Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Monitor social media and online mentions for brand sentiment, emerging issues, and conversation trends
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Systematically monitor social media and online conversations to track brand sentiment, identify emerging issues, and spot opportunities.
Based on Sprout Social methodology and Brandwatch analytics, combining:
| Claude Does | You Decide |
|---|---|
| Designs monitoring strategy | Tool selection |
| Creates keyword lists | Alert thresholds |
| Analyzes sentiment patterns | Response strategy |
| Identifies trends | Resource allocation |
| Suggests response approaches | Escalation calls |
Monitoring Categories:
| Category | What to Track | Examples |
|---|---|---|
| Brand | Company name, products | "@CompanyName", "CompanyProduct" |
| People | Executives, spokespeople | CEO name, founder |
| Industry | Sector terms, trends | Industry keywords |
| Competitors | Competitor mentions | Competitor names |
| Issues | Potential crises | Product issues, service complaints |
Keyword Types:
| Type | Purpose | Examples |
|---|---|---|
| Primary | Direct brand mentions | Brand name, product names |
| Secondary | Related conversations | Industry terms, use cases |
| Negative | Crisis indicators | "problem", "issue", "broken" |
| Competitor | Competitive intelligence | Competitor names, comparison |
Alert Levels:
| Level | Trigger | Response Time |
|---|---|---|
| Green | Normal volume, positive/neutral | Daily review |
| Yellow | Elevated volume, mixed sentiment | Same-day review |
| Orange | High volume spike, negative trend | Hours |
| Red | Viral negative, crisis indicators | Immediate |
Analysis Framework:
Reporting Cadence:
| Report | Frequency | Audience |
|---|---|---|
| Real-time alerts | As triggered | Crisis team |
| Daily digest | Daily | Marketing |
| Weekly summary | Weekly | Leadership |
| Monthly deep dive | Monthly | Strategy |
Input:
Create a social listening strategy for:
- B2B SaaS company (project management)
- Main competitors: Asana, Monday, Notion
- Recent product launch: AI features
Output:
## Social Listening Strategy: Project Management SaaS
### Monitoring Objectives
| Objective | Priority | Key Questions |
|-----------|----------|---------------|
| Brand health | High | How are we perceived? |
| Product feedback | High | What do users say about AI features? |
| Competitive intel | Medium | What are competitors doing? |
| Industry trends | Medium | Where is PM software heading? |
| Crisis detection | High | Any emerging issues? |
---
### Keyword Matrix
#### Brand Keywords
| Category | Keywords | Platforms |
|----------|----------|-----------|
| Company Name | "[Company]", "@company" | All |
| Product Name | "[Product]", "[Product] app" | All |
| Misspellings | Common variants | All |
| Hashtags | #[Company], #[Product] | Twitter, LinkedIn |
#### Product Keywords
| Category | Keywords | Why Monitor |
|----------|----------|-------------|
| AI Features | "[Company] AI", "AI project management" | Launch feedback |
| Core Features | "[Company] tasks", "[Company] boards" | Product sentiment |
| Integrations | "[Company] Slack", "[Company] integration" | Partnership health |
#### Competitive Keywords
| Competitor | Keywords | What to Track |
|------------|----------|---------------|
| Asana | "Asana vs [Company]", "switching from Asana" | Win/loss signals |
| Monday | "Monday.com", "Monday vs [Company]" | Competitive positioning |
| Notion | "Notion for projects", "Notion PM" | Category overlap |
| General | "best project management", "PM tool 2026" | Category conversations |
#### Crisis Keywords
| Category | Keywords | Alert Level |
|----------|----------|-------------|
| Outage | "[Company] down", "[Company] not working" | Red |
| Security | "[Company] hack", "[Company] breach" | Red |
| Pricing | "[Company] expensive", "[Company] price increase" | Orange |
| Churn | "leaving [Company]", "cancelled [Company]" | Yellow |
| Bugs | "[Company] bug", "[Company] broken" | Yellow |
---
### Platform Strategy
| Platform | Focus | Keywords | Frequency |
|----------|-------|----------|-----------|
| Twitter/X | Real-time sentiment | All brand, crisis | Continuous |
| LinkedIn | B2B discussions | Industry, competitor | Daily |
| Reddit | Deep user feedback | r/projectmanagement, product | Daily |
| G2/Capterra | Review sentiment | Product reviews | Weekly |
| Hacker News | Tech community | Product, competitor | As trending |
---
### Alert Configuration
#### Red Alerts (Immediate Response)
**Triggers:**
- Volume spike >300% normal
- Sentiment shift >50% negative
- Viral post (>1000 engagements)
- Keywords: "outage", "down", "breach", "hack"
**Response:**
- Slack #crisis-alerts channel
- SMS to on-call team
- Auto-pause scheduled posts
---
#### Orange Alerts (Same-Day Response)
**Triggers:**
- Volume spike >100% normal
- Negative sentiment >30%
- Trending competitor comparison
- Keywords: "expensive", "worse", "frustrated"
**Response:**
- Slack #social-alerts
- Email to marketing lead
- Review within 4 hours
---
#### Yellow Alerts (Next-Day Review)
**Triggers:**
- Volume spike >50% normal
- Notable influencer mention
- Competitor activity spike
- Keywords: "considering", "alternative", "switching"
**Response:**
- Daily digest inclusion
- Assign for monitoring
- Review within 24 hours
---
### Analysis Template
#### Daily Report
Date: [Date]
| Metric | Today | vs. Yesterday | vs. 7-Day Avg |
|---|---|---|---|
| Total Mentions | X | +/-Y% | +/-Z% |
| Brand Mentions | X | +/-Y% | +/-Z% |
| Sentiment Score | X | +/-Y | +/-Z |
---
### AI Feature Launch Monitoring
**Specific Keywords:**
- "[Company] AI"
- "AI project management"
- "[Company] automation"
- "AI features PM tool"
**What to Track:**
| Metric | Target | Alert If |
|--------|--------|----------|
| Mention volume | 2x normal | <50% or >300% |
| Sentiment | >60% positive | <40% positive |
| Feature requests | Document all | - |
| Bug reports | <5/day | >10/day |
**Competitor Response Monitoring:**
- Are competitors announcing similar features?
- How is our positioning being compared?
- What gaps are users identifying?
Input:
Analyze this week's social mentions:
- Total mentions: 450 (up 30%)
- Positive: 40%
- Neutral: 35%
- Negative: 25%
- Top negative theme: "pricing"
- One viral tweet (15K RT): "Just discovered [Company] raised prices 40%"
Output:
## Quick Sentiment Analysis
### Alert Level: ORANGE
---
### Summary
| Metric | Value | Status |
|--------|-------|--------|
| Volume | 450 (+30%) | Elevated |
| Sentiment | 40% positive | Below target |
| Negative | 25% | Above normal |
| Viral Content | Yes | Action needed |
---
### Key Finding
**Pricing is dominating the conversation**
The viral tweet about 40% price increase is:
- Driving 30% volume spike
- Shifting sentiment negative
- Spreading misinformation (if price increase was different)
---
### Immediate Actions
| Action | Owner | Timeline |
|--------|-------|----------|
| Verify price increase details | Product/Biz | Immediate |
| Draft response to viral tweet | Comms | 2 hours |
| Prepare pricing FAQ | Support | Today |
| Monitor for escalation | Social | Ongoing |
---
### Response Options
**If price increase was less than 40%:**
Hi [Name], thanks for the feedback. Just to clarify—our recent update increased [specific] pricing by [X%], not 40%. Here's what changed and why: [link]
Happy to discuss if you have questions.
**If price increase was accurate:**
Hi [Name], we hear you. We know pricing changes are never easy to absorb. Here's why we made this decision and what you're getting in return: [link]
Our team is available if you'd like to discuss your account.
---
### Escalation Watch
Monitor for:
- Influencer pile-on
- Media pickup
- Support ticket spike
- Churn mentions
Follow-up Prompts:
crisis-detector - Early warning escalationresponse-coordinator - Crisis responsereputation-recovery - Post-crisis rebuild还没有评价。欢迎分享你的使用感受。
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
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Assess customer account health using product usage, support sentiment, payment status, and relationship signals
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Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
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Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
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Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
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