Vishesh Jindal

This project is a targeting strategy that identifies customers for the line increase and decrease. For this, I analyzed customers' different risk and spend variables to develop the targeting criteria. Finally, I created an automated process to regularly generate a list of customers.

I supported a client in a flawless migration of a collection database and operation platform. Analyzing and revamping one of the leading market's collection lifecycle by implementing different segmentation and targeting opportunities helped improve the overall collection effectiveness. The expected benefit from the changes is around $4.5 million.

This gradient boosting (GBM) model predicts and provides the list of high probability attrition customers. I performed exploratory data analysis to convert the raw data into high-end informative data for the model. Before utilizing the GBM technique, I explored different modeling techniques such as classification and regression trees (CART) and logistic regression. This model helped clients identify thousands of high-risk customers and made their retention strategy highly efficient.

This automated dashboard keeps track of a number of placements happening every month across different collection segments and channels for all the markets. As the sole owner of the project, I took care of all the processes, from developing the dashboard and deploying it to automating the process using VBA. This helped the operational and strategy teams have a one-stop solution for monitoring all the collection placements.

I developed and deployed sales and analytics dashboards for a marketing technology client using AWS QuickSight. After streamlining the database processes to create dashboard-ready datasets, I performed ETL processes to load data from a third-party API to the SQL Server database.