Back in January, I pushed a LinkedIn Employee Scraper to the Apify Store. No budget. No influencer shoutouts. No Product Hunt launch. Just me, a Node.js actor, and a lot of stubborn optimism.
Three months later: 250 users, 2,665 runs, and a Rising Star badge. Here's what actually worked.
The tool itself
Quick context: the scraper takes a LinkedIn company URL and pulls employee data (names, titles, locations) using Puppeteer. It runs on Apify's platform at $0.005 per run through pay per event pricing. Nothing groundbreaking technically, but it solves a real pain point for recruiters, sales teams, and market researchers who need bulk LinkedIn data without sitting there copying names manually.
What drove the growth
1. A README that actually helps people
This sounds boring, but I'm convinced it's the #1 reason the scraper got traction. Most actors on the Apify Store have thin READMEs with maybe a paragraph and a screenshot. I wrote mine like documentation for a product:
- Real input/output examples with actual JSON
- A "common use cases" section so people could see themselves using it
- Troubleshooting tips for the issues I knew they'd hit
- Clear pricing breakdown so nobody felt tricked
The Apify Store has its own search and discovery. A well written README with the right keywords means your actor shows up when someone searches "linkedin scraper" or "employee data." Free SEO, basically.
2. Replying to people on X who needed LinkedIn data
I searched Twitter for things like "scrape linkedin employees" and "linkedin data extraction" and just... replied. Not with a sales pitch. I'd answer their question first, then mention that I built something for this exact problem.
Most people ignored it. Some clicked through. A few became regular users. The conversion rate was tiny, but it cost me nothing except time. And those users told other users.
3. Writing about it on dev.to
My first dev.to article about web scraping brought in a handful of curious developers. Not massive traffic, but developers who find your tool through a technical article tend to actually use it. They're already in problem solving mode.
The Rising Star badge
After crossing a certain threshold of users and runs, Apify gave the actor a Rising Star badge. I don't know the exact criteria, but it shows up on the store listing and probably helps with trust. People are more likely to try something that looks validated.
It also felt good, not gonna lie. When you're building solo with zero budget, small wins matter.
What I'd do differently
I should have started writing content earlier. The first two months were slow because I was just waiting for the Apify Store algorithm to do its thing. Once I started actively putting the tool in front of people (X replies, articles, forum answers), growth picked up noticeably.
I also wish I'd added more output format options sooner. A few early users asked for CSV export, and I dragged my feet on it. Every feature request from a real user is a signal. Ship it fast.
The numbers
| Metric | Value |
|---|---|
| Total users | 250 |
| Total runs | 2,665 |
| Price per run | $0.005 |
| Ad spend | $0 |
| Time to Rising Star | ~10 weeks |
If you're thinking about building something
The Apify Store is a weirdly underrated distribution channel for developer tools. You build an actor, write a solid README, set reasonable pricing, and the platform handles hosting, scaling, billing, and discovery. It's not passive income overnight, but it compounds.
I've got 27 public actors on the store now. Some get zero traffic. Some surprise me. The LinkedIn scraper surprised me the most.
Check out my full catalog on the Apify Store if you want to see what I'm building.
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