Customer Insights Analysis Using Deep Learning

Abstract

The number of organizations moving their activities online or expanding their online presence to compete in the current market is growing significantly. Increasingly, organizations are turning to online marketing strategies incorporating a comprehensive consumer engagement approach. In addition to maintaining existing customers and attracting new ones, customer segmentation is a key to organizational success. However, the unstructured data is segmented using an unsupervised learning algorithm. Traditional clustering algorithms are not guaranteed to be efficient for customer segmentation and behavior analysis. Consequently, after comparing the algorithms, only the most suitable algorithm is suggested for research KMeans, or KMeans combined with other algorithms, are the basis of most algorithms, according to the literature review. However, in the general approach, KMeans algorithms cannot handle large and high-dimensional data sets; therefore, for this research, Customer segmentation will be improved using K-means and Deep Cluster Networks (DCN). Further, traditional models of machine learning are not effective in analyzing the behavior of customers. Therefore, during the present study, a Deep Cluster Networks (DCN)-based architecture has also been used to produce effective results. Nowadays, customer Insights Analysis Deep Learning plays a very vital role.