Abstract
Customer Service Center is the second most important consideration just after the actual product. Also, customer service is one of the biggest contributors to the cost component for any firm. We aim to apply well-known data mining techniques to the problem of predicting the quality of interactions like those done in call centers and the problem of predicting the quality of service. The analysis of call center conversations will provide useful insights for enhancing Call Center Analytics to a level that will enable new metrics and key performance indicators (KPIs) beyond the standard approach. These metrics rely on understanding the dynamics of conversations by highlighting the way participants discuss topics. The main focus will be to reduce the average handling time, is a call center metric for the average duration of one transaction, typically measured from the customer’s initiation of the call and including any hold time, talk time and related tasks that follow the transaction. Get real-time solution. The main operations will be speaker diarization, speech to text, agent analysis, emotion recognition and other measures to help with the analysis. We will use RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) for emotion analysis, consisting of vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, surprise and calm). Emotion recognition is done by extracting features from the audio from its Mel-frequency cepstral coefficients (MFCCs) and passing it through a convolutional neural network. All of this will happen in real time as the call is taking place.
Highlights
For a customer, addressing the call center means addressing the company itself, and any negative experience on the part of the customer can lead to the rejection of company products and services
For the company, it is very important to ensure that a call centres function effectively and provides high quality service to its customers
Call centres collect a huge amount of data, and this provides a great opportunity for companies to use this information for the analysis of customer needs, desires, and intentions
Summary
In our increasingly industrialized and globalized world, a large number of companies include call centres in their structures and more than $300 billion is spent annually on call centres around the world. For a customer, addressing the call center means addressing the company itself, and any negative experience on the part of the customer can lead to the rejection of company products and services. For the company, it is very important to ensure that a call centres function effectively and provides high quality service to its customers. Call centres collect a huge amount of data, and this provides a great opportunity for companies to use this information for the analysis of customer needs, desires, and intentions. Such data analysis can help improve the quality of customer service and lower the costs
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