Analyzing Competitor Instagram Content Strategy

Reverse-engineers a competitor's Instagram content strategy by analyzing their last 50+ posts. Identifies what content formats, themes, and posting patterns drive their highest engagement.

Prerequisites

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

Inputs

Workflow

Progress:
- [ ] Step 1: Scrape competitor's recent posts
- [ ] Step 2: Classify content types
- [ ] Step 3: Calculate engagement metrics per content type
- [ ] Step 4: Analyze posting patterns
- [ ] Step 5: (Optional) Compare to your account
- [ ] Step 6: Deliver strategy report

Step 1: Scrape Competitor Profile

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

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

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

# Save as JSON
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": ["[COMPETITOR_HANDLE]"],
  "maxItems": 50
}

REST API fallback:

curl -X POST   "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"usernames": ["competitor_handle"], "maxItems": 50}'

Step 2: Content Classification

For each post, classify:

content_type:
  PRODUCT = post primarily shows product
  LIFESTYLE = product in context / aspirational
  EDUCATIONAL = tips, how-to, facts (carousel with text)
  SOCIAL_PROOF = testimonial, user tag, press feature
  ENTERTAINMENT = meme, trending audio, humor
  PROMOTIONAL = sale, discount, CTA-heavy
  BEHIND_SCENES = team, office, process

Format: IMAGE | VIDEO | CAROUSEL

Step 3: Calculate Metrics

engagement_rate = (likes + comments) / follower_count * 100

per_type_avg_engagement = avg(engagement_rate for all posts of that type)

content_type_share = count(posts of type) / total_posts * 100

Top performing post: highest (likes + comments * 3) — comments weighted higher as active signal.

Posting cadence:

posts_per_week = total_posts / (date_range_days / 7)
best_day = day_of_week with highest avg engagement
best_hour = hour_of_day with highest avg engagement (use post `timestamp`)

Step 4: Edge Cases

• Competitor has very few posts (< 20): Report available data; note low sample size; extend to 180-day window
• Engagement rate << 1%: Account may have bot followers or inactive audience; note this as "audience quality concern"
• All posts are product/promo: This competitor is over-indexed on promotional content — opportunity for content that educates or entertains
• Carousel shows as single image: Some scrapers return first image only; note when type = CAROUSEL for accurate content type count

Output Format

# Competitor Instagram Strategy: @[COMPETITOR_HANDLE]
Posts analyzed: [N] | Followers: [N] | Overall Eng Rate: [X%] | Date: [DATE]

## Content Mix
| Type | % of Posts | Avg Eng Rate | Best Post Example |
|------|-----------|-------------|------------------|
| Product | [X%] | [X%] | [post excerpt] |
| Lifestyle | [X%] | [X%] | |
| Educational | [X%] | [X%] | |

## Format Distribution
Images: [X%] | Carousels: [X%] | Videos/Reels: [X%]
Best format by engagement: [FORMAT] ([X%] eng rate)

## Top 5 Posts (by Engagement)
| # | Type | Format | Likes | Comments | Eng Rate | Caption Preview |
|---|------|--------|-------|----------|----------|----------------|

## Posting Cadence
Frequency: [X] posts/week | Best day: [Day] | Best hour: [HH:00]

## Key Observations
1. [Pattern observation — e.g. "Carousel educational posts get 2× engagement of product posts"]
2. [Observation]
3. [Opportunity gap]

Troubleshooting

Scraper returns only recent 12 posts: Instagram limits API access to recent posts. For 50-post analysis, run scraper and note actual count returned. Engagement rate seems wrong: Verify follower_count is current — scraper may return the follower count at time of scrape, which could differ from post-date count for historical posts. Competitor has very high engagement: Distinguish between genuine engagement and pods/bought engagement — genuine engagement shows variety in commenters; pod engagement shows the same accounts commenting repeatedly.