Prompt Claude for Analyzing Customer Reviews

Analyzing customer reviews has become a strategic lever for any company looking to improve its products, services, and customer experience. Yet manually processing hundreds or thousands of comments remains time-consuming and 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 run an e-commerce, a SaaS, or a network of retail outlets, a well-structured prompt allows you to automatically extract dominant sentiments, identify recurring themes, spot weak signals, and prioritize areas for improvement. This page offers an optimized main prompt as well as variants adapted to your skill level, to fully leverage Claude's potential in analyzing your customer feedback.

The prompt

Claude

You are an analyst specialized 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:

Sentiment analysis: Classify each review as Positive, Negative, Mixed, or Neutral. Assign a sentiment score from -1 (very negative) to +1 (very positive).
Theme extraction: Identify recurring themes (e.g., product quality, customer service, delivery, value for money, user interface). Group reviews by theme.
Weak signals: Identify issues or suggestions mentioned by few customers but potentially critical.
Key verbatims: Extract the 5 most representative quotes (positive and negative).
Executive summary: Produce a summary of 150 words maximum 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]

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Why it works

This prompt leverages the role assignment framework ("specialized analyst") to activate Claude's relevant knowledge in text analysis. The 5-step numbered structure forces a comprehensive and organized response, avoiding omissions. The request for quantitative scores combined with qualitative verbatim extraction produces an analysis that is both measurable and contextualized, directly usable in meetings or reports.

Expected result

You will obtain a structured report including a sentiment classification table 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 overview immediately shareable with your team, accompanied by three concrete recommendations prioritized by potential impact on customer satisfaction.

Variants by level

FAQ

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 proceed 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 in French. For example, a review like "Great, 3 weeks to receive a cup, a real logistical feat" will be correctly classified as negative. To further improve detection, you can specify the cultural context or the platform of origin of the reviews in your prompt. Reviews from social networks often contain more colloquial expressions than those from e-commerce platforms, and Claude adapts to these language registers.

How can I use 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 key verbatims with your product and support teams to illustrate customer feedback concretely. Use the sentiment × theme matrix to identify areas where dissatisfaction is most concentrated. Finally, regularly rerun the analysis (monthly or quarterly) with the same prompt to measure sentiment evolution and verify that your corrective actions are paying off.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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