Client Background
A growing e-commerce startup faced significant challenges in sorting through and categorizing the overwhelming volume of emails they received daily. These emails contained a mix of customer feedback, complaints, inquiries, and requests, making it difficult for the team to extract actionable insights efficiently. The sheer volume often led to delayed responses, missed opportunities to address customer concerns, and an inability to identify recurring trends or sentiment patterns.
Challenge
The large volume and diverse nature of clients' emails demanded a solution that would enable them to automate much of the heavy lifting required for sorting, categorizing, and managing these emails for appropriate responses.
To address these challenges, BSC Analytics engineers proposed a solution that leveraged GenAI services from AWS to facilitate the process and automate as much of the workload as possible. Utilizing tools such as Amazon Comprehend for natural language processing and AWS Bedrock for deploying tailored foundation models enabled automatic email categorization, sentiment analysis, and keyword extraction. Additionally, the engineers designed an end-to-end workflow that integrated these insights into a centralized dashboard, providing the company with real-time visualizations of customer sentiment and emerging trends. This streamlined the handling of email feedback and empowered the startup to improve its service quality and proactively strengthen customer relationships.
Solution
The Email Feedback Metrics Dashboard was developed to address needs, using a serverless architecture on AWS to handle feedback automatically:
Automated Email Analysis
The system automatically collects and processes emails sent to a specific address using Amazon SES, storing them in an S3 bucket.
From there, engineers trained a custom model using Amazon Comprehend for domain-specific terms, enabling precise analysis of the feedback relevant to the client’s customers and the types of emails received.
In addition, BSC Analytics set up pre-trained foundation models in AWS Bedrock to analyze email text with more nuanced sentiment analysis and categorize feedback into detailed, custom tags applied to each piece of mail.
Real-Time Insights
The sentiment analysis results and key phrases extracted from the emails are presented on an interactive dashboard built with React. This dashboard provides real-time updates, allowing users to gauge customer sentiment quickly and identify trends.
Scalable Serverless Architecture
By leveraging AWS’s serverless components, the solution scales automatically based on usage, ensuring cost efficiency and eliminating the need for server management.
Results
The deployment of the Email Feedback Metrics Dashboard provides several key benefits:
Improved Customer Understanding
The company gained immediate insights into customer sentiment, enabling faster and more informed decision-making.
Operational Efficiency
Automating the feedback process reduced manual effort, allowing resources to focus on customer service improvements.
Scalable and Cost-Effective
The serverless architecture provided a scalable solution that grows with the company’s needs, with a pay-as-you-go pricing model that optimized operational costs.
Conclusion
This case study demonstrates how the company successfully automated its customer feedback analysis using the Email Feedback Metrics Dashboard. The serverless architecture, with real-time sentiment analysis and an interactive dashboard, empowered the company to understand better and respond to customer needs, driving improved service quality and customer satisfaction.
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