A non-transitory computer storage medium storing a sequence of instructions, which when executed by a processor, causes:
- extracting, by a system, using data extraction techniques, data comprising a historical data, a revenue data and a customer data from one or more data sources over a past period;
- accessing, by the system, the data in the data sources to build an interactive diagnostic tree using predictive model of machine learning technique automatically and providing an analysis of revenue of an entity based on individual revenue streams, each of the revenue stream corresponding to a sale or use of various products and a services offered by the entity, wherein the diagnostic tree is further configured for generating average revenue per customer of the products and the services corresponding to each of the revenue stream, wherein the interactive diagnostic tree is displayed on a display, and the analysis of the diagnostic tree assists a user to target the revenue stream for the average revenue per customer boosting and assessing marketing campaign results based on the average revenue per customer trends-;
- processing, by the system the customer data and the revenue data using an artificial intelligence technique and predictive analytics from the one or more data sources and extracting an ideal customer profile (ICP) from the customer data using the predictive analytics to personify an ideal customer and display the ideal customer profile on an output interface, the ICP comprising a plurality of ICP business attribute fields and a plurality of ICP persona attribute fields comprising a customer characteristics, wherein the processing of the customer data using the predictive analytics is based on business specifications and requirement analysis of the entity;
- analyzing, by the system the customer data, using the artificial intelligence technique using one or more codes, to generate one or more statistical techniques comprising 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 configured for analyzing one or more attributes related to the customer;
- identifying, by the system executing a data mining module, target market segments and a target list of customers;
- receiving an input in real-time, by the system, comprising a current revenue of the entity, an average lifetime value of the customer, an attrition factor from an output of the processing and analyzing of the historical data, and a target revenue goal of the entity through an input device and generating realizable leads to reach the target revenue goal;
- generating automatically, by the system, using the artificial intelligence technique and machine learning technique, a required actual leads from one or more lead data sources, wherein each of the required actual leads are matched and compared to the ICP based on a match rate signal, and wherein the match rate signal is calculated based on a plurality of attributes of each of the required actual leads matching the ICP business and ICP persona attribute fields;
- estimating automatically, a marketing budget, by the system, working backward from the target revenue goal wherein the marketing budget is predicted by the system from a customer value matrix, wherein the customer value matrix is a customer acquisition cost to reach the realizable leads to the required actual leads;
- and
- automatically creating a marketing plan, by the system using the predictive analytics and the artificial intelligence technique comprising a sequence of data points captured comprising a data analysis, the realizable leads, the target revenue goal, and the marketing budget and wherein the marketing plan comprises marketing activities and a timeline for the marketing activities for a future period that is based on the marketing budget, wherein the marketing plan is displayed on the output interface;
- wherein the predictive analytics and the artificial intelligence technique sorts data in categories based on a learning from the historical data and current data to identify future business risks and opportunities;
- and the predictive analytics analyzes and weighs a relationship between several data elements to isolate each risk and potential for the entity;
- conducting, by the system, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis by the system to take an inventory of current situation of the entity and to identify the target market segments that are in alignment with the strengths of the entity;
- ranking, the marketing plan periodically by the system using the artificial intelligence technique and the machine learning technique, wherein the machine learning technique ranks the marketing plan by receiving a feedback based upon the historical data of a set of parameters over the past period to provide an insight to modify the marketing plan to reach the target revenue goal in a given time period;
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- wherein the sequence of instructions are configured for generating automatically, a growth master plan for the future period.