Sales & Conversion
Personas
Ecommerce
Time to ROI
Short-term (< 3 months)
So you're staring at Meta's audience builder, trying to figure out whether to target "dog lovers aged 25-34 who are interested in sustainable products and live within 10 miles of organic pet stores." Sound familiar?
I used to spend hours crafting these detailed audience segments. Multiple campaigns, different demographics, interests layered on top of behaviors. I was convinced that finding the "perfect audience" was the secret to profitable Facebook ads.
Then I worked with a B2C Shopify store that completely changed how I think about Meta ads targeting. We went from burning through budget on detailed audiences to achieving consistent profitability with an approach that goes against everything the "experts" teach.
Here's what happened when I stopped trying to outsmart Meta's algorithm and started working with it instead:
Why detailed targeting is actually hurting your ad performance
The shift from audience targeting to creative testing that changed everything
My simple framework for Meta ads that actually works in 2025
How to structure campaigns for maximum learning and profitability
The testing rhythm that keeps ads fresh and converting
If you're tired of guessing which audience to target and want a proven approach that lets Meta do what it does best, keep reading. This isn't another "scale to 7 figures" fantasy - it's what actually works when you optimize for real conversion instead of vanity metrics.
Industry Reality
What every marketer thinks they know about Meta targeting
Walk into any digital marketing conference and you'll hear the same advice repeated everywhere: "Success with Facebook ads is all about finding the right audience." The conventional wisdom sounds logical enough:
Layer multiple interests to create "laser-focused" audiences
Use detailed demographics to narrow down to your "ideal customer"
Create lookalike audiences based on your best customers
Exclude competitors' audiences to avoid "wasted spend"
Test different audience segments against each other
This approach exists because it feels like control. Marketers love the illusion that they can manually guide Meta's algorithm to find exactly the right people. It's satisfying to think you've cracked the code by discovering that "yoga enthusiasts who like sustainable fashion" convert better than "fitness enthusiasts who shop online."
The problem? This entire strategy is based on outdated assumptions about how Meta's algorithm works.
Privacy changes starting with iOS 14.5 fundamentally broke detailed targeting. Meta can't see what it used to see. The algorithm has evolved to rely on different signals. But most marketers are still fighting yesterday's war with today's tools.
The result? Campaigns that work for a few days, then mysteriously stop converting. Endless audience testing that never finds a consistent winner. And the frustrating feeling that you're always one audience tweak away from profitability - but never quite getting there.
What if I told you the solution isn't better targeting, but smarter creative testing instead?
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started managing Facebook ads for a B2C Shopify store, I did exactly what every "expert" told me to do. I spent weeks building detailed audience segments, convinced that precise targeting was the key to success.
The client sold lifestyle products with a broad appeal, but I was determined to find those perfect micro-audiences. I created campaigns targeting "eco-conscious millennials," "sustainable lifestyle enthusiasts," and "minimalism lovers." Each audience was carefully crafted with layered interests and specific demographics.
The results were... mediocre at best. We were getting clicks, but the conversion rates were inconsistent. Some days we'd hit our target ROAS, other days we'd barely break even. The client was frustrated because their budget wasn't delivering predictable results.
That's when I discovered something that changed everything.
I was talking to a friend who runs Facebook ads for a major e-commerce brand, and he mentioned something that sounded crazy: "We stopped targeting altogether. Just broad audiences now." I thought he was joking.
But then I started researching what was actually happening inside Meta's algorithm. Privacy changes had fundamentally altered how the platform identifies and reaches potential customers. The detailed targeting options that worked in 2019 were essentially broken by 2025.
The uncomfortable truth? Most of our "strategic" audience targeting was just educated guessing.
The platform's machine learning had become sophisticated enough to find the right people automatically - but only if we gave it the right creative signals to work with. Instead of telling Meta who to target, we needed to show it what to look for through our ad creative.
This realization led me to completely restructure our approach. Instead of fighting the algorithm with detailed targeting, I decided to work with it through systematic creative testing.
Here's my playbook
What I ended up doing and the results.
Here's exactly what I implemented for our Shopify store client:
The first thing I did was simplify our campaign structure dramatically. Instead of running multiple campaigns with different audience segments, I created one main campaign with a single broad audience. The only targeting parameters I used were basic demographics: gender, country, and age range. That's it.
But here's where it gets interesting - instead of multiple audience-based ad sets, I created multiple creative-based ad sets. Each ad set contained the same broad targeting but different creative approaches:
Lifestyle-focused creatives showing the product in use
Problem-solving creatives highlighting specific pain points
Social proof creatives featuring customer testimonials
Feature-focused creatives demonstrating product benefits
The breakthrough came when I established a weekly testing rhythm. Every single week, without fail, we produced and launched 3 new creative variations. This wasn't about quantity for quantity's sake - it was about giving Meta's algorithm fresh data points to work with.
Each creative acted as a signal to the algorithm about who might be interested. A lifestyle-focused creative would naturally attract one segment, while a problem-solving creative would attract another - all within the same broad targeting parameters.
I tracked which creative angles were generating the best results and doubled down on those themes. When a creative started showing fatigue (usually after 7-10 days), I'd pause it and launch a new variation based on the winning theme.
The magic happened when I stopped trying to control who saw the ads and started focusing entirely on what they saw. Meta's algorithm became my targeting system - I just fed it the right creative content to learn from.
Within 30 days, we had consistent daily profitability and a library of proven creative concepts that we could iterate on indefinitely.
Creative Testing
Your creative IS your targeting now - this is how the algorithm learns who to reach
Broad Targeting
Use only basic demographics (age, gender, location) and let Meta's algorithm do the heavy lifting
Weekly Cadence
Launch 3 new creative variations every week to keep the algorithm learning and prevent ad fatigue
Data-Driven Scaling
Double down on winning creative themes and pause underperformers - let performance, not opinions, guide decisions
The results spoke for themselves. Within the first month of implementing this approach, our campaign performance stabilized dramatically. We went from inconsistent daily results to predictable profitability.
Most importantly, we eliminated the constant guesswork. Instead of wondering which audience to test next, we focused entirely on creative performance. When an ad set was profitable, we knew it was because the creative was resonating - not because we'd stumbled onto the "right" demographic combination.
The client was thrilled because their budget was finally delivering consistent results. No more days of unexplained performance drops or mysterious algorithm changes tanking our campaigns.
But perhaps the biggest win was operational efficiency. Instead of managing multiple audience-based campaigns, we had one streamlined system that was easier to monitor and optimize. Our weekly creative testing rhythm created a sustainable workflow that didn't require constant audience research and experimentation.
This experience taught me that in today's advertising landscape, your creative strategy IS your targeting strategy. The platforms have the data and the intelligence - what they need from us is compelling, diverse creative content that can connect with the right people automatically.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from completely changing our Meta ads approach:
Stop fighting the algorithm - Privacy changes made detailed targeting largely ineffective. Work with Meta's machine learning instead of against it.
Creative is the new targeting - Your ad creative tells the algorithm who to find better than any audience parameter ever could.
Consistency beats perfection - Regular creative testing outperforms perfect audience research every time.
Broad audiences work better - Give the algorithm room to find people you wouldn't have thought to target.
Test themes, not just ads - Focus on different creative approaches rather than minor copy variations.
Weekly rhythm is crucial - Fresh creative prevents fatigue and keeps the algorithm learning.
Simplicity scales better - One campaign with multiple creatives is easier to manage than multiple campaigns with different audiences.
The biggest mistake I see marketers making is trying to recreate 2019 Facebook ads strategies in 2025. The platform has evolved - your approach should too.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, start with these targeting basics:
Broad professional demographics only
Focus creative on specific use cases and pain points
Test problem-solving vs. feature-focused messaging
Let creative variety do the audience segmentation
For your Ecommerce store
For e-commerce stores, implement this framework:
Target broad demographics in your key markets
Test lifestyle vs. product-focused creative angles
Use UGC and social proof in your creative rotation
Focus on creative testing over audience optimization