Choose the metric before the model finds patterns
Frame an AI customer-insight task with one HEART dimension and a measurable product question.
The move: define the product outcome before the synthesis. HEART gives teams five user-centered dimensions: Happiness, Engagement, Adoption, Retention, and Task Success. In AI customer-insight work, the value is focus. You are telling the model what kind of consequence to look for. A comment about confusing setup can matter for Adoption, Task Success, or Retention depending on the moment and segment. Why this works AI will always find patterns if you ask for patterns. The harder professional move is deciding which patterns matter for the decision in front of you. HEART prevents a common failure mode: a broad theme list…
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