Forecast Accuracy Tracker

Best-in-class sales organizations achieve 90-95% forecast accuracy. The average is closer to 75%. Anything below 70% indicates a systemic problem with pipeline quality, CRM hygiene, or the forecasting methodology itself. Accuracy below 80% makes it difficult for finance, operations, and leadership to plan headcount, inventory, and investment confidently.

Forecast bias is the directional tendency to consistently over-forecast or under-forecast. Over-forecasting (sandbagging in reverse) leads to missed targets, poor resource allocation, and loss of credibility with leadership. Under-forecasting (sandbagging) leads to under-investment in growth. Both are problems. Consistent bias suggests a structural issue - either in how reps qualify, how managers adjust, or how deals are categorised in pipeline stages.

The four most common causes are: CRM data that is manually updated and therefore incomplete or stale, pipeline stages that are defined inconsistently across reps, deals that are marked 'Commit' without the underlying MEDDIC criteria being met, and insufficient manager inspection - allowing reps to self-report without challenge. Each of these is a process problem, not a technology problem.

Airspeed addresses the root cause: data quality. It automatically updates CRM fields from every call - deal stage, next steps, key stakeholders, and qualification criteria - so pipeline data reflects what is actually happening in conversations rather than what reps remember to log. When pipeline data is accurate, forecast models built on top of it improve significantly. Airspeed customers typically see forecast accuracy improve by 15-20 percentage points within two quarters.