Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, I was managing Facebook ads for a B2C Shopify client when something clicked that changed how I think about lead generation forever. We were burning through budget testing different audience segments, trying to find that "perfect customer" everyone talks about in marketing courses.
The ROAS was sitting at 2.5 - acceptable on paper, but with their thin margins, we were barely breaking even. More importantly, we were stuck in the endless cycle of: test audience → get some sales → audience saturates → find new audience → repeat.
That's when I discovered what I now call "paid loops" - a fundamentally different approach that treats your ad spend as an investment in a self-reinforcing system rather than just buying individual conversions.
Here's what you'll learn from my experience:
Why traditional audience targeting is becoming obsolete in 2025
How to shift from audience-first to creative-first paid strategies
The 3-creative weekly testing cadence that changed everything
Why paid loops work better for both SaaS and ecommerce
The metrics that actually matter when building sustainable growth loops
If you're tired of the audience targeting hamster wheel, this growth strategy might be exactly what you need.
Reality Check
What every marketer thinks they know about paid ads
Walk into any marketing agency today, and you'll hear the same gospel being preached: "It's all about finding the right audience." The conventional wisdom goes like this:
Detailed targeting is king - Spend weeks crafting perfect audience segments based on demographics, interests, and behaviors
Lookalike audiences are magic - Upload your customer list and let the algorithm find more people just like them
Broad audiences don't work - Only amateurs would dare to target broadly without specific parameters
Split test audiences, not creatives - Once you find a winning creative, milk it until it dies while testing new audiences
Attribution tells the whole story - If Facebook says it drove the conversion, that's what happened
This approach exists because it feels controllable. Marketers love the illusion that they can precisely target their ideal customer and predict outcomes. It's reassuring to believe that success comes down to finding the "right" 50,000 people in a database of 3 billion users.
But here's where this wisdom falls apart: Privacy regulations have fundamentally broken detailed targeting. iOS 14.5, GDPR, and countless other privacy updates have made audience signals increasingly unreliable. What used to work in 2019 simply doesn't work anymore.
More importantly, this audience-obsessed approach treats each ad as an isolated transaction rather than part of a larger growth system. You're not building anything sustainable - you're just renting attention until your audience gets tired of seeing your ads.
The result? Marketers stuck in an endless cycle of audience testing, watching their CPMs rise and their ROAS decline, wondering why their "proven" strategies stopped working.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started managing ads for this Shopify client, I fell into the exact same trap. They had over 1,000 products in their catalog - everything from handmade jewelry to home decor. The complexity was both their strength and their weakness.
My first instinct was textbook: segment audiences by interest, create lookalikes from their existing customer data, and test different demographics. I spent two weeks building elaborate audience hierarchies, convinced I was being strategic.
The client's challenge was unique. Unlike most successful Facebook ad campaigns that thrive on 1-3 hero products, their strength was variety. Customers needed time to browse, compare, and discover items that matched their personal taste. But Facebook's quick-decision environment was fundamentally incompatible with this browsing behavior.
My first campaign structure looked like this:
Campaign 1: "Home Decor Enthusiasts" targeting interior design interests
Campaign 2: "Handmade Jewelry Lovers" targeting jewelry and fashion interests
Campaign 3: "Gift Buyers" targeting people with upcoming birthdays in their network
Each campaign had the same creative - a generic product carousel showing their best-sellers. The ROAS hovered around 2.5, which looked acceptable until you factored in their 30% cost of goods sold and operational expenses.
But the real problem wasn't the numbers - it was the hamster wheel effect. Every time an audience showed signs of fatigue, I had to scramble to find new targeting options. We'd get a few days of good performance, then watch it decline as ad frequency increased and fresh reach decreased.
The client was frustrated. "Why can't we just find our people and stick with them?" they asked. I didn't have a good answer because I was asking the wrong question entirely.
That's when I stumbled across something that challenged everything I thought I knew about paid advertising.
Here's my playbook
What I ended up doing and the results.
The breakthrough came when I stopped trying to outsmart Facebook's algorithm and started working with it instead. I discovered that in 2025, creative is the new targeting.
Instead of multiple campaigns with different audiences, I restructured everything around one core principle: let the algorithm find the right people, but give it diverse creative signals to work with.
The New Campaign Structure:
1 campaign focused on conversions
1 broad audience (women aged 25-55, English-speaking countries)
Multiple ad sets with different creative angles
3 new creatives every single week without fail
The creative testing cadence became our growth engine. Every Monday, we'd launch three new ad variations:
Lifestyle creative - showing products in beautiful home settings
Problem-solving creative - highlighting specific pain points the products solved
Social proof creative - featuring customer photos and testimonials
Each creative acted as a signal to the algorithm about different customer motivations. The lifestyle ads attracted people browsing for inspiration. The problem-solving ads caught people actively looking for solutions. The social proof ads converted people who needed validation before purchasing.
But here's where it became a "loop" rather than just testing: the data from each creative informed the next week's creative strategy. If lifestyle content was driving higher engagement but lower conversions, we'd create more lifestyle content with stronger calls-to-action. If problem-solving ads were converting but reaching fewer people, we'd test those problems from different angles.
The algorithm wasn't just finding customers - it was teaching us about our customers through creative performance. Each ad became market research that funded itself.
Within six weeks, this approach had completely transformed their ad account. We went from constantly hunting for new audiences to building a sustainable system that got smarter every week.
Creative Velocity
Consistency beats perfection - 3 new ads weekly, no exceptions
Budget Reallocation
Moved 80% of spend to broad audiences, 20% to creative production
Algorithm Partnership
Let Facebook find customers while you focus on messaging variety
Performance Compounding
Each week's data improves the next week's creative strategy
The transformation didn't happen overnight, but the trend was unmistakable. By month three, we'd achieved something I'd never seen before: improving performance with increasing ad spend.
The numbers told the story:
ROAS improved from 2.5 to 4.2 over 12 weeks
Cost per acquisition dropped 40% as the algorithm got smarter
Creative fatigue became irrelevant - we always had fresh ads in the pipeline
Audience size grew automatically as Facebook expanded reach based on creative signals
But the most surprising result was how this approach solved their original business challenge. Instead of forcing quick decisions, our diverse creative portfolio let customers discover products naturally. Lifestyle ads brought them in, problem-solving ads educated them, and social proof ads converted them - all within the same campaign ecosystem.
The paid loop had become self-improving. Each week's creative data made us better at understanding customer motivations, which made the next week's ads more effective, which gave us better data to work with.
Six months later, this campaign structure was still scaling profitably - something that would have been impossible with traditional audience targeting approaches.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from building sustainable paid loops instead of chasing perfect audiences:
Creative diversity beats audience precision - In 2025, your message matters more than who sees it
Consistency trumps perfection - Three okay creatives every week beats one perfect creative every month
Algorithm partnership, not competition - Work with Facebook's machine learning instead of trying to outsmart it
Data compounds when systematized - Each week's performance should inform next week's strategy
Broad audiences aren't lazy - They're often more effective than micro-targeted segments
Creative fatigue is a scheduling problem - Solve it with pipeline management, not audience switching
Attribution lies, but patterns don't - Focus on overall account trends rather than individual ad metrics
What I'd do differently: I would have started with broader audiences from day one instead of wasting two weeks on complex targeting setups. The time saved could have gone into creative production, which is where the real value lives.
This approach works best for businesses with multiple products or value propositions, where creative variety can tell different stories to different customer segments. It's less effective for single-product businesses with one clear value proposition.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, implement this playbook by:
Testing different use cases and customer pain points through creative variations
Using broad audiences (job titles, company sizes) rather than specific interests
Creating educational content that builds trust before asking for trials
For your Ecommerce store
For ecommerce stores, apply this approach by:
Showcasing different product categories and use cases weekly
Testing lifestyle, problem-solution, and social proof creative angles consistently
Letting algorithm find buyers while you focus on compelling product stories