benchmarking-instagram-influencer-engagement skill by apidojo-io

Benchmarking Instagram Influencer Engagement

Pulls recent post data for one or multiple Instagram accounts and calculates engagement rate, consistency, and performance benchmarks. Used to vet influencers before partnerships or compare accounts against each other.

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: Get account list and analysis goal
- [ ] Step 2: Run instagram-scraper for each account
- [ ] Step 3: Calculate engagement rate per account
- [ ] Step 4: Apply benchmark standards
- [ ] Step 5: Deliver comparison report

Step 1: Clarify Parameters

Ask the user for:

Instagram handles to analyze (up to 10 accounts; each handle without @)
Number of recent posts to analyze (default: 30 — enough for statistical reliability)
Analysis goal — vetting for partnership, competitive benchmarking, or general audit

Step 2: Run the Actor

Run separately for each account or pass as a list.

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

# Quick answer (prints table to chat)
node scripts/run_actor.js

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

REST API fallback:

Wait for SUCCEEDED. Fetch dataset.

Step 3: Calculate Engagement Metrics

For each account, from its posts array:

Benchmark standards by follower tier:

Tier	Followers	Good ER	Great ER
Nano	1K–10K	5–8%	>8%
Micro	10K–100K	3–5%	>5%
Mid	100K–500K	1.5–3%	>3%
Macro	500K–1M	1–2%	>2%
Mega	1M+	0.5–1%	>1%

Step 4: Flag Anomalies

Flag accounts with suspicious engagement patterns:

Low ER relative to tier: ER below 0.5% suggests inflated followers
Like/Comment imbalance: Very high likes but near-zero comments suggests like-buying
Engagement spikes: One post with 10x normal engagement followed by dead content suggests bought promotion
Sudden follower jump: Cross-reference last follower count change (if visible)

Step 5: Format Report

Output Format

Troubleshooting

Private accounts: Instagram scraper cannot access private profiles. Skip these. Low post count: Accounts with <10 recent posts have unreliable ER stats — flag this. Reels vs. posts: Reels typically get 2-3x more engagement than static posts. If the account posts mostly Reels, adjust expectations.