Extract likes, comments, shares, saves, and view counts from any Instagram Reel. Build engagement datasets for research and reporting.
Start Scraping with Apify →The core metrics are views, likes, comments, shares, and saves. Views measure reach. Likes measure immediate appeal. Comments measure conversation value. Shares measure viral potential. Saves measure long-term utility. Together they tell you not just how popular a reel is, but why it is popular. You should also scrape posting date, caption text, hashtag list, audio name, and creator handle to put the engagement numbers in context.
Opening each reel individually and writing down the numbers is feasible for ten reels. It is impossible for ten thousand. Even at a rate of one reel per minute, a thousand reels take sixteen hours. And by the time you finish, the numbers have changed. Engagement is dynamic — a reel posted yesterday might double its likes by tomorrow. Manual collection captures a snapshot, but you need a movie.
A cloud scraper visits reels in bulk, extracts all visible metrics, and stores them with timestamps. Run it once to build a baseline. Run it weekly to track growth. Run it daily to catch viral moments as they happen. The output is a structured dataset that you can query, visualize, and feed into machine learning models. You can track any public reel — your own, your competitors', or trending content from across the platform.
Export scraped engagement data to Google Sheets for team sharing, to Airtable for relational analysis, to Tableau for executive dashboards, or to Python notebooks for statistical modeling. Connect it to Slack for daily performance summaries. Pipe it into your CRM to identify high-engagement creators for partnership outreach. The data is only as valuable as the decisions it informs — make sure it flows to the people who can act on it.
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