Make.com Tutorial: Build a Google Maps Lead Generation System on Autopilot (2026 Guide)

A buddy of mine runs a 6-person agency in Austin. Marketing stuff, mostly local businesses. Last month he told me he'd spent — and I'm not exaggerating — 30 hours copying restaurant contacts from Google Maps into a Google Sheet. Thirty. Hours. That's four full workdays of clicking, copying, tabbing, pasting, and slowly dying inside.

I showed him how to wire Scrap.io into Make.com on a Friday afternoon. Nothing fancy, maybe 45 minutes of setup. By Monday morning, 4,200 leads were sitting in his Airtable — sorted by city, Google rating, email availability. He hadn't touched a keyboard all weekend.

That's what this make.com tutorial covers. Not theory. Not "imagine the possibilities." The actual step-by-step build for an automated prospecting system that scrapes Google Maps, filters the junk out, and feeds clean leads into your database while you're off doing something that actually requires a brain.

Oh and if you'd rather skip the whole build? We're giving away the pre-built scenario for free. Grab it, tweak it, run it. But stick around — understanding what each piece does means you can customize it without guessing.

Why Automate Google Maps Lead Generation in 2026?

Here's a number that should sting a little. Businesses running AI-powered marketing automation convert at 23.4%. Without automation? 9.1% (Salesforce, State of Sales 2026). That's not an improvement. That's a different sport.

Forget the big picture for a sec though. Think about what manual prospecting actually looks like.

You open Google Maps. Type "plumber Nashville." Click a listing. Copy the name. Tab to your spreadsheet. Paste. Click back. Find the website. Hunt for an email address that may or may not exist. Copy, paste, repeat. Five hundred times.

At 90 seconds per lead — and that's being generous — you're burning 12 hours. Half of those emails will bounce. Some of the businesses closed six months ago. And your back hurts from hunching over a laptop all day. Wonderful.

Meanwhile the people who automated this are compounding. Nucleus Research tracked 245% average ROI within the first 12 months of adopting automation, across 1,200 companies and 18 industries (Nucleus Research, 2026). Not from a product launch. Not from hiring five more salespeople. From automation alone.

And the tooling grew up too. The low-code development platform market was worth $26.30 billion in 2025 and is projected to hit $67.12 billion by 2030 — a 20.61% CAGR (Mordor Intelligence, 2026). Translation: the no-code way of doing this isn't a fringe hack anymore. It's where the money's going.

Make.com specifically? Over 3,000 enterprise clients, a community forum with 50,000+ members trading scenarios, and a $2.5B valuation. Not exactly a garage project. (76% of marketers already use automation tools. Sales teams? Only 44%. If you're in sales and reading this, you're behind.)

WiserNotify published a stat that stuck with me: $5.44 back for every $1 invested in automation over three years — and $8.71 in the top quartile. Where else are you getting 5x to 8x returns on a tool that costs less than your monthly coffee budget?

If you're the "make.com tutorial for beginners" type who's never touched an automation platform — don't worry. We're going piece by piece. For the bigger picture on what Google Maps automation can do beyond lead scraping, there's a separate guide on complete marketing automation with Google Maps worth bookmarking.

What You Need: Make.com + Scrap.io

Two tools. That's the entire stack.

Make.com — used to be called Integromat, for anyone who's been around a while — is a visual automation builder with 3,000+ app connectors. You drag modules onto a canvas, draw lines between them, and it runs. Like Zapier, except Make actually lets you build complex stuff without hitting weird limitations every five minutes.

Pricing is credit-based. The Core plan runs $12/month billed annually (or $16/month if you pay monthly), and that buys you 10,000 credits — one credit per module action in your scenario. For a lead gen workflow running once a day, that's more than enough. Double-check their pricing page for current numbers; they've been adjusting things since the credit overhaul.

Scrap.io is the other half. It's a dedicated Google Maps scraping tool with 225,676,406 businesses indexed. Not a static database that got scraped once in 2023 and forgotten about. Real-time data, pulled fresh from Google Maps and the websites connected to each listing at the moment you export.

What separates Scrap.io from the 15 other scrapers floating around:

• 225M+ businesses, 195 countries, 4,000+ Google Maps categories
• 10,000 requests per minute of scraping capacity. It's fast.
• Pulls more than name and phone — emails (classified by type), social profiles, website tech stack, ad pixels, review breakdowns, contact forms
• Full REST API that plugs natively into Make.com

Together, these two replace what would normally require a virtual assistant plus a data cleaning service plus a CRM integration consultant. One finds and filters your prospects. The other orchestrates the entire pipeline.

Setting Up the Integration

This part takes five minutes. Maybe ten if you're the type who reads every tooltip.

Step 1: Install the Scrap.io module in Make

Go to your Make.com dashboard, search "Scrap.io" in the app directory, click Install. Done. It's a native integration — no webhook hackery needed.

Step 2: Get your API key from Scrap.io

Log into your Scrap.io account. Profile → Security → API Keys → Create. Copy the key. Full parameter reference lives at docs.scrap.io.

New to the platform? This 2-minute walkthrough covers the account setup before you touch the API.

Step 3: Connect them

Create a new scenario in Make. Add a Scrap.io module. Paste your API key when it asks. That's... it. No OAuth flow, no JSON config, no cursing at your screen.

If you want to skip even this setup: Scrap.io publishes a free pre-built scenario that handles make.com google maps scraping end-to-end — search, enrichment, Airtable sync, pagination. Download, import, customize. Five minutes to a working pipeline.

Deep Dive: The 4 Core Scrap.io API Models

Here's where it gets interesting. Scrap.io's API exposes four distinct modules inside Make.com. Each one does something different. Mix them, chain them, use them standalone — your call.

Model 1: The Search Function

The main one. Tell it what type of business you want (say, "bakery"), where (country, state, city), and it returns results with all associated data.

Couple things that trip people up:

Results per page — your options are 1, 10, 25, or 50. Not 37. Not "all of them." Those four. It's a quirk. Live with it.

Admin1 vs admin2 vs city — because Scrap.io works across 195 countries, they use generic terms instead of "state" or "county." In the US: admin1 = state, admin2 = county. In France: admin1 = région, admin2 = département. City is city everywhere. The IDs for these come from Model 3 (we'll get there).

Now the actually important part: filters.

And there are a lot of them, all applied before a single credit is spent. Email present (yes/no), website present, contact form detected, ad pixel on the site, minimum Google review count, rating range, phone type (fixed/mobile), individual social profiles including TikTok, first-seen date, main-category-only, automatic duplicate exclusion... the output comes pre-qualified. No more exporting 10,000 rows and spending two days deleting garbage.

Real example. I ran a search last week: restaurants in Tennessee, filtered for "has email" and rating above 3.5. Google Maps holds 14,230 restaurants in the state. 3,741 of them (26.3%) have an email on their website. Narrow it to email and a 3.5+ rating and you land on 3,300 qualified leads — every one with a working email. Try getting that from a list you bought off some shady data broker.

Oh, and one more number from that same search, because it tells a story: 4,514 of those 14,230 restaurants (31.7%) have no website at all. If you sell web design, that's your entire Q4 pipeline sitting right there — and it took eight seconds to count. For free.

For the full breakdown on email extraction specifically, see our guide on how to find emails on Google Maps.

Model 2: Search Types

This one's simple. You search for a Google Maps category keyword — "restaurant," "plumber," "strip club" (no judgment) — and it returns the corresponding IDs.

Why bother? Because Google Maps has 369 types of restaurants. Searching "restaurant" gives you all of them. Searching "bakery" gives you five. You need the right ID for accurate results in Model 1. Skip this step and you'll either get way too many results or none at all.

Model 3: Search Locations

Same idea, different axis. Give it "Nashville" and a country code, and it hands back the location ID you need for your search queries.

Critical detail: if you want Nashville the city, you need to specify "city" as the entity type and use the returned ID in the city field of Model 1. I plugged a city ID into the admin1 field once. Zero results. Spent 20 minutes confused before I realized what I'd done.

Bonus: GeoSearch. You can define a radius around a point, or draw a polygon on the map for your search area. Handy for hyperlocal campaigns where city boundaries don't match your actual service area.

Model 4: Enrich — The Reverse Lookup

Honestly my favorite. Feed it a domain name, email, URL, or phone number. It returns the full Google Maps profile attached to that business — if one exists.

Why is this powerful? It works backwards. You've got a list of company websites from some other source — a CRM export, a LinkedIn scrape, whatever. Run those domains through Enrich and suddenly each one has Google ratings, review counts, categories, social links, contact data. Information you didn't have five minutes ago.

One caveat: big chains return tons of results. Feed McDonald's domain and you'll get hundreds of location bundles. Local plumber? One clean match. Adjust expectations accordingly. If you want the module-by-module reference, our Scrap.io Google Maps API guide documents every endpoint.

Building the Complete Automated Prospecting System

1. Set your variables — results per page (50 for max throughput) and your Scrap.io API key.
2. Fire the Scrap.io Search — by business type, location and filters; it returns the first batch.
3. Iterate and store — push each result into an Airtable "leads" table, fields mapped automatically.
4. Handle pagination — loop a cursor through page 2, 3, 4… until there's no data left.
5. Manage the queue — an Airtable "targets" table cycles each search through to do → in progress → done.

That's the whole architecture in five steps. Now the detail, because step 4 is where most DIY builds fall apart.

Your two variables are the initialization — results per page and API key, nothing more. The Scrap.io Search fires against whatever criteria you defined and returns the first batch. Each result gets pushed into Airtable as a new lead record: name, email, phone, website, review score, categories — all mapped automatically. The make.com airtable integration makes this dead simple.

Pagination is the trap. Your search returns 300 results but you're pulling 50 per page. You need a cursor to grab page 2, then 3, then 4. The pre-built scenario handles this automatically: stores the cursor, flips the target status from "to do" to "in progress," keeps looping until there's no more data, then marks it "done."

Queue management ties it together. The Airtable "targets" table tracks what's been scraped and what hasn't. Three statuses: to do, in progress, done. Stack up ten different searches (restaurants Nashville, bakeries Memphis, plumbers Knoxville) and the system chews through them one by one.

Once this runs? Schedule it. Daily, weekly, whatever rhythm your sales team needs. Google maps scraping automation no code — no Python, no proxies, no IP rotation headaches. Just data flowing into your pipeline on autopilot.

And it doesn't stop at lead collection. Some users bolt on email tools like Lemlist or Saleshandy, creating a closed loop: scrape → filter → enrich → validate → email → track responses. When a lead status changes in Airtable, Make triggers the next action. You can automate your email outreach entirely, layer on AI-powered cold email personalization using ChatGPT, or push those leads into your CRM automatically.

Skip the Scenario: Run the Whole Thing From Your AI Agent (MCP)

Here's the awkward part of writing a Make.com tutorial in 2026: for some jobs, you don't need Make anymore.

Scrap.io ships an official MCP server at scrap.io/mcp. MCP — Model Context Protocol — is the open standard that lets an AI like Claude, ChatGPT or Gemini talk directly to an external data source. Point your AI at that endpoint, authorize once, and you can run Google Maps searches from your AI agent in plain English. No scenario. No modules. No canvas.

What does that look like in practice? You type "find every restaurant within 15 km of downtown Nashville that has no website," and the AI builds the radius, applies the filter, and hands back the list. GeoSearch in a sentence. Counts stay free through the MCP, exactly like they do in the app — so you can size a market ("how many dentists in California have an email?") before spending a single credit. When you're ready to export, it's one credit per business, filtered before extraction like always.

Setup is four clicks. In Claude: Settings → Connectors → Add custom connector → paste scrap.io/mcp → Authorize. In ChatGPT: Settings → Apps → Developer Mode → Create app → same URL → Authorize. Any MCP-compatible client follows the same logic.

Now let me be honest, because pretending otherwise would be dishonest. The MCP does not replace Make for everything. For scheduled, recurring pipelines — scrape every Monday at 6am, paginate through 3,000 results, retry on error, sync to Airtable, kick off an email sequence — Make still wins, and it isn't close. That's the scenario you built above, and it's the right tool.

The MCP wins on a different axis: exploratory, one-off, conversational. "How big is this market?" "Pull me 50 gyms in Chicago and put them on a map." "Which counties have the most restaurants without a site?" You ask, you get an answer, you move on. No pipeline to maintain. If you want to go deeper on the AI-native workflow — including how to feed Google Maps data to your LLM for reports and personalization — we wrote a full guide on it.

Use both. Make for the machine that runs without you. MCP for the questions you didn't know you'd ask until you asked them.

Real-World Results: Who Uses This System?

Case studies sound nicer than "let me prove I'm not making this up." Fine. Let me prove I'm not making this up.

Globant (30,000+ employees) used Make.com to roll out automation company-wide. Their Head of Innovation didn't just run a pilot — he gave 30,000 people access to the platform. Non-technical teams building their own workflows. That's a scale bet most companies wouldn't touch. (Source: Make.com blog, March 2026.)

FranklinCovey automated internal processes with Make and saved — their words — "hundreds of thousands of dollars" while freeing hundreds of staff hours. The kind of savings that make finance people smile for the first time since college. (Source: Make.com case study, September 2025.)

The wildest one: Celonis built Make AI Agents for expense auditing. Annual auditing costs went from $50,000 down to $150. I had to read that twice. Fifty grand to a hundred and fifty bucks. (Source: Make.com case study, November 2025.)

My personal favorite though? Eduardo Cifre Sanchez built a voice-powered AI invoicing system for rural farmers in Spain — people who'd never used automation software in their lives. Made it with Make.com. Non-technical guy, building for non-technical users. That's the whole promise of no-code in one example. (Source: Make.com blog, December 2025.)

Now the part I like most, because it's the exact workflow this article describes. Cameron Figgins, who owns Absolute Maintenance, segments his Google Maps leads for cold outreach and leans hard on the review data. In his words:

"We would generate 100 to 150 businesses or properties within 5 to 8 miles, then bucket them into actionable segments based on factors like year built, exterior appearance, online reviews, and business category. An older multifamily complex with chipped stucco and tenant reviews mentioning leaks is going to be more receptive than a recently built office space." (Source: artisan.co, May 2026.)

That's the difference between a list and a targeting system. Figgins isn't spraying — he's reading the data before he sends a word.

And from the Scrap.io side: a web design agency filtered Google Maps for businesses without websites but WITH contact forms, then ran a contact form outreach campaign. Twenty-three percent response rate. 47 new clients in three months. That's nearly 5x what they were getting through cold email alone. The difference was targeting precision — 70+ data points per lead instead of name-and-email-and-pray. (Source: Scrap.io contact form strategy article.)

Reddit's r/automation has regular threads about Make + Google Maps builds too — people posting workflow screenshots, comparing data quality, arguing about which scraper actually returns emails. Real people building real stuff, not a landing page.

Once you've got the leads, the hard part shifts to converting those Google Maps prospects into customers — but having thousands of qualified contacts to work with is a pretty nice problem to have. For a broader look at extraction methods, the complete Google Maps scraping guide compares five approaches side by side.

Make.com vs Other Automation Tools (2026)

"Why Make and not Zapier or n8n or whatever?"

Legit question. Here's where they actually differ — not marketing fluff, real trade-offs.

Zapier is easier. Nobody denies that. But add branching logic, iterators, error handlers? Zapier starts choking. Make was designed for that level of complexity from day one.

n8n is open-source and self-hostable. Excellent if you're a developer who wants total control. No native Scrap.io module though — you'll build HTTP requests manually. Which, fine, but why bother when Make gives you drag-and-drop?

Power Automate only makes sense if your company breathes Microsoft. For automated lead generation google maps workflows? Non-starter.

One more thing worth calling out, and it's the row nobody else has. Since the AI-agents update, Scrap.io is drivable straight from Claude, ChatGPT or Gemini through its MCP server. No competitor in that table offers that natively. And a related gotcha if you're weighing Clay: its Google Maps source caps searches at around 60 results per query — great for enrichment, rough for sourcing a whole market. Clay's own lead-gen tutorial even admits it searches "one geographic region at once." Different tools, different jobs.

For make.com automation workflow examples beyond lead gen, the Make community forum is a goldmine — 50,000 members sharing scenarios for everything from invoice processing to full prospecting pipelines.

Cost Breakdown: Running This Automation

Let's do the actual math. No hand-waving.

20,000 leads for $81. That's $0.0041 per lead. Less than half a penny.

For perspective: WordStream benchmarks put B2B lead costs at $100–$400 per lead through traditional channels. A VA manually scraping Google Maps does maybe 30 leads per hour at $15–25/hour — that's 50 to 83 cents each. Intent data platforms like ZoomInfo run hundreds a month and give you a fraction of the volume.

Half a penny vs. fifty cents vs. a hundred dollars. I don't have a clever analogy. The gap is just absurd.

Starting out? Scrap.io's free trial comes with 100 leads and full API access. Enough to build the workflow, test it end to end, and see actual data before committing.

Compliance & Legal Considerations

Not the sexiest section but skip it at your own risk.

Scrap.io pulls only publicly available data — the same stuff anyone sees browsing Google Maps manually. Business names, addresses, phone numbers, websites, emails displayed on those websites. Scrap.io just does it faster and at scale. It's GDPR and CCPA compliant, works with business data only, and every data point is traceable back to its public source.

For cold outreach using that data: CAN-SPAM in the US requires clear identification, honest subject lines, a working unsubscribe link, and your physical business address. GDPR in Europe allows B2B cold email under "legitimate interest" as long as you offer easy opt-out and can justify why you're contacting that specific business.

Two things before you blast your freshly scraped list. First: verify those emails. Even one campaign with a 5%+ bounce rate can wreck your sender reputation. Second: consider contact form outreach as an alternative channel — near 100% read rates because the message lands in the owner's inbox directly, not a spam folder.

Deeper dive on legality: we wrote a full article on whether scraping Google Maps is legal. Short version: publicly accessible business data, yes. Personal data, no. Stay on the right side and you're fine.

Once compliance is handled, the data you've got gives you outreach ammunition most competitors simply don't have. You know their review count. Their rating. Whether they run Facebook ads. Whether their site uses WordPress or Shopify. Use that. Personalization based on real data beats generic "Dear Business Owner" every single time.

Scrap.io API Models — Quick Reference

Frequently Asked Questions

How to automate Google Maps lead generation?

Connect Scrap.io to Make.com via the native module. Build a scenario: search by category and location, apply filters, push results into Airtable or your CRM. Scrap.io handles extraction, Make handles orchestration. Schedule it and walk away. The API documentation covers every parameter.

Is Make.com better than Zapier for lead generation?

For Google Maps specifically? Yeah. Native Scrap.io module, cheaper entry (~$12 vs $19.99), and a builder that handles branching, iteration, and error paths without falling apart. Zapier is fine for simple stuff. Complex multi-step workflows? Make wins.

Is Make.com worth learning in 2026?

With 3,000+ integrations, native AI agents, and 50,000+ community members — hard to argue against it. The free tier gives you 1,000 credits to test. Make Academy has free courses if you want structured learning. Their Help Center covers the rest.

Do I need coding skills to use Make.com?

Nope. Everything is drag-and-drop visual. If you can use a spreadsheet, you can build Make workflows. That's the entire point of no-code lead generation — removing the developer bottleneck.

How many leads can you generate per hour?

Scrap.io handles up to 10,000 requests per minute on the scraping side, and the REST API allows 300 requests per minute per account. With Make orchestrating the pipeline, several thousand leads per hour is realistic. Exact numbers depend on your filters, page depth, and how many fields you pull.

How much does this automation cost per lead?

About four tenths of a cent. Make Core ($12/month annually) plus Scrap.io Professional ($69/month annually) gets you roughly 20,000 leads for $81 — that's $0.004 per lead. Traditional B2B lead services charge $100–$400 per lead. VA scraping runs $0.50–$0.83 each. The math is brutal.

Can I run Scrap.io from ChatGPT or Claude instead of Make.com?

Yes. Scrap.io ships an official MCP server at scrap.io/mcp, compatible with Claude, ChatGPT, Gemini and any MCP client. You can run searches, get free counts, build radius and polygon searches in plain English, and create filtered exports — no scenario to build. Make still wins for scheduled, recurring pipelines with pagination and error handling; MCP wins for exploratory research and one-off pulls.

How to use the Scrap.io API with Make.com?

Install the Scrap.io module in Make's app directory. Generate an API key in your Scrap.io account (Profile → Security → API Keys). Paste it into the connection prompt. Done. All four API models show up as native modules. Full reference at docs.scrap.io.

What tools automate Google Maps prospecting?

Scrap.io for extraction (225M+ businesses, 195 countries). Make.com for automation logic. Airtable for the database. For outreach after scraping: Lemlist or Saleshandy work well, especially with AI-driven email personalization. Some teams feed leads into HubSpot or Pipedrive.

Can I use Make.com with Airtable for lead management?

Recommended setup, actually. Make has native Airtable modules — create, read, update, search. The free Scrap.io scenario uses Airtable for both the scraping queue (targets table) and lead storage (leads table). You can trigger follow-up actions when fields change: status update → Slack ping → email sequence → CRM entry.

Where can I learn Make.com basics?

Make Academy for structured courses. Make Help Center for docs. For this specific use case, this tutorial plus the free Scrap.io scenario gets you 90% there. Community forum handles the other 10%.

Is Google Maps scraping legal?

Yes — when you stick to publicly available business data. Scrap.io pulls only information businesses choose to display on their Maps profiles and websites. Same data any human could see by browsing. Courts in the US and EU have broadly upheld this. For outreach, follow CAN-SPAM and GDPR. Full breakdown in our dedicated legal article.

Your Move

Two paths. Keep clicking through Google Maps listings until your wrist gives out. Or spend an afternoon building a system that does it while you sleep.

The free scenario exists. The API docs exist. Less than half a cent per lead vs. a hundred dollars. Not much to debate.

Freelancer, sales team, agency — doesn't matter. You can layer on automated email outreach next, or build a full marketing automation pipeline. The foundation you build today handles all of it.