Case Study: Financial Services
In an era where customer expectations are at an all-time high, businesses face the challenge of delivering prompt, efficient, and accurate customer service. The traditional model, relying heavily on large teams of customer service representatives (CSRs) to process, triage, and address customer requests in real time, is both cost-intensive and challenging to scale. Working directly with a large US based bank with a leading consumer product, Gradient was able to implement an AI based strategy to enhance triaging for customer requests using the vast amount of unstructured data from customer interactions.
The Challenge
To meet the demand for 24/7 support, the team opted to implement an AI-powered chatbot to manage the high volume of inquiries. For the chatbot to be most effective, it needs a clear and consistent approach to responding to questions. While there is an abundance of data available for the system to learn from, the challenge lies in processing this data effectively to ensure the chatbot provides an accurate and detailed response.
Unstructured Data: Although there's a vast amount of data available for the system to ingest, the data is raw and unstructured - making it difficult to process. Common types include text messages, emails, live chat, forms, blogs or social media interactions, where customers describe their issues in their own words. Last but not least, the most rich but difficult data to wrk with reside in audio recordings - making the process to extract meaningful insights and enable accurate responses challenging.
Manual and Labor-Intensive: Having to sift through unstructured data for customer support is time-consuming and labor-intensive because it lacks a clear format - making it difficult to categorize and analyze. Customer messages can vary greatly in language, tone, and context - currently requiring a team to manually break down and interpret.
The AI-Driven Solution
Leveraging Gradient's data platform, the team was able to maximize the ROI from their data despite the unstructured nature of the data.
AI-Powered Data Extraction: By using Gradient, the team automatically ingested large volumes of unstructured data from various sources, eliminating the need for manual processing and interpretation. Once the data was structured, they were able to leverage it effectively, leading to a significant improvement in the quality of the generated output.
Sentiment Overview: Gradient's data platform enabled the team with a quick sentiment overview, providing the team with deeper insights into the customer emotions and intent without having to interpret the data themselves. This enables the team to be able to prioritize issues, escalate urgent case, or offer personalized solutions more efficiently. Not to mention that sentiment analysis allows chatbots to be able to tailor responses based on the motional state of the customer, improving the overall experience.
