Analyzing Competitor YouTube Content Strategy

Reverse-engineers a competitor's YouTube channel by analyzing their last 20-50 videos. Identifies which video topics, formats, and lengths drive the most views and engagement.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
startUrls	array	Optional	[]	YouTube URLs — channels, playlists, Shorts, search results
youtubeHandles	array	Optional	[]	YouTube channel handles (e.g. @kurzgesagt)
getTrending	boolean	Optional	false	Retrieve trending videos
keywords	array	Optional	[]	Search keywords
gl	string	Optional	us	Country code for results (e.g. US, GB)
hl	string	Optional	en	Language code (e.g. en, de)
uploadDate	string	Optional	all	Upload date filter: any, hour, today, week, month, year
duration	string	Optional	all	Duration filter: any, short, long
features	string	Optional	all	Feature filter: 4k, hd, live, cc, 3d, hdr, etc.
sort	string	Optional	r	Sort order for search results
maxItems	number	Optional	Unlimited	Maximum videos to return
customMapFunction	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Scrape competitor's recent videos
- [ ] Step 2: Classify video types and topics
- [ ] Step 3: Calculate performance metrics
- [ ] Step 4: Identify patterns and top performers
- [ ] Step 5: (Optional) Compare with own channel
- [ ] Step 6: Deliver strategy report

Step 1: Scrape Channel

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

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

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

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~youtube-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~youtube-scraper"
Input:
{
  "startUrls": [{"url": "[COMPETITOR_CHANNEL_URL]"}],
  "maxResults": 30,
  "type": "video"
}

REST API fallback:

curl -X POST   "https://api.apify.com/v2/acts/apidojo~youtube-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"startUrls": [{"url": "https://www.youtube.com/@competitorhandle"}], "maxResults": 30, "type": "video"}'

Step 2: Classify Videos

video_type:
  TUTORIAL = title contains "how to", "step by step", "guide", "tutorial"
  LIST = title contains "top [N]", "[N] best", "[N] things", "mistakes"
  REVIEW = title contains "review", "tested", "worth it", "vs"
  THOUGHT_LEADERSHIP = opinion, trend analysis, "the future of", "why"
  NEWS = title contains news, announcement, breaking
  CASE_STUDY = "how [brand] grew", "inside", "behind the scenes"

video_length_tier:
  SHORT = < 5 min
  MEDIUM = 5–15 min
  LONG = 15–30 min
  DEEP_DIVE = > 30 min

Step 3: Performance Metrics

view_ratio = viewCount / subscriberCount  # corrected by subscriberCount at time of analysis

engagement_rate = (likeCount + commentCount) / viewCount * 100

publish_cadence = total_videos / (date_range_weeks)  # videos per week

Top performer: Sort by viewCount; also identify hidden gems where engagement_rate > 2× channel average despite lower views.

Step 4: Edge Cases

• Channel is very new (< 6 months, < 20 videos): Reduce videos_to_analyze to all available; note limited sample
• Views are all very low (< 1K per video): Channel may be struggling or niche is very small; provide absolute numbers, not just ratios
• One mega-viral video skews averages: Report median views alongside mean; flag outlier
• Channel posts in multiple languages: Group by language; analyze each cohort separately

Output Format

# Competitor YouTube Strategy: [CHANNEL_NAME]
Videos analyzed: [N] | Subscribers: [N] | Avg Views: [N] | Avg Eng Rate: [X%] | Date: [DATE]

## Content Mix
| Video Type | % of Videos | Avg Views | Avg Eng Rate | Best Example |
|-----------|------------|-----------|-------------|-------------|
| Tutorial | [X%] | [N] | [X%] | [title] |
| List | [X%] | [N] | [X%] | |
| Review | [X%] | [N] | [X%] | |

## Video Length Performance
| Length Tier | % | Avg Views | Avg Eng Rate |
|------------|---|-----------|-------------|
| Short (< 5min) | [X%] | [N] | [X%] |

## Top 5 Videos (by Views)
| # | Title | Type | Views | Likes | Comments | Length | Eng Rate |
|---|-------|------|-------|-------|----------|--------|---------|

## Publishing Cadence
Videos/week: [X] | Best day to publish: [Day] | Monthly trend: [↑/↓/flat]

## Key Opportunities vs. Their Strategy
1. [Gap: e.g. "No case studies — this format gets 3× their avg views when they do it"]
2. [Opportunity]
3. [Their weakness]

Troubleshooting

Scraper returns channel page but no videos: Try direct video search for channel name as keyword; some channel URLs need the /videos suffix. View counts are very low for an established channel: Channel may have declined — check publish date of latest video; may be dormant. Can't determine channel subscriber count from scrape: Use the video view-to-video count ratio as a proxy for channel health when subscriber count is unavailable.