Use Case: Request for financial information
This government department has significant peaks of work that require a ramp up of advisors from ~2,000 to 4,000 to deal with this extreme customer demand.
Gathering information for ‘request for financial information’ call type takes a lot of time, involves over 20 screens, often with the customer on-hold. Advisors can miss key information and errors can result. Customers often call back to get a ‘better’ answer or because they are not confident in what the advisor has said.
- Data gathering & analysis: The robot gathered and analyzed data in 15-20 seconds that would have taken an experienced agent 90 seconds, and an inexperienced agent 3 minutes, and presented it in a dynamic dashboard.
- Guidance: The robot determined the exact customer situation and prompted the agent with detailed guidance associated with that situation.
- Next steps: The dashboard also pre-empted the customer’s next question by proactively offering information to address it, thus reducing repeat calls.
- 2+ minutes saved per call.
- Built in < 4 weeks.
- Significantly reduced customer call-backs.
- Improved customer experience.
- Universally approved of by advisors, giving them the confidence they need to deal with this difficult call type.
- Ensured that agent behavior is consistently good for this call type and compliance with business rules is 100%.
Use Case: Update Customer Data
A 3rd party vendor is used to validate customer records, recommend details to update & rule out errors and fraud. As customer details are private & secure, the vendor has no access to the systems of record
Errors & fraud amounted to ~$1bn. Updating the changes received from the 3rd party would require the hiring of 600 employees.
- 150 Robots process the updated records without the need for human intervention.
- Currently, processing > 25k per day
- Robots can be scaled up to deal with peaks and are ready to become ‘multi-skilled’ to deal with other types of work in the future.
- Robotic Automation saved the equivalent of $13M annually but more importantly, it was an enabler to tackle a $1bn problem
- Ensures that there are zero errors in entering the records and can scale up to deal with huge peaks of work