A market leader in the consumer finance sector in Vietnam employs over 17,000 people. It serves 10 million customers via collaborations with partners through a varied partner network of more than 13,000 point-of-sales. It offers its customers a wide range of multi-accessible, flexible and effective financial lending products and services.
- Limited bandwidth allowed only 0.6–1% of the calls to be monitored and evaluated
- Poor visibility into agent performance and customer satisfaction
- Inability to recognize customer propensity to engage, buy or renew
- Lack of insights to make improvements to call center processes
- No way to measure if agent performance assessment was biased
- auMina™—Uniphore’s conversational analytics solution was chosen to replicate the QA scoring system. The solution's advanced features including real-time monitoring of 100% of calls along with derived actionable insights, enable company to evaluate and improve agent performance.
- Monitored 100% of outbound calls
- Improved agent performance through multiple KPIs, agent knowledge index and QA scoring reports
- Enabled agents to identify customer intent to buy, renew or engage
- Increased sales efficiency
- Augmented collections by increasing agents efficiency across all stages of collection calls
- Enhanced customer satisfaction through actionable insights
THE BUSINESS NEED
The company wanted to monitor every customer-agent-call interaction to optimize the efficiency at its contact center. By assessing the agent performance and the quality of a call, the company intended to increase collections, improve sales efficiency, and enhance customer service.
The contact center handled over 1.2 million outbound calls every month for sales, collections, and customer service. Due to huge volume of calls, only 0.6-1% of the calls were being monitored manually.
The company was unable to understand customers’ intent and assess agent performance. The client also had an internal scoring mechanism to receive customer feedback through a survey at the end of a call that would measure parameters such as CSAT and NPS. This internal scoring system proved to be ineffective as only 5-10% of customers responded to the internal survey.
CONVERSATIONAL ANALYTICS FOR REPLICATING QA SCORING
Uniphore’s auMina™, a conversational analytics solution that uses Natural Language Processing (NLP) and Machine Learning (ML), was chosen by the client. The conversational analytics platform enabled the client to monitor 100% of the outbound calls in Vietnamese. It also provided the client with detailed QA customizable reports highlighting customer sentiments, agent knowledge and performance.
auMina™ was also able to replicate the QA scoring of the existing parameters giving access to several dashboards to gauge whether the agents were pitching the right products, handling objections correctly, measuring customers’ interest to engage and their intent to purchase. The solution increased the collections and sales efficiency by measuring various KPIs including propensity to pay, intent to buy, objection handling, and customer satisfaction. auMina™ helped agents to enhance customer service experience by providing near real-time insights to agents and sentiment analysis, enabling smart intervention during an ongoing call.