Client Overview: Our client, a mid-sized e-commerce retailer, faced challenges with their customer support operations.
Their team of 25 customer service representatives dedicated a significant portion of their time to addressing customer inquiries and managing order-related tasks.
Approximately 80-90% of their workload involved answering customer questions, ranging from order status updates to product information.
The client sought a solution to automate parts of their communication processes, starting with the development of a Q&A bot capable of addressing a wide range of customer inquiries.
Solution: To address the client’s challenge, we implemented an automated communication system utilizing a large language model specifically designed to handle customer-specific questions. The solution involved the following steps:
- Data Preparation:
- We gathered and prepared the available unstructured customer and product data in compliance with data privacy regulations.
- The data was segmented and stored in a secure vector database, enabling semantic search over a vast body of customer and product information.
- Data Retrieval Logic:
- We developed a robust data retrieval logic integrated with the large language model. This allowed the system to fetch relevant information and provide accurate responses to customer queries.
- User Interface and Backend Integration:
- To facilitate seamless interaction, we designed a user interface specifically tailored for customer use.
- Additionally, we developed a simple backend system to enable easy management of customer and product data for the retailer.
- Request Escalation and Summary Generation:
- In cases where the bot could not address a query, the system could escalate the question to the customer support team’s ticket system.
- To streamline manual processing, the system generated a summary of the inquiry for the customer support representative’s convenience.
- Authentication and Data Security:
- To ensure secure access, we implemented an email login mechanism linked to the corresponding customer’s dataset.
- This authentication process guaranteed the privacy and security of customer data.
- Data Privacy Enhancement:
- To further improve data privacy, we employed Named Entity Recognition (NER) to anonymize personal information from the data whenever necessary, enhancing compliance with privacy regulations.
Demo: We provided a demonstration of the system to the e-commerce retailer, showcasing the secure handling of customer data and the bot’s ability to accurately respond toinquiries.
Outcomes: Through a pilot phase and subsequent refinement, the automated communication system delivered significant results for our client.
- Initially, the bot successfully answered approximately 55% of customer questions without requiring intervention from the customer support team.
- As the system accumulated more data and knowledge, this success rate increased to 75%, enabling the bot to autonomously handle the majority of customer inquiries.
This solution not only reduced the workload for the customer support team but also improved response times and customer satisfaction, leading to a more efficient and effective customer service operation.
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