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.

This post evaluates AI21's Jurassic-1 Jumbo language model and compares it to OpenAI's GPT-3 davinci model in answering trivia questions from the Water Cooler Trivia dataset. The experiment was conducted in both zero-shot and prompt settings, with the former resulting in a 55.4% accuracy rate for Jurassic-1 and the latter resulting in a 46.15% accuracy rate.

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.

Understanding customer feedback is important. But did you consider how it can be used to improve product ratings? The article introduces an impact metric called the Net Impact score, which measures the positive or negative impact of specific topics on the marketplace star rating of any product. The Net Impact score is computed using a combination of factors such as the importance of each topic, sentiment, topical sentiment distribution, and movement in average star rating.

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.