An independent insurance agency was stuck at 25 prospects per month. Every lead came from referrals or networking events. We built an AI pipeline that scans Google Maps for restaurants, auto shops, and contractors, finds the owner's email, and delivers 300+ verified contacts monthly in a clean spreadsheet. In the first 90 days, the agency closed $41K in new commercial premiums from those contacts.
The Problem: 25 Leads a Month from Handshakes
This agency had two producers and a book of mostly personal lines business. They wanted to grow the commercial side: restaurants, contractors, auto repair shops. The problem was finding them.
Every prospect they had came from either a referral or a chamber of commerce event. Good leads, but slow. 25 new contacts in a good month wasn't going to move the needle on commercial premiums.
They knew their ideal client profile cold. Small-to-midsize businesses with 5-50 employees, within 40 miles, in industries with high insurance needs. The problem wasn't knowing who to call. It was finding contact information for the actual decision-maker, not a general office number.
Why Manual Research Didn't Scale
They'd tried having a producer spend a few hours a week searching Google Maps and LinkedIn. The math doesn't work:
- Finding a business listing: 2 minutes
- Tracking down the owner's name: 5-10 minutes
- Finding a verified email: another 10-15 minutes, often nothing
- Result: maybe 8-10 usable contacts per hour of work
A producer's time is worth more than that. And even working at it, they couldn't get past 30 contacts per week without burning out on research that had nothing to do with selling insurance.
What We Built
We set up an automated lead generation pipeline. It runs on a schedule and does three things in sequence:
- Scrapes Google Maps for businesses matching their target profile, filtered by category, location, and a minimum review threshold to ensure they're established businesses
- Finds the owner or decision-maker email using a combination of business domain lookups, email pattern matching, and verification against SMTP records
- Delivers a clean spreadsheet with business name, owner name, verified email, phone, address, business category, and review count
Every email in the output is verified before it lands in the spreadsheet. Addresses that bounce the verification check get flagged separately. The agency only emails contacts with a high confidence score.
The agency feeds those contacts into their email tool every month and runs outreach campaigns. The pipeline doesn't write the emails or manage replies. Its job is simple: find the right people and confirm they're reachable. That part it does well.
"We went from begging people at events to swap cards to having a list of 300 business owners sitting in our inbox every month. I wish we'd done this two years ago."Agency Principal
The Results: 90 Days In
We handed the first batch of contacts over in week two. By the end of the first 90 days:
- ~25 new prospects per month
- Source: referrals and networking
- No consistent outreach channel
- Producer time spent on research
- No data on business type or size
- Random contact quality
- Commercial pipeline stalled
- 300+ verified contacts per month
- Source: automated Google Maps scan
- Monthly email campaigns to owners
- Producers focused on selling
- Business type, size, location on file
- 92% email deliverability
- $41K new premiums in 90 days
The $41K wasn't from some massive account. It was 11 new commercial policies, mostly restaurants and contractors in the $2,500-$6,000 annual premium range. Normal-sized wins that just weren't happening before because there was no pipeline.
What Happens to the 92% That Don't Reply
Most of the contacts don't respond to the first email. That's normal for cold outreach. The agency runs a 3-touch sequence over 3 weeks. Some respond to the second or third message. Most don't respond at all.
The point is the math. At 300 verified contacts per month and even a 3% response rate, that's 9 conversations started. Close 2 of those and you're adding commercial policies every month. The pipeline doesn't need a high conversion rate. It just needs to keep running.
What We Actually Built, Under the Hood
The core is a Python pipeline with three stages:
- Google Maps scraper pulls business listings by category and location, filters on review count and rating
- Email finder checks the business domain, runs common email pattern combinations, and queries against known public databases
- Email verifier runs an SMTP handshake to confirm the mailbox exists without actually sending a message
Output is a CSV with 8 fields per contact. No fancy dashboard. The agency imports it to their email tool each month and starts the campaign. Clean and simple.
Insurance agencies don't have a prospecting problem. They have a data problem. Their ideal clients are listed on Google Maps right now. The owners have publicly associated email addresses. The only thing missing was a way to collect that data at scale without burning a producer's entire week. An AI pipeline solves that. Once it's running, it runs every month without anyone touching it.
What This Costs
The pipeline setup is a one-time build. Monthly, the main costs are API credits for the email finder and verifier. For 300 contacts per month, that runs under $80 in API costs. The build itself takes us 7-14 business days depending on how specific the targeting needs to be.
Compare that to a lead list subscription service ($300-$600/month for often-stale data with no email verification) or hiring a VA to do manual research ($1,500+/month and still capped on output). The pipeline pays for itself fast.
Frequently Asked Questions
How does AI lead generation work for insurance agencies?
The pipeline scans Google Maps for businesses matching your target profile, finds the owner's verified email address, and delivers a spreadsheet of contacts monthly. You run your own email campaigns to those contacts. The whole process is automated and runs on a schedule without manual work each cycle.
What types of businesses can an insurance agency target with AI lead gen?
Any business on Google Maps is a valid target. Restaurants, auto shops, contractors, landscapers, retail stores, and medical offices are common for commercial insurance agencies. The pipeline filters by business category, distance from your office, and minimum review count so you're only seeing established businesses.
How accurate are the email addresses found by AI?
With email verification built into the pipeline, deliverability lands around 90-94%. Every address runs through an SMTP check before it goes into the output spreadsheet. Low-confidence addresses get flagged in a separate column rather than mixed into the main list.
How long does it take to set up an AI lead generation pipeline?
Usually 7-14 business days. That covers defining the ICP (industry, location, size), configuring the Maps scraper, connecting the email finder and verifier, setting the output format, and a test run to check quality before the first real batch goes out.
Is Google Maps scraping legal for business lead generation?
Scraping publicly listed business information from Google Maps for B2B prospecting is generally legal in the US and Canada. The outreach still needs to follow CAN-SPAM rules: honest sender identity, a physical address, and a working unsubscribe link. We configure all pipelines with that in mind from the start.