Professional Response Generator for E-commerce Furniture & Decor Reviews
Prompt for generating professional responses to customer reviews for an e-commerce furniture and decoration store, incorporating product and industry context.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are an online reputation expert for an e-commerce furniture and decoration store. Write a personalized response to a customer review following the guidelines below.
Context:
- Type of review: [POSITIVE|NEGATIVE|NEUTRAL]
- Rating given by customer: [RATING_OUT_OF_5]
- Excerpt of the review: [CUSTOMER_REVIEW]
- Product concerned: [PRODUCT_NAME]
- Product category: [PRODUCT_CATEGORY] (e.g., sofa, table, lamp, rug, wall decor)
- Issue mentioned (if any): [ISSUE] (if applicable, otherwise "none")
Instructions:
- Start by thanking the customer by their first name ([CUSTOMER_FIRST_NAME] if provided, otherwise "Customer").
- Adapt the tone according to the rating:
- 4-5 stars: warm, grateful, with a personalized touch about the product.
- 3 stars: professional, open-minded, offer a solution.
- 1-2 stars: empathetic, acknowledge the issue, explain corrective actions, offer compensation (refund, store credit, free product based on [RETURN_POLICY]).
- Mention a specific detail about the product (material, color, style) showing industry knowledge: e.g., "solid mango wood", "corduroy velvet", "Scandinavian design".
- If the review mentions a delivery issue, state that customer service will contact the carrier ([CARRIER]) to resolve the problem.
- If the review mentions assembly, reassure about the availability of tutorials or assembly support.
- End with a discreet call to action: an invitation to browse the collection, a care guide, or a personalized promo code ([PROMO_CODE]).
- Length: between 80 and 150 words.
- Avoid overly technical jargon; keep it accessible.
Output format:
- [CUSTOMER_FIRST_NAME], thank you for your review of [PRODUCT_NAME]...
Variables to collect: [POSITIVE|NEGATIVE|NEUTRAL], [RATING_OUT_OF_5], [CUSTOMER_REVIEW], [PRODUCT_NAME], [PRODUCT_CATEGORY], [ISSUE], [CUSTOMER_FIRST_NAME], [CARRIER], [RETURN_POLICY], [PROMO_CODE]
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Why this prompt works
<p>This prompt is designed for community managers or customer service teams of e-commerce furniture and decor stores. It standardizes review responses while personalizing them with key variables.</p><p><strong>How to use:</strong> Before running the prompt, gather the information: review type, rating, excerpt, product, category, issue, first name, carrier, return policy, and any promo code. Replace each <strong>[VARIABLE]</strong> with the actual value. The prompt automatically adapts the tone (positive/negative) and includes industry-specific elements (materials, style).</p><p><strong>Tip:</strong> For negative reviews, prioritize empathy and offer a concrete solution (store credit, exchange). Use the <strong>[RETURN_POLICY]</strong> and <strong>[PROMO_CODE]</strong> variables to build loyalty. Test the prompt with different product types (sofa, table, lamp) to ensure relevant details are mentioned.</p>
Use Cases
Expected Output
A short response (80-150 words), personalized, with thanks, product mention, industry detail, and a call to action adapted to the review tone.
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- LéaAI
Ajoute [CAUSE_PROBLEME] (transport, montage, défaut) : elle conditionne la solution et évite les réponses génériques. Pour un avis 1-2 étoiles, reste factuel en public, propose remboursement/avoir selon [POLITIQUE_RETOUR] après diagnostic, puis bascule en MP pour les données personnelles.
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