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Optimizing Call Center Operations with Machine Learning
Utilizing machine learning services for call volume prediction.
Overview
At a call center company, our Accelerance partner utilized Amazon Forcast to train a Deep Learning model for predicting the number of calls. This enabled us to provision the right number of attendants, resulting in several benefits for our operations.
Timeline March of 2023 and budget of 100K.
Industry
Professional, Scientific and Technical Services
Challenges
Before implementing this machine learning service, our partner faced several challenges in efficiently managing our call center operations.
One major challenge was accurate prediction of call volume, which directly impacted our ability to allocate staff effectively. This led to longer customer wait times, inefficient service delivery, and increased costs.
Solutions
To address these challenges, they leveraged the power of this machine learning consultant. They then trained a Deep Learning model using historical call data to accurately predict call volume. By analyzing various factors such as time of day, day of the week, and seasonal patterns, the model provided us with valuable insights for staff allocation.
Results
Implementing this ML service yielded significant improvements in their call center operations. With accurate call volume predictions, they were able to optimize staff allocation, which resulted in reduced customer wait times. This led to improved customer service and satisfaction. The cost management improved as they were able to allocate resources more efficiently based on predicted call volumes.
Utilizing this machine learning services for call volume prediction has been instrumental in enhancing the efficiency and effectiveness of the client’s call center operations.