content-performance-analysis

Content Performance Analysis (内容效果分析)

Overview

Content performance analysis is the systematic evaluation of individual posts and overall content portfolio to identify success patterns, understand what resonates with the audience, and make data-driven decisions about content strategy.

When to Use

Use when:

• Post performance is inconsistent or unpredictable
• Need to understand why certain content went viral
• Identifying patterns in top-performing content
• Recognizing underperforming content that needs improvement
• Comparing different content formats (carousel vs video vs single image)
• Deciding which content types to focus on
• Planning content optimization based on past performance

Do NOT use when:

• Account has fewer than 5 published posts (insufficient data)
• Looking for real-time performance during first hours (wait 3-7 days)
• Analyzing paid advertising performance (use ad analytics tools)

Core Pattern

Before (guessing what works):

❌ "This post should do well, I worked hard on it"
❌ "I don't know why this post went viral, lucky I guess"
❌ "All my content is pretty similar, performance is random"

After (data-driven content insights):

✅ "Top 5 posts all use carousel format with before/after structure"
✅ "Posts with question titles get 2.3x more comments than statement titles"
✅ "Video content underperforms images - shift strategy to graphic content"
✅ "Posts published on Tuesday outperform Sunday by 40%"

3 Analysis Dimensions Framework:

1. Engagement Data - Likes, comments, shares, saves (audience response)
2. Growth Data - New followers, profile visits (conversion impact)
3. Viral Data - Exposure, discovery traffic (reach and algorithm favor)

Quick Reference

Metric	What It Reveals	Good Benchmark	Analysis Method
Engagement Rate	Content resonance	8-12% average	(Likes+Comments+Shares+Saves)÷Views×100
Save Rate	Content value/reuse	3-5% is good	Saves÷Views×100
Comment Rate	Discussion spark	2-4% average	Comments÷Views×100
Follower Conversion	Content converts to fans	1-3%	New Followers÷Views×100
Viral Score	Algorithm favor	Views÷Followers	>10 = viral hit

Implementation

Step 1: Collect Post Performance Data

From Xiaohongshu Creator Center:

1. Open Creator Center → 内容数据
2. Select time range (last 30 days recommended)
3. Export or manually record data for each post:
   • Title
   • Content type (image/video/carousel)
   • Publish date/time
   • Views (浏览量)
   • Likes (点赞数)
   • Comments (评论数)
   • Shares (转发数)
   • Saves (收藏数)
   • New followers gained

From Qiangua Data (recommended for efficiency):

1. Account analysis → Content performance
2. Export all posts with metrics to Excel
3. Sort by different metrics to identify patterns

Step 2: Calculate Key Performance Indicators

For each post, calculate:

Engagement Rate:

Engagement Rate = (Likes + Comments + Shares + Saves) ÷ Views × 100

• Excellent: >15%
• Good: 8-15%
• Average: 5-8%
• Below Average: <5%

Save Rate (content value):

Save Rate = Saves ÷ Views × 100

• Excellent: >7%
• Good: 4-7%
• Average: 2-4%
• Low: <2%

Comment Rate (engagement depth):

Comment Rate = Comments ÷ Views × 100

• Excellent: >5%
• Good: 3-5%
• Average: 1-3%
• Low: <1%

Viral Score:

Viral Score = Views ÷ Follower Count

• Viral Hit: >10 (reached 10x beyond existing audience)
• Strong Performance: 5-10
• Expected Performance: 1-5
• Underperforming: <1

Step 3: Identify Top and Bottom Performing Content

Top Performers (analyze last 10-20 posts):

1. Sort by Engagement Rate - Find top 5
2. Sort by Viral Score - Find top 5
3. Sort by Save Rate - Find top 5

Bottom Performers:

1. Sort by Engagement Rate - Find bottom 5
2. Identify posts with Viral Score <1 (underperformed existing audience)

Step 4: Extract Success Patterns from Top Content

Analyze top 5 posts for common patterns:

Content Format Patterns:

• Single image vs carousel vs video
• Carousel slide count (3-5 slides optimal)
• Video length (under 60 seconds optimal)

Content Structure Patterns:

• Hook/intro style (question, statement, before/after)
• Main content organization (list, tutorial, story, comparison)
• Call-to-action presence and type

Title Patterns:

• Question vs statement
• Length (short vs long)
• Keyword usage
• Emotional triggers (curiosity, urgency, benefit)

Visual Patterns:

• Cover design style
• Color scheme
• Text overlay presence
• Face presence vs product-only

Topic Patterns:

• Content category (educational, entertainment, inspiration)
• Specific subtopics
• Target audience segment

Timing Patterns:

• Day of week
• Time of day
• Seasonal relevance

Document findings:

Pattern: Carousel format
- Frequency in top 5: 4/5 posts (80%)
- Average engagement: 14.2%
- Common structure: Before/after transformation

Pattern: Question-based titles
- Frequency in top 5: 3/5 posts (60%)
- Average engagement: 13.8%
- Comment rate: 4.1% (above average)

Step 5: Diagnose Underperforming Content

For bottom 5 posts, analyze:

Content Quality Issues:

• Low production value (poor images, bad lighting)
• Unclear value proposition
• Weak or confusing message
• Inadequate detail or depth

Content Format Issues:

• Suboptimal format for topic (e.g., complex topic in single image)
• Wrong carousel length (too short or too long)
• Video length issues (too long, boring intro)

Title and Cover Issues:

• Uncompelling cover (low CTR)
• Confusing or unappealing title
• Mismatch between title and content
• Lack of keywords for search

Targeting Issues:

• Content too broad/narrow for audience
• Off-brand or irrelevant to niche
• Missing audience pain point

Timing Issues:

• Posted at low-traffic times
• Competing with major events/holidays
• Seasonal mismatch

Create improvement plan for each underperforming post type:

Issue: Low CTR on single-image posts
Diagnosis: Covers lack visual hook, text-only, no face
Fix: Add face, use before/after format, bold overlay text
Test: Create 3 new posts with improved covers, measure CTR improvement

Step 6: Build Content Performance Matrix

Create a matrix to visualize performance by content type:

Content Type | Avg Engagement | Avg Viral Score | Post Count | Verdict
-------------|----------------|-----------------|------------|--------
Carousel     | 12.4%          | 6.2             | 8          | ⭐⭐⭐ Primary
Video        | 7.8%           | 3.1             | 5          | ⭐⭐ Secondary
Single Image | 9.2%           | 4.5             | 7          | ⭐⭐ Secondary

Strategy decisions:

• Primary format (⭐⭐⭐): Use for 60-70% of content
• Secondary format (⭐⭐): Use for 20-30% of content
• Deprecated format: Phase out or significantly improve

Step 7: Develop Replicable Content Formulas

Based on top performers, create 3-5 proven content formulas:

Formula 1: Problem-Solution Tutorial

• Format: 5-slide carousel
• Title: Question-based ("Struggling with X?")
• Structure: Problem → Solution → Step-by-step → Result → CTA
• Performance: 14.2% engagement, 8.3 viral score
• Best for: Educational content, how-to topics

Formula 2: Before-After Transformation

• Format: 3-slide carousel
• Title: Benefit-driven ("How I achieved X in Y days")
• Structure: Before → Process → After
• Performance: 15.8% engagement, 12.1 viral score
• Best for: Transformation topics, results showcase

Formula 3: List-Based Tips

• Format: 4-6 slide carousel
• Title: Number-based ("7 tips for X")
• Structure: Hook → 7 tips (1 per slide) → Summary → CTA
• Performance: 11.3% engagement, 5.7 viral score
• Best for: Tips sharing, quick value content

Use formulas to:

• Plan content calendar
• Maintain consistency
• Scale content production
• Predict performance

Step 8: Track Performance Trends Over Time

Build weekly content performance log:

Week | Posts | Avg Engagement | Top Format | Viral Hits | Insights
-----|-------|----------------|------------|------------|----------
W1   | 4     | 9.2%           | Carousel   | 1          | Carousels outperforming
W2   | 5     | 10.5%          | Video      | 2          | Video improvement working
W3   | 3     | 12.8%          | Carousel   | 1          | Question titles boosting comments

Trend analysis:

• Engagement rate rising? Content strategy is working
• Certain formats consistently winning? Double down
• Viral hits increasing? Algorithm favor improving
• Specific topics always perform well? Create series

Common Mistakes

Mistake	Why Happens	Fix
Judging content by views only	Views are vanity metric	Focus on engagement rate and saves - they indicate true resonance
Analyzing posts too soon (first 24 hours)	Early data is misleading	Wait 3-7 days for post to fully perform before analyzing
Comparing posts with different audience sizes	Unfair comparison	Use engagement rate % not raw numbers for fair comparison
Ignoring outlier posts (viral hits)	Assume they're flukes	Deep analyze viral hits - they contain valuable insights
Changing strategy too frequently	Impatience with slow growth	Collect data from 10+ posts before changing strategy
Focusing on averages only	Averages hide patterns	Look at top/bottom 20% to extract winning/losing patterns
Not tracking by content format	Miss format-specific insights	Always analyze by format (carousel vs video vs single)
Ignoring save rate	Saves predict long-term success	High save rate = evergreen content that drives ongoing traffic
Over-optimizing for one metric	Unbalanced strategy	Balance engagement (likes) with value (saves) and growth (followers)
Not documenting success formulas	Reinventing the wheel	Create 3-5 proven formulas, reuse consistently

Real-World Impact

Case Study: Fashion Account Turnaround

• Before: 6.8% average engagement, unpredictable performance, no clear content direction
• Analysis: Discovered carousels with before/after structure averaged 14.5% engagement vs 5.2% for single images
• Action: Shifted 80% of content to carousel format using proven formulas
• After 30 days: 11.2% average engagement (+65%), follower growth rate doubled, 3 viral hits
• Key insight: Content format matters more than production quality

Data-Backed Insights:

• Accounts that document and reuse content formulas grow 3x faster
• Posts following proven formats outperform experimental content 70% of the time
• High-save content (>7% save rate) continues getting views for 30+ days
• Top-performing content often follows 1 of 5 predictable structures
• Analyzing bottom performers reveals as much as analyzing top performers

Related Skills

REQUIRED: Use data-analytics (overall data analysis framework) REQUIRED: Use data-metrics-understanding (understand metrics)

Recommended for deeper analysis:

• traffic-analysis - Analyze which traffic sources drive best content performance
• content-planning - Apply insights to plan future content
• viral-strategy - Optimize content for viral potential

Use content-performance-analysis BEFORE:

• content-planning (align plan with proven content formulas)
• cover-design (design covers based on top-performing patterns)
• title-writing (write titles using successful patterns)
• graphic-content-creation / short-video-production (create content in winning formats)

Skills that provide context:

• account-positioning (ensure content aligns with positioning)
• persona-building (maintain consistent voice across content)