Banking Case Study: Verbio Call Automation | Verbio Technologies

Banking Case Study: Verbio Call Automation

Natural Language Understanding
Verbio offers full-scale solutions to some of the Spain's largest banks, including BBVA. In this customer success story, we look at one of the largest banks in Spain and how, since 2017, they have incorporated Verbio's call automation & call transcription solutions and a unique personal brand voice
Scope

Scope

One of Spain’s largest banks was receiving over 2,000 calls a day with over 300 different customer intents. This bank wanted to automate their internal customer service and provide service 24/7 to their customers. They also wanted to standardize and simplify their customer service categories, as they were too complex and time consuming and were reducing customer experience, resulting in customer churn.

The bank’s call center IVR was an old legacy system. With very high call volumes, customers were experiencing long wait times to get through to an agent. When they eventually got through to the system, they received a frustrating routing process, having to listen to a menu of different options, with average resolution times taking about 200 seconds. The bank wanted to join their competitors and utilize innovative technology to address the issue of long wait times for customers, while providing a very personalized on brand experience.

+90%
Accuracy
+2K
Daily Calls
+300
Intents
The Solution
Deploy call automation at speed
Verbio's complete Call Automation Solution has reduced both call wait times and call resolution times for the bank's customers and they can now self-serve 365 days a year, 24 hours a day.

Since 2017, Verbio has provided this leading retail bank with call automation and call transcription solutions, in Spanish. Verbio has also created a personal brand voice for the bank, which is unique and in line with their brand and corporate identity.

Verbio’s complete Call Automation Solution has reduced both call wait times and call resolution times for the bank’s customers and they can now self-serve 365 days a year, 24 hours a day. Instead of menu options, with long resolution times, the caller is greeted by a virtual assistant with human-like interaction through an open dialogue question, ‘how can I help you?’ The caller is then directed through a 100% self-service workflow or routed to the right agent – reducing call drop rates and improving customer experience.

In the background is Verbio’s speech recognition, Text­-to-Speech, and Natural Language Understanding (NLU) technologies. The system recognizes and understands what the caller is saying. Using Deep Neural Networks means that the voice recognition engine is capable of differentiating languages, dialects, accents and intonation.

This leading bank wanted to extend their brand values and offer their customers a very personalized customer experience that was non-robotic and different to their competitors. Verbio’s Text-to-Speech technology provided the bank with their very own unique brand voice for their virtual assistant. This helped to provide a human-like experience and a brand voice that customers would connect to and remember.

Another service this bank utilized was Verbio’s call transcription technology. With Verbio’s 90+% transcription accuracy rates, this provided valuable insight for the bank. This analysis identified the main reasons customers were calling the bank and how their call center resources were being utilized. This information also helped agents to upsell and increase overall sales.

This successful implementation of Verbio’s call automation technology, has resulted in a streamlined process where customers can self­serve 24/7. The bank now has increased their customer service and brand experience with their unique virtual assistant voice and reduced frustrating wait times. Customers are now being directed to the right support straight away. This improved CX also enabled the bank to save millions of euros in operating costs, with human agents being utilized more effectively and for more complex issues.

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