SD-301e · Module 1

Multi-Input Forecast Models

3 min read

Stage probability is the baseline. Layering additional inputs improves accuracy, but only if the inputs are reliable. The second input is velocity — how fast the deal is moving through stages compared to the historical norm. A deal that reaches Stage 3 in fifteen days when the average is thirty has higher probability. A deal that took sixty days has lower. The third input is engagement — meeting frequency, stakeholder count, response latency. The fourth is deal characteristics — size, industry, new versus expansion. Each layer narrows the confidence interval. Four inputs typically reduce forecast error from 12% to 8%. Diminishing returns kick in after four.

Do This

  • Start with stage probability as the baseline, then layer velocity and engagement
  • Add inputs one at a time and measure whether each improves accuracy on historical data
  • Rebuild the model quarterly to capture changes in your sales process and market

Avoid This

  • Throw twenty inputs into the model and assume more complexity means more accuracy
  • Use inputs that reps self-report unreliably — the model amplifies data quality issues
  • Build the model once and never recalibrate — your sales process changes, the model must follow