Audit advanced Works with: Any AI

Sales Forecast Accuracy and Commit Discipline Review

Table output Has example

Check how well forecasts match results.

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Review forecasts. Forecast and actual bookings by period and by rep code: [FORECAST_DATA]. How forecasts are produced: [FORECAST_METHOD].

Calculate error as actual minus forecast, absolute percentage error and bias for each period and in total, showing the arithmetic. Identify periods and rep codes with consistent over or under forecasting and stages where deals slip.

Return a table, causes to investigate, rules for what counts as a commit, and a forecast range method. Small samples make patterns uncertain.

Add a worked table: forecast 100, actual 90, error minus 10, absolute percentage error 11.1 percent; forecast 120, actual 108, error minus 12, 11.1 percent; forecast 80, actual 84, error plus 4, 4.8 percent. Bias = (minus 10 minus 12 plus 4) divided by 3 = minus 6, an over-forecast. Average error = 9.0 percent. Recommend a forecast range and rules that deals count as commit only with a dated next step and a named buyer.

Add a closing step: agree a forecast range method and review accuracy monthly, reporting average absolute error.

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Invented periods. Forecast 100, actual 90: error minus 10, 10 / 90 = 11.1 percent. Forecast 120, actual 108: error minus 12, 11.1 percent. Forecast 80, actual 84: error plus 4, 4.8 percent.
Average absolute percentage error = (11.1 + 11.1 + 4.8) / 3 = 9.0 percent. Bias = (-10 - 12 + 4) / 3 = minus 6: over-forecasting.
Rule: a deal counts as commit only with a named buyer and a dated next step.
Report a range rather than a single number.

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