Measures how closely predicted sales match actual sales achieved within a time frame.

What it Measures ?

How close our sales predictions were

Relevant StakeHolders 

Sales Ops, Finance Team

Why it Matters ?

Compares forecasted and actual sales to improve planning and execution.

In-depth Use Case / Real-world Example

Sales Forecast Accuracy is crucial for manufacturers to plan production, inventory, staffing, and cash flow effectively. Suppose a company forecasts ₹60 crores in Q1 sales based on its pipeline but ends up closing ₹52 crores. The forecast accuracy is (52 ÷ 60) × 100 = 86.7%. Accurate forecasts help in making data-driven decisions, especially in manufacturing where supply chain and resource planning depend heavily on expected demand. A high variance can lead to excess inventory, stockouts, or missed revenue targets. By analyzing this KPI over time, companies can refine their forecasting models, use predictive analytics, and improve cross-functional collaboration between sales, operations, and finance.