Text Analytics

The process of extracting meaningful insights from unstructured text feedback.

Category

Analytics & Insights

Full Definition

Text analytics (also called text mining) is the process of analyzing unstructured text data to extract meaningful patterns, themes, and insights. In customer feedback, it transforms thousands of open-ended comments into actionable intelligence.

Text Analytics Capabilities: - Theme Extraction: Identifying main topics discussed - Sentiment Analysis: Determining emotional tone - Entity Recognition: Extracting mentions of products, people, locations - Keyword Extraction: Finding frequently used terms - Topic Modeling: Grouping related feedback automatically - Trend Analysis: Tracking how themes change over time

Common Use Cases

Large-scale feedback analysis
Trend discovery
Competitive intelligence
Voice of customer programs

Real-World Examples

1
Telecom

Scenario

A telecom company receives 10,000 support tickets monthly. Text analytics automatically categorizes them: 35% billing, 28% service outage, 22% plan changes, 15% other.

Outcome

They discover billing tickets increased 50% after a new invoice format. They revert to the old format and add an explainer video.

2
Hospitality

Scenario

Text analytics on 2,000 hotel reviews extracts: "room cleanliness" (mentioned 823 times), "friendly staff" (756 times), "breakfast variety" (412 times with declining sentiment).

Outcome

Hotel maintains high standards on cleanliness and staff (strengths) but overhauls breakfast menu (emerging weakness).

3
Automotive

Scenario

A car manufacturer analyzes owner forum posts and service reviews. "Infotainment system" appears in 45% of negative posts, with keywords "frozen," "reboot," and "slow."

Outcome

Engineering team prioritizes a software update for the infotainment system. Issue resolved before it becomes a major recall.

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