• Developed a time series model that can predict the number of applications received based on advertising spend.
• Data modeling included Ad stock rate, Advertising Saturation, Decay and Lag factors.
• Identified areas of spending with minimal ROI thereby cutting down spending by 27% and achieving the target with only 73% of the marketing budget.
• Exceeded expectations by outperforming the target of reducing the sourcing budget by 10%.