Use Case
Customer Service Automation
Manual processes in customer service significantly inflate operational costs due to prolonged engagement durations, varying levels of CSR expertise, and repeated incidents from unresolved issues. Implementing an intelligent automation framework can mitigate these challenges by classifying interactions into live agent resolution, bot-assisted CSR journeys, and fully automated resolutions.
This approach not only enhances interaction volume and resolution rates but also drastically reduces the cost per serve. By leveraging real-time data, machine learning, and unique automation workflows, companies can achieve a minimal bot volume mix of 60%, aiming to lower costs by up to 90%
Problem
Manual Processes Inflate Costs
Engagement Duration
"Cost per serve" tracks interaction expenses. Longer interactions mean higher costs, emphasizing the need for prompt resolutions.
CSR Expertise
Not all CSRs can handle multiple chats concurrently; this skill is limited to a select few, with an average capacity of 4 chats.
Region & Language
Another factor impacting the cost per serve is the language used during interactions.
Resolution
Failure to accurately resolve a customer's issue often results in repeated incidents. This exponentially inflates the cost spent on resolving the same problem for the same customer.
Solution Ecosystem
An intelligent automation solution, which aims at containing the entire customer services cycle, possessing following characteristics:
- Enriched and diverse interactions with customers for comprehensive support using Gen AI.
- Real-time access to a large database for continuous learning from past interactions.
- Unique automation workflows tailored to resolve a variety of customer queries.
- Real-time analytics provided to CS agents for immediate insights.
- Problem resolution capabilities incorporating ML for complex issues.
Impact of CS Automation
- Enhanced interaction volume, resolution rates, and NPS.
- Targeting a standardized minimal threshold of achieving at least 60% Bot Volume Mix, aiming to lower Cost per Serve by up to 1/6th.
- Leveraging Automated Resolution cycles to decrease Cost per Serve by up to 90% compared to original live agent support.
