Large Language Models (LLM) for Net Promoter Score (NPS) Analysis
2023-2024
Context
In the competitive laptop gaming industry, understanding customer feedback is essential for improving products and services. The Net Promoter Score (NPS) is a key metric for measuring customer loyalty and satisfaction, indicating how likely customers are to recommend a product. However, manually analyzing vast amounts of feedback is time-consuming and challenging. Large language models (LLMs) like ChatGPT and Google Bard offer solutions by automating and enhancing the analysis of NPS. This presentation aims to train specialists to use LLMs for NPS analysis, demonstrated through practical examples and data visualization.
Content
We introduce a method for utilizing LLMs to analyze NPS efficiently. By leveraging tools like ChatGPT, specialists can automate the categorization of customer feedback, extract key sentiments, and identify actionable insights. The process involves collecting diverse feedback from various platforms, crafting effective prompts for sentiment analysis, and visualizing the results using data visualization tools. This approach streamlines the analysis, saves time, and enhances the depth of understanding customer satisfaction.
Conclusion
Integrating LLMs into NPS analysis significantly enhances the efficiency of understanding customer feedback. Automating categorization and sentiment analysis saves time and offers deeper insights into customer satisfaction and loyalty. This presentation demonstrates the practicality of AI-driven tools for NPS analysis, empowering specialists to improve products based on comprehensive customer insights. While recognizing the limitations of LLMs, this training equips professionals with the skills to leverage AI for better decision-making and product development.