Analyze customer feedback with AI: themes, counts and opposing experiences

A worked example: where the task becomes difficult

A fictional software team has two hundred feedback messages about onboarding. Some customers dislike the setup form, while others fail at invitation acceptance. Combining both as “onboarding is confusing” hides different fixes. Remove unnecessary personal identifiers, retain stable message references and ask the agent to distinguish reported problems, feature requests and praise. Review the categories before running the complete dataset.

Decisions to make before implementation

Define whether one message can belong to several themes and how duplicates are handled. Separate a customer count from a message count when one person writes repeatedly. Keep severity, frequency and commercial impact distinct. A rare account-access failure may matter more than many color preferences. Decide how ambiguous or sarcastic feedback is flagged for human review rather than forced into a confident category.

A practical sequence for the work

Use the sequence below as a task boundary, not as a claim that the example has been executed. Work with approved inputs and the project’s actual architecture. If a required integration or permission is unavailable, keep that stage visibly incomplete rather than generating a plausible substitute result.

• Prepare a permitted dataset with stable references and minimized personal data.
• Develop categories on a sample and inspect disagreements with human coding.
• Classify the complete set with explicit uncertainty and duplicate handling.
• Connect proposed improvements to representative examples and measured counts.

A detailed brief you can adapt for your agent

Replace the illustrative context with your approved facts and controlled inputs. Keep the stated boundaries when adapting the brief. The expected deliverable matters more than a particular tool name: ask for an explanation grounded in the inspected material and evidence for the requested outcome.

Analyze this redacted onboarding-feedback dataset. Propose a coding scheme on the first sample, then classify with stable message references. Separate invitation failures from form confusion. Preserve uncertain and opposing examples. Do not invent quotes or calculate unsupported percentages. Return theme counts, denominator definitions and an evidence-backed improvement queue.

Failure modes that an attractive preview can hide

A model can invent quotes or report percentages without counting. Require exact source references for excerpts and calculate totals independently. Do not erase positive feedback when investigating complaints, and do not equate silence with satisfaction. Feedback collected through a support channel may overrepresent frustrated customers compared with the whole user base.

Technical references: NIST: AI Risk Management Framework

Acceptance checks and the evidence to retain

Provide the category definitions, traceable classifications, counts and unresolved examples. Product decisions should reference the specific journey affected rather than a generic sentiment score.

Specific answers

Common questions