Driver-Based Forecasting: Selecting the Right Drivers

Driver-Based Forecasting: Selecting the Right Types of Drivers for Your Business

Driver-Based Forecasting Definition: Driver-based forecasting is a business planning technique that links operational activities to key variables or drivers such as revenue or expenses and then mathematically calculates how the company's success would be affected by different scenarios.

Driver-Based Forecasting Model

Ask yourself this question: What makes your business go? Too often within the FP&A world, business managers let the tail wag the dog and build business plans that are simply chasing where they found success before. But from the enterprise level down to each department, understanding the underlying drivers of what makes your business a success can result in a more accurate — and more lucrative — financial planning and budgeting process.

What makes driver-based forecasts tricky is that they can feel subjective and vary from industry to industry and company to company.  Drivers will likely range from macro-economic indicators to industry-specific metrics to unique ones for a specific company, and potentially even drivers closely aligned to a business function. The key is that driver data should be measurable for use in mathematical models and reflect how an enterprise would respond to different core variables.  It is up to forecasters to determine their own criteria for selecting core drivers. Key selection criteria could include ease and timeliness of availability, materiality and causality, and ease of understanding to the business.

Driver data typically takes three main forms: predictive, causal, and life cycle. These can be combined to create the most accurate forecasts.


Predictive refers to how data from one time period predicts business activity in a future period. An example might be that new home construction starts in a particular area is a leading indicator by 6 months of sales of appliances and other home furnishings.


Causal refers to the relationship between two data points and how they impact each other. In the case of school administrators trying to estimate the number of cafeteria meals that will be served in a given time period, they might look at historical data showing what meals sold the most based on the day of week and meal time. 

Life cycle

Life cycle drivers track trends of activity over time. For example, a given customer account requires initial work to sell and set up, then has different levels of activity over its useful life and finally requires a replacement.  We have used this method recently for projecting health care costs for a given patient population over time in chronic treatment. These cycles are reasonably predictable and thus a good source of data to help pinpoint drivers.

Generally for enterprises, fewer drivers is better, so getting the right ones is important. FP&A professionals can build forecasts with 'chains of drivers', with data feeding into each other and helping predict outcomes based on different time horizons, scenarios andanalytical dimensions.

An example of a longer time horizon might be how forecast population and economic growth drives future airline passenger traffic, which in turn predicts future aircraft sales, which finally predicts long-term need for spare parts. Or a more short-term horizon might be how a sports team launches a new logo and sales of team-wear increase as products become available.

Through examining the factors that drive the growth of your specific industry, you can select the drivers that will create the most accurate forecasts. The key is knowing where to look to glean the most valuable insights from the data.