In a perfect world, contact centre demand would be a simple and predictable flat line. In reality it’s a complex concoction shaped by factors such as:
- Seasonality patterns
- Marketing Spikes
- Behavioural Shifts
- Residual Noise - the layer of unavoidable randomness
No matter how advanced our algorithms are, a forecast is still only an informed expectation rather than a guarantee. When we staff only for the “expected average” we leave the customer experience and agent wellbeing vulnerable to the inevitable moments where reality deviates from the plan. Categorising the Complexity: To manage this “concoction” we can categorise what drives our demand:
- Known Knowns: Historical patterns and seasonality that we can model with high confidence
- Known Unknowns: Events we know will happen like a marketing launch or a system issue, but we cannot always time with 100% certainty
- Unknown Unknowns (Residual Noise): The
This classification doesn’t have to be just a way of looking at the past, it can also be a tool for navigating the future. For example, when we quantify that residual noise leads to 10% fluctuations in our data, we have the option to bake it (or part bake) directly into our capacity plan to ensure we are staffed for both the predictable and the unpredictable. By building a staffing buffer that considers this residual noise, we are not “overstaffing” we are introducing a calculated safety net which is designed to absorb the Known Unknowns and Residual Noise.
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