Data Mining and Predictive Analytics Terminology Summary
Data mining is currently booming in all kinds of businesses and organizations. Therefore we have created a summary of common terms used in this field, we hope you enjoy it.
Analytical CRM/aCRM:? Used to support decision-making to improve a company's interactions with customers or to increase the value of those interactions. Gathering, analyzing, and applying knowledge about customers and how to effectively engage with them. See >>>
Big Data:? Big Data is both an abused buzzword and a real trend in today's society. The term refers to the ever-increasing amount of data that is being captured, processed, aggregated, stored, and analyzed on a daily basis. Wikipedia describes "Big Data" as "the sum of data sets so large and complex that existing database management tools have difficulty handling them (...)".
Business Intelligence: Applications, facilities, tools, and processes for analyzing data and presenting information to help executives, management, and others in an organization make more informed business decisions.
Churn Analysis/Attrition Analysis: The process of describing which customers are likely to stop using a company's products/businesses and identifying those whose churn will result in the greatest losses. The results of the churn analysis are used to prepare new offers for customers who are likely to be lost.
Conjoint Analysis/ Trade-off Analysis:? Compares several different variants of the same product/service based on actual consumer usage. It predicts the acceptance of a product/service after it is launched and is used in activities such as product line management and pricing.
Credit Scoring:? Assesses the credit worthiness of an entity (company or individual). This is used by the bank (borrower) to determine whether the borrower will repay the loan.
Cross / Up selling:? A marketing concept. The sale of complementary goods (complementary selling) or additional goods (value-added selling) to a specific consumer based on his or her characteristics and past behavior.
Customer Segmentation & Profiling:? Based on existing customer data, categorize and group customers with similar characteristics and behaviors. Describe and compare the groups.
Data Mart:? Data stored by a particular organization, on a specific topic or department, such as sales, financial, and marketing data.
Data Warehouse: A centralized repository of data that captures and stores data from multiple business systems of an organization.
Data Quality:? The processes and techniques involved in ensuring the reliability and usefulness of data. High-quality data should faithfully represent the transactional processes behind it and fulfill its intended use in operations, decision-making, and planning.
Extract-Transform-Load (ETL): A process in data warehousing. Getting data from one source, transforming the data as per the requirement for the next use and later placing the data in the right target database.
Fraud Detection: Identifying suspected fraudulent transfers, subscriptions, and other illegal activities against a specific organization or company. Triggered alerts are pre-designed in the IT system and warnings appear when such activities are attempted or carried out.
Hadoop:? Another favorite in the big data space today. apache Hadoop is an open source software architecture for distributed storage and processing of huge data sets on clusters of computers with existing commercial hardware. It makes large-scale data storage and faster data processing possible.
Internet of Things (IoT):? A widely distributed network of electronic devices of many kinds (personal, home, industrial) for many purposes (medical, leisure, media, shopping, manufacturing, environmental regulation). These devices exchange data and coordinate their activities with each other over the Internet.
Lifetime Value (LTV): The expected discounted profit that a customer generates for a company over his/her lifetime.
Machine Learning: ? A discipline that studies automatic learning from data so that computers can adjust their operations based on the feedback they receive. Closely related to artificial intelligence, data mining, and statistical methods.
Market Basket Analysis: The identification of combinations of goods or services that often occur together in a transaction, such as products that are often purchased together. The results of this type of analysis are used to recommend additional products, inform display decisions, etc.
Market Basket Analysis (MBA).
On-Line Analytical Processing (OLAP):? Tools that enable users to easily create and view reports that summarize relevant data and analyze it from multiple perspectives.
Predictive Analytics:? Extracts information from existing data sets in order to recognize patterns and predict future earnings and trends. In business, predictive models and analytics are used to analyze current data and historical facts to better understand consumers, products, partners, and identify opportunities and risks for a company.
Real Time Decisioning (RTD): Helps organizations make optimal sales/marketing decisions in real time (near latency-free). For example, a Real Time Decisioning system (scoring system) can score and rank customers at the moment they interact with a company using a variety of business rules or models.
Retention / Customer Retention:? Refers to the percentage of customer relationships that are established and then maintained over time.
Social Network Analysis (SNA):? Depicts and measures the relationships and flows between people, groups, organizations, computers, URLs, and other kinds of connected information/knowledge entities. These people or groups are nodes in a network, and the connecting lines between them indicate relationships or flows.SNA provides a way to analyze interpersonal relationships, both mathematically and visually.
Survival Analysis:? Estimating how long a customer will continue to use a business, or the likelihood of churning in subsequent periods. This type of information enables a business to determine customer retention for the time period to be predicted and to introduce appropriate loyalty policies.
Text Mining:? The analysis of data containing natural language. Statistical calculations are performed on words and phrases in the source data in order to express the text structure in mathematical terms, after which the text structure is analyzed using traditional data mining techniques.
Unstructured Data: Data that either lacks a pre-defined data model or is not organized according to pre-defined specifications. The term usually refers to information that cannot be placed in a traditional columnar database, such as e-mail messages, comments.
Web Mining / Web Data Mining? : The use of data mining techniques to automatically discover and extract information from Internet sites, documents, or services.
The above is a summary of data mining and predictive analytics terminology shared by the editorial staff, more information can be concerned about the Global Green Ivy to share more dry goods