In the forecasting world, we often forget that our sophisticated models are only as good as the data feeding them. The most crucial element isn't your fancy statistical approach... it's understanding your source data inside out.
Missed Philip Stubbs's weWFM full episode:
Digging below the surface reveals surprising truths:
What's labelled as "call volume" might actually be attempts rather than connects, drastically changing your forecast accuracy.
Data verification should be your first step, not an afterthought. Roll up your sleeves and examine those database tables yourself.
The gap between what people tell you the data represents and what it actually contains is where major forecasting errors hide.
Beyond the obvious, I've found:
Cross-functional partnerships between WFM teams and database administrators create a forecasting superpower few organisations leverage.
Implementing a formal data validation process before modelling can eliminate up to 80% of forecast variance in contact centres.
What's your experience with data validation? Have you ever discovered your forecasting was based on misunderstood metrics?
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