1. A method for building an integrated customer analytics solution for an enterprise comprising one or more data sources in a client-server architecture over a network, the client-server architecture comprising one or more user devices and one or more servers for processing requests received from the one or more user devices, the method comprising: retrieving and processing customer data from the one or more data sources, wherein retrieving and processing the customer data comprises: gathering the customer data from the one or more data sources, wherein at least two data sources are disparate data sources which store data using different formats; storing the gathered customer data in the form of data structures comprising one or more data fields in an operational data store; extracting the customer data from the operational data store if the data structure corresponds to a predetermined data structure; transforming the extracted customer data based on one or more predetermined formats; transforming the existing one or more data fields of the operational data store into a first set of new data fields based on business specifications and requirement analysis of the enterprise; loading the transformed customer data and the first set of new data fields into a data mart; and updating the data mart with a second set of new data fields and new customer data corresponding to the second set of new data fields over a period of time of predetermined customer lifecycle stages, wherein the updating of the data mart is based on the business specifications and requirement analysis of the enterprise; analyzing the processed customer data stored in the data mart using one or more codes to generate one or more statistical techniques, the statistical techniques being at least one of descriptive statistics, cluster analysis, forecasting, survival analysis and logit model, wherein the one or more codes are developed using a statistical package module, and further wherein the generated one or more statistical techniques facilitate analyzing one or more predetermined attributes related to the customer; deriving, by a processor, one or more statistical model outputs using the one or more generated statistical techniques, wherein the statistical model outputs represent one or more metrics corresponding to the analyzed attributes; generating one or more statistical models corresponding to the one or more statistical model outputs, the one or more statistical models being associated with one or more scores which are computed based on the statistical model outputs, wherein the one or more scores facilitate to predict likelihood of customer behavior towards products, services and other customer related aspects associated with the enterprise; generating one or more reports based on at least one of: the processed customer data and the one or more statistical model outputs; and building one or more analytical modules for the predetermined customer lifecycle stages comprising the one or more reports and the one or more statistical models, wherein the one or more analytical modules constitute the integrated customer analytics solution.
8. The method of claim 1 , wherein the one or more analytical modules comprises: transaction behavior analysis module, campaign performance and management module, attrition and loyalty analysis module, cross-sell and product holding analysis module, customer profitability and lifetime value analysis module and service request analysis module.