Turn Talk into Action with GenAI Powered Call Analytics

Turning ‘Talk’ into Action through GenAI-powered Call Analytics

Organizations depend on call analytics software to understand their customers’ preferences, enhance customer services, conduct more efficient operations, comply with legal and regulatory requirements, refine training, and more. In an era where customer interactions shape the brand narrative, the time has arrived to look beyond the transcripts and leverage Generative Al to mine actionable insights from call recordings.

Recently, we developed a prototype for an American data analytics, software, and consumer intelligence company that demonstrates how advanced Al models can go beyond traditional analytics, offering an unprecedented depth of understanding in customer-agent interactions. Our innovative approach elevates customer service from reactive troubleshooting to proactive engagement. By identifying systematic issues early on, agents are empowered with precise feedback, ultimately enhancing the overall customer experience.

Enhancing Call Insights with GenAI

Our prototype processes entire call recordings, extracting insights like customer sentiment, agent performance, call summaries, issue status, and product mentions. It’s highly scalable, has integration capabilities, and automation such as storing output in a database, creating dashboards and chatbots. Its advanced capabilities include:

Sentiment Analysis

Beyond basic sentiment classification, the model scores emotions on a scale of 1 to 10, revealing subtle shifts in mood throughout the call. For example, in a call involving a technical issue with a pin pad, the customer's initial frustration was noted, but the sentiment improved significantly once the problem was resolved.

Agent Performance Metrics

Each call is assessed on communication clarity, patience, and problem-solving effectiveness. This granular evaluation helps to identify top performers including specific areas for each agent to improve, such as clarifying initial instructions to avoid repeated steps.

Detailed Call Summaries

Summarizations capture the essence of each interaction, detailing the problem, steps taken, and final resolution. This concise report is invaluable for stakeholders who need quick insights without diving into full call recordings.

Outcome Tracking and Recommendations

The model provides actionable recommendations to both agents and customers, enhancing future interactions. In the example of the pin pad issue, a suggestion was made to double-check cable connections to prevent similar problems.

Streamlining Analytics through Data Automation and Integration

After insights are generated, they are pushed automatically into databases or Excel sheets, making data storage and accessibility seamless. This opens the door to a range of output formats tailored to specific needs:

Using platforms like Zoho, we created dashboards that visualize agent performance, sentiment trends, and issue resolution rates. These visual representations help stakeholders quickly grasp the broader picture and make data-driven decisions.

Our IGNIS GenAI bot, integrated with the prototype, allows its users to query the data directly. Questions like the following are answered in real time, transforming static data into dynamic and interactive insights:

• What is the average duration of calls? Additionally, could you provide information on the shortest and longest calls recorded?
• Could you provide details regarding call ID 1001V?
• Who is the top-performing agent, and what factors contribute to their success?
• Are there any specific calls that warrant concern? If so, please explain the reasons.
• Can you summarize the overall sentiment expressed across all calls?

The system can send curated reports via email, highlighting key metrics, emerging issues, and agent performance summaries. These reports keep stakeholders updated without requiring manual data extraction or analysis.

Scalability and Future Enhancements

Our prototype currently utilizes Python packages and GPT-4 Turbo API, and will scale with future advancements, including the anticipated GPT-4o's audio capabilities. Once available, these enhancements will enable even deeper analysis of vocal tone, pacing, and more nuanced sentiment detection. The entire process-from data collection to insight generation-can be fully automated, scheduled to run at regular intervals to keep analytics fresh and relevant.

Want a demo? Connect with Team IGNIS to learn more.