Optimizing Call Centers with Machine Learning
Providing consistent and quality support to customers is an important part of the customer experience. If a customer has a problem with a product or service, calling customer support should not be something customers dread.
Yet, many call centers agents lack the training or information necessary to promptly answer customer questions. When customer service agents cannot quickly answer customer questions, the customer is put on hold while the agent searches for the answer. This also creates a queue of other customers waiting to talk to the agent.
What if you could predict call volumes for planned or unplanned events and reduce the number of calls to the call center? Through the powerful technology of practical artificial intelligence (Ai) and machine learning, you can identify those customers likely to call and their reason for calling.
Call reasons are often not recorded or accurately classified by customer service agents. Natural Language Processing (NLP) is part of machine learning that can be used to extract key features from call logs to determine the true call reasons. This allows the machine learning model to better learn the call reason patterns.
ElectrifAi’s pre-built machine learning model, Call Center Reductions, is particularly efficient at solving call center problems. This model consists of ensemble modeling techniques, clustering, and reinforcement learning to understand reasons for calling and the optimal approach to take.
How does the Call Center Reduction model really work? Let’s look at some challenges and the solutions to those problems.
- Large consumer, telecom, and financial services providers receive upwards of 20 million inbound customer calls per annum.
- Up to 70% of calls go to a customer service agent for resolution.
- 50% of calls for not have a reason classifier.
- The average handling time (AHT) is 3-4 minutes per inbound call.
- ElectrifAi developed a call reason group logic through multiple rounds of model reinforcement, including multi-level models for different reason group granularity.
- Real-time model scoring predicts the reasons for a customer to call and proactively engage with that customer prior to the call.
- A proven framework and models for call classification and optimizing call center operations.
What data does the Call Center Reduction model use?
- Call transaction
- Billing and payment information
- Customer service information query (balance/statement)
- · Demographics
How does this data fit together? The model outputs:
- A predicted score of propensity for each customer to call
- A need states segment
- A reason code
Knowing how this pre-built machine learning model works is interesting, but we bet you’re wondering if it has been used in the real world? Yes, this model has a proven history of:
- Predicting call volumes for planned and unplanned events to make optimal operational decisions.
- Reducing 14% incremental billing calls and 6% incremental service calls in a test-controlled environment.
- The projected cost reduction was $6-10 million on a base of $150 million.
Proven pre-built machine learning models are the best way to proceed in your digital transformation. Fast and accurate results provide actionable insights to help your company reduce costs, increase revenue, and decrease risk.
Many people have tried to establish their own data science team and, while that can work, it can cost a lot of time and money. Machine learning can take months or even years to build depending on the project complexity and if the data scientists understand the business requirements and industry pain points.
Founded in 2004, ElectrifAi has been able to help many companies with their machine learning needs. Are you ready to join those successful companies that have achieved the benefits of machine learning? Contact us today for a custom demo!
Artificial intelligence (Ai) is the computer's ability to perform tasks commonly associated with human intelligence, such as the ability to reason, discover meaning, generalize, or learn from past experience.
Call centers handle a large volume of phone calls, especially for taking orders and providing customer service.
Clustering is a machine learning technique that groups data points together.
Ensemble modeling is the process of running two or more related but different analytical machine learning models to obtain better predictive performance than running one machine learning algorithm alone.
Machine Learning is meant to handle large datasets, where manual analysis is impractical. A model built for a large data set will not perform as well on a small data set. Machine learning improves automatically through experience.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is the part of artificial intelligence (Ai) that gives computers the ability to process and understand text and spoken words similarly to humans.
Need State Segment
A need state is a group of consumers who seek similar product benefits and attributes for a specific use occasion. Segmentation allows the product to be tailored to the needs, desires and uses of customers.
Classification is the process of predicting the class of given data points. The reason classifiers are those data points that help the machine learning model output recommendations.
Reason codes are numerical or word-based codes that describe the reasons, in this case, for a customer calling customer support.
Reinforcement learning is a machine learning training method that perceives and interprets its environment by taking actions and learning through trial and error.
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