Automation Revenue Forecasting

About Client

The client is a global event management company based in Chicago with a strong presence in the Americas, Europe, and the Middle East.


The client’s existing forecasting model had a few unfavorable issues that impacted its accuracy-

  • The system lacked automation
  • The process was more people driven rather than being data driven
  • The error percentage between the numbers from the forecast model and actual numbers recorded from their Oracle Financial ERP went above 30% in some cases.
  • The model only supported monthly forecasting with no option for weekly 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


  • The new revenue forecasting model was completely automated and based on location.
  • A centralized solution was implemented for all business units so that there is a single source of truth across the organization.
  • Efficiency of the system doubled. Previously, only 5 months from the 1 year forecast were below the 10% error rate.The number increased to 10 months.
  • The model helped the client with a flexible system which also shared weekly forecast numbers
  • One of the extra benefits to client from the holiday impact analysis was that the business team could now plan better for the holiday season based on their location

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