Customer Behavioral Analysis through AI Agent

Consumer behavioral analytic is essential to the intelligent decision analytical system which every commercial organization relies on. This demand-driven analysis and prediction can primarily influence the supply chain management of the organization. Using methods such as data mining and machine learning (ML) approaches, data analysts explore the hidden patterns in consumer behavior to forecast sales. Time series forecasting is finding its real time scientific value when the present e-commerce sites cannot be able to on-going the velocity of data with any traditional modes of analysis due to it present in the very recent inception layer so the new techniques on data mining and machine learning along with time series forecasting paved better way for discovering hidden layers. I am not going to write down the references to this chapter and also the links from your blog because this chapter basically comes out of those except for the analysis which is more useful to predict next basket prediction, willingness to buy a product and personalized list suggestion which all are centered on the customers for either making them satisfied and loyalty to a particular e-shopping site then nothing will be able to be contributed to you. In this chapter we discuss methods employed in previous literature addressing consumer behavior prediction using random forest, support vector machine, logistic regression, decision trees and neural networks. Here, the performance of the ML algorithms is compared and it chooses the Logistic Regression to be the best modelling algorithm. Knowing what customers want and need – and ideally, predicting it – is, unsurprisingly, a perennial struggle for marketers.