How things used to be
Employee scheduling is one of the most significant factors for determining a customer service hotline’s quality. Allocating too few workers results in long waiting times and dissatisfied customers, while assigning too many eats into the budget. It is difficult to predict the number of calls that will be made on a single day. Until now, companies have had to fall back on their managers’ intuition in making these estimations, or have relied on simple average values, with all the unreliability and fluctuating service quality these methods entail.
Then AI came along
Optimising telephone services using a better method of predicting caller numbers is a task that is perfect for AI applications, as they work best with large data volumes, a clearly defined task and simple performance measurement criteria. These AI-based estimations constitute a more scientific method than the ones outlined above.
Caller figures from the previous few years make up the data basis. A machine learning system uses these to search for patterns and detects connections between the number of calls and factors such as the day of the week, time, holiday period, public holidays, weather and advertising activities. The activity prediction for the service hotline is continuously compared with the actual values and parameters are adjusted accordingly.
The situation today
Customers are put in contact with customer service employees faster and resolve their queries more promptly. This results in higher satisfaction levels and reduces the likelihood that they will change providers. At the same time, work organisation supported by machine learning offers a more reliable basis to both employees and managers at call centres in terms of planning. This helps in holiday planning, for example, which can involve a lot of work.
