Elevating customer service: AWS Connect and generative AI

4-minute read

Quick summary: Generative AI–based tools can deliver the efficient, personalized experiences today’s customers demand—if they’re well trained on high-quality data. AWS Connect enables customer service organizations to feed AI platforms with call analytics that can unleash opportunities for improving customer satisfaction.

In a business landscape where customer loyalty and retention are paramount, customer service plays a pivotal role. A recent study revealed that 97 percent of consumers and 98 percent of contact center managers believe the quality of customer service interactions directly influences whether customers remain loyal to a brand. These figures underscore the undeniable connection between exceptional customer service and brand success.

What is it that gives certain organizations a competitive edge in the customer service landscape? Eighty-nine percent of customer support leaders emphasize the importance of personalized service as a key differentiator. Tailoring interactions to individual needs and preferences has become an essential aspect of modern customer service.

In this article, we delve into a powerful combination of technologies that can help businesses take their customer service to new heights: AWS Connect and generative AI platforms. We’ll explore how these technologies come together to deliver the data needed for creating efficient, optimized experiences that not only meet but exceed expectations as they shape the future of customer service operations.

In the following sections, we’ll explore how generative AI platforms can harness the data provided by AWS Connect to take customer service experiences to the next level.

What is AWS Connect?

A cloud-based contact center service from Amazon Web Services (AWS), Connect enables businesses to set up and manage a customer contact center in the cloud without the need for extensive hardware or complex setup. AWS Connect offers tools for handling customer interactions via various channels such as voice, chat, and SMS, allowing for flexibility in how businesses engage with their customers.

The service includes features like call routing, interactive voice response (IVR), analytics, and integration with other AWS services. It’s designed to be scalable and customizable, allowing businesses to adjust their contact center capabilities based on their needs, such as adding or reducing the number of agents or adjusting routing strategies.

In March 2023, AWS announced the addition of voice analytics to Connect’s capabilities via AWS Chime SDK. This feature uses machine learning (ML) to extract sentiment from caller and agent speech signals by analyzing verbal and linguistic information as well as acoustic and tonal qualities. The data is delivered into the business’ data lake of choice, from which it can be accessed by analytics platforms and also feed into training sets for generative AI tools.

Enhancing generative AI tool training with call analytics

Call analytics play a pivotal role in improving the performance and effectiveness of generative AI–based customer service interactions, whether they occur through real-time agent-customer conversations or through automated chatbot interactions. By analyzing customer conversations, businesses can identify areas where both agent tools and chatbots can improve to enable more personalized, efficient, and satisfying experiences.

Here are a few ways call analytics can help train and improve generative AI–based customer service tools:

• Identify common customer queries and requests: Call analytics can reveal the most frequently asked questions, topics of discussion, and customer requests, which is valuable for training both agent tools and chatbots to recognize and respond to common inquiries accurately and efficiently.
• Analyze customer sentiment and satisfaction: Connect’s voice analytics function can assess customer sentiment throughout conversations, identifying instances of frustration, dissatisfaction, or positive interactions. This data guides the development of generative AI tools to ensure appropriate responses to different emotional cues.
• Uncover conversational patterns and trends: By identifying patterns in customer language, conversational flows, and common issues, call analytics guides the development of customer service tools to follow natural conversational patterns and address typical customer concerns.
• Detect knowledge gaps and areas for improvement: Call analytics can identify instances where agents or chatbots struggle to provide accurate answers or handle specific requests. This information pinpoints areas where AI tools can better support agents and guides the expansion and improvement of chatbot training data.
• Evaluate chatbot performance against real-world interactions: Customer service teams can leverage call analytics to compare chatbot interactions with human-agent-to-customer conversations, assessing their effectiveness in handling inquiries, resolving issues, and providing positive customer experiences. These comparisons can uncover areas for improvement, enabling continuous learning and adaptation to better meet customer needs.

By incorporating call analytics into the generative AI tool training process, both customer service agents and chatbots can become more effective and personalized in their interactions with customers, leading to improved customer satisfaction and loyalty.

Inventing the future of customer service experiences