The Lead Generation Platform
Of The Future

If you're running a B2B software, an agency, a local business directory, or any platform that relies on selling to other businesses, you already know the bottleneck: finding and qualifying potential sellers or clients is brutally time-consuming work.

At Salesnode, we've seen this pattern over and over again. Founders and sales teams spend dozens of hours each week combing through Google Maps listings, copying business names into spreadsheets, hunting for email addresses one by one. It's important work — but it doesn't have to be manual work.

In this post, we're going to walk you through a fully automated prospecting workflow that builds your database on autopilot, pulling real business data from Google Maps and enriching it with contact information — all without touching a single listing by hand. This workflow is designed specifically for people building B2B marketplaces, and it works particularly well for local business directories and local marketplaces — though the same strategy applies to any type of marketplace.

Why Google Maps Is Still the Best Starting Point

When we work with clients on market research or building out the first version of their prospecting database, the first place we always point them is Google My Business (now Google Maps / Google Business Profile).

It remains one of the richest publicly available directories of local and regional businesses. When you search for something like "co-working spaces in Wellington, New Zealand," you get a list of verified listings with ratings, addresses, phone numbers, website links, and operating hours — all in one place. Oftentimes clients come in with a rough idea of who their potential sellers are, but Google My Business consistently surfaces businesses they hadn't found yet.

The problem is that going through these listings one by one, clicking into each one, and manually adding them to your own database is exactly the kind of low-leverage, tedious work that doesn't scale. What we want is a way to extract all of that data programmatically — and that's where Apify comes in.

The Tools Behind This Workflow

The workflow relies on three components connected through Make.com, a no-code automation platform:

Apify is a web scraping and automation platform whose tools are called "actors." The one we use here is the Google Maps Extractor, which programmatically pulls business listings from Google Maps at scale — returning structured data including business name, address, neighborhood, rating, review count, website URL, phone number, and more. Apify also has actors for scraping Facebook, LinkedIn, TikTok, and other platforms, which opens up a wide range of use cases beyond this one.

Any Mail Finder (or similar email enrichment tools) solves a specific gap: Google Maps data often doesn't include a direct email address, but it does include a website URL. Any Mail Finder takes that domain, crawls the site, and cross-references available data to surface contact email addresses — turning a partial record into an actionable lead.

Make.com connects everything together. Two separate workflows handle the two stages of the process: one to trigger the scraper, and one to retrieve and store the results.

The Workflow, Step by Step

Step 1: Define Your Search Parameters in Your Database

The workflow is driven by your database — whether that's Airtable, Salesnode Tables, or any other structured tool. Rather than hardcoding a single city or search query into your automation, you store search parameters as records. This means you can queue up dozens of searches across different cities, business categories, and regions, and process them all systematically.

Each record contains the search term (e.g., "co-working space"), the target city or region (e.g., "Wellington, New Zealand"), and any filters you want to apply — minimum star rating, excluding closed businesses, and so on.

Step 2: Trigger the Scraper

When a new search record is ready, the first Make.com workflow fires. It calls the Apify API, passing in the parameters from your database record as JSON. This starts the Google Maps Extractor actor, which begins scraping matching listings from Google Maps.

A useful trick here: within Apify, you can toggle between a visual input form and raw JSON. Switch to JSON view, copy that structure, and you have an API-ready payload. Replace the static values — like the city name — with dynamic variables pulled from your database record, and your single workflow can handle any search you throw at it. Worth noting that this step only starts the scraper; it doesn't return any results yet.

Step 3: Retrieve Results When the Run Completes

The second Make.com workflow handles data retrieval. Using Apify's "watch actor runs" module, it triggers automatically whenever a scraping job finishes — no polling, no manual checks.

Once triggered, it calls the Apify API to retrieve the dataset from that run. Each result in that dataset contains the business name, address, neighborhood, star rating, review count, website URL, phone number, and additional fields depending on the listing. The results come back as individual bundles, one per listing, ready to be processed.

Step 4: Enrich with Email Addresses

Website URLs are valuable, but a direct contact email is what makes a record truly actionable. For each result, we pass the domain through Any Mail Finder, which searches for associated email addresses and appends them to the record before it gets stored.

Not every domain will yield an email address — that's expected. But even a 40–60% match rate dramatically accelerates outreach compared to hunting for contacts manually.

Step 5: Store Everything in Your Database

The final step in the workflow creates a new record for each result in your database of choice — Airtable, Salesnode Tables, or wherever you manage your leads. If you're using Salesnode Tables, you can also connect Apify directly and sync results on actor run completion, skipping the retrieval step in Make entirely.

Either way, the end result is the same: name, address, website, phone, email, and rating — all structured, all searchable, and all ready for your sales or outreach process. Run ten searches across ten cities and you've potentially populated hundreds of qualified leads in the time it would have taken to manually research a handful.

What Makes This Approach Scalable

The real power here isn't any single tool — it's the architecture. Because search parameters live in your database, adding new searches is as simple as adding a new row. Because the workflow is event-driven, it handles itself without babysitting. And because Apify supports actors for LinkedIn, Facebook, TikTok, and more, the same workflow structure can be adapted as your prospecting strategy evolves — job boards, social profiles, industry directories, whatever the use case demands.

This approach works across a wide range of B2B verticals: local service providers for a vertical marketplace, restaurants and hospitality businesses for a booking platform, contractors and tradespeople for a services directory. Anywhere Google Maps has meaningful business density, this workflow can fill your pipeline.

The Bottom Line

Building a prospecting database used to mean hours of manual research, copy-pasting, and data cleanup. With the right automation stack, that same work runs in the background while your team focuses on what actually drives revenue: building relationships and closing deals.

At Salesnode, this kind of systematic, data-driven approach to lead generation is exactly what we help B2B teams implement. If you're spending more time finding leads than talking to them, it's time to rethink the workflow.