Forecast Accuracy

Forecast Accuracy: How closely forecasted revenue matched actual revenue.

Formula

Forecast Accuracy = 1 - |Forecasted - Actual| / Actual

Computable definition

The canonical formula in the GASP formula grammar. The MCP server compiles it to SQL over the datasets listed below.

1 - abs(sum(field.forecasted_revenue) - sum(field.actual_revenue)) / sum(field.actual_revenue)

Unit: percent (the formula yields an unscaled ratio)

Data requirements

Grain: Monthly aggregate · Frequency: Monthly · Aggregation: aggregate (month) · Staged: no · Main source: CRM.

Field	Dataset	Type
`actual_revenue`	forecast	decimal
`forecasted_revenue`	forecast	decimal
`period_start`	forecast	date

Example SQL (Postgres)

SELECT (1 - (CAST(ABS(((SELECT COALESCE(SUM(forecasted_revenue), 0) FROM forecast WHERE period_start >= :period_start AND period_start <= :period_end) - (SELECT COALESCE(SUM(actual_revenue), 0) FROM forecast WHERE period_start >= :period_start AND period_start <= :period_end))) AS DOUBLE PRECISION) / NULLIF((SELECT COALESCE(SUM(actual_revenue), 0) FROM forecast WHERE period_start >= :period_start AND period_start <= :period_end), 0))) AS forecast_accuracy

Access: recommended sensitivity tier confidential, access scope department-scoped. Guidance your policy engine enforces.

From an agent: with the GASP MCP server, call lookup_metric({ "name": "Forecast Accuracy" }) or generate_query({ "metric": "Forecast Accuracy", "adapter": "<your adapter JSON>" }).

Benchmarks

Below 70%: Poor forecasting
70-85%: Average
85-95%: Good
Above 95%: Excellent

What It Tells You

How predictable revenue is, which sets how much weight plans can put on the forecast.

Connected in the GASP relationship graph.

Downstream: what this drives

Forecast Accuracy drives Pipeline Coverage (Sales)

← Back to RevOps metrics