analyzing-tiktok-hashtag-performance …

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Analyzing TikTok Hashtag Performance

Scrapes TikTok hashtag pages to pull top-performing videos, engagement data, and creator information. Compares multiple hashtags side-by-side to identify which ones deliver the best reach for a given content category.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`startUrls`	array	Optional	`[]`	TikTok URLs — user profiles, hashtags, music pages, search, locations
`keywords`	array	Optional	`[]`	Search keywords/terms to find posts
`sortType`	string	Optional	`RELEVANCE`	Sort order for keyword results: `RELEVANCE`, `MOST_LIKED`, `DATE_POSTED`
`location`	string	Optional	—	ISO 3166-1 alpha-2 country code for regional filtering (e.g. `US`, `GB`)
`maxItems`	number	Optional	Unlimited	Maximum posts to return across the run
`includeSearchKeywords`	boolean	Optional	`false`	Add the matched search keyword field to each post
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Define hashtags to analyze
- [ ] Step 2: Run tiktok-scraper for each hashtag
- [ ] Step 3: Calculate hashtag-level metrics
- [ ] Step 4: Identify top content and creators
- [ ] Step 5: Deliver strategy recommendations

Step 1: Clarify Parameters

Ask the user for:

Hashtags to analyze — up to 10 (without #)
Posts per hashtag (default: 30 — enough for reliable stats)
Goal — choosing hashtags for a post, auditing a competitor's hashtag strategy, or general research

If the user hasn't provided hashtags yet and wants recommendations, ask for:

Content niche (e.g., "fitness", "cooking", "personal finance")
Then generate a mix of: 2 mega hashtags (100M+ views), 3 mid-tier (10M–100M), 3 niche (1M–10M), 2 micro (<1M)

Step 2: Run the Actor Per Hashtag

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

bash

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tiktok-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~tiktok-scraper"
Input:
{
  "keywords": ["[hashtag1]", "[hashtag2]", "[hashtag3]"],
  "shouldDownloadCovers": false
}

REST API fallback:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "keywords": ["[hashtag1]", "[hashtag2]", "[hashtag3]"]
  }'

Wait for SUCCEEDED. Fetch dataset.

Step 3: Calculate Hashtag Metrics

For each hashtag, from its video results:

total_views_sampled = sum(video.playCount)
avg_views_per_video = total_views_sampled / num_videos
avg_likes_per_video = sum(video.diggCount) / num_videos
avg_comments_per_video = sum(video.commentCount) / num_videos
engagement_rate = (avg_likes + avg_comments) / avg_views * 100
competition_level = num_videos_per_day (estimate from timestamps)

Use challengeInfo.stats.videoCount (if available) as total hashtag size proxy.

Step 4: Identify Top Content and Creators

For each hashtag, surface:

Top 3 videos by play count (with creator handle and video URL)
Top 3 creators by frequency in the hashtag's top content
Common content formats in top videos (based on descriptions/captions)

Step 5: Format Output

Output Format

# TikTok Hashtag Performance Analysis
Hashtags analyzed: [N] | Posts sampled per hashtag: [30] | Date: [DATE]

## Hashtag Comparison Table

| Hashtag | Avg Views | Avg Likes | Eng. Rate | Competition | Verdict |
|---------|-----------|-----------|-----------|-------------|---------|
| #[name] | [N]       | [N]       | [X.X%]    | [Low/Med/High] | [Use / Test / Avoid] |

## Detailed Breakdown

### #[hashtag1]
- Avg views per post: [N]
- Engagement rate: [X.X%]
- Competition level: [Low / Medium / High] — approx [N] new posts/day
- Top video: "[creator]" — [N] views | [url]
- Dominant content format: [e.g., tutorial, reaction, storytelling]
- **Recommendation:** [Use as primary / Layer with broader tags / Avoid — too saturated]

### #[hashtag2]
[same structure]

## Recommended Hashtag Strategy
For maximum reach on [CONTENT NICHE], use this combination:
- Primary (1-2 hashtags): [#hashtag] — broad reach driver
- Secondary (2-3 hashtags): [#hashtag] — niche relevance
- Micro (1-2 hashtags): [#hashtag] — community engagement

## Top Creators in These Hashtags
[Creators who appear most in top-performing content across all analyzed hashtags]
1. @[handle] — [N] top videos found | [N] followers

Troubleshooting

Very low view counts: Hashtag may be misspelled or very new. Verify spelling and try alternate versions. All results look the same: Mega-hashtags (#fyp, #foryou) surface algorithmically promoted content, not organic. Use niche hashtags for better signal. Engagement rate seems too high or low: Engagement rate varies heavily by content type — compare within the same content format for fair benchmarking.

analyzing-tiktok-hashtag-performance