“At UnionBank, we take delivering the best customer experience very seriously. To ensure our products, services and interactions across channels keep on delighting our customers, we make sure we continuously get feedback from them and we work closely with our Data Science and AI counterparts to help us analyze the vast data we get to allow us to keep on providing what our customers love and quickly address those that need improvement,” said Albert Cuadrante, UnionBank’s Chief Marketing Officer.
One of the key aspects of this DSAI-powered tool from UnionBank, the Net Promoter Score (NPS) Free-text Analysis Tool, is that it can automate the identification of user needs and gains through direct feedback analysis. NPS is an important consumer metric used by the Bank to estimate the loyalty and satisfaction of its customers towards its products and services. Customers can rate the Bank with a satisfaction score ranging from 0 (lowest) to 10 (highest) and give feedback or reviews to expound on their scores and recent experiences.
The solution is designed in a way that allows it to understand the sentiments of customers through free text found in various channels and platforms, such as the UnionBank Online mobile app or surveys answered in branches.
“The information that we are able to get from our various feedback channels, whether through online surveys, comments on social media and actual interviews, allow us to have more personalized offers and engagements with our different customers, ultimately deepening their relationship with us,” said Cuadrante.
Based on the tool’s performance over a period of eight months, UnionBank said it was able to streamline the analysis of more than 10,000 monthly survey responses and over 35,000 monthly inquiries and complaints from customers. This has proven to be invaluable for the bank’s product teams in understanding the customers’ key concerns and prioritizing actions to be implemented in a fast and efficient manner.
“The tool, which leverages NLP (Natural Language Processing), customer data analytics, and clustering techniques, identifies the top issues raised by multiple customer segments in just three to five minutes, saving around one to two weeks and significant man-hours of manually screening and processing texts,” said Gabriel Isaac Ramolete, a Data Scientist from UnionBank’s AI and Innovation Center of Excellence (AI & Innovation CoE).
"It is challenging for humans to read thousands of feedback texts to understand its impact on customer experience. Technologies like NLP function best at manually extracting insights from big and unstructured data. The tool that we developed and deployed at UnionBank acts as a decision support system that helps with prioritizing and making more informed decisions”, said Dr. Adrienne Heinrich, UnonBank’s AI & Innovation CoE Head.
To help the product teams analyze root causes and provide proactive recommendations that can help customers, the tool has an automated dashboard featuring sentiment analysis for reported customer experiences and product performance summaries, which is made readily available to the Bank’s customer experience teams.
“Because of this, we were able to raise the bank's NPS above its local competitors, and more importantly, leave more human-critical tasks to the bank's employees, so they can ideate, communicate, and execute actions optimally,” Dr. Heinrich concluded.
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