๐๐จ๐ฌ๐ญ ๐๐ข๐ง๐๐ง๐๐ ๐ญ๐๐๐ฆ๐ฌ ๐ฎ๐ฉ๐๐๐ญ๐ ๐ญ๐ก๐๐ข๐ซ ๐๐จ๐ซ๐๐๐๐ฌ๐ญ๐ฌ ๐๐ฏ๐๐ซ๐ฒ ๐ฆ๐จ๐ง๐ญ๐ก โ ๐๐ฎ๐ญ ๐ซ๐๐ซ๐๐ฅ๐ฒ ๐ฅ๐จ๐จ๐ค ๐๐๐๐ค ๐ญ๐จ ๐ฌ๐๐ ๐ก๐จ๐ฐ ๐๐๐๐ฎ๐ซ๐๐ญ๐ ๐ญ๐ก๐ ๐จ๐ฅ๐ ๐จ๐ง๐๐ฌ ๐ฐ๐๐ซ๐.
That is normal. A forecast made 10 or 11 months before close is not expected to be perfect. But it still matters because early forecasts influence budgets, hiring, cash planning, and investment decisions.
๐๐ก๐ ๐ซ๐๐๐ฅ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง ๐ข๐ฌ ๐ง๐จ๐ญ:
โWas the first forecast right?โ
๐๐ก๐ ๐๐๐ญ๐ญ๐๐ซ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง ๐ข๐ฌ:
โHow much confidence should we place in a forecast at each stage of the planning cycle?โ
That is why I built this Power BI page that tracks every forecast version for each month and compares it against the actual result.
Each line shows one monthโs forecast journey:
from the early estimate,
to the monthly updates,
to the final number.
The pattern is clear.
๐๐๐ซ๐ฅ๐ฒ ๐๐จ๐ซ๐๐๐๐ฌ๐ญ๐ฌ ๐๐ซ๐ ๐ฐ๐ข๐๐๐ซ ๐๐ง๐ ๐ฆ๐จ๐ซ๐ ๐ฎ๐ง๐๐๐ซ๐ญ๐๐ข๐ง.
As the actual month gets closer, the forecast tightens and moves toward the truth.
In this dataset:
โข Final estimate accuracy: ~0.6% MAPE
โข 11-month forecast accuracy: ~1.44% MAPE
โข Long-range forecasts were about 2.3ร less accurate
โข 36 of 66 forecasts came in below actual, showing a mild conservative bias
๐๐ฎ๐ญ ๐ญ๐ก๐ข๐ฌ ๐ซ๐๐ข๐ฌ๐๐ฌ ๐ ๐๐ข๐ ๐ ๐๐ซ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง:
Are we only asking, โHow much did we miss by?โ
Or are we also asking, โDo we keep missing in the same direction?โ
In this case, most forecasts came in below actual. That suggests a mild conservative bias.
And that matters because consistently low forecasts can cause the business to plan too small, hire too late, or invest too cautiously before the growth becomes obvious.
Built in Power BI using Deneb / Vega-Lite
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