Text Analytics
The broad discipline of extracting meaningful insights from unstructured text data, including sentiment, themes, intent, and urgency.
Text analytics is the umbrella term for techniques that transform unstructured text—customer comments, support tickets, reviews, social posts—into structured, analyzable data. It encompasses sentiment analysis, theme extraction, intent detection, urgency scoring, and entity recognition.
In customer experience programs, text analytics unlocks the value hidden in open-ended feedback. While quantitative scores (NPS, CSAT) tell you how customers feel, text analytics tells you why. This "why" is what drives actionable improvement.
The evolution of text analytics has been dramatic. Early approaches relied on keyword matching and manual coding. Modern approaches use large language models (LLMs) that understand context, sarcasm, industry jargon, and nuance. These models can process feedback in multiple languages and detect subtle shifts in customer sentiment.
Effective text analytics requires volume. The insights become more reliable and actionable as more feedback is processed. This is why automated, always-on text analytics—analyzing every response as it arrives—is more valuable than periodic manual reviews that sample a fraction of the data.
Related Terms
Sentiment Analysis
AnalysisThe use of natural language processing to automatically determine whether a piece of customer feedback expresses positive, negative, or neutral sentiment.
Theme Extraction (Topic Modeling)
AnalysisThe automated process of identifying recurring topics and themes across large volumes of customer feedback text.
Verbatim Analysis
AnalysisThe process of reviewing and analyzing customers’ exact words from open-ended survey responses and other text-based feedback.
Predictive Analytics
AnalysisThe use of historical data, statistical models, and machine learning to forecast future customer behavior such as churn risk, expansion likelihood, or satisfaction trends.
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