APPLIED ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

The click-through rate on web ads measures the number of clicks they receive from all customers who view them from web browsers.This paper with the aim of anticipating customer preferences proposes an approach that uses AI and ML to design and implement hyper-personalized experiences to automate the complex tasks related to predicting customer behaviors to better understand them and offer them intelligent web advertising.The proposed approach is based on granular customer segmentation dynamic content adaptation precise product recommendations and predictive analysis. To implement cognitive functions data comes from Chatbots processed with data analysis multivariate testing attribution modeling and predictive optimization.A case study is discussed and the solution proposed and programmed with the Python language and its machine learning libraries: Pandas NumPy Matplotlib and Scikit-learn.