Browser

benchmarking-instagram-influencer-engagement

Name: benchmarking-instagram-influencer-engagement
Author: apidojo-io

Benchmarks and compares Instagram influencer engagement rates using apidojo's Instagram scraper on Apify. Triggers when the user asks to: compare engagement rates of Instagram accounts, check if an influencer has real or fake followers, analyze Instagram account performance metrics, benchmark a creator against competitors on Instagram, find Instagram accounts with unusually high or low engagement, verify influencer stats before a paid partnership, or audit an Instagram account's post performance. Returns follower count, average likes, average comments, engagement rate, and recent post performance. Ideal for influencer agencies, brand marketing teams, and campaign performance analysts.

What it does

Benchmarking Instagram Influencer Engagement

The skill document

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 \
  --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": ["[handle1]", "[handle2]", "[handle3]"],
  "maxItems": 30
}

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": ["[handle1]", "[handle2]"],
    "maxItems": 30
  }'

Wait for SUCCEEDED. Fetch dataset.

Step 3: Calculate Engagement Metrics

For each account, from its posts array:

avg_likes = sum(post.likesCount) / num_posts
avg_comments = sum(post.commentsCount) / num_posts
engagement_rate = (avg_likes + avg_comments) / follower_count * 100
posting_frequency = num_posts / date_range_days

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

# Instagram Engagement Benchmark Report
Accounts analyzed: [N] | Posts per account: [30] | Date: [DATE]

## Summary Comparison
| Account | Followers | Avg Likes | Avg Comments | Eng. Rate | Tier Benchmark | Status |
|---------|-----------|-----------|--------------|-----------|----------------|--------|
| @[name] | [N]       | [N]       | [N]          | [X.X%]    | [good/great/low] | ✅/⚠️/❌ |

## Detailed Analysis

### @[handle1]
- Followers: [N] | Tier: [Nano/Micro/Mid/Macro/Mega]
- Avg Likes: [N] | Avg Comments: [N]
- **Engagement Rate: [X.X%]** — [above/at/below] benchmark for this tier
- Posting frequency: [X] posts/week
- Best-performing post: [url] ([N] likes, [N] comments)
- ⚠️ Anomalies: [none / describe if any]
- **Verdict:** [Recommended / Proceed with caution / Do not recommend]

### @[handle2]
[same structure]

## Benchmark Verdict Key
- ✅ Healthy engagement — safe to partner with
- ⚠️ Below benchmark — investigate further before committing budget
- ❌ Suspicious patterns — likely inflated audience, do not recommend

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.

Related skills

AdMapix

Fetch raw ad creative, app, ranking, and revenue data from AdMapix as structured JSON.

by fly0pants4.3k installs296 stars

Join meeting

Join a video meeting as an AI bot with voice, avatar, and screenshare across four operating modes.

by johnpatternai22 installs8 stars

diagram-generator

Generate and edit Draw.io, Mermaid, and Excalidraw diagrams from natural language using a structured JSON spec.

by nssa.io1.0k installs47 stars

SEO (Site Audit + Content Writer + Competitor Analysis)

Site audit, content writing, and competitor analysis for organic search rankings.

by Iván574 installs31 stars

Productivity

Find why your productivity system keeps failing, then apply the smallest fix — capacity math, bottleneck routing, durable local notes.

by Iván855 installs69 stars

Memory

Stores durable facts in a categorized, plain-markdown vault on disk, alongside your agent's built-in memory.

by Iván559 installs18 stars

More from apidojo-io

Browse all skills

tracking-twitter-thought-leaders

Identifies and tracks thought leaders and key voices in any industry on Twitter/X using apidojo's scrapers. Triggers when the user asks to: find the top voices in an industry on Twitter, identify thought leaders in a niche, discover who has the most influence in a topic area on X, find experts tweeting about a subject, build a list of influencers to engage with on Twitter, track who is gaining followers fastest in a category, or identify key opinion leaders in a field for PR or partnership outreach. Returns name, handle, follower count, engagement rate, bio keywords, and recent top tweets. Ideal for PR teams, community managers, and B2B content marketers.

by apidojo-io1 installs

building-twitter-prospect-lists

Builds targeted B2B prospect lists from Twitter/X profiles and posts using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find Twitter users with a specific job title or keyword in bio, build a list of founders or executives on Twitter, find people tweeting about a topic for outreach, identify potential customers on X, scrape Twitter profiles matching an ICP description, find decision-makers in a specific industry on Twitter, or export a list of leads from Twitter bios. Returns name, username, bio, follower count, location, and recent tweet samples per prospect. Ideal for B2B SDRs, growth hackers, founder-led sales teams, and partnership managers.

by apidojo-io1 installs

extracting-tiktok-comments-for-research

Extracts and analyzes TikTok comments from any video or creator using apidojo's TikTok Comments scraper on Apify. Triggers when the user asks to: scrape TikTok comments from a video, analyze what viewers say about a TikTok post, extract comment data for sentiment analysis, find top comments on a viral TikTok video, collect TikTok user feedback from comments, build a dataset of TikTok community reactions, study audience sentiment on TikTok content, or research what a target audience cares about from TikTok comments. Returns commenter username, comment text, likes on comment, reply count, and timestamp. Ideal for market researchers, brand managers, content creators, and academic researchers.

by apidojo-io1 installs

finding-trending-twitter-topics-for-content

Finds trending Twitter topics and conversations for content ideation using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find trending topics on Twitter for content, discover what is being discussed in a niche on X right now, identify Twitter conversations to join with content, find trending hashtags in an industry on Twitter, research what topics are generating engagement in a space on X, discover viral tweet themes for blog or video content, or find what your target audience is talking about on Twitter this week. Returns trending topics, tweet volume signals, top engagement posts, and content angle suggestions. Ideal for content marketers, social media managers, newsletter writers, and real-time content teams.

by apidojo-io1 installs

finding-youtube-micro-influencers

Finds YouTube micro-influencers and niche creators using apidojo's YouTube scraper on Apify. Triggers when the user asks to: find YouTube channels under 100k subscribers in a niche, discover emerging YouTube creators for sponsorship deals, find YouTubers covering a specific topic, identify high-engagement YouTube channels with smaller audiences, build a YouTube influencer outreach list, compare YouTube channel growth rates in a category, or find YouTube channels for affiliate or brand partnership programs. Returns channel name, subscriber count, total views, average views per video, and contact info. Ideal for brand sponsors, podcast networks, and affiliate marketing teams.

by apidojo-io1 installs

finding-youtube-sponsorship-candidates

Finds YouTube channels suitable for brand sponsorships using apidojo's YouTube scraper on Apify. Triggers when the user asks to: find YouTube channels to sponsor, discover YouTubers who accept brand deals in a niche, identify YouTube influencers for mid-roll or integration sponsorships, find channels that already run sponsors in a product category, research YouTube sponsorship opportunities for a brand, identify high-CPM YouTube audiences for B2B or SaaS sponsorships, or build a YouTube outreach list for a sponsorship campaign. Returns channel name, subscriber count, avg views, engagement rate, niche, and sponsorship history. Ideal for brand partnerships managers, SaaS marketing teams, and sponsorship agencies.

by apidojo-io1 installs