DEV Community: Bhojraj Pilaniya

How to Create an Instagram Business and Creator Research Dataset

Bhojraj Pilaniya — Sat, 29 Aug 2026 12:59:13 +0000

Instagram profiles contain useful public signals for creator discovery and business research: biographies, follower counts, business categories, websites, and publicly listed contact details. The challenge is turning a list of usernames into consistent records that can be filtered and reviewed.

Manual research works for a few profiles, but bulk projects need a stable schema and a repeatable process.

Start with a focused username list

Do not begin by collecting unrelated profiles. Build a source list around a niche, campaign, location, customer segment, or competitor set. A focused input makes the resulting dataset easier to qualify.

The Instagram Profile Scraper & Business Lead Extractor accepts plain usernames, @usernames, or full Instagram profile URLs. It can process multiple profiles in one run.

Example input:

{
  "usernames": [
    "openai",
    "instagram"
  ],
  "includeAboutSection": false
}

The normalized output includes username, profile URL, full name, biography, followers, following, post count, verification, business status, category, website, and public business email or phone when available.

Build qualification rules

Follower count alone is a weak qualification signal. Combine it with category, biography keywords, posting activity, verification, website availability, and public business status. For creator discovery, you may prioritize niche relevance over audience size. For local business research, category and external website can be more important.

Keep missing values as missing rather than treating them as negative signals. Many legitimate profiles do not publicly display contact information.

Export and review the results

Apify datasets can be exported in CSV, Excel, and JSON formats. They can also be consumed through the API for scheduled workflows. Before importing records into a CRM, deduplicate by username or profile URL and manually review the shortlisted profiles.

Use only publicly available information for legitimate research, respect privacy and platform requirements, and avoid unsolicited bulk messaging. The dataset is most valuable when it reduces repetitive research while leaving final qualification to a person.

Run the profile-research workflow on Apify: Instagram Profile Scraper & Business Lead Extractor.

How to Use LinkedIn Profile Posts for Better Sales Personalization

Bhojraj Pilaniya — Sat, 29 Aug 2026 12:51:14 +0000

Generic outreach often fails because it gives the recipient no reason to care. Public posts can provide relevant context about a prospect's current interests, projects, events, and professional priorities.

The goal is not to collect everything a person has posted. It is to identify a small number of recent, relevant signals that help a researcher understand the prospect before making contact.

Define the research question

Start with a list of target LinkedIn profile URLs and a clear purpose. You may want to track executives in a target account, follow industry experts, identify speakers discussing a topic, or prepare for an upcoming meeting.

Choose a recent time window and a reasonable maximum number of posts. This produces a manageable dataset and reduces irrelevant historical content.

The LinkedIn Profile Posts Scraper for Lead Research extracts public profile posts in bulk with text, dates, authors, post URLs, and engagement counts.

Example input:

{
  "profileUrls": [
    "https://www.linkedin.com/in/satyanadella/"
  ],
  "maxPostsPerUrl": 20,
  "postedLimit": "3months",
  "includeQuotePosts": true,
  "includeReposts": false
}

The output is deduplicated and stored in a consistent Apify dataset that can be exported or accessed through the API.

Turn posts into research signals

Classify each post into categories such as hiring, product, event, customer, partnership, leadership, or opinion. Retain the post URL so every signal can be verified in its original context.

For personalization, select one relevant post and explain why it connects to the intended conversation. Avoid pretending to know the person or using unrelated personal details. Useful personalization is specific, truthful, and professionally relevant.

Create a lightweight monitoring system

For a recurring target list, schedule the Actor and compare results by post URL. Send only new posts into a review queue or weekly digest. Engagement counts can help prioritize attention, but relevance should remain the main filter.

This workflow also supports thought-leader monitoring, executive activity tracking, content research, and meeting preparation. Used responsibly, it replaces repetitive browsing with a structured research feed while preserving human judgment.

Try it on Apify: LinkedIn Profile Posts Scraper for Lead Research.

How to Find High-Intent B2B Prospects in LinkedIn Post Comments

Bhojraj Pilaniya — Sat, 29 Aug 2026 12:50:19 +0000

Cold prospect lists tell you who matches a demographic profile. Comments can provide an additional signal: who actively engaged with a specific topic, product, event, or problem.

That does not make every commenter a lead. It does, however, create a useful starting point for research because the person has already shown contextual interest.

Choose the right source posts

The quality of the source post determines the quality of the resulting list. Look for posts discussing a specific business problem, industry event, product category, or technical decision. Broad motivational posts may attract engagement but rarely produce focused B2B intent.

Use posts from relevant companies, industry experts, event organizers, and communities. Start with a small sample and inspect the discussion before scaling the extraction.

Capture the context, not just the name

A useful comment dataset should retain the comment text, author name, headline, profile URL, publication date, likes, reply status, source post URL, and comment URL. Keeping the original text matters because it explains why the prospect was included.

The LinkedIn Post Comments Scraper & Engaged Leads collects those fields from one or more public post URLs and returns clean records in an Apify dataset.

Example input:

{
  "postUrls": [
    "https://www.linkedin.com/posts/example-post/"
  ],
  "maxCommentsPerPost": 100,
  "postedLimit": "month",
  "includeReplies": true
}

The output can be exported as CSV or JSON, or passed to another workflow through the API.

Score comments for relevance

After extraction, classify each comment by topic and intent. A thoughtful question, implementation problem, vendor comparison, or request for a recommendation is generally more valuable than a short congratulatory response.

Combine the comment context with the author headline and your ideal-customer criteria. Then prioritize records that match both the target role and a relevant discussion signal. This keeps the workflow focused on research rather than indiscriminate messaging.

Use engaged-lead data responsibly

Public engagement is not consent for mass outreach. Review each record, keep the original context, and personalize any follow-up. Respect platform terms, privacy requirements, and local marketing laws.

A good social-selling workflow helps a human understand the conversation. It should not turn every visible profile into an automated target.

Try the engaged-lead workflow on Apify: LinkedIn Post Comments Scraper & Engaged Leads.