Prompt Claude for Analyzing Customer Reviews
Customer review analysis has become an essential strategic lever for any company looking to improve its products, services, and customer experience. Yet manually processing hundreds or thousands of comments remains a time-consuming task prone to interpretation bias. Claude, Anthropic's AI assistant, excels at qualitative analysis of natural language text. With its ability to understand nuances, sarcasm, implicit emotions, and cultural contexts, Claude transforms a corpus of raw reviews into actionable insights. Whether you manage an e-commerce site, a SaaS, or a network of stores, a well-structured prompt allows you to automatically extract dominant sentiments, identify recurring themes, spot weak signals, and prioritize improvement areas. This page offers an optimized main prompt as well as variants tailored to your level of expertise, to fully leverage Claude's potential in analyzing your customer feedback.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are a specialist in customer experience and text mining. I will provide you with a list of customer reviews. For each batch of reviews, perform the following analysis:
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Sentiment Analysis: Classify each review as Positive, Negative, Mixed, or Neutral. Assign a sentiment score from -1 (very negative) to +1 (very positive).
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Topic Extraction: Identify recurring themes (e.g., product quality, customer service, delivery, value for money, user interface). Group reviews by theme.
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Weak Signals: Identify issues or suggestions mentioned by few customers but potentially critical.
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Key Verbatims: Extract the 5 most representative quotes (positive and negative).
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Executive Summary: Produce a summary of maximum 150 words with strengths, weaknesses, and 3 priority recommendations ranked by estimated impact.
Format your response in clearly separated sections with tables where relevant.
Here are the reviews to analyze:
[PASTE_YOUR_REVIEWS_HERE]
Personalize this prompt with Léa
Answer 3 questions and Léa tailors the prompt to your situation.
Why this prompt works
This prompt leverages the role assignment ("specialist analyst") to activate Claude's relevant knowledge in text analysis. The 5-step numbered structure forces a comprehensive and organized response, preventing omissions. The request for quantitative scores combined with qualitative verbatim extraction produces analysis that is both measurable and contextualized, directly usable in meetings or reports.
Use Cases
Variants
Expected Output
You will obtain a structured report including a classification table by sentiment with scores, a mapping of identified themes with their frequency, a list of weak signals to monitor, and selected verbatims. The final executive summary will give you an immediate overview shareable with your team, accompanied by three concrete recommendations prioritized by potential impact on customer satisfaction.
Frequently Asked Questions
How many customer reviews can I analyze at once with Claude?
Claude can process several thousand reviews in a single conversation thanks to its large context window. For very large volumes (over 5,000 reviews), it is recommended to work in thematic or chronological batches, then request a consolidated summary. You can also provide the reviews in a text or CSV file that you paste directly into the conversation. The key is to structure your data with a clear separator between each review to ensure accurate analysis.
Can Claude detect sarcasm and cultural nuances in reviews?
Yes, Claude is particularly effective at detecting sarcasm, irony, and idiomatic expressions. For example, a review like 'Wow, 3 weeks to receive a cup, a true logistical feat' will be correctly classified as negative. To further improve detection, you can specify the cultural context or the review source platform in your prompt. Reviews from social media often contain more colloquial expressions than those from e-commerce platforms, and Claude adapts to these language registers.
How can I leverage the analysis results to improve my product?
Claude's results are directly actionable. Start with the priority recommendations from the executive summary: they are ranked by estimated impact. Share the key verbatims with your product and support teams to concretely illustrate customer feedback. Use the sentiment × theme matrix to identify areas where dissatisfaction is most concentrated. Finally, rerun the analysis regularly (monthly or quarterly) with the same prompt to measure sentiment evolution and verify that your corrective actions are paying off.
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ChatGPT Prompt for Analyzing a Survey
Survey analysis is a crucial step for transforming raw data into actionable insights. Whether you collected responses via Google Forms, Typeform, or any other tool, ChatGPT can help you identify trends, segment respondents, and draw relevant conclusions in minutes. Where an analyst would spend hours cross-referencing variables and writing a report, AI significantly speeds up the process while maintaining methodological rigor. This prompt is designed to guide ChatGPT through a structured analysis of your survey results: synthesis of quantitative data, interpretation of open-ended responses, identification of significant correlations, and formulation of concrete recommendations. It works equally well for a customer satisfaction survey, a market study, or an internal questionnaire. The proposed approach combines descriptive statistical analysis and thematic qualitative analysis, offering you a complete and nuanced view of your results without requiring advanced data science skills.