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 a cornerstone in customer centricity. Though analytics is used for performing customer analysis for decades, the manual approach to data management and analysis has 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 the ensemble learning method for classification based on the monthly income and spending behaviour of customers. Secondly, we analyse customer satisfaction by applying an unsupervised learning clustering method based on similarity through different performance measures.