Sentiment Analysis is a key component of Customer Feedback Analysis. It involves using natural language processing and text analysis to identify and extract subjective information from customer feedback. This helps businesses understand whether the overall sentiment is positive, negative, or neutral, and to what degree.

Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES) are metrics used in Customer Feedback Analysis to quantify customer satisfaction and loyalty. NPS measures the likelihood of customers recommending a company's products or services. CSAT gauges overall satisfaction, while CES assesses the ease of customer interactions with the company.

Text Mining and Opinion Mining are advanced techniques used in Customer Feedback Analysis. Text Mining involves extracting useful information from textual data, while Opinion Mining focuses specifically on determining the sentiment behind customer opinions. These techniques help businesses uncover deeper insights from large volumes of feedback.

Qualitative Data Analysis is another crucial aspect, involving the examination of non-numeric data like customer comments and reviews. Customer Journey Mapping helps visualize the customer's experience across different touchpoints, allowing businesses to identify pain points and areas for improvement in the customer journey.

Competitive Analysis and Voice of the Customer (VoC) are strategic components of Customer Feedback Analysis. Competitive Analysis helps businesses understand their standing relative to competitors, while VoC programs capture and analyze customer expectations and preferences to guide product development and service enhancements. These insights contribute to a comprehensive understanding of customer feedback and inform strategic decision-making.