It is often thought that Demand forecasting is an activity that can be undertaken separately from capacity planning and the influence this will have on customer queueing. This, however, is not realistic, and how long customers have to queue, and their patience to queue might see customers abandon the queue and reconnect at a future point.
True inbound volume (we refer to it as the new volume from now on) is more appropriate when one makes forecasts since it is independent of the service levels in the contact center. In contrast, the total inbound volumes are influenced by the service levels, the skill level of the agent, and staffing decisions of the contact center, due to the redial and reconnect customer behaviors.
On a much simpler basis, it does not even require customers to abandon to influence future volume arrival. This is especially true in high touch (when the same customers need to contact regularly) customer contact centers. For example, in a high-touch contact center, if the contact center is difficult to reach in the mornings, customers will likely shift contact arrival towards the afternoon/evenings. These gradual changes are easy to capture, and the regular forecasting process usually picks them up.
A significant fraction of the inbound contact volume involves customers reconnecting more than once in a contact center. There are several reasons for customers to reconnect, including abandoned customers not getting their questions answered in their initial attempts, a customer checking the status of their previous request, or perhaps the quality of the agent who last handled their request not resulting in resolution.
Therefore, it is essential to separate what is “true demand” (the number of unique customers contacts), what is reconnect as a result of first contact resolution failure, and what is a reconnect as a result of a previously abandoned attempt. True demand data is best to use for trend and seasonality factoring, while the reconnects probabilities are expected to be more stable over time since they represent normal customer behavior.
We conclude that the first call resolution and service levels are likely to be having a considerable impact on the number of contacts received the accuracy of the forecast derived. For this reason, it might be interesting to see which part of the variability in the deseasonalized actuals can be explained by the service level or first contact resolution.
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