Operational planning made easy at ÖAMTC
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Machine learning ensures even utilization in the ÖAMTC call center by predicting the call volume.
How A1 Digital approached the project
Use case definition & ML model creation
In the first step, a use case was specified in joint workshops.Based on historical data on calls (including length of calls, waiting times, etc.) A1 Digital iteratively developed a machine learning model to predict call volume.
Testing of algorithms & models
A1 Digital then used its own self-parameterising ML platform to test different algorithms as well as different models (e.g. a neural network and the Random Forest classification method) and their configurations.
Model confirmation
The result was a solid model that was able to forecast the probable call volume and thus the personnel requirements over certain periods based on various input parameters such as the number of calls in the last week, the weather in the previous year's period and the holiday periods.
POC
With an extensive proof-of-concept, A1 Digital then demonstrated that the developed ML model can actually accurately predict the number of calls in the desired time horizon.
“The proof-of-concept has shown us that machine learning can change our work in a sustainable and positive way. The way A1 Digital's AI experts have condensed the multitude of data into a reliable, expandable ML solution convinced us to get A1 Digital on board for this ML project.”
The Customer:
The Austrian Automobile, Motorcycle and Touring Club ÖAMTC sees itself as a point of contact and promoter of the interests of its members in all questions relating to mobility. Under this premise, the Yellow Angels on the street and in the call center focus their daily activities on the service concept. As part of the telephone and information service, call center employees are the first point of contact for members and customers.
The Challenge:
The expected call volume plays a major role in optimal personnel deployment planning in the call center. The wrong number of call center employees can lead to long customer waiting times during peak periods or waste staff time during quiet periods. However, the very complex Excel-based model previously used for personnel deployment planning did not allow for dynamic changes and reached its limits due to the multi-dimensionality of the process. The algorithms necessary for flexible adaptation for processing data, for example on weather development and seasonal holiday behavior, could not be used.
The Solution:
The ÖAMTC saw the solution to the problem in the use of machine learning (ML). The core functions of this technology include recognizing patterns of behavior among callers and comparing this data with a variety of different parameters. As experienced digitalization experts, the Mobility Club commissioned A1 Digital to develop a proposal for the innovative use of ML technology for personnel planning.