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The forecast does not match reality. Six causes, and five of them are the data rather than the method.
Symptoms
- Volume consistently over or under what arrives.
- The shape of the day is wrong even when the total is close.
- Particular days are always wrong.
One: not enough history
A new deployment forecasting from a few weeks. Expected, and fixed by importing history rather than by changing anything.
Two: abnormal periods in the history
An outage, a campaign, an unusual month. The forecast learned that those are normal and predicts them again.
Exclude them and regenerate.
Three: holidays not configured
The classic cause of "particular days are always wrong". A missing holiday is a quiet day the forecast treats as a collapse in demand, every year.
Four: business hours wrong
If the hours do not match reality, contact arriving outside them is either ignored or attributed oddly. The shape goes wrong before the total does.
Five: something changed in the business
A new product, a price change, a marketing push, a channel added. The forecast is predicting the old business faithfully.
This is not a fault; it is the forecast needing to be told.
Six: the method does not suit the pattern
Last, not first. Only worth changing after the five above are ruled out, because a different algorithm on bad data produces a differently wrong answer.
How to find it
Look at which intervals are wrong. Whole days wrong points at holidays or hours. Peaks wrong points at seasonality. Everything wrong by a similar proportion points at a change in the business.
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