Customer analytics often combines structured information, purchases, retention, and usage, with unstructured data such as reviews, support conversations, and survey responses.
NLP and language models can help categorize feedback at scale, but sentiment alone may not explain what customers actually do. A complaint about a feature, for example, does not automatically mean the customer will churn.
I’m interested in how teams connect these different data sources without treating correlation as causation.
Have you found a reliable way to link themes in customer feedback to measurable outcomes such as retention, repeat purchases, or support costs? What validation approach would you recommend?