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.
ELT is the initials of the words “extract, load, and transform.” Indicates a data integration process that extracts, uploads, and transforms data from one or more sources into a repository such as a data warehouse or data lake.
Synthetic data generation is the process of generating datasets that mimic the statistical properties and patterns of real data, but are completely artificially created.
Big data is very large unstructured or structured data and is the most complex to analyze. For this you need advanced big data technology and big data tools that can work with large amounts of unstructured data. The amount of data collected is constantly increasing.
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