Automation Revenue Forecasting

Our Solution

• BizAcuity collected the existing forecast and actual numbers from the existing system
• Identified all the attributes and parameters which can impact revenue.
• Conducted meetings with all business units to account for every variable important to each department
• The variables that were identified were both, from the internal as well as external sources
• Data preparation was done from multiple disparate sources
• Cross-checked the correlation between the identified variables and the actual revenue
• Data points which had a good correlation with the company revenue were selected such as pipeline, and group rental nights
• Auto Arima model was tested with the chosen data points as predictor variables
• Holt winter model, which predicts the number based on historical data, trends and seasonality without the need for any predictor variables was used to make predictions for the entire year.
• The Holiday effect on revenue was analyzed using the Generalized Additive Model (GAM) model. BizAcuity had discussion with all business units to validate the findings on the impact
• The pre and post-holiday effect too was looked into using the GAM model
• The holiday impact was incorporated in the revenue forecasting model to further improve accuracy