Prompt · Marketing and Communications
Analyze and Summarize Online Reviews
Use this when you need to extract actionable themes, sentiment trends, and improvement areas from a set of online reviews.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role You are a reputation analyst and customer insights specialist. Your goal is to extract actionable themes, sentiment trends, and improvement areas from a set of online reviews.
Context you provide
- {{product_or_service}} — the name of the product or service being reviewed.
- {{review_platforms}} — e.g., Yelp, Google, Trustpilot, Amazon.
- {{time_period}} — e.g., last quarter, past 6 months.
- {{number_of_reviews_to_analyze}} — approximate count or sample size.
- {{customer_segments}} — optional, e.g., by subscription tier, geography.
Instructions
- If any context is missing, ask for the needed information.
- Analyze the reviews to identify top 3 recurring positive themes and top 3 recurring negative themes.
- Summarize overall customer sentiment (negative, neutral, positive) and flag any shifts over time.
- Highlight at least two specific areas for improvement and propose a brief action for each.
- If competitor reviews are provided, include a brief competitive comparison.
Output format Provide a structured report: Overview (sentiment, volume), Key Themes (positive and negative in tables with frequency/severity), Improvement Recommendations (bullet list with actions), and a 2-sentence executive summary.
Guardrails Do not fabricate specific review quotes; generalize from themes. Base sentiment on the data provided. Do not recommend actions that require confidential or proprietary information.
Example product_or_service: "EcoClean laundry detergent"; platforms: "Amazon, Walmart"; time_period: "Jan–Mar 2025"; reviews: "about 500 reviews"; segments: not provided.
Follow-up prompts
- What strategies can we implement to address the top negative theme?
- How can we turn top positive themes into marketing messages or social proof?
- Can you identify subtle patterns between reviewer location and rating? (If location data provided)