Is Regression/ Causal Modeling for Forecasting Underutilized?

As readers know, we basically have two ways of doing forecasting: 1. Extrapolating from historical trends – univariate forecasting (ie. Time Series Forecasting) 2. Including independent variables such as price that we believe influence movements in sales – causal modeling or regression modeling.

Comparing the two approaches, the chief advantage of univariate forecasting is that it is simpler. When historical patterns have been very regular for a long time, the univariate approach may be good enough. But we probably will only be able to speculate about what is causing these historical patterns, and usually the data aren't that easy on us.  Sudden jumps or declines or other breaks with the past aren't unusual. Causal modeling can help us understand the key sales drivers and a good causal model will do better at forecasting future periods.

Yet, according to Institute of Business Forecasting & Planning, IBF's benchmarking studies, fewer than 20% of organizations use causal modeling for forecasting.  Why is this?  The surveys did not ask why causal modeling is or is not used but my own experience suggests several reasons, including:

  • Causal data are not available or are spotty
  • The data are available but expensive or difficult to obtain in a regular or timely fashion
  • Determining future values of the causal variables to use is problematic.  The forecaster may not have finalized marketing plans, for instance.  Exogenous variables such as economic conditions are another example.
  • Lack of internal specialist modeling resources
  • A perception that causal modeling really doesn't work any better than than univariate or time series forecasting