Abstract

Organizations today want to be extrapolative they want to gain information and insights on every minutia of customers.  Predictive analytics is one of the tools which has proved to be cornerstone in customer centricity. Though analytics is used for performing the customer analysis for decades, the manual approach of data management and analysis have constrained the functionalities. Professionals now leverage the individual information from demographics to purchase history and perform predictive analytics for optimal decision making, business marketing and thus business growth. Here we apply ensemble learning method for classification based on the monthly income and spending behaviour of customers. Secondly, we analyse customer satisfaction by applying unsupervised learning clustering method based on similarity through different performance measures.  

Keywords: Predictive analytics, customer segmentation, K-means algorithm, classification algorithm.