The process of dividing a customer base into distinct groups based on shared characteristics to tailor marketing strategies.

A subset of artificial intelligence that enables systems to learn from data and improve performance without explicit programming.

The systematic examination of data to extract meaningful insights, trends, and patterns for informed decision-making.

Techniques used to group similar data points together, facilitating the identification of patterns within datasets.

A popular clustering algorithm that partitions data into K distinct clusters based on feature similarity.

A clustering method that builds a hierarchy of clusters, either agglomeratively or divisively, to visualize relationships.

The steps taken to clean, transform, and prepare raw data for analysis, ensuring quality and relevance.

An approach to analyze datasets to summarize their main characteristics, often using visual methods.

A metric used to evaluate the quality of clustering by measuring how similar an object is to its own cluster compared to other clusters.

The process of scaling data to fit within a specific range, improving the performance of machine learning algorithms.

Transforming categorical data into a numerical format that machine learning algorithms can process.

A plan of action designed to promote and sell a product or service to target customers effectively.

A specific group of consumers identified as the intended recipient of a marketing message or campaign.

The graphical representation of information and data, making complex data more accessible and understandable.

Data-derived findings that can be directly applied to improve decision-making and drive business outcomes.

The process of assessing the accuracy and reliability of a model or analysis through various metrics and tests.

A measure of the profitability of marketing investments, calculated by comparing revenue generated to marketing costs.

Technologies and strategies used for data analysis of business information to support better decision-making.

A process where colleagues evaluate each other's work for quality and effectiveness, enhancing learning and improvement.

The practice of using data to tell a compelling narrative, making insights more relatable and impactful.

A system where outputs of a process are circled back as inputs, allowing for continuous improvement and refinement.

The practice of measuring, managing, and analyzing marketing performance to maximize effectiveness.

Groups of customers identified as having the greatest potential for profitability and engagement.

The evaluation of two or more items to determine their similarities and differences, often used in assessing clustering methods.

The condition of a dataset, determined by its accuracy, completeness, reliability, and relevance for analysis.