A Three Channel Outbound Machine Using Claude Code and Google Maps for Local Leads
A $200 per month system that scrapes Google Maps for local business leads, enriches emails, and sends 2,000 cold emails per day on autopilot.
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The Strategy
Levi Munneke lays out a complete outbound system that uses Claude Code running on a cheap VPS to scrape Google Maps for local business leads, enrich contact emails, and feed them into a three channel outreach machine across email, LinkedIn, and Twitter. The core insight is that Google Maps has 265 million business listings and those businesses receive far fewer cold emails than the companies on Apollo getting bombarded with outreach. The scraping layer is built entirely through Claude Code prompts. You tell it to build a Python scraper that hits Google Maps for a specific service category and city, pulls business name, address, phone, website, rating, and review count, then crawls each website to extract the owner contact email. Two RapidAPI endpoints handle the data: Business Data Finder for the listings and Website Contacts Finder for the emails. The scraper runs on a cron job at 6 AM every morning, producing 500 to 2,000 fresh enriched leads daily. The email infrastructure uses 35 domains with 3 inboxes each (105 total) sending 2,000 emails per day through Instantly or Smartlead. Sequences are personalized based on Google Maps data, like noting a missing website or low review count. LinkedIn adds a warm layer with 30 to 40 soft connects per day. Twitter monitors locals discussing business problems for high intent DM opportunities. A Telegram bot sends daily stats. The math Levi projects: 1,600 to 1,700 daily touches across all three channels, roughly 50,000 per month, with a 2 to 6 percent reply rate producing 20 to 40 qualified discovery calls monthly. At a 25 percent close rate and $5K to $10K retainers, that is 5 to 10 new clients per month. Infrastructure cost is $200 to $400 per month total.
How It Works
Spin up a VPS on Hostinger or Hetzner for $7 to $12 per month running Ubuntu. Install Claude Code and tmux for persistent sessions that survive connection drops. Set up Tailscale VPN for remote access from phone or laptop.
Prompt Claude Code: "Build a Python scraper that hits Google Maps for [service category] in [city], pulls name, address, phone, website, rating, review count, then crawls each website to extract the owner contact email, outputs to CSV. Tell me which APIs from RapidAPI to use." Claude Code builds the scraper and identifies two RapidAPI endpoints: Business Data Finder and Website Contacts Finder.
Set the scraper to run on a cron job at 6 AM daily. Each run produces 500 to 2,000 fresh enriched local business leads.
Set up email infrastructure: 35 domains with 3 inboxes each (105 total inboxes) for 2,000 sends per day capacity.
Connect the output CSV to Instantly or Smartlead. Build sequences personalized from Maps data. Example hook: "Couldn't find a website attached to your Google profile so I built you a free demo, open to checking it out?"
Follow up sequences fire at 3, 5, 8, and 14 days for leads who show interest but do not reply. Auto qualify replies and book into your calendar.
LinkedIn warm layer: use Claude Code to cross reference Maps leads against LinkedIn. Send 30 to 40 soft connects per day at human volume. No pitching, just building familiarity before the email lands.
Twitter/X bonus channel: use Claude Code for keyword monitoring of locals discussing slow business, bad leads, or poor Google visibility. Queue DMs for high intent prospects.
Set up a Telegram bot for daily alerts showing sends, replies, and bookings.
Maintenance: tweak city and keyword targets when volume dips. Scale by adding new service categories and cities.
Results
These are projected numbers, not verified personal results. Levi projects 1,600 to 1,700 daily touches across three channels, approximately 50,000 monthly targeted touches, a 2 to 6 percent reply rate on hyper local personalized cold email producing 20 to 40 qualified discovery calls per month. At a 25 percent close rate and $5K to $10K retainers, that projects to 5 to 10 new agency clients per month. Infrastructure cost is $200 to $400 per month. One closed retainer covers years of overhead.
Our Take
The Google Maps angle is the real differentiator here. These are businesses that are not in Apollo and are not getting 40 identical outbound sequences a week. The data enrichment from Maps itself (low review count, missing website, no photos) gives you personalization hooks that feel like genuine observations rather than templated flattery. The three channel approach is aggressive but the math on volume checks out if deliverability holds. The main risk is that this is entirely projection based. Levi does not share his own client results or close rates from this exact system. The email infrastructure (35 domains, 105 inboxes) also requires careful warmup and deliverability management that is glossed over. Best suited for agency owners who already understand cold email deliverability and want a differentiated lead source that competitors are not using.
Frequently Asked Questions
The practical questions a builder or operator is likely to ask before trying a strategy like this.
What does this marketing agencies lead response AI agent actually do?
This marketing agencies lead response AI agent is a real workflow where the agent takes on an operational job, not just a brainstorming task. A Three Channel Outbound Machine Using Claude Code and Google Maps for Local Leads shows what that looks like in practice. A $200 per month system that scrapes Google Maps for local business leads, enriches emails, and sends 2,000 cold emails per day on autopilot. The practical value comes from the agent handling repeatable business work with enough autonomy that a human only steps in after context has already been gathered.
Who should use a marketing agencies lead response AI agent like this?
This example is most relevant for marketing agencies operators. It is especially relevant for businesses where speed to lead, after-hours coverage, or consistent intake quality directly affects revenue. The category here is Lead Gen, which means the best fit is a team looking to turn a manual bottleneck into a repeatable system with a marketing agencies lead response AI agent.
Which tools are used in this marketing agencies lead response AI agent setup?
The source names Claude Code, Google Maps, Instantly, Smartlead, LinkedIn, Telegram, Tailscale. That matters because one of the strongest signals in this directory is whether the operator shared the actual stack. Named tools make a marketing agencies lead response AI agent strategy far more useful than vague claims about “an AI system” doing the work.
How hard is it to implement a marketing agencies lead response AI agent like this?
Advanced difficulty is the current read. The listing suggests a launch window of days. Startup cost is listed as $200+/mo. We were able to extract 10 concrete workflow steps from the source. We would treat a marketing agencies lead response AI agent like this as a workflow that needs real business context, testing, and exception handling rather than something you should copy blindly from one prompt.
What results can a marketing agencies lead response AI agent produce?
These are projected numbers, not verified personal results. Levi projects 1,600 to 1,700 daily touches across three channels, approximately 50,000 monthly targeted touches, a 2 to 6 percent reply rate on hyper local personalized cold email producing 20 to 40 qualified discovery calls per month. At a 25 percent close rate and $5K to $10K retainers, that projects to 5 to 10 new agency clients per month. Infrastructure cost is $200 to $400 per month. One closed retainer covers years of overhead.
How credible is this marketing agencies lead response AI agent case study?
Right now the evidence comes from an X post. That is enough for us to study and curate the workflow, but not enough on its own to treat this marketing agencies lead response AI agent like an audited case study. We look for named tools, concrete results, and enough workflow detail to understand what was actually deployed, then we add our own editorial judgment on top.
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