Your customers have had enough of being placed on hold

An AI that knows how many people will call you next week

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.

We quickly realised that our intuition was far inferior to a systematic evaluation of the available data by a system based on machine learning. We were able to significantly increase the reliability of our predictions in just a few weeks.
Kai Völker, Board member of Barmenia Versicherungen

For which companies is the approach suitable?

Any company with its own service hotline.

For which decision makers is the approach interesting?

All managers working in departments related to customer services and marketing.

Backgrounds for the technically interested

Regression problem with a weak temporal dependency, Stand-alone solution with minimal integration in the existing IT landscape, White-box model (regression tree)

Do you have any questions?

Artificial intelligence is not a replacement for human discussion

Are you wondering what possibilities AI can open up in your company? Would you like to learn more about its applications and the technology? We cannot give you a pack full of stock responses, but we can share with you our specialist knowledge, our curiosity about your company and our passion for technology.

We would be delighted to talk to you.


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