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.
GPT (Generative Pre-trained Transformer) is a model developed in the field of artificial intelligence, especially for natural language processing (NLP) applications.
Deep learning, also known as deep neural learning or deep neural network, is an artificial intelligence (AI) function that mimics the way the human brain works to process data and create patterns that facilitate decision-making.
One of the main keys to success in machine learning and artificial intelligence projects is the correct configuration of settings known as hyperparameters.
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