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Analysis

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.

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