# Instagram Influencer Analytics Scraper (`seemuapps/instagram-influencer-analytics-scraper`) Actor Compute engagement rate, content mix, and audience signals for any public Instagram creator from a list of usernames. - **URL**: https://apify.com/seemuapps/instagram-influencer-analytics-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 5 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $35.00 / 1,000 creator analyzeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Instagram Influencer Analytics Scraper **Calculate Instagram engagement rate, posting cadence, content mix, sponsored posts, and top-performing content for any public creator profile.** No login. No cookies. No proxies. Just paste a list of usernames and get decision-ready creator analytics in seconds. Built for talent teams, brand marketers, influencer agencies, and growth analysts who need consistent influencer metrics at scale — not raw post dumps you have to clean up yourself. ### Why use this Actor - **Engagement rate done right** — separate engagement rates for feed posts (vs. followers) and Reels (vs. views), because mixing them produces meaningless numbers - **Sponsored content detection** — see which brands a creator has already worked with before you send your outreach - **Authenticity signals** — save-to-like and comment-to-like ratios catch fake or disengaged audiences that follower count alone misses - **Posting cadence and recency** — instantly filter out creators who've gone dormant - **Top-performing posts surfaced** — three highest-engagement posts with URLs, so you can reference them in your brief - **Batch processing** — submit hundreds of usernames in one run; failed handles produce a single error record without stopping the rest - **Clean structured output** — one JSON record per creator, ready for Google Sheets, Airtable, BI tools, or AI workflows ### What you get Every public Instagram username produces one dataset record with these sections: - **Profile snapshot** — follower count, following count, post count, verified status, full name, bio link, profile picture URL - **Engagement metrics** — engagement rate by followers, engagement rate by views, average likes per post, average comments per post, total interactions, interactions per 1,000 followers - **Posting cadence** — last post date, days since last post, posts per week, posts per month - **Content mix** — feed photos, feed videos, feed carousels, Reels count - **Quality ratios** — save-to-like ratio, comment-to-like ratio - **Sponsored content** — paid partnership count, ad keyword count, sponsored percentage, list of recent brand partners - **Topic signals** — top hashtags and top @mentions (collab partners, brands, recurring themes) - **Top 3 posts** — highest-engagement posts with URLs, captions, like and comment counts, view counts - **Coverage signals** — data-quality flags so you know how reliable each metric is Export to JSON, CSV, Excel, XML, or HTML directly from the Apify console. ### Use cases - **Influencer vetting** — calculate engagement rate, check authenticity signals, and see brand-deal history before reaching out - **Campaign planning** — compare 50+ creators side-by-side with consistent metrics in one CSV - **Talent discovery and shortlisting** — filter by posts-per-week, engagement rate, and Reels skew to find creators who match your brief - **Competitive intelligence** — track how brand-aligned creators are posting, what they're tagging, and which competitors are sponsoring them - **Creator scorecards and reporting** — build internal dashboards with standardized metrics across hundreds of accounts - **Data for AI agents and LLMs** — clean structured JSON that drops directly into prompt context for outreach copy, scoring, or recommendations ### How to use 1. Open the Actor and paste one or more public Instagram usernames into the **Usernames** field (with or without `@`) 2. Optionally adjust **Feed posts sample size** (default 72) and **Reels sample size** (default 36) — larger samples produce more reliable averages 3. Click **Start** 4. Open the **Dataset** tab once the run finishes — one record per username, ready to export Private accounts and missing handles produce a `{ username, error }` record so a single bad input never stops the rest of the batch. ### Sample output ```json { "username": "natgeo", "creator": { "id": 787132, "username": "natgeo", "fullName": "National Geographic", "followersCount": 269597381, "followsCount": 194, "verified": true, "postsCount": 31620, "profilePicUrl": "https://…", "external_url": "http://visitstore.bio/natgeo" }, "engagement": { "overall_interactions": 1791003, "clips_engagement_rate_by_views": 5.95, "feed_avg_engagement_rate_by_followers": 0.03, "clips_avg_views": 2165395.5, "feed_avg_engagement": 84806, "clips_avg_engagement": 128888.5, "posts_contribution_pct": 67, "clips_contribution_pct": 33, "engagement_skew_clips_over_feed": 1.52, "avg_interactions_per_content": 99500.17, "interactions_per_1k_followers": 6.643 }, "cadence": { "last_post_at": "2026-05-08T19:09:25.000Z", "days_since_last_post": 0.4, "posts_per_week": 5.19, "posts_per_month": 22.26, "sample_window_days": 24.3 }, "content_mix": { "feed_photos": 4, "feed_videos": 7, "feed_carousels": 1, "reels": 6 }, "quality": { "avg_save_to_like_ratio": 0.0428, "avg_comment_to_like_ratio": 0.0077, "save_data_coverage": 0.333 }, "sponsored": { "paid_partnership_count": 1, "caption_ad_keyword_count": 1, "total_sponsored": 1, "sponsored_pct": 5.6, "recent_partners": ["rolex"] }, "topics": { "top_hashtags": [ { "tag": "tucciinitaly", "count": 2 }, { "tag": "americasnationalparks", "count": 2 } ], "top_mentions": [ { "handle": "disneyplus", "count": 9 }, { "handle": "hulu", "count": 4 } ] }, "top_posts": [ { "url": "https://www.instagram.com/p/DYFBt6HAP1O/", "product_type": "clips", "taken_at": "2026-05-08T13:01:31.000Z", "like_count": 501273, "comment_count": 6320, "view_count": 5113029, "caption_excerpt": "Wishing a Happy Birthday to Sir David Attenborough…" } ], "coverage": { "total_items": 18, "clips_count": 6, "feed_count": 12, "clips_view_coverage": 1, "feed_view_coverage": 0.583 } } ```` ### Metric reference #### Engagement metrics | Field | What it tells you | |---|---| | `feed_avg_engagement_rate_by_followers` | Industry-standard feed engagement rate (likes + comments per post ÷ followers, %) | | `clips_engagement_rate_by_views` | Reels engagement rate using views as the denominator (the correct one for short-form video) | | `clips_avg_views` | Average view count across the Reels sample — a key reach signal | | `engagement_skew_clips_over_feed` | Reels-to-feed engagement ratio. Above 1.0 = Reels-dominant. Below 1.0 = feed-dominant. | | `interactions_per_1k_followers` | Total interactions normalized per 1,000 followers — best for comparing creators of different audience sizes | Engagement rate benchmarks by tier (industry-standard): - **Nano** (< 10K followers): 3–6% feed engagement is typical - **Micro** (10K–100K): 1–3% - **Macro** (100K–1M): 0.5–1.5% - **Mega** (1M+): 0.1–0.5% A feed engagement rate well below the band for the creator's tier often indicates an inflated or disengaged audience. #### Cadence `days_since_last_post` is the recency check most marketers gate on first. Above 14 days usually means the creator has slowed down — filter these out before deeper analysis. `posts_per_week` and `posts_per_month` are derived from the timestamp range of the analyzed sample. #### Quality ratios — the real authenticity signals Follower count and even engagement rate can be inflated. Two ratios are much harder to fake: - **`avg_save_to_like_ratio`** — share of likes that converted into saves. High ratios (>0.05) signal utility content the audience comes back to. - **`avg_comment_to_like_ratio`** — share of likes that turned into comments. High ratios (>0.02) signal genuinely engaged audiences vs. passive likers. A creator with strong save and comment ratios has a real audience. Use these alongside engagement rate, not in place of it. `save_data_coverage` reports the share of analyzed posts that returned a save count — treat the save ratio with caution if coverage is below 0.5. #### Sponsored content - **`paid_partnership_count`** — posts using Instagram's official paid-partnership tag (`is_paid_partnership: true` or `sponsor_tags`) - **`caption_ad_keyword_count`** — posts whose caption contains `#ad`, `#sponsored`, `[ad]`, "paid partnership with", etc. - **`total_sponsored`** — union of the two (deduplicated) - **`sponsored_pct`** — share of analyzed sample that is sponsored content - **`recent_partners`** — handles of brands tagged via Instagram's official paid-partnership system, ordered by frequency A creator with `sponsored_pct` above ~30% is heavily monetized. Below 5% likely hasn't done many brand deals. The `recent_partners` list is the fastest way to vet brand fit and exclusivity conflicts. #### Topics - **`top_hashtags`** — most-used hashtags across the sample. Reveals niche, ongoing campaigns, and themed content series. - **`top_mentions`** — most-tagged @ handles. Reveals collab partners, recurring brand relationships, and the creator's network. #### Top posts Three highest-engagement posts in the sample, ranked by likes + comments. Each entry has the post URL, product type, timestamp, raw counts, and a caption excerpt — useful when briefing creators ("we noticed your Reel about X performed exceptionally well"). #### Coverage signals Read this before citing the metrics: - **`clips_view_coverage`** — share of Reels for which view data was returned (0–1). Below 1 means the Reels engagement rate was computed on partial view data. - **`feed_view_coverage`** — share of feed posts with view data. Often `0` because Instagram does not consistently expose feed view counts. This is expected behavior, not a bug. ### FAQ **Do I need an Instagram account or login?** No. The Actor works on public profile data only — no Instagram account, cookies, or login required. **How do I check if my audience or competitor's audience is real?** Run their handle through this Actor and look at three things together: `feed_avg_engagement_rate_by_followers` (compared to the tier benchmarks above), `avg_comment_to_like_ratio` (>0.02 is healthy), and `avg_save_to_like_ratio` (>0.05 signals utility content). All three low = likely fake or disengaged followers. **Can this calculate Instagram engagement rate at scale?** Yes. Submit hundreds of usernames in a single run. Each produces one row, so the dataset CSV opens directly as a creator leaderboard. **Does it work for private accounts?** No. Private profiles return an `error` record. The Actor only analyzes public Instagram data. **Can it detect sponsored or branded content?** Yes — both Instagram's official paid-partnership tag and caption keywords (`#ad`, `#sponsored`, `[paid partnership]`, etc.). See the `sponsored` section of the output for partner handles and counts. Note that undisclosed sponsored content cannot be detected by any tool. **How fresh is the data?** Live — every run hits Instagram's current public data. Engagement metrics reflect the most recent feed posts and Reels in the sample window, not lifetime account performance. **Can I use this output with AI agents or LLMs?** Yes. The structured JSON output drops cleanly into prompt context for outreach generation, creator scoring, or recommendation pipelines. Each creator is one self-contained record. **How many posts does it analyze per creator?** Up to 72 feed posts and 36 Reels by default. Both are configurable per run — larger samples produce more reliable averages but cost more. **What input formats are supported?** A list of Instagram usernames, with or without the `@` prefix. Whitespace and case are normalized automatically. **Do I get the post URLs?** Yes — the `top_posts` section includes the full Instagram URL for each highest-engagement post, ready to share in a brief or pitch deck. ### Tips for getting the most out of results - Compare `feed_avg_engagement` and `clips_avg_engagement` to decide which format to brief a creator for - Use `interactions_per_1k_followers` when comparing creators across very different audience sizes - Treat `clips_engagement_rate_by_views` with caution if `clips_view_coverage` is below 0.5 - Filter out creators with `days_since_last_post` above your acceptable cutoff before deeper analysis - High save-to-like AND high comment-to-like ratios is the strongest signal of an authentic, engaged audience - Check `recent_partners` before pitching — if a competitor brand appears, your offer may need to address exclusivity - Test on a small batch of 3–5 usernames before scaling to a large list ### Limitations - Only public Instagram profiles can be analyzed — private accounts return a logged failure record - Metrics are computed from the most recent feed posts and Reels in the sample window, not lifetime account performance - Instagram does not expose view counts on most feed posts, so `feed_view_coverage` is typically `0` - Save count data is only sometimes returned for older posts — `save_data_coverage` reports availability - Sponsorship detection works on captions and Instagram's official paid-partnership tag — undisclosed sponsored content cannot be detected by any tool ### Support For bugs, feature requests, or custom output shapes, use the **Issues** tab on this Actor's page. # Actor input Schema ## `usernames` (type: `array`): Public Instagram usernames to analyze (with or without @). Private accounts will produce a logged failure record. ## `feedSampleSize` (type: `integer`): Maximum number of recent feed posts to analyze per creator. Larger samples cost more HikerAPI calls. ## `clipsSampleSize` (type: `integer`): Maximum number of recent Reels to analyze per creator. Larger samples cost more HikerAPI calls. ## Actor input object example ```json { "usernames": [ "natgeo" ], "feedSampleSize": 72, "clipsSampleSize": 36 } ``` # Actor output Schema ## `results` (type: `string`): Each record has the shape { username, creator, engagement, cadence, content\_mix, quality, sponsored, topics, top\_posts, coverage } on success, or { username, error } on failure (e.g., private or missing accounts). # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "usernames": [ "natgeo" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/instagram-influencer-analytics-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "usernames": ["natgeo"] } # Run the Actor and wait for it to finish run = client.actor("seemuapps/instagram-influencer-analytics-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "usernames": [ "natgeo" ] }' | apify call seemuapps/instagram-influencer-analytics-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/instagram-influencer-analytics-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Influencer Analytics Scraper", "description": "Compute engagement rate, content mix, and audience signals for any public Instagram creator from a list of usernames.", "version": "1.0", "x-build-id": "unmSjI0dPwvGGz9zS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~instagram-influencer-analytics-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-instagram-influencer-analytics-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~instagram-influencer-analytics-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-instagram-influencer-analytics-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~instagram-influencer-analytics-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-instagram-influencer-analytics-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "usernames" ], "properties": { "usernames": { "title": "Usernames", "type": "array", "description": "Public Instagram usernames to analyze (with or without @). Private accounts will produce a logged failure record.", "items": { "type": "string" } }, "feedSampleSize": { "title": "Feed posts sample size", "minimum": 0, "maximum": 200, "type": "integer", "description": "Maximum number of recent feed posts to analyze per creator. Larger samples cost more HikerAPI calls.", "default": 72 }, "clipsSampleSize": { "title": "Reels (clips) sample size", "minimum": 0, "maximum": 200, "type": "integer", "description": "Maximum number of recent Reels to analyze per creator. Larger samples cost more HikerAPI calls.", "default": 36 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```