CASE STUDY: IDENTIFYING THE BEST DIGITAL MEDIA BASED ON LEAD CONVERSIONS OF DIGITAL MARKETING DATA USING DECISION TREE ALGORITHM

The digital revolution and market dynamics, organizations started focusing more on developing strategies to find new customer segments through digital marketing mediums [1]. Winning a new customers needs collaborative efforts in the current competitive industry. Also, the rapid live changes of growing digital marketing is even more challenging for organization to know right insights of data. In this context, it is crucial for an organization to have knowledge about customers, attract more useful customers and increase the bottom line. Information Technology (IT)-driven big data analytics now has the potentiality to discover knowledge hidden inside very large amount of data to help in making business decisions which are customized to any organization needs. This paper is strive to create a machine learning model for customers training lead data which will help in identifying and predicting customer conversion and important attributes influencing lead conversion to promote the digital media[2]. For the development predictive data model CRISP methodology followed and explained each step in clearly. The Machine learning algorithm Decision Tree technique applied to find to find the prospective leads. Used Python for data Preprocessing, model development and prediction. In detail about the machine learning model & discussed the implementation steps involved in model development. To validate the model by using classification metrics which are confusion matrix and ROC curve and outcome of the metrics discussed [3]. The benefit of Machine learning model to get the better lead conversion and the benefits of the model discussed.