Loading...
Loading...
Use when monitoring Xiaohongshu marketing campaign performance, tracking promotion effectiveness, analyzing advertising ROI, measuring influencer collaboration results, evaluating activity success rates, or optimizing marketing spend allocation
npx skill4agent add vivy-yi/xiaohongshu-skills effect-monitoring❌ "Launched campaign, spent ¥10,000, not sure how it performed"
❌ "Influencer posted, got some likes, unclear if it drove sales"
❌ "Multiple campaigns running, can't tell which is working"
❌ "No tracking, just hoping for the best"✅ "Campaign A: ¥10,000 spend, ¥50,000 revenue, 5x ROAS, 12% conversion"
✅ "Influencer B: ¥5,000 fee, 100,000 views, 2.3% CTR, 350 conversions"
✅ "Campaign B outperformed A by 40% - reallocate budget next month"
✅ "Real-time dashboard shows campaign C underperforming, paused immediately"| Metric Type | Key Metrics | Benchmarks | Tools |
|---|---|---|---|
| Exposure | Views, impressions, reach | 10,000+ views for paid | Platform analytics |
| Engagement | Likes, comments, shares, saves | 5-10% engagement rate | Platform analytics |
| Click-Through | CTR, link clicks | 2-5% CTR for ads | Platform analytics |
| Conversion | Sales, leads, sign-ups | 1-3% conversion rate | E-commerce analytics |
| ROI | ROAS, ROCE, cost per acquisition | 3-5x ROAS good | Custom calculations |
| Cost | CPM, CPC, cost per lead | ¥50-150 CPM | Platform analytics |
Primary Objective (choose one):
- Brand Awareness: Maximize reach and impressions
- Engagement: Maximize likes, comments, shares
- Traffic: Drive clicks to website or store
- Conversion: Generate sales, leads, or sign-ups
- Customer Acquisition: Acquire new customers at target cost
Secondary Objectives:
- Grow follower count by X amount
- Generate user-generated content
- Build email list or private domain
- Increase brand mentions and sentiment
- Launch new product successfully
Target Metrics:
- ROAS (Return on Ad Spend): 3-5x target
- CPA (Cost Per Acquisition): ¥50-200 target
- CTR (Click-Through Rate): 2-5% target
- Conversion Rate: 1-3% target
- Engagement Rate: 5-10% targetCampaign Types to Monitor:
1. Paid Advertising (信息流广告, 搜索广告)
2. KOL/Influencer Collaborations
3. Activity Campaigns (促销活动, 打卡活动)
4. Content Marketing (品牌内容, 种草内容)
5. Live Commerce (直播带货)
Tracking Timeline:
- Pre-Launch: Baseline metrics (3-7 days before)
- Launch: Real-time monitoring (first 24 hours critical)
- Mid-Campaign: Daily check-ins, optimize as needed
- Post-Campaign: Final analysis and report (7 days after)Xiaohongshu Professional Account (专业号):
✅ Enable professional account analytics
✅ Set up UTM parameters for external links
✅ Create campaign tags for different initiatives
✅ Enable e-commerce tracking (if applicable)
✅ Set up conversion tracking pixels
Key Platform Metrics to Track:
- Content performance by post
- Audience demographics and behavior
- Traffic sources and referrals
- Follower growth and churn
- Engagement trends over timeGoogle Analytics / Website Analytics:
✅ Set up goal tracking (purchases, sign-ups)
✅ Enable e-commerce tracking
✅ Create UTM tagged URLs for campaigns
✅ Set up custom dimensions for campaign attribution
✅ Configure cross-domain tracking
CRM / Customer Data:
✅ Tag customers by source (Xiaohongshu, campaign, influencer)
✅ Track customer lifetime value by acquisition channel
✅ Monitor repeat purchase rate by source
✅ Calculate customer acquisition cost accurately
Third-Party Tools:
✅ Huitun Data (灰豚数据) - Competitor and campaign tracking
✅ Chanmama (蝉妈妈) - Live commerce analytics
✅ Xinhong Data (新红数据) - Influencer performance trackingCampaign Name: [Brand] [Type] [Date] [Objective]
Example: "BrandA_KOLCollab_June2024_Awareness"
UTM Parameters:
utm_source=xiaohongshu
utm_medium=cpc | influencer | activity | content
utm_campaign=[campaign_name]
utm_content=[influencer_name | post_id]
Unique Tracking Links:
- Create unique link for each influencer
- Create unique link for each creative variation
- Create unique link for each placement/channelKey Exposure Metrics:
1. Impressions (曝光量)
- Total times content displayed
- Benchmark: 50,000+ for paid campaigns
2. Reach (触达人数)
- Unique users who saw content
- Benchmark: 30,000+ unique users
3. View-Through Rate (VTR)
- Percentage of impressions viewed
- Benchmark: 60-80% for video content
4. Frequency (频次)
- Average times each user saw content
- Optimal: 2-4 exposures per user
Monitoring Frequency:
- First 24 hours: Check every 2-4 hours
- Days 2-7: Check twice daily
- Week 2-4: Check daily
- After week 4: Weekly check-ins sufficientCost Efficiency Metrics:
1. CPM (Cost Per Mille / Cost Per 1,000 Impressions)
- Formula: (Total Spend / Impressions) × 1,000
- Benchmark: ¥50-150 CPM for Xiaohongshu
2. CPC (Cost Per Click)
- Formula: Total Spend / Clicks
- Benchmark: ¥1-5 CPC for good targeting
3. Cost Per View (CPV)
- Formula: Total Spend / Video Views
- Benchmark: ¥0.01-0.05 per view
4. Cost Per Engagement (CPE)
- Formula: Total Spend / Total Engagements
- Benchmark: ¥0.5-2 per engagement
Alert Thresholds:
- CPM > ¥200: Check targeting, creative may be fatigued
- CPC > ¥10: Audience too narrow or creative not compelling
- CPV > ¥0.10: Content not resonating, revise creativeCore Engagement Metrics:
1. Likes (点赞数)
- Basic engagement signal
- Benchmark: 3-5% of views
2. Comments (评论数)
- Deep engagement signal
- Benchmark: 0.5-1% of views
- Quality: Positive sentiment > 80%
3. Shares/Forwards (分享数)
- Viral potential signal
- Benchmark: 0.2-0.5% of views
- High share = strong resonance
4. Saves (收藏数)
- Purchase intent signal
- Benchmark: 1-2% of views
- High saves = high conversion potential
5. Engagement Rate
- Formula: (Likes + Comments + Shares + Saves) / Views
- Benchmark: 5-10% good, 10%+ excellentComment Sentiment Tracking:
Positive Indicators:
- "种草了" (got hooked/seeded)
- "想要" (want it)
- "链接在哪" (where's the link)
- "已经买了" (already bought)
- Emoji usage (❤️, 🔥, 👍)
Negative Indicators:
- "太贵了" (too expensive)
- "不好用" (doesn't work well)
- "广告" (ad/commercial)
- "踩雷" (disappointment)
Sentiment Score Formula:
Sentiment % = (Positive Comments / Total Comments) × 100
Benchmark:
- Excellent: >80% positive
- Good: 60-80% positive
- Needs improvement: <60% positiveCompare Influencer Performance:
Influencer Engagement Quality Score =
(Average Engagement Rate / Influencer's Follower Count) × 1,000
Benchmark:
- Mega-influencers (1M+ followers): 1-3
- Macro-influencers (100K-1M): 3-10
- Micro-influencers (10K-100K): 10-50
- Nano-influencers (1K-10K): 50-200
Red Flags:
- Engagement rate < 1% of follower count (possible fake followers)
- Comments are generic (好美, 喜欢, 支持) without substance
- Like:comment ratio > 20:1 (normal is 10:1)
- Sudden spike in followers then drop (bought followers)Link Click Metrics:
1. CTR (Click-Through Rate)
- Formula: (Clicks / Impressions) × 100
- Benchmark: 2-5% for paid campaigns
2. Click-to-Conversion Rate
- Formula: (Conversions / Clicks) × 100
- Benchmark: 1-3% for e-commerce
3. Bounce Rate
- Percentage who click but leave immediately
- Benchmark: <40% good, <60% acceptable
4. Time on Site
- Average time spent after clicking
- Benchmark: 2+ minutes good signalE-commerce Conversion Metrics:
1. Total Sales Revenue (GMV)
- Gross merchandise value from campaign
- Compare to campaign cost for ROAS
2. ROAS (Return on Ad Spend)
- Formula: Revenue / Ad Spend
- Benchmark:
* 1-2x: Breakeven or slight profit
* 3-5x: Good performance
* 5-10x: Excellent performance
* 10x+: Exceptional, scale campaign
3. CPA (Cost Per Acquisition)
- Formula: Ad Spend / Number of Customers Acquired
- Benchmark:
* Low-ticket (<¥100): ¥20-50 CPA
* Mid-ticket (¥100-500): ¥50-150 CPA
* High-ticket (>¥500): ¥150-500 CPA
4. AOV (Average Order Value)
- Formula: Total Revenue / Number of Orders
- Compare to regular AOV
- Campaign AOV > Regular AOV = good sign
5. Customer Lifetime Value (CLV)
- Formula: Average Purchase Value × Purchase Frequency × Customer Lifespan
- Compare CLV to CPA
- Target: CLV > 3× CPA for sustainable growthLead Metrics (for non-ecommerce):
1. Total Leads Generated
- Form submissions, sign-ups, inquiries
- Benchmark: 2-5% conversion from clicks
2. Cost Per Lead (CPL)
- Formula: Ad Spend / Number of Leads
- Benchmark: ¥50-200 CPL depending on industry
3. Lead Quality Score
- Track lead qualification rate
- Benchmark: 30-50% become qualified leads
4. Conversion to Customer
- Percentage of leads who become customers
- Benchmark: 10-30% depending on sales cycleTotal Campaign ROI Analysis:
Campaign Costs:
- Ad spend: ¥X
- Influencer fees: ¥Y
- Content production: ¥Z
- Platform fees: ¥A
- Team time: ¥B
Total Cost = X + Y + Z + A + B
Campaign Returns:
- Direct revenue: ¥R
- Attributed revenue (assisted conversions): ¥S
- Customer lifetime value: ¥C
- Earned media value (organic from paid): ¥E
Total Return = R + S + C + E
ROI Formulas:
1. Simple ROAS = R / Total Cost
2. Attributed ROAS = (R + S) / Total Cost
3. Full ROI = (R + S + C + E - Total Cost) / Total Cost
Decision Matrix:
ROAS < 2x: Unprofitable, optimize or pause
ROAS 2-3x: Marginal, improve creatives/targeting
ROAS 3-5x: Good, scale gradually
ROAS 5-10x: Excellent, scale aggressively
ROAS 10x+: Exceptional, maximize scaleCampaign Attribution Approaches:
1. Last-Click Attribution
- Credit goes to final touchpoint before purchase
- Simple but undervalues awareness campaigns
2. First-Click Attribution
- Credit goes to initial touchpoint
- Values discovery but ignores nurturing
3. Multi-Touch Attribution (Recommended)
- Distributes credit across all touchpoints
- Xiaohongshu often plays mid-funnel role
- More accurate for multi-channel campaigns
4. Time Decay Attribution
- Touchpoints closer to purchase get more credit
- Reflects recency effect on decision-making
Recommended: Use multi-touch attribution for complete pictureDaily Report Template:
Campaign Name: [Name]
Date: [Date]
Days Active: [X]
Today's Performance:
- Spend: ¥X (¥Y total to date)
- Impressions: X (Y total)
- Clicks: X (Y total)
- Conversions: X (Y total)
- Revenue: ¥X (¥Y total)
- ROAS: X.x (Y.y total to date)
Key Changes vs Yesterday:
- Performance: ↑↓ X%
- Insights: [What happened, why]
Alerts: [Any issues or opportunities]
Actions Taken: [Optimizations performed]Weekly Report Template:
Campaign: [Name]
Week: [Date Range]
Executive Summary:
[2-3 sentences on overall performance]
Performance Table:
| Metric | This Week | Last Week | Change |
|--------|-----------|-----------|--------|
| Spend | ¥X | ¥Y | ±Z% |
| Revenue| ¥X | ¥Y | ±Z% |
| ROAS | X.x | Y.y | ±Z% |
| CTR | X.x% | Y.y% | ±Z% |
| Conv. | X.x% | Y.y% | ±Z% |
Top Performing:
- Creatives: [Which creatives worked best]
- Audiences: [Which audiences converted best]
- Placements: [Which placements performed best]
Low Performing:
- Underperforming elements to fix
Optimization Actions:
[Changes made this week]
Next Week's Plan:
[Planned adjustments]Post-Campaign Analysis Template:
Campaign: [Name]
Duration: [Start Date] - [End Date]
Campaign Objectives:
[What we set out to achieve]
Objectives Achieved:
- Objective 1: [Status and details]
- Objective 2: [Status and details]
Overall Performance:
- Total Spend: ¥X
- Total Revenue: ¥Y
- ROAS: X.x
- vs Target: (above/below target by X%)
Key Metrics Breakdown:
[Full metrics table with benchmarks]
Learnings and Insights:
1. What worked:
- [Specific success factors]
2. What didn't work:
- [Specific failures]
3. Surprises:
- [Unexpected outcomes]
4. Audience Insights:
- [Demographics, behaviors discovered]
5. Creative Insights:
- [Messages, formats that resonated]
Recommendations for Next Campaign:
1. [Specific recommendation 1]
2. [Specific recommendation 2]
3. [Specific recommendation 3]
Budget Allocation Recommendation:
[How to optimize future spend]| Mistake | Why Happens | Fix |
|---|---|---|
| Only tracking vanity metrics (likes, views) | Easy to measure, feel good | Focus on business metrics (sales, leads, ROAS) |
| Not setting up proper tracking beforehand | Excitement to launch | Set up ALL tracking before spending any money |
| Ignoring attribution, claiming all sales | Want to show good results | Use proper attribution modeling for accuracy |
| Checking too frequently, overreacting | Anxiety, desire to optimize | Set check intervals, look at trends not daily blips |
| Comparing campaigns with different objectives | Apples-to-oranges comparison | Only compare campaigns with similar goals |
| Not tracking long-term value (CLV) | Focus on immediate results | Track customer lifetime value, not just first purchase |
| Ignoring qualitative feedback | Hard to quantify | Monitor comments, sentiment, customer feedback |
| Pausing campaigns too early | Impatience, early underperformance | Give campaigns 7-14 days to optimize |
| Not A/B testing creatives or audiences | Seems good enough | Always test variations, optimize winners |
| Failing to document learnings | Moving to next campaign | Document insights for future campaigns |