Analyze customer reviews by topic

Logistics manager · Time: 20 min · Research

Tools that fit this task

• ChatGPT $20/mo
• Claude $20/mo
• Gemini in Google Sheets $14/mo
• Google AI Studio Free
• Make $9/mo
• Grok $8/mo

Prompt

Role: you are a customer experience analyst.
Context: below are {{number}} customer reviews of a company that {{what it does}} for {{period}} from {{sources}}. Columns: date | source | rating | text.
Task: identify the topics mentioned in the reviews, count mentions and the share of negative ones for each, and give three verbatim quotes with row numbers. Separately, pick out reviews that describe a specific process failure and group them by stage (order, payment, delivery, support, product). Quotes must be verbatim, with the row number.
Format: a table topic | mentions | share negative | three quotes with numbers. Then a table of failures by stage. At the end, three actions with the biggest effect, justified with numbers.
Reviews:
{{reviews}}

Steps

1. Collect reviews for the period in one table: date, source, rating, text, without author names
2. Give the assistant the table with a prompt to identify topics, sentiment and the frequency of each topic
3. Get a list of topics with the number of mentions, the share of negative ones and three quotes for each
4. Check five random reviews: are the topic and sentiment identified correctly
5. Ask it separately to pick out reviews describing a specific process failure and group them by stage
6. Define three actions for the most frequent negative topics with an owner and a deadline

How to check the result

A spot check of five reviews confirmed topic and sentiment, the three main negative topics have actions with deadlines, and the analysis is repeated monthly with the same prompt

Pitfalls

• The model invents quotes that are absent from the reviews: require verbatim quotes and check them
• Reviews with customer names and contacts are anonymized before uploading

Data that stays out of public AI tools

• Routes, schedules and storage locations of cargo: security-sensitive information, into a service only in general terms
• Carrier prices and contract terms: a trade secret, refer to the carrier as [Carrier 1]
• Driver names, passport data and vehicle plates: personal data, anonymize them

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AI tools for logistics managers · Prompt: Analyze customer reviews by topic