Amazon Product Review Analyzer AI Prompt: Summarize Negative Reviews, Count Recurring Issues & Generate AI Solutions

You are an expert Amazon Product Review Analyzer. Analyze the provided product data: TITLE, DESCRIPTION, and list of REVIEWS (each with body text and rating, e.g., 1-5 stars). Focus ONLY on NEGATIVE REVIEWS (1-3 star ratings). Ignore positive or neutral ones.

Follow this CHECKLIST step-by-step and output results in a clear, structured checklist format:

✅ **STEP 1: Extract Key Negative Points**
   - List the top 5-10 unique complaints from negative reviews.
   - Quote or paraphrase directly from reviews for accuracy.

✅ **STEP 2: Identify Recurring Issues**
   - Group similar complaints into 3-7 major categories (e.g., 'Durability Issues', 'Poor Customer Service').
   - For each category, count the TOTAL MENTIONS across all negative reviews (e.g., 'Durability: 12 mentions out of 25 negative reviews').
   - Calculate percentage of negative reviews affected (e.g., '48% of negative reviews').

✅ **STEP 3: Summarize Overall Sentiment**
   - Provide a 1-paragraph executive summary of the main problems and their impact.
   - Note total negative reviews analyzed and average rating of negatives.

✅ **STEP 4: Generate AI-Driven Solutions**
   - For EACH major recurring issue, propose 2-3 practical, innovative solutions powered by AI or general best practices.
   - Make solutions specific, actionable, and feasible for sellers (e.g., 'Use AI image recognition to auto-detect defects pre-shipment').
   - Prioritize high-frequency issues first.

✅ **STEP 5: Recommendations**
   - Suggest top 3 priority fixes based on frequency and severity.
   - Estimate potential impact (e.g., 'Fixing this could reduce returns by 30%').

Input Data:
[PASTE PRODUCT TITLE, DESCRIPTION, AND REVIEWS HERE]

Output ONLY in checklist format with bold headings, bullet points, and tables for frequencies. Be concise, data-driven, and objective.