# TikTok engagement rate vs the norm for its size

**Use case:** 

Engagement rate by views and by followers, views per follower, consistency and posting rate for each account, next to what is usual for creators of the same size.

## Input

```json
{
  "usernames": [
    "duolingo",
    "ryanair",
    "chipotle"
  ],
  "videosToAnalyze": 10,
  "followersToSample": 200,
  "commentsToAnalyze": 150
}
```

## Output

```json
{
  "username": {
    "label": "Username"
  },
  "followerTier": {
    "label": "Size"
  },
  "followers": {
    "label": "Followers",
    "format": "number"
  },
  "following": {
    "label": "Following",
    "format": "number"
  },
  "likes": {
    "label": "Likes",
    "format": "number"
  },
  "videos": {
    "label": "Videos",
    "format": "number"
  },
  "engagementRatePct": {
    "label": "Engagement rate % (by views)",
    "format": "number"
  },
  "engagementRateNormPct": {
    "label": "Usual for size %"
  },
  "engagementVsNorm": {
    "label": "Engagement vs usual"
  },
  "engagementRateByFollowersPct": {
    "label": "Engagement rate % (by followers)",
    "format": "number"
  },
  "engagementRateByFollowersNormPct": {
    "label": "Usual for size %"
  },
  "viewsToFollowersPct": {
    "label": "Views to followers %",
    "format": "number"
  },
  "viewsToFollowersNormPct": {
    "label": "Usual for size %"
  },
  "viewsVsNorm": {
    "label": "Views vs usual"
  },
  "medianViews": {
    "label": "Median views",
    "format": "number"
  },
  "avgViews": {
    "label": "Average views",
    "format": "number"
  },
  "avgLikes": {
    "label": "Average likes",
    "format": "number"
  },
  "avgComments": {
    "label": "Average comments",
    "format": "number"
  },
  "avgShares": {
    "label": "Average shares",
    "format": "number"
  },
  "avgSaves": {
    "label": "Average saves",
    "format": "number"
  },
  "likesPerViewPct": {
    "label": "Likes per view %",
    "format": "number"
  },
  "viewsConsistency": {
    "label": "Views consistency"
  },
  "postsPerWeek": {
    "label": "Posts per week",
    "format": "number"
  },
  "daysSinceLastPost": {
    "label": "Days since last post",
    "format": "number"
  },
  "videosAnalyzed": {
    "label": "Videos analyzed",
    "format": "number"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The TikTok Fake Followers Checker: Influencer Audit & Engagement Actor is ready to run with the inputs already set for "TikTok engagement rate vs the norm for its size". [Sign up or log in](https://console.apify.com/create-task-from-example/TPmlCn12BU5gmHTnh) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "TikTok engagement rate vs the norm for its size" task

The TikTok Fake Followers Checker: Influencer Audit & Engagement Actor will start running based on the input automatically. You can also change all the input fields, like tiktok usernames or profile links, recent videos to analyze, and more, to match your specific use case.

### 3. Receive the "TikTok engagement rate vs the norm for its size" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as username, size, followers, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "TikTok engagement rate vs the norm for its size" task via API](https://apify.com/moggedhq/tiktok-fake-followers-checker.md).

## About this Actor

This example demonstrates how to use [TikTok Fake Followers Checker: Influencer Audit & Engagement](https://apify.com/moggedhq/tiktok-fake-followers-checker.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/moggedhq/tiktok-fake-followers-checker.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/moggedhq/tiktok-fake-followers-checker.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).