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Analytics and Generative AI for Operational Functions – Customer Support


One Minute with Ruble Joseph

VP - Global Data Science Consulting at eClerx

Customer support has evolved drastically in the last few years. With the expansive growth of analytics and AI, businesses are now able to identify inefficiencies faster and more accurately, generating higher customer satisfaction and lower costs.

In this one-minute video, Ruble Joseph, VP of Global Data Science Consulting at eClerx Digital, highlights how we have worked with clients using proprietary analytics and AI tools and solutions to deliver impactful results. 




Transcription of the Video

The way organizations provide customer support has evolved significantly from calls, chats, SMS & WhatsApp, e-services websites, virtual agents, and much more.

Analytics and AI can help identify inefficiencies in these processes that are troubleshooting steps and optimal resolution paths, maintaining the balance between cost and customer satisfaction. and monitor and improve the performance of call centers and support centers. Guided resolution engines that identify optimal resolution paths, e-services journey optimization engines that take the customers on a self-resolution journey, virtual agents and chat bots that integrate agent learning along with AI-driven mechanisms.

In the customer support function for a leading client, we have been able to achieve more than $30 million in cost savings through improvement in metrics such as first-stage resolution, handling time, repeat contacts, and dispatches by more than 10 percent.

Similar outcomes can be achieved in other niche areas such as inventory management, partner operations, finance backend operations, and other operationally-intense processes, leveraging AI and process re-engineering.