How can I build a workflow in Make to connect real estate agents to buyers by scraping Zillow and Craigslist, considering recent platform blocks?
Zillow and Craigslist have blocked simple HTTP scraping from Make, so the old method no longer works. To scrape them now you need custom browser automation (e.g., Puppeteer or Playwright) to bypass IP blocks and obfuscation. I recommend learning via Appify’s web scraping academy (JavaScript‑focused) or using a pre‑built Zillow scraper from their store for basic data. Once you have the scraped items, connect them to an email module in Make to outreach buyers. Note that extracting email/phone numbers remains unreliable with current scrapers.
He explains that if you're targeting real estate agents (B2C), you rarely go B2C; usually you work B2B. For B2B, scrape property developers or companies that do multifamily home acquisition, then sell them your property‑scraping service and deliver the data via a dashboard. If you truly need B2C leads for agents, consider a speed‑to‑lead system: as soon as an agent shows interest, immediately SMS or call them to set up a meeting. He also notes that the only reliable way to get this data is to scrape property developers and sell the service to them, or use a speed‑to‑lead approach for agents.
I've launched a campaign for real-estate brokers. One system is a BTOC outreach where you offer to buy people's homes—you need a pre-existing list of owners. The second is a custom AI follow-up system that nurtures old leads on both the buy and sell sides. You get the agent's CRM (most are basic) and, ideally, connect via webhook or API to poll contacts weekly, monthly, or yearly. Instead of sending generic templated messages like 'Hello Peter, thank you for purchasing a home with us last year...', you can customize follow-ups—for example, an SMS that says, 'Hey Peter, hope you're settling in well in your new neighborhood. Just checking in to make sure everything's okay with your new home.' This personalized approach feels higher-value, builds more goodwill, and can be implemented today as a sellable service.
I’d use Apollo, which returns phone numbers natively, and pair it with Lead Magic for enrichment. Set up your Apollo search with terms like “landscaper” and the city you’re targeting. You can start with the whole U.S. if you need scale. For cleaning, you can strip whitespace, prepend a plus‑one if needed, and run the list through a simple script or GPT‑3.5 to format numbers in seconds. I don’t recommend cold‑calling or SMS; instead send cold emails to qualify interest, then follow up with a call once they’re engaged.
To gather leads on Apollo for your target niches, start by building a list in Apollo (e.g., real estate agencies, hiring agencies, digital marketing agencies). Export that list’s URL and paste it into a scraper like Apify (or Ampify) to pull the data and extract email addresses—this is cheaper than scraping LinkedIn directly. If your initial list is too small (e.g., only 50 leads), broaden the filters (remove overly specific sectors) to increase volume. Export the results to CSV, then run them through a workflow that combines the emails with profile fields (such as headline, title) and uses AI to generate personalized one‑line icebreakers. Finally, feed those icebreakers and emails into a platform like Instantly to automate your outreach.