Behavior analysis is a type of data analysis that tries to understand how and why people behave that way. Behavior analysis measures how consumers relate to digital applications (e-commerce, mobile, social media, gaming, IoT). Data such as demographics or past activities are collected to generate insights and predict future trends. The principles of behavior analysis are applied not only to the behavior of people, but also to the interactions and dynamics between processes, machines, equipment, and even macroeconomic trends.
Behavioral analytics combines typical event tracking with audience segmentation to associate users with triggered events and map customer journeys, using the large amount of raw data collected by the analytics platform.
Behavior Analysis is an important subset of business analytics that helps us understand how and — why — consumers and apps behave this way. It is particularly prevalent in e-commerce, online gaming, web, mobile and IoT applications — and provides the data needed to improve the customer journey towards transformation.
Behavior analytics measure data such as navigation paths, clicks, social media interactions, and purchase/cart abandonment. A/B testing is an example of using analytics to make data-driven decisions.
Correlation analysis refers to the application of statistical analysis and other mathematical techniques to evaluate or measure the relationships between variables.
Data virtualization is the reproduction of real data in a virtual environment. In this way, tests, experiments and analyses performed when working with real data can be carried out without compromising the accuracy and security of the real data.
Data mining is ultimately the process of analyzing hidden data patterns according to different perspectives for grouping useful information, collected and consolidated in common areas such as data warehouses, data mining algorithms, facilitating business decision-making, and other information requirements, in order to reduce costs and increase profits.
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