Section 1
The job is coding, not summarising
Ask a model to summarise a thousand reviews and you get a paragraph that sounds reasonable and cannot be acted on. Everything is mostly positive with some concerns about delivery. The useful operation is coding: assigning each piece of feedback to a category, so you can count. Counting is what turns text into a management input. Eleven percent of tickets last month were about a failed integration, up from four, is a decision. A pleasant summary is not. So the output you want is structured, one row per item, with a category, a sentiment, a severity, and the original text preserved for reference. Structured extraction from messy inputs is the same shape of task as [Using AI for Real-Time Fraud Detection](/blog/using-ai-for-real-time-fraud-detection), with a slower clock.
Section 2
Build the taxonomy from the data, then freeze it
Two mistakes bracket this step. The first is inventing categories in a meeting, which produces buckets that reflect your org chart rather than the customer's experience. The second is letting the model invent fresh categories every run, which makes months incomparable. The workable middle: sample two hundred real items, read them yourself, and let the categories emerge. Then write definitions with examples of what belongs and what does not. Then freeze the list and run it at scale. Add a mandatory other bucket and watch its size. When other exceeds roughly one item in ten, the taxonomy has aged and needs a deliberate revision, versioned and dated so old comparisons stay honest.
Section 3
Verify against a human-coded sample
Classification quality cannot be assessed by reading the output and finding it plausible. It always reads as plausible. Hand-code a hundred items yourself, then run the model over the same hundred and compare. You will learn where it systematically errs, and the errors are usually specific: sarcasm read as praise, a mention of a competitor filed as a complaint about you, one category quietly absorbing another. Repeat this check every time you change the prompt, the model, or the taxonomy. It takes an afternoon. Skipping it means a silent shift in classification behaviour will show up in your dashboard as a trend, and someone will make a decision on it.
Section 4
Wire it into a decision, not a dashboard
Feedback analysis dies as a monthly report nobody opens. It survives when each category has an owner and a threshold. Make the rule explicit. If the failed-integration category exceeds a set count in a week, it opens an engineering ticket automatically. If a named account produces two severe items in a month, the account manager gets a message. If a theme appears in both churned-customer interviews and support tickets, it goes on the roadmap review agenda by default. Coded feedback also feeds churn and expansion signals, which is where [Leveraging AI Automation for Predictive Sales Analytics](/blog/leveraging-ai-automation-for-predictive-sales-analytics) picks it up. One more pipeline is worth building: route the strongest positive verbatims to the marketing and sales team with a permission request attached. That is the cheapest source of proof material a company has, and it usually sits unused in a helpdesk.
Section 5
Privacy, consent, and the quiet leak
Feedback text is the least controlled personal data in most companies. It contains names, order numbers, health details, complaints about staff, and occasionally card details a customer pasted in. Before any of it reaches an external model, strip identifiers, and keep the mapping in your own system. Know where the vendor processes data and whether it is retained. Check your privacy notice actually covers this use, because a promise to use feedback to improve service does not automatically cover sending it to a third party. When you publish what you learn, use aggregate patterns rather than individual quotes unless permission is explicit. Turning those patterns into something a board can absorb is the craft in [Using AI and Data Analytics to Enhance Storytelling](/blog/using-ai-and-data-analytics-to-enhance-storytelling).
Section 6
Metrics: movement, not mood
A satisfaction score on its own tells you the temperature and nothing about the cause. The value of coded feedback is that it explains the score. Track category volume over time, severity mix within each category, the share of feedback linked to a resolved change, and time from a theme appearing to a fix shipping. Track the other bucket as a health check on the taxonomy, and the agreement rate between the model and your periodic human sample as a health check on the pipeline. The review question is simple. What did we change last quarter because of feedback, and did the relevant category shrink afterwards. A program that cannot answer that is producing reading material, not intelligence.