Today, the customer service industry primarily relies on survey tools to capture feedback after different types of interactions—whether through email, live chat, chatbots, or phone calls. Customers are typically prompted at the end of these interactions, often via an on-screen request or a follow-up email, to provide their input. This feedback is then measured using a standard “1-to-5” rating scale, known as Customer Satisfaction (CSAT), to assess the quality of the service experience. The reality is that most customers choose not to provide explicit feedback. When responses are collected for only a fraction of interactions—for example, 10 out of 100—the data becomes inherently biased. This raises a critical question: can such limited data truly be relied upon to measure customer satisfaction or to train and improve Customer Service Representative (CSR) performance?

Smarter way to measure satisfaction
With Generative AI, a new approach emerges—Generated Satisfaction Score (GSAT). Unlike traditional CSAT, which depends on customers responding to a limited set of questions, GSAT leverages conversational data, tone, sentiment, and contextual cues to generate a real-time satisfaction rating. This methodology leverages artificial intelligence to evaluate a predefined set of business-specific factors that influence customer satisfaction—eliminating the dependency on customers to submit feedback. GSAT can be automatically triggered in alignment with user preferred workflows—for example, when a service ticket is closed, a chat session concludes, or a call is completed.

AI-powered GSAT in Siebel CRM
With our 25.8 release update, we are offering out-of-the box capabilities that enable organizations to evolve from static, survey-driven feedback to real-time, AI-powered experience intelligence. The GSAT Score is derived from the complete set of interactions—both inbound and outbound—between customers and agents. Key indicators such as Customer Effort, Agent Effort, Agent Knowledge, and Empathy are evaluated after each engagement—whether a service request, chat, email, or purchase.

  • Customer Effort Score: This metric measures the perceived difficulty a customer faces when interacting with the company. The AI analyzes conversational cues for signs of frustration, such as multiple repetitions of the same question or frequent transfers, to determine if the customer’s journey was seamless or complex.

  • Agent Effort Score: AI assesses the agent’s experience by evaluating the ease with which they resolve an issue. This is gauged by factors like the number of internal resources they had to access or the number of transfers required to handle a request. This metric is rated on a 1–5 scale, where 1 indicates minimal effort and 5 signifies maximum effort.

  • Agent Empathy Score: This measures the degree of empathy conveyed by the agent. The AI detects empathetic language, such as acknowledging the customer’s feelings or using phrases like “I understand” or “I’m sorry to hear that.”

  • Agent Knowledge Score: AI evaluates how effectively an agent uses their knowledge to address customer inquiries. This is determined by the speed and accuracy of their responses and their ability to provide correct information on the first attempt.

Benefits of AI-powered GSAT scoring:

  • Provides immediate visibility into customer satisfaction data without the need to manually review emails or interaction records.

  • Enhances agent performance by delivering timely, data-driven insights into individual and team effectiveness.

  • Enables organizations to pinpoint friction points across the customer journey and implement targeted, strategic improvements.

 

To find out more, watch the webinar: Empowering Decisions with AI Multimodal & GSAT Score in Siebel CRM and download the PDF for easy reference at any time.

Get started with your AI journey in Siebel CRM today!, please don’t hesitate to contact me if you need more information.