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.
Neural Architecture Search (NAS), derin öğrenme modellerinin mimarisini otomatik olarak keşfetmek için kullanılan devrim niteliğinde bir yaklaşımdır.
The classic definition of a digital twin is: “A digital twin is a virtual model designed to accurately reflect a physical object.”
Data Mart is a slice of the data warehouse logical model that serves a narrow group of users. Many data subsets only need a subset of data from the full tables in the data warehouse.
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