Forecasting Solutions
Summary
Forecast modeling thrives with sufficient historical data, thorough cross-department business insights, and a focused problem approach. For reliable results, models should incorporate key factors identified by business teams and data scientists. Where historical data or predictable growth is lacking, confidence increases by isolating and testing input factors under diverse scenarios.
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Process
Forecast modeling typically most benefits from:
a. Adequate historical data (not always available for a young company)
b. Extremely thorough business intuition collected from every business department
c. The right problem focus (ex: forecast each market geography separately, or even each segment within a market geography)
Model Development
For forecasting models, it is possible to quickly “whip up” a forecasting model that includes the key factors intuited by the business teams and the data scientist. However, unless many years of stable historical data exist and future growth is highly predictable, more confidence in the model might be obtained by stepping through input factors individually and analyzing the effects of these inputs under a wide variety of circumstances.