Turn Social Feedback into Product Insights
Build a product feedback analysis system using XPOZ MCP and Claude. Turn
The Problem
Your customers share honest product feedback on social media every day. They praise features, complain about friction, and suggest improvements—but this valuable signal rarely reaches your product team:
Feedback lives in silos
Support tickets capture complaints, but the spontaneous praise and feature requests on Twitter and Instagram never make it into product planning sessions
Survey fatigue biases your data
The customers who respond to NPS surveys aren't representative—social feedback captures perspectives from the silent majority who won't fill out forms
Context gets stripped away
By the time feedback reaches product managers through internal channels, the original emotion, screenshots, and conversation threads are lost
Volume makes manual review impossible
No product team can read thousands of social posts to find the patterns that matter
The Workflow
This workflow combines XPOZ for social listening, Claude for intelligent categorization, and structured outputs that integrate with your product development process.
Example Queries
Ask Claude in natural language. Here are some examples with the underlying API calls:
Find feature requests for your product
>"Search Twitter for posts mentioning "Acme" AND ("wish" OR "would love" OR "need" OR "please add") from the last 30 days. Show the post text, author, and engagement metrics."
Identify what users love about your product
>"Find tweets praising Acme with words like "amazing," "love," or "game changer." Include the author's follower count and the post's like count to prioritize high-visibility endorsements."
Track competitor comparison conversations
>"Find Instagram posts and comments where users compare Acme to [Competitor]. Get the full comment threads to understand the context of these comparisons."
Monitor feedback volume trends
>"Count how many tweets mentioned "Acme" with negative sentiment keywords each week for the past quarter. Has complaint volume increased after our last release?"
Why XPOZ
No Platform API Keys Needed
Access Twitter and Instagram feedback data without obtaining developer credentials from each platform. XPOZ handles authentication through the remote MCP server.
Natural Language Queries
Ask Claude to "find feature requests from the last month" instead of constructing complex API calls. The Model Context Protocol translates your questions into the right tool calls.
Unified Multi-Platform Access
Monitor Twitter and Instagram through one interface. Users share feedback wherever they're most comfortable—your listening should cover all channels.
Conversation Context Preserved
Pull complete discussion threads including replies, quotes, and comments. Product teams get the full context of user feedback, not just isolated snippets.
CSV Export for Deep Analysis
Export large result sets for statistical analysis, visualization, or sharing with stakeholders. Pagination and rate limits are handled automatically.
Frequently Asked Questions
You define your product taxonomy—feature names, workflows, user types—and Claude uses that context to automatically tag incoming feedback. The system learns your product vocabulary and applies it consistently across thousands of posts. You can refine categories as your product evolves.
Yes. By running queries across different time periods, you can compare feedback volume and sentiment before and after releases, identify emerging themes, and measure whether product changes addressed user complaints. Export results to CSV for time-series analysis and visualization.
Claude generates structured reports formatted for product planning sessions. These include theme summaries, supporting quotes, volume metrics, and links to original posts. You can schedule regular feedback digests or run ad-hoc queries before planning meetings. Reports can be exported or integrated into tools like Notion or Confluence.
XPOZ supports language filtering in queries. You can monitor English feedback or specify other languages to capture insights from international users. Claude can analyze feedback in multiple languages and provide English summaries.
Twitter profiles include authenticity scoring that helps filter out bot accounts. You can also filter by engagement metrics—feedback with higher like counts or from accounts with established followings tends to be more genuine. Claude's analysis step further filters for substantive feedback versus random mentions.
Get Started
Set up your product feedback system in minutes:
Connect XPOZ: Add the remote MCP server at `https://mcp.xpoz.ai/mcp` to Claude Desktop or Claude.ai
Define your vocabulary: List your product name, feature names, and competitor names to monitor
Run your first query: Ask Claude to find feedback about your product from the last week
Review and refine: Adjust your queries based on what surfaces and build toward regular feedback reports
Start with 100,000 free results per month—enough to establish your feedback baseline and refine your workflow before scaling.
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