Competitor Analysis on Etsy: Unbiased Insights from Customer Feedback

Enhanced Prompt:

Title: Competitor Analysis on Etsy: Unbiased Insights from Customer Feedback

Objective:
To conduct a detailed and unbiased analysis of your competitors' customer feedback and ratings on Etsy and third-party review platforms, ensuring actionable insights for product improvement, pricing strategies, and marketing efforts.

Deliverables:

1. Comprehensive analysis report including competitor product performance metrics (e.g., Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), Sentiment Analysis).
2. Identification of key trends, strengths, and weaknesses in competitor offerings.
3. Detailed customer feedback analysis, including common themes, pain points, and recommendation strategies.
4. Data visualization dashboard for easy tracking and reporting.
5. Recommendations for optimizing product offerings, pricing, and marketing strategies based on competitor insights.

Industry Best Practices and Standards:

• Adhere to Etsy’s policies and guidelines for customer feedback and ratings.
• Use ethical scraping techniques to gather data without violating third-party terms of service.
• Ensure compliance with data privacy laws (e.g., GDPR, CCPA) when handling competitor data.
• Utilize industry-standard tools and methodologies for sentiment analysis and competitor benchmarking.

Quality Expectations:

• Accuracy: Ensure data is verified and validated through multiple sources.
• Completeness: Gather feedback from all relevant platforms (Etsy, Google Reviews, social media, etc.).
• Relevance: Focus on feedback directly related to products, services, and pricing.
• Timeliness: Analyze data within the last 6-12 months to ensure relevance.

Technical Considerations:

• Use tools like Google Analytics, SEMrush, Hootsuite, and social media monitoring platforms to gather competitor data.
• Implement web scraping tools (e.g., Scrapy, Octoparse) to extract structured data from third-party platforms.
• Ensure data extraction is optimized for large volumes of data and scalable for future use.
• Optimize