Transforming revenue forecasting and risk management through data-driven predictive modeling

Revenue forecasting is a vital aspect of risk management for various organizations, particularly for those in the industrial sector with complex contracts reliant on the sales of multiple products. Given the significant impact of errors in revenue predictions on cash flow, stakeholder trust, and corporate reputation, there is a need to explore practical solutions for improving the accuracy of forecasts, especially those at longer lead times as organizations look to improve predictions at all horizons. However, due to the complex nature of the forecasting process, it is common for organizations to rely on a highly labor-intensive, spreadsheet-based approach to generate revenue forecasts. Furthermore, many organizations currently do not adopt the use of predictive models to aid forecasting, relying instead solely on data volume-based, judgmental methods. Given the rapid advances in data mining and other analytical techniques, organizations have been slow to adapt.

Our research aims to propose predictive analytics techniques that organizations may exploit to generate practical solutions rooted in a data-driven foundation to improve revenue forecasting accuracy. We explore the benefits of implementing predictive models for demand forecasting and contract revenue forecasting for service organizations to generate data-driven baseline estimates that the involved business planners can then adjust and improve to include additional judgment-driven insights based on information that predictive algorithms may not have access to, such as knowledge related to contract servicing history, seasonality, and personnel needs. Generating data-driven baseline estimates located systematically in advance of the contracting close date can lead to major improvements to the shortened timelines usually associated with the budgeting and forecasting process for companies.