins of visitors to a website, such as search engines, direct visits, social media, and referrals from other sites. Crucial for understanding and optimizing website traffic and marketing strategies.
A cognitive bias where individuals underestimate their own abilities and performance relative to others, believing they are worse than average. Important for understanding self-perception biases among designers and designing systems that support accurate self-assessment.
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.
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.
The percentage of leads that convert into customers. Crucial for measuring the effectiveness of marketing and sales efforts.
The process of turning a lead into a customer. Important for driving business growth and measuring marketing effectiveness.
A moment of significant change in a process or system, where the direction of growth, performance, or trend shifts markedly. Important for recognizing critical transitions in design or business strategies, enabling timely adjustments and informed decision-making.
A cognitive bias where individuals favor others who are perceived to be similar to themselves, affecting judgments and decision-making. Crucial for understanding biases in team dynamics and decision-making processes among designers.
The process of self-examination and adaptation in AI systems, where models evaluate and improve their own outputs or behaviors based on feedback. Crucial for enhancing the performance and reliability of AI-driven design solutions by fostering continuous learning and improvement.
Business-to-Consumer (B2C), a business model where products or services are sold directly to individual consumers. Essential for understanding consumer markets and developing direct marketing strategies.
The speed at which leads move through the sales funnel. Crucial for understanding and optimizing the sales process.
The process of distinguishing a product or service from its competitors in a way that is meaningful to the target market. Important for creating a unique value proposition and gaining a competitive edge.
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.
Product Development is the process of bringing a new product to market or improving an existing one. Crucial for innovation, meeting customer needs, and maintaining a competitive edge.
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 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.
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 statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
Search Engine Marketing (SEM) is a digital marketing strategy used to increase a website's visibility in search engine results pages (SERPs) through paid advertising. Essential for driving targeted traffic and improving online presence.
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 research method that focuses on collecting and analyzing numerical data to identify patterns, relationships, and trends, often using surveys or experiments. Essential for making data-driven decisions and validating hypotheses with statistical evidence.
A logical fallacy that occurs when one assumes that what is true for a part is also true for the whole. Important for avoiding incorrect assumptions in design and decision-making.