THE PRACTICAL GUIDES
AI customer-feedback analysis in Australia: topics, sentiment and useful counts
An AI customer-feedback project should help someone decide what to improve or review. A positive-to-negative sentiment score alone does not identify the delivery problem, missing product information or unresolved request inside a comment. Define the useful output and the unit being counted before buying a dashboard.
This guide helps Australian buyers brief text analysis using existing directory providers' published capabilities. It concerns organising feedback for people to act on. It is separate from an AI phone receptionist or knowledge-search assistant.
Decide which question each output answers
Start with one collection, such as approved post-purchase comments, and one review meeting or queue. Ask whether the team needs recurring topics, a summary of a particular issue, comments requiring follow-up or a broad sentiment measure. Those are different outputs and should not share an unexplained success figure.
A topic label such as “fulfilment” describes what a comment discusses. Sentiment describes the attitude expressed in it. An action flag identifies something a person should review. A comment can discuss several topics and include both praise and a problem.
Agree whether one row is a comment, a sentence, a support conversation or a distinct customer. If those units are mixed, a dashboard can report a change produced by counting rules rather than a change in experience. Keep a traceable link between each approved input and its reviewed labels.
An original six-row feedback exercise
The following comments are fictional commissioning material. Suppose the agreed unit is one distinct, usable comment, duplicates are removed and topic labels may overlap.
| Row and fictional comment | Expected review treatment | Counted unit |
|---|---|---|
| A: “Arrived two days late. The item works well.” | Fulfilment issue and product praise; retain the mixed message. | One usable comment. |
| B: “The wrong size arrived and the replacement is still missing.” | Fulfilment issue and a follow-up flag for a person. | One usable comment. |
| C: “Please put the measurements beside the buy button.” | Product-information request; do not invent an angry customer. | One usable comment. |
| D: “Fast delivery, and the sizing chart was accurate.” | Fulfilment and product-information praise. | One usable comment. |
| E: “No comment.” | Exclude from this usable-text analysis under the agreed rule. | No usable comment. |
| F: An exact duplicate of B. | Exclude as a duplicate under this exercise's rule. | No additional comment. |
Check the denominator before presenting a percentage
The exercise contains six rows but four distinct usable comments. Three of those four mention fulfilment: A and B raise issues, while D praises it. A report of fulfilment mentions would therefore show 3/4, or 75%. A report of fulfilment issues would show 2/4, or 50%. Both can be correct while answering different questions.
Product information appears in C and D, giving 2/4, or 50%, under that label. Topic percentages can add to more than one hundred because a comment may have several labels. None of these figures establishes the percentage of all customers who are dissatisfied: the example has no customer population or representative sample.
Ask a supplier to reproduce the individual decisions and totals before demonstrating a polished chart. If it uses a different reasonable rule for empty or duplicate comments, make that choice visible and compare the resulting denominator. Silent changes to those rules make later reports hard to assess.
Mixed sentiment needs the text beside it
Google's primary Natural Language documentation explains that a score near zero can reflect low emotion or positive and negative content cancelling each other. It also distinguishes sentiment from identifying specific emotions. A single numeric score should not turn A into “nothing to investigate” or label C as anger.
Keep the relevant excerpt, topic and reason available to a reviewer. Agree what happens with sarcasm, unfamiliar language, short comments and several subjects in one message. A system may need to abstain or route an uncertain item to a person instead of forcing every input into one confident category.
Two published capabilities to discuss
Resolute AI advertises natural-language processing and analytics on its official site, with feasibility work, demonstrations, code and documentation. Ask whether its proposed feasibility phase will test your topic definitions and return reviewable examples, rather than only a summary chart.
DiUS advertises classification, analytics, governed data foundations and production AI on its data and AI page. Ask how preparation, human review, reporting and continuing operation would fit the proposed scope.
These pages establish advertised capabilities, not independently measured feedback-analysis results. Neither establishes a comparable fixed project fee for this brief. Obtain the actual deliverables, data boundaries, evaluation work, ownership and continuing costs in a proposal.
Commission a review process and operating handover
Keep a simple manually labelled or keyword-based reference for comparison. Review examples of missed issues, incorrect labels and unnecessary follow-up flags, with separate measures for the categories that matter. An overall accuracy figure can conceal poor performance on a small but important issue type.
Specify who approves the taxonomy, corrects labels and handles emerging topics. Agree the permitted inputs, retention and access, the refresh schedule, and how changes to models or rules are recorded. A dashboard should retain enough provenance to explain why a comment appears in an action queue.
Set the action boundary explicitly: identifying a review item does not authorise an automated refund, outbound message or account change. Begin with a person checking the suggestion and an agreed way to pause the process. Use the quote comparison and data-readiness guide for the accompanying scope.
Sources reviewed 27 October 2026, Melbourne time. The six-row exercise is original and fictional; no customer collection, business system or model was used. Browse AI implementation providers or all AI buying guides.
Editorial guidance with linked sources. Reviewed 2026-10-27.