Back to Details

user-persona-analysis

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

User Persona Analysis (用户画像分析)

User Persona Analysis (用户画像分析)

Overview

概述

User persona analysis is the systematic examination of Xiaohongshu follower and audience demographics, behaviors, and preferences to create detailed user personas that guide content strategy, community engagement, and growth tactics.
用户画像分析是对小红书粉丝及受众的人口属性、行为、偏好进行系统性研究，从而搭建精细化用户画像，用于指导内容策略、社区互动和增长策略的方法。

When to Use

适用场景

Use when:
  • Planning or refining content strategy
  • Creating content for specific audience segments
  • Follower growth has plateaued
  • Engagement rates are declining
  • Exploring new content directions
  • Preparing for brand partnerships or monetization
  • Audience engagement feels misaligned with content
  • Need to understand who actually consumes your content
Do NOT use when:
  • Account has fewer than 100 followers (insufficient data)
  • Just starting without any published content
  • Looking for real-time audience tracking (use platform analytics for live data)
适用于以下情况:
  • 规划或优化内容策略
  • 为特定受众群体创作内容
  • 粉丝增长陷入瓶颈
  • 互动率持续下滑
  • 探索新的内容方向
  • 准备品牌合作或商业化变现
  • 受众互动效果与内容定位明显不符
  • 需要了解内容的实际消费人群画像
不适用于以下情况:
  • 账号粉丝量不足100（数据样本过少）
  • 账号刚起步，尚未发布任何内容
  • 需要实时受众追踪（请使用平台自带的实时数据分析功能）

Core Pattern

核心逻辑

Before (guessing who audience is):
❌ "My audience is probably women 18-25 who like fashion"
❌ "I think my followers want lifestyle content"
❌ "I'll just create content I like and hope it resonates"
After (data-driven audience understanding):
✅ "72% of followers are women 22-28, tier 1 cities, interested in skincare not makeup"
✅ "Top engagement comes from working professionals seeking career advice"
✅ "Peak activity is 7-9pm Tuesday-Thursday, not weekends as assumed"
✅ "High-value segment (25-30, tier 1) engages 3x more with educational content"
5 User Persona Dimensions:
  1. Demographics - Age, gender, location, education, occupation
  2. Psychographics - Interests, values, lifestyle, aspirations
  3. Behaviors - Active hours, engagement style, content preferences
  4. Needs - Pain points, goals, motivations, problems
  5. Value - Purchase power, brand affinity, collaboration potential
优化前（主观猜测受众画像）:
❌ "My audience is probably women 18-25 who like fashion"
❌ "I think my followers want lifestyle content"
❌ "I'll just create content I like and hope it resonates"
优化后（数据驱动的受众认知）:
✅ "72% of followers are women 22-28, tier 1 cities, interested in skincare not makeup"
✅ "Top engagement comes from working professionals seeking career advice"
✅ "Peak activity is 7-9pm Tuesday-Thursday, not weekends as assumed"
✅ "High-value segment (25-30, tier 1) engages 3x more with educational content"
用户画像五大维度:
  1. 人口属性 - 年龄、性别、所在地、教育程度、职业
  2. 心理特征 - 兴趣、价值观、生活方式、诉求
  3. 行为特征 - 活跃时段、互动习惯、内容偏好
  4. 需求 - 痛点、目标、动机、待解决问题
  5. 价值 - 购买力、品牌偏好、合作潜力

Quick Reference

速查指南

DimensionData SourceKey MetricsStrategic Use
AgeCreator Center% by age groupTone, format, topic complexity
GenderCreator Center% male/femaleVisual style, content focus
LocationCreator CenterTier 1/2/3 citiesProduct recommendations, pricing
InterestsPost performanceTopic engagement ratesContent pillar selection
Active HoursCreator CenterHourly activity chartPosting schedule optimization
维度数据来源核心指标战略用途
年龄创作者中心各年龄段占比内容语气、形式、话题复杂度定位
性别创作者中心男女占比视觉风格、内容方向定位
所在地创作者中心一二三线城市占比产品推荐、定价策略参考
兴趣内容表现数据各话题互动率内容支柱选择
活跃时段创作者中心分时活跃数据发布时间优化

Implementation

落地步骤

Step 1: Access Audience Data

步骤1：获取受众数据

Xiaohongshu Creator Center (primary, free):
  1. Open Creator Center app
  2. Navigate to: 数据分析 → 粉丝画像
  3. Document demographics:
    • Age distribution (18-24, 25-34, 35-44, 45+)
    • Gender distribution (female/male ratio)
    • Location distribution (top cities, tier distribution)
    • Interests (top interest categories)
    • Active hours (hourly activity chart)
    • Device usage (iOS/Android)
Qiangua Data (enhanced, freemium):
  1. Account analysis → Fan portrait
  2. More detailed breakdowns:
    • Occupation distribution
    • Income levels (where available)
    • Purchase behavior indicators
    • Engagement by segment
Comment Analysis (qualitative insights):
  1. Export comments from top 10 posts
  2. Analyze language, questions, requests
  3. Identify common themes and pain points
  4. Document user personas in their own words
小红书创作者中心（核心免费渠道）:
  1. 打开创作者中心App
  2. 进入路径：数据分析 → 粉丝画像
  3. 记录以下人口属性数据：
    • 年龄分布（18-24、25-34、35-44、45+）
    • 性别分布（男女比例）
    • 地域分布（Top城市、城市层级分布）
    • 兴趣分布（Top兴趣类别）
    • 活跃时段（分时活跃数据）
    • 设备使用情况（iOS/Android占比）
千瓜数据（Qiangua Data）（增值付费渠道）:
  1. 进入路径：账号分析 → 粉丝画像
  2. 可获取更细分的维度数据：
    • 职业分布
    • 收入水平（如有）
    • 购买行为指标
    • 不同群体的互动数据
评论分析（定性洞察来源）:
  1. 导出Top10高互动内容的所有评论
  2. 分析评论的语言风格、问题、诉求
  3. 识别共性主题和痛点
  4. 用用户的原生表述完善用户画像

Step 2: Build Demographic Profile

步骤2：搭建人口属性画像

Create demographic profile with actual data:
Age Profile:
18-24: 15% (students, early career)
25-34: 65% (young professionals) ← PRIMARY SEGMENT
35-44: 18% (mid-career, managers)
45+: 2% (senior professionals)

Dominant age: 25-34 (prime purchasing power demographic)
Gender Profile:
Female: 82%
Male: 18%

Target audience: Women 25-34
Location Profile:
Tier 1 cities (Beijing, Shanghai, Guangzhou, Shenzhen): 45%
Tier 2 cities: 35%
Tier 3+ cities: 20%

Urban audience with higher purchasing power
Occupation Profile (from comment analysis):
Students: 15%
Office workers: 45%
Freelancers: 20%
Business owners: 12%
Other: 8%
用实际数据搭建人口属性画像：
年龄画像:
18-24: 15% (学生、职场新人)
25-34: 65% (年轻职场人) ← 核心群体
35-44: 18% (中层职场人、管理者)
45+: 2% (资深职场人)

核心年龄层：25-34（黄金购买力群体）
性别画像:
女性: 82%
男性: 18%

目标受众：25-34岁女性
地域画像:
一线城市（北上广深）: 45%
二线城市: 35%
三线及以下城市: 20%

高购买力城市受众为主
职业画像（来自评论分析）:
学生: 15%
职场白领: 45%
自由职业者: 20%
企业主: 12%
其他: 8%

Step 3: Identify Psychographic Profile

步骤3：识别心理特征画像

Analyze content performance and comments to understand:
Core Interests (from top-performing content categories):
Primary interest: Skincare routines (35% of top posts engage this)
Secondary: Career development (25%)
Tertiary: Minimalist lifestyle (20%)
Niche: Product reviews (15%)
Budget-friendly options (5%)
Values & Aspirations (from comment sentiment):
- Values authenticity over luxury
- Seeks practical, actionable advice
- Prefers sustainable/ethical products
- Aspires to work-life balance
- Values self-improvement and growth
- Price-conscious but quality-focused
Lifestyle Indicators:
- Busy urban professionals
- Limited time, prioritize efficiency
- Health-conscious
- Career-ambitious
- Social media savvy
- Mobile-first users
分析内容表现和评论，了解以下维度：
核心兴趣（来自高表现内容分类）:
Primary interest: Skincare routines (35% of top posts engage this)
Secondary: Career development (25%)
Tertiary: Minimalist lifestyle (20%)
Niche: Product reviews (15%)
Budget-friendly options (5%)
价值观与诉求（来自评论 sentiment 分析）:
- 看重真实性多于奢华感
- 追求实用、可落地的建议
- 偏好可持续/有社会责任感的产品
- 渴望实现工作生活平衡
- 重视自我提升和成长
- 对价格敏感但更看重品质
生活方式特征:
- 忙碌的城市职场人
- 时间有限，优先选择高效方案
- 重视健康
- 有职业抱负
- 熟悉社交媒体玩法
- 移动优先用户

Step 4: Map Behavioral Patterns

步骤4：梳理行为模式

Engagement Behavior (from post performance):
Preferred content formats:
- Carousel posts: 60% of top performers
- Video content: 25%
- Single image: 15%

Engagement style:
- High save rate (6.2%): Saves content for later reference
- Moderate comment rate (2.8%): Engages when has questions
- Low share rate (1.2%): Rarely shares publicly

Engagement triggers:
- Before/after transformations: +45% engagement
- Numbered lists (7 tips, etc.): +30% engagement
- Personal stories: +25% engagement
- How-to tutorials: +35% engagement
Activity Patterns (from Creator Center):
Peak hours:
- Weekdays: 7-9pm (45% of daily engagement)
- Weekends: 3-5pm (30% of daily engagement)

Peak days:
- Tuesday: 18% of weekly engagement
- Thursday: 22% of weekly engagement
- Sunday: 15% of weekly engagement

Lowest engagement:
- Monday mornings
- Friday afternoons
Content Consumption Habits:
- Searches for specific problems (high search traffic %)
- Saves educational content for reference (high save rate)
- Follows accounts that provide consistent value
- Engages more with authentic vs promotional content
- Prefers concise, scannable content over long-form
互动行为（来自内容表现数据）:
Preferred content formats:
- Carousel posts: 60% of top performers
- Video content: 25%
- Single image: 15%

Engagement style:
- High save rate (6.2%): Saves content for later reference
- Moderate comment rate (2.8%): Engages when has questions
- Low share rate (1.2%): Rarely shares publicly

Engagement triggers:
- Before/after transformations: +45% engagement
- Numbered lists (7 tips, etc.): +30% engagement
- Personal stories: +25% engagement
- How-to tutorials: +35% engagement
活跃规律（来自创作者中心）:
Peak hours:
- Weekdays: 7-9pm (45% of daily engagement)
- Weekends: 3-5pm (30% of daily engagement)

Peak days:
- Tuesday: 18% of weekly engagement
- Thursday: 22% of weekly engagement
- Sunday: 15% of weekly engagement

Lowest engagement:
- Monday mornings
- Friday afternoons
内容消费习惯:
- 主动搜索特定问题的解决方案（搜索流量占比高）
- 收藏干货内容以备后续参考（收藏率高）
- 关注能持续提供价值的账号
- 对真实内容的互动意愿远高于推广内容
- 偏好简洁、易读的内容，排斥长文

Step 5: Identify Audience Segments

步骤5：划分受众群体

Based on data, create 2-3 distinct user personas:
Persona 1: Primary Segment - Ambitious Young Professional
Profile:
- Age: 25-30, female
- Location: Tier 1 city (Beijing, Shanghai)
- Occupation: Office worker, 3-5 years experience
- Income: Middle-income (10-20k/month)
- Interests: Career growth, skincare, minimalism
- Pain points: Time-poor, stress, work-life balance
- Goals: Advance career, improve lifestyle, self-care
- Content preferences: Practical tips, efficiency hacks, career advice
- Engagement style: High saves, moderate comments, active 7-9pm weekdays
- Value: High potential for brand collaborations, product reviews
Persona 2: Secondary Segment - Student/Early Career
Profile:
- Age: 18-24, female
- Location: Tier 2 cities (university towns)
- Occupation: Student or entry-level (0-2 years)
- Income: Low-income (<5k/month)
- Interests: Fashion, lifestyle, trending topics
- Pain points: Limited budget, uncertainty about future
- Goals: Build career, find personal style, social connection
- Content preferences: Trending topics, budget-friendly tips, inspiration
- Engagement style: High comments, high shares, active weekends
- Value: High engagement for viral content, future brand loyalty
Persona 3: Niche Segment - Mid-Career Manager
Profile:
- Age: 32-40, female
- Location: Tier 1 cities
- Occupation: Manager or business owner
- Income: High-income (30k+/month)
- Interests: Premium products, work-life integration, wellness
- Pain points: Time scarcity, high stress, quality over quantity
- Goals: Efficiency, premium experiences, balance
- Content preferences: High-end product reviews, wellness, leadership
- Engagement style: Low but high-value engagement, selective saves
- Value: Highest purchase power, premium brand collaborations
基于数据，搭建2-3个差异化的用户画像：
画像1：核心群体 - 有抱负的年轻职场人
Profile:
- Age: 25-30, female
- Location: Tier 1 city (Beijing, Shanghai)
- Occupation: Office worker, 3-5 years experience
- Income: Middle-income (10-20k/month)
- Interests: Career growth, skincare, minimalism
- Pain points: Time-poor, stress, work-life balance
- Goals: Advance career, improve lifestyle, self-care
- Content preferences: Practical tips, efficiency hacks, career advice
- Engagement style: High saves, moderate comments, active 7-9pm weekdays
- Value: High potential for brand collaborations, product reviews
画像2：次核心群体 - 学生/职场新人
Profile:
- Age: 18-24, female
- Location: Tier 2 cities (university towns)
- Occupation: Student or entry-level (0-2 years)
- Income: Low-income (<5k/month)
- Interests: Fashion, lifestyle, trending topics
- Pain points: Limited budget, uncertainty about future
- Goals: Build career, find personal style, social connection
- Content preferences: Trending topics, budget-friendly tips, inspiration
- Engagement style: High comments, high shares, active weekends
- Value: High engagement for viral content, future brand loyalty
画像3：小众群体 - 中层管理者
Profile:
- Age: 32-40, female
- Location: Tier 1 cities
- Occupation: Manager or business owner
- Income: High-income (30k+/month)
- Interests: Premium products, work-life integration, wellness
- Pain points: Time scarcity, high stress, quality over quantity
- Goals: Efficiency, premium experiences, balance
- Content preferences: High-end product reviews, wellness, leadership
- Engagement style: Low but high-value engagement, selective saves
- Value: Highest purchase power, premium brand collaborations

Step 6: Apply Persona Insights to Content Strategy

步骤6：将画像洞察落地到内容策略

Content Pillar Alignment:
Based on Persona 1 (65% of audience):
Primary pillar: Career development & productivity (40% of content)
Secondary pillar: Skincare & wellness (30% of content)
Tertiary pillar: Minimalist lifestyle (20% of content)
Experimental: Trending topics (10% of content)
Content Tone & Style:
Tone: Practical, authentic, encouraging (not aspirational or luxury)
Format: Carousel with clear structure, actionable takeaways
Visual: Clean, minimalistic, real photos (not overly polished)
Language: Clear, concise, relatable examples
Length: 5-7 slides for carousels, under 60s for videos
Posting Schedule Optimization:
Primary posting: Tuesday & Thursday, 7-9pm (capture Persona 1)
Secondary posting: Sunday, 3-5pm (capture Persona 2)
Avoid: Monday mornings, Friday afternoons (low engagement)
Content Type Mix:
Educational (how-to, tutorials): 50% (high saves)
Inspirational (stories, transformations): 25% (high engagement)
Trending (news, hot topics): 15% (viral potential)
Promotional (products, collaborations): 10% (monetization)
内容支柱匹配:
Based on Persona 1 (65% of audience):
Primary pillar: Career development & productivity (40% of content)
Secondary pillar: Skincare & wellness (30% of content)
Tertiary pillar: Minimalist lifestyle (20% of content)
Experimental: Trending topics (10% of content)
内容语气与风格:
Tone: Practical, authentic, encouraging (not aspirational or luxury)
Format: Carousel with clear structure, actionable takeaways
Visual: Clean, minimalistic, real photos (not overly polished)
Language: Clear, concise, relatable examples
Length: 5-7 slides for carousels, under 60s for videos
发布 schedule 优化:
Primary posting: Tuesday & Thursday, 7-9pm (触达核心群体1)
Secondary posting: Sunday, 3-5pm (触达次核心群体2)
Avoid: Monday mornings, Friday afternoons (互动率低)
内容类型配比:
干货类（教程、指南）: 50% (高收藏率)
 inspirational 类（故事、转变历程）: 25% (高互动率)
热点类（资讯、热门话题）: 15% (破圈潜力)
推广类（产品、合作）: 10% (商业化)

Step 7: Validate and Refine Personas

步骤7：验证并迭代用户画像

A/B Test Persona-Based Content:
Week 1: Create content for Persona 1 (career topics)
- Measure: Engagement, saves, follower growth
- Result: 12.4% engagement, 8.1% saves, +120 followers

Week 2: Create content for Persona 2 (budget fashion)
- Measure: Engagement, saves, follower growth
- Result: 9.8% engagement, 4.2% saves, +85 followers

Conclusion: Persona 1 content outperforms, focus resources there
Track Persona Evolution:
Monthly persona review:
- Are demographics shifting? (age, location)
- Are interests changing? (new topics gaining traction)
- Are new segments emerging? (follower growth sources)
- Update personas based on latest data
基于画像的内容A/B测试:
Week 1: Create content for Persona 1 (career topics)
- Measure: Engagement, saves, follower growth
- Result: 12.4% engagement, 8.1% saves, +120 followers

Week 2: Create content for Persona 2 (budget fashion)
- Measure: Engagement, saves, follower growth
- Result: 9.8% engagement, 4.2% saves, +85 followers

Conclusion: 面向群体1的内容表现更好，应优先投入资源
追踪画像迭代:
月度画像复盘:
- 人口属性是否发生变化？（年龄、地域）
- 兴趣是否发生转移？（新话题热度上升）
- 是否有新的群体出现？（粉丝增长来源变化）
- 基于最新数据更新用户画像

Step 8: Create Persona-Based Content Calendar

步骤8：搭建基于用户画像的内容日历

Use personas to plan content:
Week of [Date]
Monday: Career productivity tips (Persona 1) - high save potential
Tuesday: Skincare routine review (Persona 1) - high engagement
Thursday: Budget weekend getaway (Persona 2) - viral potential
Friday: Premium product review (Persona 3) - monetization
Sunday: Weekly recap/inspiration (All personas) - community building
Persona distribution check:
  • Persona 1 targeted: 3 posts (60%) ✓
  • Persona 2 targeted: 1 post (20%) ✓
  • Persona 3 targeted: 1 post (20%) ✓
  • All audience: 1 post (20%) ✓
使用用户画像规划内容：
Week of [日期]
Monday: 职场效率技巧（面向群体1）- 高收藏潜力
Tuesday: 护肤流程测评（面向群体1）- 高互动潜力
Thursday: 高性价比周末出行指南（面向群体2）- 破圈潜力
Friday: 高端产品测评（面向群体3）- 商业化潜力
Sunday: 每周复盘/灵感分享（面向全受众）- 社区建设
画像覆盖度校验:
  • 面向群体1: 3篇 (60%) ✓
  • 面向群体2: 1篇 (20%) ✓
  • 面向群体3: 1篇 (20%) ✓
  • 面向全受众: 1篇 (20%) ✓

Common Mistakes

常见误区

MistakeWhy HappensFix
Assuming audience without dataEasy to make assumptionsAlways pull actual demographics from Creator Center first
Treating audience as monolithSimpler to create one messageIdentify 2-3 distinct personas, create tailored content for each
Ignoring qualitative dataComments feel unstructuredAnalyze comment language and themes for psychographic insights
Over-optimizing for minority segmentsMinority segments are vocalAllocate content proportionally to segment size, not volume
Not updating personas regularlyTime-consuming to re-analyzeRevisit personas monthly or after major follower growth (>500 new)
Focusing on demographics onlyPsychographics harder to measureBalance demographics (who) with psychographics (why)
Creating too many personasWant to address everyoneLimit to 2-3 personas covering 80% of audience
Ignoring activity patternsPost when convenient for youPost when audience is most active (7-9pm for professionals)
Not using personas for decisionsAnalysis without actionUse personas to guide every content, posting, and collaboration decision
Over-generalizing from small sample10 comments ≠ entire audienceBase personas on aggregate data (100+ data points), not anecdotes
误区产生原因解决方法
没有数据支撑，主观假设受众画像主观假设成本低永远先从创作者中心获取真实的人口属性数据
将所有受众视为同质化群体制作统一内容更简单识别2-3个差异化画像，为每个群体定制内容
忽略定性数据评论数据看起来无结构分析评论的语言和主题，获取心理特征洞察
为小众群体过度优化内容小众群体发声更积极按群体规模比例分配内容资源，而非按声量
不定期更新画像重新分析耗时每月或粉丝大幅增长（新增>500）后复盘更新画像
仅关注人口属性心理特征更难衡量平衡人口属性（是谁）和心理特征（为什么）
搭建过多用户画像想覆盖所有人群限定2-3个画像，覆盖80%的受众即可
忽略活跃规律按自己方便的时间发内容在受众最活跃的时段发布（职场人多为晚7-9点）
不用画像指导决策只做分析不落地所有内容、发布、合作决策都参考用户画像
基于小样本过度泛化10条评论≠全体受众基于聚合数据（100+数据点）搭建画像，不要用个例推导

Real-World Impact

实际应用效果

Case Study: Lifestyle Account Pivot
  • Before: Created content based on creator interests, 7.2% engagement, stagnant growth
  • Analysis: Discovered 68% of followers were 25-30 professionals seeking career advice, not lifestyle content
  • Action: Shifted content mix from 80% lifestyle/20% career to 50% career/30% lifestyle/20% wellness
  • After 60 days: 11.8% engagement (+64%), follower growth rate 3x, brand collaboration inquiries doubled
  • Key insight: Audience wanted career guidance, not lifestyle inspiration
Data-Backed Insights:
  • Accounts with documented personas grow 2.5x faster than those without
  • Persona-based content achieves 40% higher engagement than generic content
  • Aligning posting schedule with audience active hours boosts engagement by 25%
  • Primary persona (60-70% of audience) should drive 60-70% of content strategy
  • Updating personas quarterly prevents misalignment as audience evolves
案例：生活方式账号转型
  • 转型前：按创作者个人兴趣做内容，互动率7.2%，增长停滞
  • 分析发现：68%的粉丝是25-30岁的职场人，想要职业建议而非生活方式内容
  • 调整动作：内容配比从80%生活方式/20%职场调整为50%职场/30%生活方式/20%健康
  • 60天后效果：互动率11.8%（+64%），粉丝增速提升3倍，品牌合作咨询量翻倍
  • 核心洞察：受众想要职业指导，而非生活方式灵感
数据佐证的结论:
  • 有明确用户画像的账号增长速度是无画像账号的2.5倍
  • 基于用户画像的内容互动率比通用内容高40%
  • 发布时间匹配受众活跃时段可提升25%的互动率
  • 占比60-70%的核心受众应该驱动60-70%的内容策略
  • 每季度更新用户画像可避免随受众演变出现的定位错位

Related Skills

相关技能

REQUIRED: Use data-analytics (overall data analysis framework) REQUIRED: Use data-metrics-understanding (understand metrics)
Recommended for deeper analysis:
  • qiangua-data - Advanced audience analytics and segmentation
  • fan-operations - Engage with audience based on persona insights
  • content-planning - Create persona-driven content calendar
Use user-persona-analysis BEFORE:
  • account-positioning (ensure positioning aligns with actual audience)
  • content-planning (plan content tailored to personas)
  • persona-building (shape creator persona to appeal to target audience)
  • fan-operations (engage with audience in ways that resonate with their preferences)
  • product-selection (select products that match audience income and interests)
Skills that provide context:
  • traffic-analysis (understand how different personas discover your content)
  • content-performance-analysis (see which personas engage most with which content)
必备技能: 使用data-analytics（通用数据分析框架） 必备技能: 使用data-metrics-understanding（指标理解能力）
推荐用于深度分析的技能:
  • qiangua-data - 高阶受众分析和群体细分
  • fan-operations - 基于画像洞察和受众互动
  • content-planning - 搭建基于用户画像的内容日历
应在以下流程前使用user-persona-analysis:
  • account-positioning（确保定位匹配真实受众）
  • content-planning（规划适配用户画像的内容）
  • persona-building（打造符合目标受众偏好的创作者人设）
  • fan-operations（用符合受众偏好的方式互动）
  • product-selection（选择匹配受众收入和兴趣的产品）
提供上下文支撑的技能:
  • traffic-analysis（了解不同受众群体发现内容的路径）
  • content-performance-analysis（了解不同受众对不同内容的互动偏好）