Feedback-analysis

Unlock the power of Text & Sentiment Analysis to tap into your customers' voice. Traditional methods grapple with manual tagging and linguistic limitations. In contrast, advanced Large Language Models (LLMs) offer a rich, nuanced comprehension of language across diverse verticals and concepts. Dive into this guide to explore the evolution, advantages, and future trajectory of sentiment analysis in the Zendesk platform.

Understanding and measuring customer satisfaction is critical for success in today's competitive business landscape. One widely-used metric for gauging satisfaction and loyalty is the Net Promoter Score (NPS). In this blog, we'll delve into what NPS scores are, how to calculate NPS scores, and strategies to improve them. We'll also explore the role of sentiment analysis in understanding customer feedback. So, let's dive in and unlock the true potential of NPS scores.

Analyzing customer reviews is important to identify areas of improvement, gauge overall customer satisfaction, and communicate brand messaging. However, effective analysis becomes more challenging as the volume and variety of review data continue to grow. This article outlines the current state-of-the-art methods to analyze reviews and also looks at AI text mining solutions for customer feedback analytics. The process of review analysis is broken down into data gathering, analysis, and reporting, with different challenges in each phase.

Intercom is a chat support tool used by online businesses to provide quality customer service. By coding and tagging chat conversation transcripts, businesses can identify gaps in documentation, difficult website sections, and common feature requests. This analysis can be leveraged to reduce customer questions and complaints, as well as increase user happiness. Alternatively, businesses can use natural language processing and text analysis techniques to automatically extract patterns from Intercom data.