Understanding Call Center Analytics

Azure OpenAI (AOAI) can be a valuable tool for call center analytics. By analyzing transcripts of calls between customers and agents, GPT models can identify patterns and insights that can help call centers to improve their operations.

Here are some examples of what AOAI models can do:

• Identify frequently asked questions and suggest potential responses, which can help agents answer calls more quickly and effectively.

• Analyze the sentiment in customer interactions, allowing call center managers to identify areas where customer satisfaction may be lacking and make necessary improvements.

• Provide insights into call volume trends, wait times, and call duration, allowing call centers to optimize staffing levels and resource allocation.

• Extract relevant information from the conversation to automatically create tickets in the CRM.

• Provide a relevant knowledge base for customer-facing chatbots that can address a larger number of questions/claims before switching to a physical operator.

With its powerful NLP capabilities, AOAI can help call centers improve their efficiency and effectiveness, leading to improved customer satisfaction and business outcomes.

For our sample scenario, let’s imagine we are a car insurance company. On a daily basis, we receive large numbers of calls from customers who have been in car accidents and need support to determine what to do with their insurance. After every call, our operators need to open a ticket on our CRM.

Our goal is that of simplifying the overall procedure by implementing the following features:

• From the call transcript, extract relevant parameters to automatically create a ticket.

• Get the main topic of the conversation to classify it in the proper category.

• Get the sentiment of the customer.

• Generate a guide for responding to particularly tough conversations.

Here is a visual representation of this: