Streamlining Community Engagement for a Product Manager

You are helping me execute the "Streamlining Community Engagement for a Product Manager" workflow.

Context: Improve community engagement by automating content extraction and analysis from online forums.

Persona this is for: Liam, a Product Manager at a 200-person EdTech company

Problem:
Liam spends over 10 hours weekly manually reviewing posts in a closed community forum to gather feedback for product improvements. He sifts through approximately 150 posts per week, often missing key insights amid noise. This labor-intensive process leads to delayed product updates and lost engagement opportunities.

Approach:
By implementing the Community Signal Detection Agent, Liam can automate the extraction of relevant posts based on predetermined keywords and topics. Coupled with the Azure DevOps MCP server, he can seamlessly integrate this feedback into his project management workflow. This will allow him to focus more on strategic decisions rather than manual data collection.

Walk through these steps in order. Pause between steps if you need an input I have not given you.
  1. Step 1: Set up the Community Signal Detection Agent to crawl the relevant online forum and define the categories of interest.
  2. Step 2: Configure the extraction criteria based on the product feedback topics that are most important for Liam's projects.
  3. Step 3: Connect the outputs of the Community Signal Detection Agent with Azure DevOps to create actionable work items for the product development team.
  4. Step 4: Test the extraction process to ensure that the right posts are captured and integrated into the Azure DevOps dashboard.
  5. Step 5: Train Liam and his team on how to use the new setup to proactively monitor community feedback and adapt product strategies.

Tools / assets referenced (call colaberry_get_asset to fetch each if not already in context):
  - agents: community-signal-detection-agent -- automates content extraction from online community posts
  - mcp: Azure DevOps -- integrates community feedback into project management workflow

Expected outcome: Reduction of manual review time from 10 hours to 2 hours per week, saving 8 hours weekly.

Begin step 1. Ask only if you need missing inputs.