Instagram Leads Scraper

🚀 Instagram Lead Generator (HTML-based)

🔎 This Actor searches Google for Instagram profiles in your niche, opens each public profile in a lightweight HTML view, and extracts structured lead data (including emails).

🎯 It is designed to give you a small, highly relevant list of profiles per keyword, rather than scraping the entire Instagram graph.

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🎯 Goal

• 🔍 Find Instagram profiles in specific niches, optionally filtered by country and minimum follower count.
• 📧 Collect business emails when available, so you can build outreach lists.
• 📦 Produce clean, structured output in an Apify dataset ready for export to CSV, JSON, or direct API usage.

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⭐ Key Features

• 🌐 Google dork–based discovery: Uses carefully crafted Google queries to discover Instagram profiles for each keyword.
• 📬 Email-focused mode:
  • When collectEmails = true, it collect popular email plateforms (e.g. "gmail.com", "outlook.com") and the Actor only saves profiles where an email was found.
  • When collectEmails = false, the Actor saves all matching profiles, with or without emails.
• 🎚️ Per-keyword lead cap: Strictly respects maxLeadsPerKeyword per keyword so you control volume and cost.
• ⚡ HTML-only scraping: Loads only the main document for each profile to minimize proxy bandwidth.
• 🆓 Free Tier Available: GET 100 EMAILS FOR FREE per run! Perfect for testing or small campaigns.

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⚙️ Input Parameters

🏷️ Parameter	🧩 Type	✅ Required	🔢 Default	📝 Description
keywords	string	Yes	—	Niche keywords describing the Instagram profiles you want to find. Format: comma-separated or newline-separated string. Example: fitness coach, personal trainer, crossfit gym
country	string	No	—	Optional location filter appended to each Google query as an exact phrase. Examples: "USA", "UK", "Canada", "Dubai", "Berlin". Leave empty for worldwide results.
maxLeadsPerKeyword	integer	No	50	Maximum number of profiles stored per keyword. If collectEmails = true, counts only profiles with extracted emails. If collectEmails = false, counts all profiles that pass the follower filter. Must be between 1 and 10000.
collectEmails	boolean	No	true	If true: adds email-domain modifiers to Google queries, extracts emails, and saves only profiles with emails. If false: skips email modifiers and extraction, saves all profiles regardless of email.

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📤 Output Data Format

Typical fields:

{
    "email": "njr.collabs@gmail.com",
    "follower_count": 1000000,
    "following_count": 329,
    "full_name": "Junior Braceiro",
    "total_post_count": 617,
    "profile_image_url": "https://scontent.cdninstagram.com/v/t51.82787-19/587766597_18538957498015230_6994925832083064221_n.jpg",
    "profile_url": "https://www.instagram.com/thenoahjr/",
    "bio": "1M Followers, 329 Following, 617 Posts - Junior Braceiro (@thenoahjr) on Instagram: \"Wellness | Lifestyle | Gratitude\nLiving with discipline, guided by faith\nMy Training @thenoahjrfit\n📩 njr.collabs@gmail.com\"",
    "username": "thenoahjr",
    "is_business": false,
    "source_keyword": "fitness"
  }

👥 Who is this Actor for?

• 🏢 Agencies and freelancers looking to build targeted Instagram outreach lists for specific niches (e.g. fitness coaches, real estate agents, local businesses).

• 🚀 Founders and growth marketers who want a repeatable way to discover relevant Instagram accounts with or without publicly listed emails.

• 🎯 Lead-generation specialists who want a “smart” scraper that prioritizes quality and relevance over raw volume.

• 📧 Cold Email Campaigns: Build targeted email lists for B2B services or product launches.

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🛠️ How to Use

1️⃣ Add your keywords

Use descriptive, niche-focused keywords like:

• fitness coach
• nutritionist
• real estate agent
• vegan restaurant

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2️⃣ Optionally set a country / location

Use a country or city name as you would type it into Google:

• USA, UK, Canada, Dubai, Berlin

Leave empty to search worldwide.

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3 Set maxLeadsPerKeyword

Controls how many profiles per keyword are saved to the dataset.

Behavior:

• If collectEmails = true: this is the maximum number of profiles with emails per keyword.
• If collectEmails = false: this is the maximum number of all profiles per keyword (regardless of email).

Typical values: 20–500 depending on how broad your keywords are.

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4 Decide whether to collect emails

✅ Recommended: start with collectEmails = true if you mainly care about email leads.

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5 Run the Actor

• ▶️ Click Run in Apify Console.
• 📊 After the run finishes, open the Dataset tab to inspect and download the results.

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📌 Input Parameters

📝 Notes:

• 📧 email is present only when collectEmails = true and an email is found on the profile.
• 🏷️ source_keyword and source_query help you trace where each lead came from.
• 📊 Counts (follower_count, following_count, total_post_count) are parsed from the public profile description and may be null if not available.

📤 You can export this dataset to CSV, JSON, XLSX, or access it programmatically via the Apify API.

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❓ FAQ

❔ Q: I’m not getting many results. What should I adjust first?

A: Try:

• 🔄 Broadening your keywords (e.g. fitness coach instead of vegan fitness coach berlin).
• ⬆️ Increasing maxLeadsPerKeyword.

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❔ Q: I’m getting profiles without emails even though I enabled “Collect emails”. Why?

A: With the current implementation, when collectEmails = true the Actor:

• 📡 Biases Google towards email-bearing profiles.
• 🔎 Extracts emails from profile text.
• 💾 Only saves profiles where an email was actually found.

If you see items without an email field, check that you are looking at a run where collectEmails was enabled and that you are inspecting the correct dataset.

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❔ Q: I want all profiles, even without emails. How do I do that?

A: Set collectEmails = false. The Actor will:

• 🚫 Stop adding email-domain modifiers to the Google queries.
• ⏭️ Skip email extraction.
• 💾 Save all profiles that pass the follower filter, up to maxLeadsPerKeyword per keyword.

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❔ Q: Why do follower numbers sometimes look rounded (e.g. 25,000 instead of 25,123)?

A: Instagram often displays follower numbers in compact form (e.g. 25.1K). The scraper parses these approximated values, so expect some rounding.