Applied Data Analytics for Impact

 Introduction Customer text data is often analysed in isolation. Topics are extracted but not prioritised. Sentiment is measured but lacks context. High frequency words dominate attention without explaining why they matter. Individually, these techniques are useful. Together, they often fail to answer the real analytical question: What themes matter most, how do customers feel about them, and how is that changing? The challenge is not choosing the “best” NLP method. It’s combining complementary signals into a coherent analytical view . Why this is required Decision makers rarely act on text analysis alone. They act when text insights are: interpretable prioritised comparable over time or segments Without integration: sentiment scores feel abstract topic models feel academic frequency counts feel noisy Applied NLP turns unstructured language into structured signals that can sit alongside CRM metrics , rather than compete with them. NLP as a layer...