Analyze Feedback

作者 amplitude96fc7d4c58bb無授權條款42 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

Synthesizes customer feedback into actionable themes including feature requests, bugs, pain points, and praise. Use when planning product roadmap, understanding user sentiment, investigating specific issues, or preparing voice-of-customer reports.

AI 產生的概覽

運用 Amplitude 回饋工具,將客戶回饋歸納為主題、情緒與優先順序建議。

功能
引導代理檢視已連接的客戶回饋來源,依類型與日期擷取主題洞察,並深入查看每個主題背後的實際回饋內容。它也會將主題與使用者分群(例如使用年資、方案類型、使用頻率)相互關聯。最終產出結構化的客戶之聲報告,涵蓋緊急問題、功能請求、好評、情緒評分與優先順序建議。
適用情境
適用於規劃產品藍圖、調查特定問題或功能請求、了解客戶情緒,或準備客戶之聲簡報時。
執行需求
需要存取 Amplitude 回饋與分群工具(use_amplitude_ai_feedback、use_amplitude_cohorts)以及已連接的回饋管道。不含指令碼,僅為操作說明。

Analyze Feedback

Perform comprehensive feedback reviews, investigate specific feature requests, understand customer sentiment, then prepare concise but actionable voice-of-customer presentations

Instructions

Step 1: Understand Available Sources

Use Amplitude:use_amplitude_ai_feedback with facet: "sources" to see which feedback channels are connected (surveys, support, app reviews, etc.).

Step 2: Get Themed Insights

Use Amplitude:use_amplitude_ai_feedback with facet: "insights" and appropriate filters:

  • Filter by types: request, complaint, lovedFeature, bug, painPoint
  • Filter by date range for recent feedback
  • Filter by source for channel-specific analysis

Step 3: Drill Into Specific Themes

For top insights, use Amplitude:use_amplitude_ai_feedback with facet: "mentions" to see the actual user feedback driving each theme.

Step 4: Connect to User Segments

Use Amplitude:use_amplitude_cohorts with action: "list" to understand if feedback themes correlate with:

  • User tenure (new vs. established)
  • Plan type (free vs. paid)
  • Usage level (power users vs. casual)

Step 5: Present Findings

Structure as:

  1. Summary: Concise one-liner explaining the number of feedback sources and mentions analyzed along with the key takeaways from the analysis.
  2. Urgent Issues 🚩: Top bugs, issues, or pain-points noted by customers. Share 3-4 themes here unless prompted otherwise.
  3. Top Feature Requests 💡: Top feature requests noted by customers. Share 3-4 themes here unless prompted otherwise.
  4. Praises ❤️: Top praises or loved features noted by customers. Share 1-2 themes here unless prompted otherwise.
  5. Sentiment Analysis: Share a very concise overview of the themes, sentiment on a scale from 1-5 (5 being highest), and snippets of evidence.
  6. Prioritized Recommendations: Very concise section recapping the top 3-7 specific actionable recommendations (unless prompted otherwise) to follow-up on. Inlcude [p0],[p1],[p2],[p3] in front of each title to help size priority with p0 being most urgent and p3 being least.

Best Practices

  • Be comprehensive in your investigation and analysis but concise and actionable in your response.
  • Do not repeat duplicate sections or the same takeaway multiple times.
  • Include 1-2 representative quotes for each theme. Prioritize the best quotes that explains the theme. Include when it was received and what source it came from. If the quote is long, only quote the relevant section tied to the theme.
  • When describing a theme, include the mention volumes, key sources, and recency in a concise manner.
  • Each theme should have consistent formatting like: Concise But Descriptive Theme Name (X mentions) Actionable one-line description or multiple concise bullet-points explaining what the specific issue or request or praise is, what time period the feedback was relevant for, and key sources the theme came from
    • "Customer quote backing the theme" - Source name (Received date)
    • "Customer quote backing the theme" - Source name (Received date)
  • For the Prioritized Recommendations section, each recommendation should just be 1 concise but actionable bullet-point instead of a long theme overview.
  • Do not recap what you did at the very end and just end after the concise prioritized recommendations.
  • Connect feedback to behavioral data when possible.

來源與署名

來源:amplitude/mcp-marketplace位於plugins/amplitude/skills/analyze-feedback提交96fc7d4

授權條款: 無授權條款

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