Large Fintech Increases CSAT by 30% and Captures Unrealized Revenue by Uncovering Operational Issues

Author:

Level AI Team

Last updated:

February 22 2023

Blog /Artificial Intelligence / Large Fintech Increases CSAT by 30% and Captures Unrealized Revenue by Uncovering Operational Issues

It’s no secret that increasing customer satisfaction (CSAT) is an important factor for the success of any organization, especially those in highly competitive industries such as fintech. That’s why we would like to share with you a remarkable story about one large fintech that has grown its CSAT by 30% while also capturing unrealized revenue. In this post, we will discuss how the company leveraged Level AI analytics to uncover valuable feedback and business insights.

The Challenge

A large fintech client noticed a significant decrease in their CSAT scores in a relatively short period, but they lacked immediate insight into why this was happening.

The Analysis

The client first isolated and reviewed interaction samples with negative sentiments and low customer scores within the identified time range. The review revealed a high volume of payment-related issues, but it was unclear which types of payment problems were causing the sudden spike in low ratings.

They then tapped into the power of automated conversation categorization from Level AI to quickly identify and analyze customer payment issues, which enabled them to gain visibility into daily trends of problems that caused drops in CSAT scores.

Call data analysis revealed that the highest volume of calls was around payment processing and refund issues. The client reviewed call samples from days with increased activity via auto-summaries and conversation flags, discovering that all such calls were related to processing errors when making new purchases, receiving refunds, or making payments. It was clear that there was a problem with the backend infrastructure. Since the company used multiple vendors, they needed to dig deeper to see where customers with processing issues were coming from to identify which vendor or processor was causing errors.

The client conducted another custom analysis of the vendors that had escalated payment issues over the same time range. The pattern of drastic increase matched the dates of the customer reports and confirmed that the problem was caused by the same payment processor major vendors were using, which affected one-third of customers.

The Result

With this insight, the client worked with the specific vendors to resolve the payment processing issues and create a more efficient partner escalation plan. The client continued to monitor the customer sentiment and ratings after the problem was solved and was surprised to see some unexpected revenue gains. On top of decreased customer frustration and a 30% improvement in the CSAT score, the client also saw a jump in revenue with the increase in successful purchase and payment transactions.

For more details about this use case and how four other companies use Level AI to uncover hidden insights with contact center analytics, download the e-book, From Insights to Action: Uncover Hidden Insights and Transform Contact Center Operations.