Numbers Don't Lie. Neither Do We.
Two case studies. Real clients. Real ad accounts. Here's what happens when you stop paying Meta to find your audience and start handing it one.
All results are from real client campaigns on real ad accounts. No simulations. No projections. Actual performance data.
National Annuities Insurance Company
The problem: This company was running Meta ads using the platform's built-in targeting. Meta's algorithm was spending their budget testing thousands of people to figure out who might be interested in an annuity. Most of those people couldn't afford one. Every ad impression during that "learning phase" was wasted money.
What we did: We built a Consumer Signals audience filtered by three financial criteria: credit score above a certain threshold, household income in their target range, and net worth that indicated the person could actually buy an annuity. Then we uploaded that audience directly to Meta as a Custom Audience. The algorithm skipped discovery and went straight to showing ads to verified, financially qualified prospects.
Channel: Meta Ads with Consumer Signals custom audience upload
Same budget. Same creative. Same campaign structure. The only variable that changed was the audience. That's the difference between paying Meta to guess and giving it a verified list.
Want Results Like These for Your Business?
We build custom audiences from 89 data providers. Tell us your vertical and we'll pull a sample audience on the call.
Garage Door Repair Company, Colorado
The problem: This company was paying $100 per lead on Meta. That means every time someone filled out their contact form through a Meta ad, it cost them $100 in ad spend to get there. At that price, Meta wasn't a profitable channel.
What we did: We built a custom audience using four filters: homeowner status (renters don't need garage door repair), home value above a minimum threshold, zip codes within their service area, and recent intent signals (people who had actively searched for garage door repair within the last 30 days). The result was a list of real homeowners who needed the service right now, lived nearby, and could afford it.
Before Big Easy Data, Meta wasn't a viable channel for this company. Period. Now it's their primary lead source.
The Pattern Holds Across Every Vertical
Your results depend on your creative, your offer, and your funnel. But the math stays the same. Give the platform a better audience, get a better cost per lead. Every time. See how our data stacks up against every competitor on our comparison page.
Why a Better Audience Means a Lower Cost Per Lead
If you're new to paid advertising or have never uploaded a custom audience before, here is the short version of why these numbers happen.
Step 1: You launch an ad campaign the normal way
You go into Meta (or Google, TikTok, etc.) and say "show my ad to homeowners between 30 and 55 in Colorado." The platform doesn't actually know who matches that description. So it starts testing. It shows your ad to thousands of people and watches who clicks, who converts, who bounces. This testing period is called the learning phase. During the learning phase, you pay for every impression and click, but most of those people will never convert. This is why your cost per lead (CPL) is high.
Step 2: You upload a pre-built audience instead
Instead of telling Meta to guess, you upload a file containing real, verified people who match your exact criteria. We build that file for you. It contains encrypted contact info (hashed emails and phone numbers) for people who meet your behavioral, financial, geographic, and demographic filters. When you upload this file to Meta as a Custom Audience, the platform skips the learning phase entirely. It already knows exactly who to target. No testing needed.
Step 3: Your CPL drops because waste disappears
Every impression now goes to a person you already know is qualified. No more paying for the algorithm to learn. No more budget burned on people who were never going to buy. The same ad spend reaches better people, which means more leads for the same money, or the same leads for less money. That's the 30 to 61% cost per lead reduction you see in our case studies.
We've Done This Before. In Your Industry.
These are verticals where we've built audiences and delivered results. With 7,000+ consumer categories and 1,800+ behavior signals, if your customer is a US consumer, we can find them.
Will this work for my industry?
We have 7,238 consumer signals across 25 categories. If your customers are consumers in the United States, we can build your audience. See all signal categories.
$100M/Year HVAC Company
Pixel deployed. Every resolved visitor returned with full contact info and financial profile. They stopped guessing who visited. They started calling them.
National Annuities Insurance
Financially qualified audiences on Meta. 61% CPL reduction. 190% more leads. Same budget.
Garage Door Repair
$100 CPL to $17. Homeowner filters, home value data, and intent signals turned an unprofitable channel into their best one.
Personal Injury Law Firms
Behavior signals and geo-targeting to reach people actively searching for legal help. Local case volume went up. Cost per case went down.
Med Spas & Plastic Surgery
Audiences filtered by household income, net worth, and discretionary spending. You only pay to reach people who can actually book.
Hotels & Hospitality
Travel-intent audiences built from behavior signals and geographic data. Fill rooms with people already planning a trip.
Solar Companies
Homeowners only. Filtered by home value, mortgage data, and environmental interest. No more renters wasting your ad spend.
Insurance Providers
Life, auto, home, specialty. Audiences pre-qualified by credit score, income, and coverage gaps. Your agents talk to buyers, not browsers.
Your Turn. Let's Build Your Audience.
Tell us your vertical, your budget, and your target customer. We'll show you exactly what's possible with 89 data providers and 7,000+ consumer categories behind your ads.