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benchmarking-instagram-influencer-engagement

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Benchmarking Instagram Influencer Engagement

Instagram网红参与度基准测试

Pulls recent post data for one or multiple Instagram accounts and calculates engagement rate, consistency, and performance benchmarks. Used to vet influencers before partnerships or compare accounts against each other.
获取一个或多个Instagram账号的近期帖子数据，计算参与率、发布稳定性和表现基准。用于在合作前审核网红资质，或对比不同账号的表现。

Prerequisites

前提条件

  • APIFY_TOKEN
    environment variable set
  • Optional: Apify MCP server installed
  • 已设置
    APIFY_TOKEN
    环境变量
  • 可选：已安装Apify MCP服务器

Inputs

输入参数

ParameterTypeRequiredDefaultNotes
startUrls
array✅
[]
Instagram URLs — profiles, hashtags, locations, audio pages, reels
until
stringOptional—Scrape posts until this date (YYYY-MM-DD)
maxItems
numberOptionalUnlimitedMaximum posts to return
customMapFunction
stringOptional—JavaScript function to transform each output object
参数类型是否必填默认值说明
startUrls
数组✅
[]
Instagram链接 — 个人主页、话题标签、地点、音频页面、短视频（Reels）
until
字符串可选—爬取截止日期（格式：YYYY-MM-DD）
maxItems
数字可选无限制返回的最大帖子数量
customMapFunction
字符串可选—用于转换每个输出对象的JavaScript函数

Workflow

工作流程

Progress:
- [ ] Step 1: Get account list and analysis goal
- [ ] Step 2: Run instagram-scraper for each account
- [ ] Step 3: Calculate engagement rate per account
- [ ] Step 4: Apply benchmark standards
- [ ] Step 5: Deliver comparison report
进度：
- [ ] 步骤1：获取账号列表和分析目标
- [ ] 步骤2：为每个账号运行instagram-scraper
- [ ] 步骤3：计算每个账号的参与率
- [ ] 步骤4：应用基准标准
- [ ] 步骤5：交付对比报告

Step 1: Clarify Parameters

步骤1：明确参数

Ask the user for:
  • Instagram handles to analyze (up to 10 accounts; each handle without @)
  • Number of recent posts to analyze (default: 30 — enough for statistical reliability)
  • Analysis goal — vetting for partnership, competitive benchmarking, or general audit
向用户确认以下信息：
  • 要分析的Instagram账号名（最多10个；每个账号名不带@符号）
  • 要分析的近期帖子数量（默认：30篇 — 足以保证统计可靠性）
  • 分析目标 — 合作资质审核、竞品基准对比或常规审计

Step 2: Run the Actor

步骤2：运行Actor

Run separately for each account or pass as a list.
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
bash
undefined
可为每个账号单独运行，或批量传入列表。
推荐方式 — 使用run_actor.js（自动处理等待、输出和文件保存）：
bash
undefined

Quick answer (prints table to chat)

快速输出（在聊天中打印表格）

node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'

Save as CSV

保存为CSV文件

node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.csv --format csv
node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.csv --format csv

Save as JSON

保存为JSON文件

node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json
> `APIFY_TOKEN` must be set in environment or `.env` file.

**If Apify MCP is available:**
Tool: apify:run-actor Actor: "apidojo~instagram-scraper" Input: { "usernames": ["[handle1]", "[handle2]", "[handle3]"], "maxItems": 30 }

**REST API fallback:**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "usernames": ["[handle1]", "[handle2]"],
    "maxItems": 30
  }'
Wait for 
SUCCEEDED
. Fetch dataset.
node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json
> 必须在环境变量或`.env`文件中设置`APIFY_TOKEN`。

**如果Apify MCP可用：**
工具：apify:run-actor Actor: "apidojo~instagram-scraper" 输入： { "usernames": ["[handle1]", "[handle2]", "[handle3]"], "maxItems": 30 }

**备用REST API方式：**
```bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "usernames": ["[handle1]", "[handle2]"],
    "maxItems": 30
  }'
等待任务状态变为
SUCCEEDED
，然后获取数据集。

Step 3: Calculate Engagement Metrics

步骤3：计算参与度指标

For each account, from its posts array:
avg_likes = sum(post.likesCount) / num_posts
avg_comments = sum(post.commentsCount) / num_posts
engagement_rate = (avg_likes + avg_comments) / follower_count * 100
posting_frequency = num_posts / date_range_days
Benchmark standards by follower tier:
TierFollowersGood ERGreat ER
Nano1K–10K5–8%>8%
Micro10K–100K3–5%>5%
Mid100K–500K1.5–3%>3%
Macro500K–1M1–2%>2%
Mega1M+0.5–1%>1%
对于每个账号，从其帖子数组中计算：
avg_likes = sum(post.likesCount) / num_posts
avg_comments = sum(post.commentsCount) / num_posts
engagement_rate = (avg_likes + avg_comments) / follower_count * 100
posting_frequency = num_posts / date_range_days
按粉丝层级划分的基准标准：
层级粉丝数量良好参与率优秀参与率
微型（Nano）1K–10K5–8%>8%
小型（Micro）10K–100K3–5%>5%
中型（Mid）100K–500K1.5–3%>3%
大型（Macro）500K–1M1–2%>2%
超大型（Mega）1M+0.5–1%>1%

Step 4: Flag Anomalies

步骤4：标记异常情况

Flag accounts with suspicious engagement patterns:
  • Low ER relative to tier: ER below 0.5% suggests inflated followers
  • Like/Comment imbalance: Very high likes but near-zero comments suggests like-buying
  • Engagement spikes: One post with 10x normal engagement followed by dead content suggests bought promotion
  • Sudden follower jump: Cross-reference last follower count change (if visible)
标记存在可疑参与模式的账号：
  • 参与率低于层级基准：参与率低于0.5%可能意味着粉丝注水
  • 点赞/评论比例失衡：点赞数极高但评论数接近零，可能存在刷赞行为
  • 参与率突增：某篇帖子参与率是正常水平的10倍，后续内容表现低迷，可能存在购买推广行为
  • 粉丝数量突增：交叉对比最近的粉丝数量变化（如果可见）

Step 5: Format Report

步骤5：格式化报告

Output Format

输出格式

undefined
undefined

Instagram Engagement Benchmark Report

Instagram参与度基准测试报告

Accounts analyzed: [N] | Posts per account: [30] | Date: [DATE]
分析账号数量：[N] | 每个账号分析帖子数：[30] | 日期：[DATE]

Summary Comparison

汇总对比

AccountFollowersAvg LikesAvg CommentsEng. RateTier BenchmarkStatus
@[name][N][N][N][X.X%][good/great/low]✅/⚠️/❌
账号粉丝数量平均点赞平均评论参与率层级基准状态
@[name][N][N][N][X.X%][良好/优秀/偏低]✅/⚠️/❌

Detailed Analysis

详细分析

@[handle1]

@[handle1]

  • Followers: [N] | Tier: [Nano/Micro/Mid/Macro/Mega]
  • Avg Likes: [N] | Avg Comments: [N]
  • Engagement Rate: [X.X%] — [above/at/below] benchmark for this tier
  • Posting frequency: [X] posts/week
  • Best-performing post: [url] ([N] likes, [N] comments)
  • ⚠️ Anomalies: [none / describe if any]
  • Verdict: [Recommended / Proceed with caution / Do not recommend]
  • 粉丝数量：[N] | 层级：[微型/小型/中型/大型/超大型]
  • 平均点赞：[N] | 平均评论：[N]
  • 参与率：[X.X%] — [高于/符合/低于]该层级基准
  • 发布频率：[X] 篇/周
  • 表现最佳帖子：[链接]（[N]个点赞，[N]条评论）
  • ⚠️ 异常情况：[无 / 如有则描述]
  • 结论：[推荐合作 / 需进一步调查 / 不推荐合作]

@[handle2]

@[handle2]

[same structure]
[相同结构]

Benchmark Verdict Key

基准结论说明

  • ✅ Healthy engagement — safe to partner with
  • ⚠️ Below benchmark — investigate further before committing budget
  • ❌ Suspicious patterns — likely inflated audience, do not recommend
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  • ✅ 参与度健康 — 可安全合作
  • ⚠️ 低于基准 — 投入预算前需进一步调查
  • ❌ 模式可疑 — 受众可能注水，不推荐合作
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Troubleshooting

故障排除

Private accounts: Instagram scraper cannot access private profiles. Skip these. Low post count: Accounts with <10 recent posts have unreliable ER stats — flag this. Reels vs. posts: Reels typically get 2-3x more engagement than static posts. If the account posts mostly Reels, adjust expectations.
私人账号： Instagram爬虫无法访问私人主页，请跳过此类账号。 帖子数量过少： 近期帖子少于10篇的账号，其参与率数据不可靠 — 需标记此情况。 短视频（Reels） vs. 普通帖子： Reels的参与度通常是静态帖子的2-3倍。如果账号主要发布Reels，需调整预期。