Customers are the link to a business's success. Any organization must focus on a huge number of customers, for this customer satisfaction and loyalty should be incorporated along with the long-term goals of the organization. As the backbone for all marketing activities, customer analytics comprises techniques like predictive modeling, data visualization, information management, and segmentation. Customer analytics is becoming critical. Customers have access to information anywhere, any time – where to buy, what to shop for, what proportion to pay, etc. The deeper the understanding of customers' buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviors are going to be – and therefore the more successful you'll be at delivering relevant offers that attract rather than alienate customers. Generally, organizations spend a lot of money to acquire new customers but they do not realize that the majority of the sales and profits come from their existing customers. In this thesis, the existing customers are analyzed and given a customer lifetime score, and based on this score, the customers are nurtured by the sales team to increase the profits. In order to maximize sales and conversions, customer segmentation and product recommendation engine is used effectively. Customer segmentation is the process of dividing the customers into many groups that supported common characteristics so companies can market to every group effectively and appropriately. Segmentation is performed using k means clustering. Segmentation allows marketers to raised tailor their marketing efforts to varied audience subsets. It is important to predict the customer segment for any new customer which can be done using supervised classification algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K Nearest Neighbour, and Support Vector Classifier.