- Implemented an ARIMA-based predictive model for revenue forecasting, utilizing Python for data analysis and modeling.
- Preprocessed and aggregated a diverse dataset, identifying critical revenue trends and patterns, and adjusted for variations in client data points and onboarding periods.
- Enhanced the ARIMA model to consider business days only, improving accuracy in forecasting by eliminating non-business day revenue data.
- Performed seasonal decomposition segmented by clients' NAICS codes, allowing for industry-specific trend analysis and better prediction accuracy.
- Created a Tableau Dashboard for effective visualization of forecasting results, enabling clear interpretation of seasonal components, residuals, and industry-specific trends, thereby enhancing data-driven decision-making strategies.
- Tableau Dashboard at the following link: https://public.tableau.com/app/profile/alessandrosciorilli/viz/PitneyBowesAnalysis/Dashboard2