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analyzing-competitor-instagram-content-strategy

Name: analyzing-competitor-instagram-content-strategy
Author: apidojo-io

Analyzes a competitor's Instagram content strategy and performance using apidojo's Instagram scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on Instagram, benchmark a competitor's Instagram engagement, see what content types perform best for a competitor on Instagram, reverse-engineer a competitor's Instagram content calendar, identify content gaps vs. a competitor on Instagram, compare posting frequency or content themes, or understand why a competitor's Instagram is growing. Returns post frequency, content type mix, top-performing posts, hashtag strategy, and engagement benchmarks. Ideal for social media managers, brand strategists, and content marketing teams.

What it does

Analyzing Competitor Instagram Content Strategy

The skill document

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

Parameter	Type	Required	Default	Notes
`startUrls`	array	✅	`[]`	Instagram URLs — profiles, hashtags, locations, audio pages, reels
`until`	string	Optional	—	Scrape posts until this date (YYYY-MM-DD)
`maxItems`	number	Optional	Unlimited	Maximum posts to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

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.

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