Customer feedback analysis can provide crucial insights for a business, but manually analyzing and categorizing these large volumes of unstructured data is a time-consuming task that is prone to inconsistencies and subjectivity. The process can be streamlined through the use of large language models (LLMs), comprehensive machine learning models that can interpret, generate, and analyze text across various industries and applications.
Amazon Bedrock is a service facilitating the integration of LLMs into enterprise applications. It offers model customization, context-specific responses, and the capability for running complex multi-step tasks. Developers can use Bedrock to bring generative AI applications to life, avoiding the burden of infrastructure management.
Amazon QuickSight, another Amazon service, can create customer feedback analysis and other business intelligence needs, even without the need to manage the underlying infrastructure. Through this service, complex data can be visualized and insights can be shared across the organization effortlessly.
Major advantages of adopting generative AI approaches for customer feedback analysis include superior accuracy, ability to use minimal or no labeled data, and enhanced model generalization. These models are also more flexible, adapt better to new categories, and can generate text for rare or unseen categories. All these save time and resources for businesses when compared to traditional ML methods.
This article provides a detailed overview of creating a generative AI application for customer review analysis, including workflow orchestration, prompt engineering, and result visualization. The architecture and application can be customized to a business’s specific needs and industry requirements.
Practical applications of the framework include analyzing and categorizing customer feedback, streamlining customer service processes, analyzing product information for e-commerce purposes, and enhancing product recommendation systems. The goal of using this technology is to enhance operational efficiencies, improve decision-making and ultimately enhance customer satisfaction.
